Initial release: parquet traces + manifests + grid cases

.gitignore

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+# macOS
+.DS_Store
+._*
+.AppleDouble
+.LSOverride
+.Spotlight-V100
+.Trashes
+.fseventsd
+
+# Windows
+Thumbs.db
+ehthumbs.db
+Desktop.ini
+
+# Linux
+*~
+.directory
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+*.egg-info/
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+.ipynb_checkpoints/
+
+# Editors / IDEs
+.idea/
+.vscode/
+*.swp
+*.swo
+*.bak
+*.tmp
+
+# Local virtual envs (should never be committed to a dataset repo)
+.venv/
+venv/
+env/
+
+# Hugging Face / huggingface_hub local cache (in case it gets created here)
+.cache/
+.huggingface/
+
+# Logs / scratch
+*.log
+scratch/
+tmp/

README.md

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+---
+pretty_name: PowerZoo Dataset
+license: other
+license_name: mixed-upstream-licences
+language:
+  - en
+size_categories:
+  - 1M<n<10M
+tags:
+  - power-systems
+  - electricity
+  - reinforcement-learning
+  - benchmark
+  - time-series
+  - smart-grid
+  - data-center
+  - load-forecasting
+  - opf
+task_categories:
+  - time-series-forecasting
+  - tabular-regression
+  - reinforcement-learning
+configs:
+  - config_name: aemo_5min_demand
+    data_files: parquet/AEMO_5min_Demand_2025_2026.parquet
+  - config_name: aemo_forecast
+    data_files: parquet/AEMO_Forecast_vs_Actual_2025.parquet
+  - config_name: ausgrid_zone_substation
+    data_files: parquet/Ausgrid_Zone_Substation_FY25_imputed_15min.parquet
+  - config_name: gb_forecast_actual_demand
+    data_files: parquet/GB_Forecast_Actual_Demand_2023_2025_30min.parquet
+  - config_name: gb_gen_by_type
+    data_files: parquet/GB_Gen_by_Type_2016_2025_30min.parquet
+  - config_name: gb_neso_demand
+    data_files: parquet/GB_NESO_Demand_2009_2025_30min.parquet
+  - config_name: gb_market_mid
+    data_files: parquet/MID_GB_30min_aligned_to_gen.parquet
+  - config_name: alibaba_dc_2018
+    data_files: parquet/alibaba_dc_2018_300s.parquet
+  - config_name: alibaba_gpu_2020
+    data_files: parquet/alibaba_gpu_2020_300s.parquet
+  - config_name: azure_dc_v2
+    data_files: parquet/azure_dc_v2_300s.parquet
+  - config_name: google_dc_2019
+    data_files: parquet/google_dc_2019_300s.parquet
+---
+
+# PowerZoo Dataset
+
+A curated, harmonised collection of **real-world power-system and data-centre traces**, plus a library of **canonical electrical-network case files** (transmission and distribution), used by the *PowerZoo* / *PowerZooJax* benchmark suite for reinforcement-learning research on power systems.
+
+This release is the data companion submitted to *******. Source code for environments, baselines and reproducibility scripts is released separately under a ***; this dataset repository is self-contained and can be loaded standalone.
+
+> *****.** Author names, affiliations and contact information are intentionally omitted during the review period, in compliance with ***.
+
+---
+
+## 1. Why this dataset?
+
+Power-system RL benchmarks have historically suffered from three gaps:
+
+1. **Synthetic load curves** that do not capture the multi-scale structure of real demand (sub-hour ramping, weekly seasonality, event-driven spikes).
+2. **Inconsistent grid topologies** scattered across `MATPOWER`, `pandapower`, OpenDSS, IEEE PES test feeders, etc., each with its own conventions.
+3. **No canonical link** between high-fidelity demand traces and a fixed set of grid topologies, making cross-paper comparison difficult.
+
+PowerZoo Dataset addresses all three by shipping (a) cleaned, parquet-encoded traces from public regulators and cloud providers under a single schema, (b) a unified Python representation of standard transmission and distribution test cases, and (c) lightweight JSON manifests that wire the two together for reproducible experiment configuration.
+
+---
+
+## 2. Repository layout
+
+```
+PowerZooDataset/
+├── README.md                  # this file
+├── parquet/                   # harmonised time-series traces
+│   ├── *.parquet              # primary data files
+│   └── *.json                 # per-file provenance metadata (rows, dtypes, source URL, generation timestamp)
+├── manifests/                 # loader manifests (column maps, derivations, normalisation)
+│   └── <dataset_id>.json
+└── powergrid_case/            # electrical-network case definitions (Python)
+    ├── CaseBase.py            # ClearCase base class + ext. DataFrame
+    ├── transmission/          # HV / sub-transmission test systems
+    │   ├── Case5.py, Case14.py, Case29GB.py, Case118.py,
+    │   ├── Case300.py, Case552GB.py, Case1354pegase.py, Case2383wp.py
+    │   └── __init__.py
+    └── distribution/          # MV distribution test feeders
+        ├── Case33bw.py, Case118zh.py, Case123.py, Case141.py,
+        ├── Case533mt_hi.py, Case533mt_lo.py
+        └── __init__.py
+```
+
+### 2.1 Parquet traces
+
+
+| File                                                 | Domain                                        | Resolution | Rows      | Columns | Bytes   |
+| ---------------------------------------------------- | --------------------------------------------- | ---------- | --------- | ------- | ------- |
+| `AEMO_5min_Demand_2025_2026.parquet`                 | AU NEM demand (5 regions)                     | 5 min      | 737,400   | 5       | 6.5 MB  |
+| `AEMO_Forecast_vs_Actual_2025.parquet`               | AU NEM probabilistic forecast vs. actual      | 30 min     | 89,145    | 10      | 1.6 MB  |
+| `Ausgrid_Zone_Substation_FY25_imputed_15min.parquet` | NSW zone substations (175 sites)              | 15 min     | 6,095,040 | 4       | 60 MB   |
+| `GB_NESO_Demand_2009_2025_30min.parquet`             | GB NESO historical demand                     | 30 min     | 285,454   | 22      | 4.7 MB  |
+| `GB_Forecast_Actual_Demand_2023_2025_30min.parquet`  | GB day-ahead forecast vs. actual              | 30 min     | 48,283    | 3       | 0.8 MB  |
+| `GB_Gen_by_Type_2016_2025_30min.parquet`             | GB generation by fuel type                    | 30 min     | 180,048   | 13      | 6.2 MB  |
+| `MID_GB_30min_aligned_to_gen.parquet`                | GB APX/N2EX mid prices & volumes              | 30 min     | 48,283    | 6       | 0.8 MB  |
+| `alibaba_dc_2018_300s.parquet`                       | Alibaba production cluster (CPU/mem/net/disk) | 5 min      | 2,243     | 6       | 0.1 MB  |
+| `alibaba_gpu_2020_300s.parquet`                      | Alibaba GPU cluster (GPU/CPU util)            | 5 min      | 415       | 3       | <0.1 MB |
+| `azure_dc_v2_300s.parquet`                           | Azure VM trace v2 (CPU, assigned memory)      | 5 min      | 8,640     | 3       | 0.2 MB  |
+| `google_dc_2019_300s.parquet`                        | Google Borg 2019 (CPU/mem/CPI)                | 5 min      | 8,064     | 5       | 0.3 MB  |
+
+
+Each `*.parquet` ships with a sibling `*.json` capturing source URL, source organisation, generation timestamp, exact column dtypes, region/category enumerations and (where applicable) timezone conventions.
+
+### 2.2 Manifests
+
+Manifests in `manifests/` are the contract used by PowerZoo / PowerZooJax loaders. A manifest declares:
+
+- `parquet_file` and the matching `metadata_json`
+- `column_map`: rename rules from raw column → canonical schema (e.g. `OPERATIONAL_DEMAND → load.actual_mw`)
+- `index_map`: which raw columns serve as `datetime` / `region` / `issue_time` / `target_time`
+- `derived`: closed-form derivations from raw columns (e.g. `wind.available_mw = "Wind Offshore + Wind Onshore"`)
+- `normalize`: per-channel scaling factors applied at load time
+- `time_mode`: `calendar` (absolute UTC) or `profile` (cyclical, anchored to `data_epoch`)
+- `region_values`, `date_range`, `source_url`, `source_organization`
+
+The 11 manifests cover every parquet file shipped here. Note that `gb_neso_demand` uses a two-column index (`SETTLEMENT_DATE` + `SETTLEMENT_PERIOD` 1–50) instead of a single `datetime` column — the manifest's `datetime_recipe` field documents the exact reconstruction (Europe/London tz-localise then convert to UTC; SP=49–50 absorb the autumn clock-change repeat).
+
+### 2.3 Power-grid case files
+
+`powergrid_case/` contains a unified Python representation for both transmission and distribution test systems. Every case subclasses `ClearCase` and exposes four `pandas.DataFrame` tables in MATPOWER-compatible units (MW, MVAr, p.u.):
+
+- `nodes` — `id, type (1=PQ / 2=PV / 3=Ref), Pd, Qd, x, y`
+- `units` — `id, bus_id, mc_a, mc_b, mc_c, p_max, p_min` (quadratic cost + capacity)
+- `lines` — `id, from, to, x, floor, cap` (reactance + thermal limits)
+- `loads` — `id, bus_id, mc_a, mc_b, mc_c, d_max, d_min` (price-responsive demand)
+
+Each file declares `BUS_COUNT`, `VOLTAGE_LEVEL`, `SOURCE` and `DESCRIPTION` as class-level metadata. The values below are reproduced verbatim from those declarations (so the file itself is the source of truth):
+
+
+| File                             | Voltage | Buses | `SOURCE` | `DESCRIPTION`                                                      |
+| -------------------------------- | ------- | ----- | -------- | ------------------------------------------------------------------ |
+| `transmission/Case5.py`          | HV      | 5     | MATPOWER | IEEE 5-bus test system                                             |
+| `transmission/Case14.py`         | HV      | 14    | MATPOWER | IEEE 14-bus test system                                            |
+| `transmission/Case29GB.py`       | HV      | 29    | custom   | GB reduced 29-bus transmission network                             |
+| `transmission/Case118.py`        | HV      | 118   | MATPOWER | IEEE 118-bus test system                                           |
+| `transmission/Case300.py`        | HV      | 300   | MATPOWER | IEEE 300-bus test system                                           |
+| `transmission/Case552GB.py`      | HV      | 552   | GB       | Great Britain 552-bus transmission (distinct from 29-bus Case29GB) |
+| `transmission/Case1354pegase.py` | HV      | 1354  | MATPOWER | European PEGASE 1354-bus system                                    |
+| `transmission/Case2383wp.py`     | HV      | 2383  | MATPOWER | Polish 2383-bus winter peak system                                 |
+| `distribution/Case33bw.py`       | MV      | 33    | MATPOWER | IEEE 33-bus Baran & Wu radial distribution                         |
+| `distribution/Case118zh.py`      | MV      | 118   | MATPOWER | 118-bus Zhang distribution system                                  |
+| `distribution/Case123.py`        | MV      | 123   | MATPOWER | IEEE 123-bus three-phase distribution                              |
+| `distribution/Case141.py`        | MV      | 141   | MATPOWER | 141-bus Caracas distribution system (Khodr et al., EPSR 2008)      |
+| `distribution/Case533mt_hi.py`   | MV      | 533   | MATPOWER | 533-bus Swedish distribution (high load)                           |
+| `distribution/Case533mt_lo.py`   | MV      | 533   | MATPOWER | 533-bus Swedish distribution (low load)                            |
+
+
+---
+
+## 3. Loading
+
+### 3.1 Direct parquet load (no extra dependency on PowerZoo)
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="PowerZooJax/PowerZooDataset",
+    repo_type="dataset",
+    filename="parquet/AEMO_5min_Demand_2025_2026.parquet",
+)
+df = pd.read_parquet(path)
+print(df.head())
+```
+
+### 3.2 Via `datasets` (per-config)
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset(
+    "PowerZooJax/PowerZooDataset",
+    name="aemo_5min_demand",
+    split="train",
+)
+```
+
+### 3.3 Via the PowerZooJax `DataLoader`
+
+If you have the (separately released) benchmark package installed, instantiate `DataLoader` with this repository's `parquet/` and `manifests/` directories:
+
+```python
+from powerzoojax.data import DataLoader
+
+loader = DataLoader(
+    data_dir="/path/to/PowerZooDataset/parquet",
+    manifest_dir="/path/to/PowerZooDataset/manifests",
+)
+print(loader.list_available_datasets())
+df = loader.load_actual_series("aemo_5min_demand")
+```
+
+### 3.4 Power-grid cases
+
+```python
+from powerzoo.case.distribution.Case141 import Case141
+case = Case141()
+case.check()
+case.get_node_ptdf()
+```
+
+---
+
+## 4. Schema conventions
+
+- **Time stamps.** Calendar-mode parquet files store timestamps as `datetime64[ns, UTC]`, with the exception of `GB_NESO_Demand_2009_2025_30min.parquet`, which uses two columns (`SETTLEMENT_DATE` + `SETTLEMENT_PERIOD`) — see the `datetime_recipe` in `manifests/gb_neso_demand.json`. The Ausgrid metadata explicitly declares `timezone_local = "Australia/Sydney"` and `timezone_stored = "UTC"`; for the other calendar sources, only the stored UTC representation is documented in metadata.
+- **Profile-mode traces.** The data-centre traces (Alibaba / Azure / Google) are tagged `time_mode = "profile"` and `cyclical = true` in their manifests, anchored to a `data_epoch` (Alibaba DC: `2018-01-06`, Alibaba GPU: `2020-07-01`, Azure: `2019-01-01`, Google: `2019-05-01`). They are intended as periodic exogenous signals, not absolute calendar series.
+- **Imputation.** `Ausgrid_Zone_Substation_FY25_imputed_15min.parquet` contains imputed values (as indicated by its filename suffix and source path); the imputation method is not documented inside the metadata shipped here. Users requiring raw observations should pull from the upstream Ausgrid release.
+- **Units.** Power columns in the parquet traces are in MW. The GB market-mid file uses prices in `mid_price_`* columns (the upstream Elexon convention is £/MWh; consult the source URL for any unit caveats). Data-centre utilisation columns are 0–100 (percent) before the manifest's `normalize` factor is applied.
+
+---
+
+## 5. Source acknowledgements & licensing
+
+This repository **redistributes derivative datasets** built from publicly accessible upstream releases. Each upstream is governed by its own terms; users are responsible for complying with them. The `source_url` and `source_organization` fields in every `parquet/*.json` and `manifests/*.json` give the canonical pointer.
+
+
+| Trace                                 | Upstream                               | Original portal                                                                                                                                                                                      |
+| ------------------------------------- | -------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| AEMO 5-min demand & POE forecasts     | Australian Energy Market Operator      | [https://visualisations.aemo.com.au/aemo/nemweb/](https://visualisations.aemo.com.au/aemo/nemweb/)                                                                                                   |
+| Ausgrid zone-substation load          | Ausgrid                                | [https://www.ausgrid.com.au/Industry/Our-Research/Data-to-share/Distribution-zone-substation-data](https://www.ausgrid.com.au/Industry/Our-Research/Data-to-share/Distribution-zone-substation-data) |
+| GB demand actual & day-ahead forecast | Elexon (BMRS)                          | [https://data.elexon.co.uk/bmrs/api/v1](https://data.elexon.co.uk/bmrs/api/v1)                                                                                                                       |
+| GB generation by fuel type            | Elexon (BMRS)                          | [https://data.elexon.co.uk/bmrs/api/v1/generation/actual/per-type](https://data.elexon.co.uk/bmrs/api/v1/generation/actual/per-type)                                                                 |
+| GB historical demand 2009–2025        | National Energy System Operator (NESO) | [https://www.neso.energy/data-portal/historic-demand-data](https://www.neso.energy/data-portal/historic-demand-data)                                                                                 |
+| Alibaba cluster trace 2018            | Alibaba Group                          | [https://github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018](https://github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018)                                                     |
+| Alibaba GPU trace 2020                | Alibaba Group                          | [https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020](https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020)                                             |
+| Azure public dataset v2               | Microsoft                              | [https://github.com/Azure/AzurePublicDataset](https://github.com/Azure/AzurePublicDataset)                                                                                                           |
+| Google cluster data 2019              | Google                                 | [https://github.com/google/cluster-data](https://github.com/google/cluster-data)                                                                                                                     |
+
+
+The packaging artefacts authored for this benchmark (the manifest schema, case-file Python representations, schema harmonisation logic) are intended to be released under a permissive open-source licence at camera-ready time; the underlying parquet data inherits the upstream licence in every case. **Do not assume any single licence applies to the data themselves.** When in doubt, redownload from the upstream URL above and treat that copy as authoritative.
+
+No personally identifiable information is present in any trace; demand and generation series are aggregated at substation, regional or national level.
+
+---
+
+## 6. Intended use & limitations
+
+- **Intended use.** Reinforcement-learning research, load / generation forecasting, market-clearing simulation, OPF benchmarking, distribution-system control, data-centre power-shaping research, demand-response studies.
+- **Out-of-scope.** Short-term operational decisions for real grids, retail tariff design, settlement of real markets. The forecasts and prices included are historical educational artefacts, not signals for live trading.
+- **Known limitations.**
+  - Several calendar-mode traces extend slightly past the `date_range` declared in their manifest (e.g. AEMO 5-min runs through 2026-02-12; GB demand/generation through April 2026). The manifest `date_range` is a planning bound, not a strict assertion of file extent — consult the parquet itself for the authoritative range.
+  - The Ausgrid trace is imputed; the imputation procedure is not documented in this repository.
+  - The data-centre traces are stored at 5-minute (300 s) resolution. The upstream releases provide additional fields and finer resolutions; this repository ships only the columns mapped by each manifest.
+  - The grid-case files use parameters consistent with the cited source systems but are not guaranteed to be byte-identical to any specific MATPOWER / IEEE PES / PEGASE release; they should not be used for compliance studies of real networks.
+
+---
+
+## 7. File integrity
+
+To regenerate provenance metadata locally:
+
+```bash
+python -c "
+import pyarrow.parquet as pq, glob, os
+for p in sorted(glob.glob('parquet/*.parquet')):
+    m = pq.read_metadata(p)
+    print(f'{os.path.basename(p):60s}  rows={m.num_rows:>10,}  cols={m.num_columns:>3}  bytes={os.path.getsize(p):>10,}')
+"
+```
+
+Expected output is reproduced in §2.1.
+
+---
+
+## 8. Changelog
+
+- **v0.1 (***)** — initial public release: 11 harmonised parquet traces, 11 loader manifests, 14 grid-case files (8 transmission + 6 distribution).
+

manifests/aemo_5min_demand.json

@@ -0,0 +1,24 @@
+{
+  "name": "aemo_5min_demand",
+  "source": "aemo",
+  "data_type": "actual_series",
+  "time_mode": "calendar",
+  "resolution": "5min",
+  "parquet_file": "AEMO_5min_Demand_2025_2026.parquet",
+  "metadata_json": "AEMO_5min_Demand_2025_2026.json",
+  "column_map": {
+    "OPERATIONAL_DEMAND": "load.actual_mw"
+  },
+  "index_map": {
+    "INTERVAL_DATETIME": "datetime",
+    "REGIONID": "region"
+  },
+  "derived": {},
+  "normalize": {},
+  "data_epoch": null,
+  "cyclical": false,
+  "region_values": ["NSW1", "QLD1", "SA1", "TAS1", "VIC1"],
+  "date_range": ["2024-09-18", "2026-12-31"],
+  "source_url": "https://visualisations.aemo.com.au/aemo/nemweb/index.html#",
+  "source_organization": "Australian Energy Market Operator (AEMO)"
+}

manifests/aemo_forecast.json

@@ -0,0 +1,28 @@
+{
+  "name": "aemo_forecast",
+  "source": "aemo",
+  "data_type": "forecast_panel",
+  "time_mode": "calendar",
+  "resolution": "30min",
+  "parquet_file": "AEMO_Forecast_vs_Actual_2025.parquet",
+  "metadata_json": "AEMO_Forecast_vs_Actual_2025.json",
+  "column_map": {
+    "OPERATIONAL_DEMAND_POE10": "load.forecast_p10_mw",
+    "OPERATIONAL_DEMAND_POE50": "load.forecast_p50_mw",
+    "OPERATIONAL_DEMAND_POE90": "load.forecast_p90_mw",
+    "OPERATIONAL_DEMAND": "load.actual_mw"
+  },
+  "index_map": {
+    "REGIONID": "region",
+    "FORECAST_DATETIME": "issue_time",
+    "INTERVAL_DATETIME": "target_time"
+  },
+  "derived": {},
+  "normalize": {},
+  "data_epoch": null,
+  "cyclical": false,
+  "region_values": ["NSW1", "QLD1", "SA1", "TAS1", "VIC1"],
+  "date_range": ["2025-01-26", "2025-12-31"],
+  "source_url": "https://visualisations.aemo.com.au/aemo/nemweb/index.html#",
+  "source_organization": "Australian Energy Market Operator (AEMO)"
+}

manifests/alibaba_dc_2018.json

@@ -0,0 +1,33 @@
+{
+  "name": "alibaba_dc_2018",
+  "source": "alibaba",
+  "data_type": "actual_series",
+  "time_mode": "profile",
+  "resolution": "300s",
+  "parquet_file": "alibaba_dc_2018_300s.parquet",
+  "column_map": {
+    "cpu_util_percent": "datacenter.cpu_util",
+    "mem_util_percent": "datacenter.mem_util",
+    "net_in": "datacenter.net_in",
+    "net_out": "datacenter.net_out",
+    "disk_io_percent": "datacenter.disk_io"
+  },
+  "index_map": {
+    "time_stamp": "datetime"
+  },
+  "derived": {},
+  "normalize": {
+    "datacenter.cpu_util": 100,
+    "datacenter.mem_util": 100,
+    "datacenter.net_in": 100,
+    "datacenter.net_out": 100,
+    "datacenter.disk_io": 100
+  },
+  "data_epoch": "2018-01-06T00:00:00",
+  "cyclical": true,
+  "region_values": [],
+  "date_range": null,
+  "metadata_json": null,
+  "source_url": "https://github.com/alibaba/clusterdata/tree/master/cluster-trace-v2018",
+  "source_organization": "Alibaba Group"
+}

manifests/alibaba_gpu_2020.json

@@ -0,0 +1,27 @@
+{
+  "name": "alibaba_gpu_2020",
+  "source": "alibaba",
+  "data_type": "actual_series",
+  "time_mode": "profile",
+  "resolution": "300s",
+  "parquet_file": "alibaba_gpu_2020_300s.parquet",
+  "column_map": {
+    "gpu_util_percent": "datacenter.gpu_util",
+    "cpu_util_percent": "datacenter.cpu_util"
+  },
+  "index_map": {
+    "time_stamp": "datetime"
+  },
+  "derived": {},
+  "normalize": {
+    "datacenter.gpu_util": 100,
+    "datacenter.cpu_util": 100
+  },
+  "data_epoch": "2020-07-01T00:00:00",
+  "cyclical": true,
+  "region_values": [],
+  "date_range": null,
+  "metadata_json": null,
+  "source_url": "https://github.com/alibaba/clusterdata/tree/master/cluster-trace-gpu-v2020",
+  "source_organization": "Alibaba Group"
+}

manifests/ausgrid_zone_substation_fy25_imputed.json

@@ -0,0 +1,203 @@
+{
+  "name": "ausgrid_zone_substation_fy25_imputed",
+  "source": "ausgrid",
+  "data_type": "actual_series",
+  "time_mode": "calendar",
+  "resolution": "15min",
+  "parquet_file": "Ausgrid_Zone_Substation_FY25_imputed_15min.parquet",
+  "column_map": {
+    "load_mw": "load.actual_mw"
+  },
+  "index_map": {
+    "interval_start": "datetime",
+    "zone_substation": "region"
+  },
+  "derived": {},
+  "normalize": {},
+  "data_epoch": null,
+  "cyclical": false,
+  "region_values": [
+    "Aberdeen 66_11kV",
+    "Adamstown 132_11kV",
+    "Argenton 132_11kV",
+    "Auburn 33_11kV",
+    "Avoca 66_11kV",
+    "Avondale 33_11kV",
+    "Baerami 33_11kV",
+    "Balgowlah North 132_11kV",
+    "Bankstown 132_11kV",
+    "Bass Hill 33_11kV",
+    "Beacon Hill 33_11kV",
+    "Belmore Park 132_11kV",
+    "Belrose 33_11kV",
+    "Berkeley Vale 132_11kV",
+    "Berowra 132_11kV",
+    "Blakehurst 33_11kV",
+    "Botany 33_11kV",
+    "Brandy Hill 132_11kV",
+    "Branxton 66_11kV",
+    "Broadmeadow 132_11kV",
+    "Brookvale 33_11kV",
+    "Burwood 132_11kV",
+    "Campbell St 132_11kV",
+    "Camperdown 33_11kV",
+    "Campsie 33_11kV",
+    "Cardiff 33_11kV",
+    "Careel Bay 33_11kV",
+    "Caringbah 33_11kV",
+    "Castle Cove 132_11kV",
+    "Cessnock South 33_11kV",
+    "Charlestown 132_11kV",
+    "Charmhaven 132_11kV",
+    "Chatswood 33_11kV",
+    "City Central 132_11kV",
+    "City North 132_11kV",
+    "City South 132_11kV",
+    "Clovelly 132_11kV",
+    "Concord 33_11kV",
+    "Cronulla 132_11kV",
+    "Croudace Bay 33_11kV",
+    "Crows Nest 132_11kV",
+    "Croydon 132_11kV",
+    "Dalley St 132_11kV",
+    "Darling Harbour 132_11kV",
+    "Darlinghurst 33_11kV",
+    "Dee Why West 33_11kV",
+    "Denman 66_11kV",
+    "Double Bay 132_11kV",
+    "Drummoyne 132_11kV",
+    "Edgeworth 33_11kV",
+    "Empire Bay 66_11kV",
+    "Engadine 132_11kV",
+    "Epping 66_11kV",
+    "Erina 66_11kV",
+    "Flemington 132_11kV",
+    "Galston 132_11kV",
+    "Gateshead 33_11kV",
+    "Gore Hill 33_11kV",
+    "Green Square 132_11kV",
+    "Greenacre 132_11kV",
+    "Gwawley Bay 132_11kV",
+    "Harbord 33_11kV",
+    "Homebush Bay 132_11kV",
+    "Hornsby 132_11kV",
+    "Hunters Hill 66_11kV",
+    "Hurstville North 132_11kV",
+    "Jannali 33_11kV",
+    "Jesmond 132_11kV",
+    "Jewells 33_11kV",
+    "Killarney 33_11kV",
+    "Kingsford 132_11kV",
+    "Kirrawee 132_11kV",
+    "Kogarah 132_11kV",
+    "Kotara 33_11kV",
+    "Kurnell South 132_11kV",
+    "Kurri 132_11kV",
+    "Lake Munmorah 132_11kV",
+    "Leichhardt 132_11kV",
+    "Leightonfield 33_11kV",
+    "Lemington 66_11kV",
+    "Lidcombe 33_11kV",
+    "Lindfield 33_11kV",
+    "Lisarow 33_11kV",
+    "Long Jetty 66_11kV",
+    "Lucas Heights 33_11kV",
+    "Macquarie Park 132_11kV",
+    "Maitland 33_11kV",
+    "Manly 33_11kV",
+    "Maroubra 132_11kV",
+    "Marrickville 132_11kV",
+    "Maryland 132_11kV",
+    "Mascot 33_11kV",
+    "Matraville 33_11kV",
+    "Mayfield West 132_11kV",
+    "Meadowbank 132_11kV",
+    "Medowie 33_11kV",
+    "Menai 132_11kV",
+    "Merriwa 33_11kV",
+    "Metford 33_11kV",
+    "Milperra 132_11kV",
+    "Miranda 33_11kV",
+    "Mitchell Line 66_11kV",
+    "Mitchells Flat 66_11kV",
+    "Mona Vale 33_11kV",
+    "Moonan 33_11kV",
+    "Morisset 132_11kV",
+    "Mortdale 33_11kV",
+    "Mosman 132_11kV",
+    "Mt Hutton 33_11kV",
+    "Mt Thorley 66_11kV",
+    "Muswellbrook 33_11kV",
+    "Narrabeen 33_11kV",
+    "Nelson Bay 33_11kV",
+    "New Lambton 33_11kV",
+    "Newcastle CBD 33_11kV",
+    "Newdell 66_11kV",
+    "Newport 33_11kV",
+    "Noraville 33_11kV",
+    "North Head 33_11kV",
+    "North Sydney 132_11kV",
+    "Nulkaba 33_11kV",
+    "Olympic Park 132_11kV",
+    "Paddington 33_11kV",
+    "Paxton 33_11kV",
+    "Peats Ridge 33_11kV",
+    "Pelican 33_11kV",
+    "Pennant Hills 132_11kV",
+    "Port Botany 33_11kV",
+    "Potts Hill 132_11kV",
+    "Punchbowl 33_11kV",
+    "Pymble 33_11kV",
+    "RNS Hospital 132_11kV",
+    "Rathmines 132_11kV",
+    "Raymond Terr NEW 33_11kV",
+    "Revesby 132_11kV",
+    "Riverwood 33_11kV",
+    "Rockdale 132_11kV",
+    "Rose Bay 132_11kV",
+    "Rothbury 132_11kV",
+    "Rouchel 33_11kV",
+    "Rutherford 33_11kV",
+    "Sans Souci 33_11kV",
+    "Scone 66_11kV",
+    "Sefton 132_11kV",
+    "Singleton 66_11kV",
+    "Singleton North 66_11kV",
+    "Somersby 132_11kV",
+    "St Ives 33_11kV",
+    "St Peters 132_11kV",
+    "Stockton 33_11kV",
+    "Strathfield South 132_11kV",
+    "Summer Hill 33_11kV",
+    "Surry Hills 33_11kV",
+    "Swansea 33_11kV",
+    "Tanilba Bay 33_11kV",
+    "Tarro 33_11kV",
+    "Telarah 33_11kV",
+    "Terrey Hills 33_11kV",
+    "Thornton 33_11kV",
+    "Tighes Hill 33_11kV",
+    "Tomago 33_11kV",
+    "Tomalpin 33_11kV",
+    "Tomaree 33_11kV",
+    "Top Ryde 132_11kV",
+    "Toronto West 132_11kV",
+    "Turramurra 33_11kV",
+    "Umina 66_11kV",
+    "Vales Point 33_11kV",
+    "Wamberal 132_11kV",
+    "Waverley 132_11kV",
+    "West Gosford 132_11kV",
+    "Williamtown 33_11kV",
+    "Woy Woy 66_11kV",
+    "Wyong 132_11kV",
+    "Zetland 132_11kV"
+  ],
+  "date_range": [
+    "2024-04-30",
+    "2025-04-29"
+  ],
+  "metadata_json": "Ausgrid_Zone_Substation_FY25_imputed_15min.json",
+  "source_url": "https://www.ausgrid.com.au/Industry/Our-Research/Data-to-share/Distribution-zone-substation-data",
+  "source_organization": "Ausgrid"
+}

manifests/azure_dc_v2.json

@@ -0,0 +1,24 @@
+{
+  "name": "azure_dc_v2",
+  "source": "azure",
+  "data_type": "actual_series",
+  "time_mode": "profile",
+  "resolution": "300s",
+  "parquet_file": "azure_dc_v2_300s.parquet",
+  "column_map": {
+    "cpu_usage": "datacenter.cpu_util",
+    "assigned_mem": "datacenter.mem_util"
+  },
+  "index_map": {
+    "time_stamp": "datetime"
+  },
+  "derived": {},
+  "normalize": {},
+  "data_epoch": "2019-01-01T00:00:00",
+  "cyclical": true,
+  "region_values": [],
+  "date_range": null,
+  "metadata_json": null,
+  "source_url": "https://github.com/Azure/AzurePublicDataset/blob/master/AzurePublicDatasetV2.md",
+  "source_organization": "Microsoft Corporation"
+}

manifests/gb_forecast_actual_demand.json

@@ -0,0 +1,27 @@
+{
+  "name": "gb_forecast_actual_demand",
+  "source": "gb",
+  "data_type": "actual_series",
+  "time_mode": "calendar",
+  "resolution": "30min",
+  "parquet_file": "GB_Forecast_Actual_Demand_2023_2025_30min.parquet",
+  "column_map": {
+    "Actual": "load.actual_mw",
+    "DAForecast": "load.forecast_da_mw"
+  },
+  "index_map": {
+    "startTime": "datetime"
+  },
+  "derived": {},
+  "normalize": {},
+  "data_epoch": null,
+  "cyclical": false,
+  "region_values": [],
+  "date_range": ["2023-07-05", "2026-04-05"],
+  "metadata_json": "GB_Forecast_Actual_Demand_2023_2025_30min.json",
+  "source_urls": [
+    "https://data.elexon.co.uk/bmrs/api/v1/demand/actual/total",
+    "https://data.elexon.co.uk/bmrs/api/v1/forecast/demand/total/day-ahead"
+  ],
+  "source_organization": "Elexon Limited"
+}

manifests/gb_gen_by_type.json

@@ -0,0 +1,25 @@
+{
+  "name": "gb_gen_by_type",
+  "source": "gb",
+  "data_type": "actual_series",
+  "time_mode": "calendar",
+  "resolution": "30min",
+  "parquet_file": "GB_Gen_by_Type_2016_2025_30min.parquet",
+  "column_map": {
+    "Solar": "solar.available_mw"
+  },
+  "index_map": {
+    "startTime": "datetime"
+  },
+  "derived": {
+    "wind.available_mw": "Wind Offshore + Wind Onshore"
+  },
+  "normalize": {},
+  "data_epoch": null,
+  "cyclical": false,
+  "region_values": [],
+  "date_range": ["2016-01-01", "2026-04-08"],
+  "metadata_json": "GB_Gen_by_Type_2016_2025_30min.json",
+  "source_url": "https://data.elexon.co.uk/bmrs/api/v1/generation/actual/per-type",
+  "source_organization": "Elexon Limited"
+}

manifests/gb_market_mid.json

@@ -0,0 +1,24 @@
+{
+  "name": "gb_market_mid",
+  "source": "gb",
+  "data_type": "actual_series",
+  "time_mode": "calendar",
+  "resolution": "30min",
+  "parquet_file": "MID_GB_30min_aligned_to_gen.parquet",
+  "column_map": {
+    "mid_price_APXMIDP": "market.mid_price_apx",
+    "mid_price_N2EXMIDP": "market.mid_price_n2ex",
+    "mid_volume_APXMIDP": "market.mid_volume_apx",
+    "mid_volume_N2EXMIDP": "market.mid_volume_n2ex"
+  },
+  "index_map": {
+    "startTime": "datetime"
+  },
+  "derived": {},
+  "normalize": {},
+  "data_epoch": null,
+  "cyclical": false,
+  "region_values": [],
+  "date_range": ["2023-07-05", "2026-04-05"],
+  "metadata_json": "MID_GB_30min_aligned_to_gen.json"
+}

manifests/gb_neso_demand.json

@@ -0,0 +1,44 @@
+{
+  "name": "gb_neso_demand",
+  "source": "gb",
+  "data_type": "actual_series",
+  "time_mode": "calendar",
+  "resolution": "30min",
+  "parquet_file": "GB_NESO_Demand_2009_2025_30min.parquet",
+  "metadata_json": "GB_NESO_Demand_2009_2025_30min.json",
+  "column_map": {
+    "ND": "load.actual_mw",
+    "TSD": "load.tsd_mw",
+    "ENGLAND_WALES_DEMAND": "load.england_wales_mw",
+    "EMBEDDED_WIND_GENERATION": "wind.embedded_mw",
+    "EMBEDDED_WIND_CAPACITY": "wind.embedded_capacity_mw",
+    "EMBEDDED_SOLAR_GENERATION": "solar.embedded_mw",
+    "EMBEDDED_SOLAR_CAPACITY": "solar.embedded_capacity_mw",
+    "NON_BM_STOR": "storage.non_bm_mw",
+    "PUMP_STORAGE_PUMPING": "storage.pump_mw",
+    "IFA_FLOW": "interconnector.ifa_mw",
+    "IFA2_FLOW": "interconnector.ifa2_mw",
+    "BRITNED_FLOW": "interconnector.britned_mw",
+    "MOYLE_FLOW": "interconnector.moyle_mw",
+    "EAST_WEST_FLOW": "interconnector.east_west_mw",
+    "NEMO_FLOW": "interconnector.nemo_mw",
+    "NSL_FLOW": "interconnector.nsl_mw",
+    "ELECLINK_FLOW": "interconnector.eleclink_mw",
+    "VIKING_FLOW": "interconnector.viking_mw",
+    "GREENLINK_FLOW": "interconnector.greenlink_mw",
+    "SCOTTISH_TRANSFER": "interconnector.scottish_transfer_mw"
+  },
+  "index_map": {
+    "SETTLEMENT_DATE": "settlement_date",
+    "SETTLEMENT_PERIOD": "settlement_period"
+  },
+  "datetime_recipe": "datetime_utc = tz_localize(SETTLEMENT_DATE + (SETTLEMENT_PERIOD - 1) * 30min, tz='Europe/London', ambiguous='infer', nonexistent='shift_forward').tz_convert('UTC'); SETTLEMENT_PERIOD ranges 1..50 to absorb GB DST clock changes (46 on spring-forward day, 50 on fall-back day).",
+  "derived": {},
+  "normalize": {},
+  "data_epoch": null,
+  "cyclical": false,
+  "region_values": [],
+  "date_range": ["2009-01-01", "2025-04-13"],
+  "source_url": "https://www.neso.energy/data-portal/historic-demand-data",
+  "source_organization": "National Energy System Operator (NESO)"
+}

manifests/google_dc_2019.json

@@ -0,0 +1,26 @@
+{
+  "name": "google_dc_2019",
+  "source": "google",
+  "data_type": "actual_series",
+  "time_mode": "profile",
+  "resolution": "300s",
+  "parquet_file": "google_dc_2019_300s.parquet",
+  "column_map": {
+    "cpu_util": "datacenter.cpu_util",
+    "mem_util": "datacenter.mem_util",
+    "assigned_mem": "datacenter.assigned_mem",
+    "cycles_per_instruction": "datacenter.cycles_per_instruction"
+  },
+  "index_map": {
+    "time_stamp": "datetime"
+  },
+  "derived": {},
+  "normalize": {},
+  "data_epoch": "2019-05-01T00:00:00",
+  "cyclical": true,
+  "region_values": [],
+  "date_range": null,
+  "metadata_json": null,
+  "source_url": "https://github.com/google/cluster-data/blob/master/ClusterData2019.md",
+  "source_organization": "Google LLC"
+}

parquet/AEMO_5min_Demand_2025_2026.json

@@ -0,0 +1,32 @@
+{
+  "source_file": "AEMO_5min_Demand_2025_2026.csv",
+  "parquet_file": "AEMO_5min_Demand_2025_2026.parquet",
+  "source_url": "https://visualisations.aemo.com.au/aemo/nemweb/index.html#",
+  "source_organization": "Australian Energy Market Operator (AEMO)",
+  "generated_at": "2026-03-25T16:17:26.313070",
+  "shape": {
+    "rows": 737400,
+    "columns": 5
+  },
+  "columns": [
+    "REGIONID",
+    "INTERVAL_DATETIME",
+    "OPERATIONAL_DEMAND",
+    "OPERATIONAL_DEMAND_ADJUSTMENT",
+    "WDR_ESTIMATE"
+  ],
+  "dtypes": {
+    "REGIONID": "object",
+    "INTERVAL_DATETIME": "datetime64[ns, UTC]",
+    "OPERATIONAL_DEMAND": "int64",
+    "OPERATIONAL_DEMAND_ADJUSTMENT": "int64",
+    "WDR_ESTIMATE": "int64"
+  },
+  "regions": [
+    "NSW1",
+    "QLD1",
+    "SA1",
+    "TAS1",
+    "VIC1"
+  ]
+}

parquet/AEMO_5min_Demand_2025_2026.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4d3129e3a2e14523e4b19fda24deabb993247d29296b9e57814bba7bf9acbb2d
+size 6820957

parquet/AEMO_Forecast_vs_Actual_2025.json

@@ -0,0 +1,42 @@
+{
+  "source_file": "AEMO_Forecast_vs_Actual_2025.csv",
+  "parquet_file": "AEMO_Forecast_vs_Actual_2025.parquet",
+  "source_url": "https://visualisations.aemo.com.au/aemo/nemweb/index.html#",
+  "source_organization": "Australian Energy Market Operator (AEMO)",
+  "generated_at": "2026-03-25T16:17:26.444637",
+  "shape": {
+    "rows": 89145,
+    "columns": 10
+  },
+  "columns": [
+    "REGIONID",
+    "FORECAST_DATETIME",
+    "INTERVAL_DATETIME",
+    "LOAD_DATE",
+    "OPERATIONAL_DEMAND_POE10",
+    "OPERATIONAL_DEMAND_POE50",
+    "OPERATIONAL_DEMAND_POE90",
+    "OPERATIONAL_DEMAND",
+    "OPERATIONAL_DEMAND_ADJUSTMENT",
+    "WDR_ESTIMATE"
+  ],
+  "dtypes": {
+    "REGIONID": "object",
+    "FORECAST_DATETIME": "datetime64[ns, UTC]",
+    "INTERVAL_DATETIME": "datetime64[ns, UTC]",
+    "LOAD_DATE": "datetime64[ns, UTC]",
+    "OPERATIONAL_DEMAND_POE10": "int64",
+    "OPERATIONAL_DEMAND_POE50": "int64",
+    "OPERATIONAL_DEMAND_POE90": "int64",
+    "OPERATIONAL_DEMAND": "float64",
+    "OPERATIONAL_DEMAND_ADJUSTMENT": "float64",
+    "WDR_ESTIMATE": "float64"
+  },
+  "regions": [
+    "NSW1",
+    "QLD1",
+    "SA1",
+    "TAS1",
+    "VIC1"
+  ]
+}

parquet/AEMO_Forecast_vs_Actual_2025.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:41298fbbd1c722b392d71501b6d9eb2600813a98abda7509cc9ae39fca92bea9
+size 1660988

parquet/Ausgrid_Zone_Substation_FY25_imputed_15min.json

@@ -0,0 +1,208 @@
+{
+  "source_dir": "Ausgrid Distribution Zone Substation Data FY25_imputed",
+  "source_files": 175,
+  "parquet_file": "Ausgrid_Zone_Substation_FY25_imputed_15min.parquet",
+  "source_url": "https://www.ausgrid.com.au/Industry/Our-Research/Data-to-share/Distribution-zone-substation-data",
+  "source_organization": "Ausgrid",
+  "generated_at": "2026-04-10T05:34:20.253058",
+  "timezone_local": "Australia/Sydney",
+  "timezone_stored": "UTC",
+  "interval_convention": "interval_start is UTC start of 15-minute block; CSV column HH:MM is interval end in local civil time. Local\u2192UTC uses pandas tz_localize (ambiguous=False, nonexistent=shift_forward).",
+  "shape": {
+    "rows": 6095040,
+    "columns": 4
+  },
+  "columns": [
+    "interval_start",
+    "zone_substation",
+    "load_mw",
+    "fiscal_year"
+  ],
+  "dtypes": {
+    "interval_start": "datetime64[ns, UTC]",
+    "zone_substation": "object",
+    "load_mw": "float64",
+    "fiscal_year": "Int64"
+  },
+  "zone_substation_values": [
+    "Aberdeen 66_11kV",
+    "Adamstown 132_11kV",
+    "Argenton 132_11kV",
+    "Auburn 33_11kV",
+    "Avoca 66_11kV",
+    "Avondale 33_11kV",
+    "Baerami 33_11kV",
+    "Balgowlah North 132_11kV",
+    "Bankstown 132_11kV",
+    "Bass Hill 33_11kV",
+    "Beacon Hill 33_11kV",
+    "Belmore Park 132_11kV",
+    "Belrose 33_11kV",
+    "Berkeley Vale 132_11kV",
+    "Berowra 132_11kV",
+    "Blakehurst 33_11kV",
+    "Botany 33_11kV",
+    "Brandy Hill 132_11kV",
+    "Branxton 66_11kV",
+    "Broadmeadow 132_11kV",
+    "Brookvale 33_11kV",
+    "Burwood 132_11kV",
+    "Campbell St 132_11kV",
+    "Camperdown 33_11kV",
+    "Campsie 33_11kV",
+    "Cardiff 33_11kV",
+    "Careel Bay 33_11kV",
+    "Caringbah 33_11kV",
+    "Castle Cove 132_11kV",
+    "Cessnock South 33_11kV",
+    "Charlestown 132_11kV",
+    "Charmhaven 132_11kV",
+    "Chatswood 33_11kV",
+    "City Central 132_11kV",
+    "City North 132_11kV",
+    "City South 132_11kV",
+    "Clovelly 132_11kV",
+    "Concord 33_11kV",
+    "Cronulla 132_11kV",
+    "Croudace Bay 33_11kV",
+    "Crows Nest 132_11kV",
+    "Croydon 132_11kV",
+    "Dalley St 132_11kV",
+    "Darling Harbour 132_11kV",
+    "Darlinghurst 33_11kV",
+    "Dee Why West 33_11kV",
+    "Denman 66_11kV",
+    "Double Bay 132_11kV",
+    "Drummoyne 132_11kV",
+    "Edgeworth 33_11kV",
+    "Empire Bay 66_11kV",
+    "Engadine 132_11kV",
+    "Epping 66_11kV",
+    "Erina 66_11kV",
+    "Flemington 132_11kV",
+    "Galston 132_11kV",
+    "Gateshead 33_11kV",
+    "Gore Hill 33_11kV",
+    "Green Square 132_11kV",
+    "Greenacre 132_11kV",
+    "Gwawley Bay 132_11kV",
+    "Harbord 33_11kV",
+    "Homebush Bay 132_11kV",
+    "Hornsby 132_11kV",
+    "Hunters Hill 66_11kV",
+    "Hurstville North 132_11kV",
+    "Jannali 33_11kV",
+    "Jesmond 132_11kV",
+    "Jewells 33_11kV",
+    "Killarney 33_11kV",
+    "Kingsford 132_11kV",
+    "Kirrawee 132_11kV",
+    "Kogarah 132_11kV",
+    "Kotara 33_11kV",
+    "Kurnell South 132_11kV",
+    "Kurri 132_11kV",
+    "Lake Munmorah 132_11kV",
+    "Leichhardt 132_11kV",
+    "Leightonfield 33_11kV",
+    "Lemington 66_11kV",
+    "Lidcombe 33_11kV",
+    "Lindfield 33_11kV",
+    "Lisarow 33_11kV",
+    "Long Jetty 66_11kV",
+    "Lucas Heights 33_11kV",
+    "Macquarie Park 132_11kV",
+    "Maitland 33_11kV",
+    "Manly 33_11kV",
+    "Maroubra 132_11kV",
+    "Marrickville 132_11kV",
+    "Maryland 132_11kV",
+    "Mascot 33_11kV",
+    "Matraville 33_11kV",
+    "Mayfield West 132_11kV",
+    "Meadowbank 132_11kV",
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powergrid_case/CaseBase.py

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+"""Power market case base module
+
+This module contains basic data structures and computation methods for power market simulation.
+"""
+from typing import List, Union
+import pandas as pd
+import numpy as np
+from scipy.sparse import coo_matrix
+import scipy.sparse as sp
+
+
+class DataFrame(pd.DataFrame):
+    """Extended DataFrame for power market data display
+    
+    Inherits from pandas.DataFrame with label feature and custom display format.
+    """
+
+    def __init__(self, columns: List[str], data: Union[List, np.ndarray]):
+        """Initialize DataFrame
+        
+        Args:
+            columns: column names
+            data: data content
+        """
+        super(DataFrame, self).__init__(data=data, columns=columns)
+        self.loc[:, '#id'] = self.index
+        self.index = self['id']
+        self.label = ''
+
+    def __repr__(self) -> str:
+        """Custom string representation"""
+        return f'{"=" * 20} {self.label} {"=" * 20}\n{self.to_string(index=False)}'
+
+    def __str__(self) -> str:
+        """String representation"""
+        return self.__repr__()
+
+    def set_label(self, label: str) -> 'DataFrame':
+        """Set label
+        
+        Args:
+            label: label name
+            
+        Returns:
+            self to support method chaining
+        """
+        self.label = label
+        return self
+
+
+class ClearCase(object):
+    """Base class for power market clearing cases
+    
+    Contains basic data for nodes, units, lines and loads,
+    along with matrix computations and data processing methods.
+
+    Subclasses should override the class-level metadata attributes below
+    to enable automatic discovery, filtering, and compatibility validation.
+    """
+
+    GRID_TYPE: str = ""
+    BUS_COUNT: int = 0
+    PHASE: str = "1"
+    VOLTAGE_LEVEL: str = ""
+    SOURCE: str = ""
+    DESCRIPTION: str = ""
+
+    def __init__(self, mock=True):
+        """Initialize clearing case"""
+        self.init_flag = False
+        self._node_gsdf = None
+        self._node_ptdf = None
+        self._plotter = None
+        self.mocker = None
+        self.nodes_map_units = None
+        self.flexloads_num = len(getattr(self, 'flexloads', []))
+        if mock:
+            from .CaseMocker import CaseMocker
+            self.mocker, _ = CaseMocker(self).mock_c()
+            
+        # if lines['cap'] == 0 or lines['floor'], set to 1000000
+        self.lines.loc[self.lines['cap'] == 0, 'cap'] = 1000000
+        self.lines.loc[self.lines['floor'] == 0, 'floor'] = -1000000
+
+    def init(self) -> 'ClearCase':
+        """Initialize case data
+        
+        Set node ID mapping and basic flags.
+        
+        Returns:
+            self to support method chaining
+        """
+        self.init_flag = True
+        self.units['#bus_id'], self.loads['#bus_id'] = self.get_nodes_id(
+            self.units['bus_id'], self.loads['bus_id']
+        )
+        if self.flexloads_num > 0:
+            # The trailing comma is required because the function returns a list object which needs ',' unpacking.
+            self.flexloads['#bus_id'], = self.get_nodes_id(self.flexloads['bus_id'])
+        self.lines['#from'], self.lines['#to'] = self.get_nodes_id(
+            self.lines['from'], self.lines['to'])
+        if not hasattr(self, 'name'):
+            self.name = 'Case'
+        return self
+
+    def __repr__(self) -> str:
+        """String representation"""
+        return '\n'.join(self.to_str())
+
+    # =============================================================================
+    # Plotter property
+    # =============================================================================
+
+    @property
+    def plotter(self):
+        """Get the CasePlotter instance (lazy initialization)
+        
+        Returns:
+            CasePlotter instance for visualizing this case
+        """
+        if self._plotter is None:
+            from .CasePlotter import CasePlotter
+            self._plotter = CasePlotter(self)
+        return self._plotter
+
+    # =============================================================================
+    # Data validation methods
+    # =============================================================================
+
+    def check(self) -> None:
+        """Check data integrity
+        
+        Verify that each table contains the required columns.
+        
+        Raises:
+            AssertionError: raised when required columns are missing
+        """
+        nodes_col = {'id'}
+        units_col = {'id', 'bus_id', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min'}
+        lines_col = {'id', 'from', 'to', 'x', 'floor', 'cap'}
+        loads_col = {'id', 'bus_id', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'}
+
+        self.assert_cols(nodes_col, getattr(self, "nodes"))
+        self.assert_cols(units_col, getattr(self, "units"))
+        self.assert_cols(lines_col, getattr(self, "lines"))
+        self.assert_cols(loads_col, getattr(self, "loads"))
+
+    @staticmethod
+    def assert_cols(min_col_set: set, df_to_check: pd.DataFrame) -> None:
+        """Assert that a DataFrame contains at least a set of columns
+        
+        Args:
+            min_col_set: minimal required column set
+            df_to_check: DataFrame to check
+            
+        Raises:
+            AssertionError: raised when required columns are missing
+        """
+        assert set(df_to_check.columns) & min_col_set == min_col_set, \
+            f'最少需要以下列：{min_col_set}'
+
+    def get_nodes_id(self, *args) -> List[np.ndarray]:
+        """Get node ID mapping
+        
+        Args:
+            *args: node ID arrays
+            
+        Returns:
+            list of mapped node IDs
+        """
+        return [self.nodes.loc[np.array(a).flatten(), '#id'].values for a in args]
+
+    # =============================================================================
+    # Matrix computation methods
+    # =============================================================================
+
+    def get_node_gsdf(self) -> pd.DataFrame:
+        """Get node GSDF matrix (cached)
+        
+        Returns:
+            generation shift distribution factor matrix per node
+        """
+        if self._node_gsdf is None:
+            self.cal_gsdf(False)
+            self._node_gsdf = self.node_gsdf
+        return self._node_gsdf
+
+    def get_node_ptdf(self) -> pd.DataFrame:
+        """Get node PTDF matrix (cached)
+        
+        Returns:
+            power transfer distribution factor matrix per node
+        """
+        if self._node_ptdf is None:
+            self.cal_gsdf(True)
+            self._node_ptdf = self.node_ptdf
+        return self._node_ptdf
+
+    def cal_gsdf(self, ptdf: bool = False) -> 'ClearCase':
+        """Compute GSDF and optionally PTDF matrices
+        
+        Args:
+            ptdf: whether to compute PTDF matrices as well
+        
+        Returns:
+            self to support method chaining
+        """
+        if not hasattr(self, 'init_flag') or not self.init_flag:
+            self.init()
+
+        # Build line-node incidence matrix
+        lines_nodes = coo_matrix(
+            (np.repeat((1, -1), len(self.lines)),
+             (np.tile(np.arange(len(self.lines)), 2),
+              self.lines[['#from', '#to']].values.T.flatten()))
+        ).toarray()
+
+        # Line susceptance matrix
+        lines_B = np.diag(-1 / self.lines['x'])
+
+        # Node admittance matrix (excluding reference node)
+        self.node_Y = lines_nodes.T.dot(lines_B).dot(lines_nodes)[1:, 1:]
+        node_Y_inv = np.linalg.inv(self.node_Y)
+
+        # Compute GSDF matrix
+        node_gsdf_1 = lines_B.dot(lines_nodes.dot(
+            np.vstack((np.zeros(len(self.nodes) - 1), node_Y_inv))
+        ))
+        self.node_gsdf = pd.DataFrame(
+            np.hstack((np.zeros((len(self.lines), 1)), node_gsdf_1)),
+            index=self.lines['id'],
+            columns=self.nodes['id']
+        )
+
+        # GSDF matrices for units and loads
+        self.unit_gsdf = self.node_gsdf[self.units['bus_id']]
+        self.unit_load_gsdf = pd.concat((
+            self.node_gsdf[self.units['bus_id']],
+            -self.node_gsdf[self.loads['bus_id']]
+        ), axis=1)
+
+        # Compute PTDF matrix (only for constrained lines)
+        if ptdf:
+            self.ptdf_lines_flag = np.where(
+                (self.lines.loc[:, 'floor'] == 0) & (self.lines.loc[:, 'cap'] == 0),
+                False, True
+            )
+            self.ptdf_lines = self.lines.loc[self.ptdf_lines_flag]
+            self.node_ptdf = self.node_gsdf[self.ptdf_lines_flag]
+            self.unit_load_ptdf = pd.concat((
+                self.node_ptdf[self.units['bus_id']],
+                -self.node_ptdf[self.loads['bus_id']]
+            ), axis=1)
+        return self
+
+    def get_A_matrix(self) -> np.ndarray:
+        """Get adjacency matrix
+        
+        Returns:
+            network adjacency matrix
+        """
+        if not hasattr(self, 'init_flag') or not self.init_flag:
+            self.init()
+        row = np.hstack((self.lines['#from'], self.lines['#to']))
+        col = np.hstack((self.lines['#to'], self.lines['#from']))
+        A = coo_matrix(
+            (np.ones(len(row)), (row, col)),
+            shape=(len(self.nodes), len(self.nodes))
+        ).toarray()
+        return A
+
+    def get_D_matrix(self) -> np.ndarray:
+        """Get degree matrix
+        
+        Returns:
+            diagonal degree matrix whose diagonal is node degrees
+        """
+        if not hasattr(self, 'init_flag') or not self.init_flag:
+            self.init()
+        A = self.get_A_matrix()
+        d = np.sum(A, axis=1)
+        D = np.diag(d)
+        return D
+
+    def normalize_adjacency(self, adjacency: np.ndarray) -> np.ndarray:
+        """Normalize adjacency matrix
+        
+        Args:
+            adjacency: adjacency matrix
+            
+        Returns:
+            normalized adjacency matrix
+        """
+        adj = sp.coo_matrix(adjacency)
+        rowsum = np.array(adj.sum(1))  # node degree per row
+        rowsum[rowsum == 0] = 1  # avoid division by zero
+        degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5).flatten())
+        normalized_adjacency = adj.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo()
+        return normalized_adjacency.todense()
+
+    def get_L_matrix(self) -> np.ndarray:
+        """Get normalized Laplacian matrix
+        
+        Returns:
+            normalized Laplacian matrix
+        """
+        if not hasattr(self, 'init_flag') or not self.init_flag:
+            self.init()
+        A = self.get_A_matrix()
+        A_self = A + np.eye(len(A))
+        D_half_inv = np.diag(np.power(np.sum(A_self, axis=1), -0.5))
+        L = np.eye(A_self.shape[0]) - np.dot(np.dot(D_half_inv, A_self), D_half_inv)
+        return L
+
+    # =============================================================================
+    # Node mapping methods
+    # =============================================================================
+
+    def get_nodes_map_units_and_loads(self) -> np.ndarray:
+        """Get node-to-units and node-to-loads mapping matrix
+        
+        Returns:
+            mapping matrix (units positive, loads negative)
+        """
+        if not hasattr(self, 'init_flag') or not self.init_flag:
+            self.init()
+
+        # Node-to-unit relationship
+        self.nodes_map_units = coo_matrix(
+            (np.ones(len(self.units)), (self.units['#bus_id'], self.units['#id'])),
+            shape=(len(self.nodes), len(self.units))
+        ).toarray()
+
+        # Node-to-load relationship
+        self.nodes_map_loads = coo_matrix(
+            (np.ones(len(self.loads)), (self.loads['#bus_id'], self.loads['#id'])),
+            shape=(len(self.nodes), len(self.loads))
+        ).toarray()
+
+        return np.hstack([self.nodes_map_units, -self.nodes_map_loads])
+
+    def get_nodes_units_map(self) -> np.ndarray:
+        """Get node-to-units mapping matrix
+        
+        Returns:
+            node-to-units mapping matrix
+        """
+        if not hasattr(self, 'init_flag') or not self.init_flag:
+            self.init()
+
+        self.nodes_map_units = coo_matrix(
+            (np.ones(len(self.units)), (self.units['#bus_id'], self.units['#id'])),
+            shape=(len(self.nodes), len(self.units))
+        ).toarray()
+        return self.nodes_map_units
+
+    def get_nodes_loads_map(self) -> np.ndarray:
+        """Get node-to-loads mapping matrix
+        
+        Returns:
+            node-to-loads mapping matrix
+        """
+        if not hasattr(self, 'init_flag') or not self.init_flag:
+            self.init()
+
+        self.nodes_map_loads = coo_matrix(
+            (np.ones(len(self.loads)), (self.loads['#bus_id'], self.loads['#id'])),
+            shape=(len(self.nodes), len(self.loads))
+        ).toarray()
+        return self.nodes_map_loads
+
+    def get_nodes_flexloads_map(self) -> np.ndarray:
+        """Get node-to-flexloads mapping matrix
+        
+        Returns:
+            node-to-flexloads mapping matrix
+        """
+        if not hasattr(self, 'init_flag') or not self.init_flag:
+            self.init()
+
+        self.nodes_map_flexloads = coo_matrix(
+            (np.ones(len(self.flexloads)), (self.flexloads['#bus_id'], self.flexloads['#id'])),
+            shape=(len(self.nodes), len(self.flexloads))
+        ).toarray()
+        return self.nodes_map_flexloads
+
+    # =============================================================================
+    # Utility methods
+    # =============================================================================
+
+    def length(self) -> List[int]:
+        """Get lengths of each data table
+        
+        Returns:
+            [nodes, units, lines, loads]
+        """
+        return [len(getattr(self, v)) for v in ["nodes", "units", "lines", "loads"]]
+
+    def to_str(self) -> List[str]:
+        """Convert to string representation
+        
+        Returns:
+            list of string representations of tables
+        """
+        v_list = [getattr(self, v).set_label(v) for v in ["nodes", "units", "lines", "loads"]]
+        v_str_list = [str(v) for v in v_list]
+        return v_str_list
+
+    # =============================================================================
+    # Cost calculation methods
+    # =============================================================================
+    def cal_unit_piecewise_by_margin_cost(self, band_num: int = 5) -> np.ndarray:
+        """Compute piecewise linear parameters based on marginal cost
+        
+        Args:
+            band_num: number of segments
+            
+        Returns:
+            piecewise cost matrix
+        """
+        a, b, c = self.units['mc_a'], self.units['mc_b'], self.units['mc_c']
+        p_max, p_min = self.units['p_max'], self.units['p_min']
+
+        # Compute segment ratios
+        xs = np.tile((p_max - p_min) / p_max / band_num, (band_num, 1)).T
+        ys = []
+
+        # Compute marginal cost per segment
+        for x in np.cumsum(xs, 1).T:
+            y = list(a * (p_min + p_max * x) ** 2 + b * (p_min + p_max * x) + c)
+            # Ensure costs are monotonically increasing
+            if len(ys) > 0:
+                for i in range(len(y)):
+                    if y[i] <= ys[-1][i]:
+                        y[i] = ys[-1][i] + 1
+            ys.append(y)
+
+        return np.array(ys).T
+
+    def cal_units_cost(self, units_power: Union[List, np.ndarray]) -> np.ndarray:
+        """Compute generation cost for units
+        
+        Args:
+            units_power: unit output array
+            
+        Returns:
+            unit generation cost array
+        """
+        units_power = np.array(units_power) if isinstance(units_power, list) else units_power
+        units_cost = (
+                (self.units['mc_a'].values / 3) * units_power ** 3 +
+                (self.units['mc_b'].values / 2) * units_power ** 2 +
+                self.units['mc_c'].values * units_power
+        )
+        return units_cost
+
+    # =============================================================================
+    # Graph connectivity methods
+    # =============================================================================
+    
+    def is_connected_graph(self) -> bool:
+        """Check if the power network forms a connected graph.
+
+        Returns:
+            True if the graph is connected, False otherwise
+        """
+        return len(self.get_graph_components()) <= 1
+
+    def get_graph_components(self) -> List[List]:
+        """Get all connected components of the power network graph.
+
+        Only in-service lines (status == 1, or missing status column) are
+        considered.
+
+        Returns:
+            List of connected components, each component is a list of node IDs
+        """
+        if len(self.nodes) == 0:
+            return []
+
+        adjacency: dict = {node_id: [] for node_id in self.nodes['id'].values}
+
+        for _, line in self.lines.iterrows():
+            if line.get('status', 1) == 1:
+                from_bus, to_bus = line['from'], line['to']
+                adjacency[from_bus].append(to_bus)
+                adjacency[to_bus].append(from_bus)
+
+        visited: set = set()
+        components: List[List] = []
+
+        for node_id in self.nodes['id'].values:
+            if node_id not in visited:
+                component = []
+                queue = [node_id]
+                visited.add(node_id)
+                while queue:
+                    current = queue.pop(0)
+                    component.append(current)
+                    for neighbor in adjacency[current]:
+                        if neighbor not in visited:
+                            visited.add(neighbor)
+                            queue.append(neighbor)
+                components.append(component)
+
+        return components
+
+    def get_3ph_matrix(self, config: pd.Series, matrix_type: str = 'Z') -> np.ndarray:
+        """Extract 3x3 matrix from line configuration
+        
+        For three-phase systems, extract impedance (Z), capacitance (C), or other 
+        3x3 matrices from line configuration data.
+        
+        Args:
+            config: Line configuration series with elements like Z11, Z12, ..., Z33
+            matrix_type: Type of matrix to extract ('Z' for impedance, 'C' for capacitance, etc.)
+            
+        Returns:
+            3x3 numpy array (complex128 for 'Z', float64 for others)
+            
+        Raises:
+            AttributeError: If line_config does not exist for this case
+            KeyError: If required matrix elements are not in config
+            
+        Example:
+            >>> config = case.line_config.iloc[0]
+            >>> z_matrix = case.get_3ph_matrix(config, 'Z')
+            >>> z_matrix.shape
+            (3, 3)
+        """
+        if not hasattr(self, 'line_config'):
+            raise AttributeError(
+                f"Case {self.name} does not have 'line_config' attribute. "
+                "This method is only available for three-phase cases."
+            )
+        
+        # Create matrix with appropriate data type
+        dtype = np.complex128 if matrix_type == 'Z' else np.float64
+        
+        # Generate all element names: Z11, Z12, ..., Z33
+        element_names = [f'{matrix_type}{i + 1}{j + 1}' for i in range(3) for j in range(3)]
+        
+        # Validate all elements exist (fail fast)
+        missing_elements = [name for name in element_names if name not in config.index]
+        if missing_elements:
+            raise KeyError(
+                f"Elements {missing_elements} not found in config. "
+                f"Available elements: {list(config.index)}"
+            )
+        
+        # Extract values and reshape to 3x3 matrix (vectorized)
+        matrix_3x3 = np.array([config[name] for name in element_names], dtype=dtype).reshape(3, 3)
+        
+        return matrix_3x3
+    
+    def get_line_impedance_matrices(self, reshape_2d: bool = False) -> np.ndarray:
+        """Get impedance matrices for all lines in three-phase system
+        
+        For each line, extract its 3x3 impedance matrix from line_config and 
+        multiply by line length to get total impedance.
+        
+        Args:
+            reshape_2d: If True, reshape output to (n_lines * 3, 3) instead of (n_lines, 3, 3)
+            
+        Returns:
+            numpy array of shape (n_lines, 3, 3) or (n_lines * 3, 3) if reshape_2d=True
+            Each element is the total impedance matrix (config impedance * length)
+            
+        Raises:
+            AttributeError: If line_config or lines does not exist
+            
+        Example:
+            >>> z_matrices = case.get_line_impedance_matrices()
+            >>> z_matrices.shape
+            (113, 3, 3)  # For 113 lines
+            >>> z_matrices_2d = case.get_line_impedance_matrices(reshape_2d=True)
+            >>> z_matrices_2d.shape
+            (339, 3)  # 113 * 3 rows, 3 columns
+        """
+        if not hasattr(self, 'line_config'):
+            raise AttributeError(
+                f"Case {self.name} does not have 'line_config' attribute. "
+                "This method is only available for three-phase cases."
+            )
+        
+        if not hasattr(self, 'lines'):
+            raise AttributeError(
+                f"Case {self.name} does not have 'lines' attribute."
+            )
+        
+        # Build impedance matrices using list comprehension
+        z_matrices = np.array([
+            self.get_3ph_matrix(self.line_config.loc[int(line['config_name'])]) * line['length']
+            for _, line in self.lines.iterrows()
+        ])
+        
+        # Optionally reshape to 2D
+        if reshape_2d:
+            return z_matrices.reshape(-1, 3)
+        
+        return z_matrices
+

powergrid_case/distribution/Case118zh.py

@@ -0,0 +1,452 @@
+"""Power System Case: 118zh
+
+Data Source: MATPOWER case file
+Base MVA: 10.0 MVA
+Base kV: 11.0 kV
+
+Unit conversions applied:
+  - Pd/Qd converted from kW/kVar to MW/MVAr (divided by 1000)
+  - Pd converted from kVA to MW/MVAr (divided by 1000)
+  - r/x converted from Ohms to p.u. (Z_pu = Z_ohm * Sbase / Vbase^2)
+
+
+Nodes (bus) columns:
+    id, type, Pd, Qd, Gs, Bs, Vm, Va, baseKV, Vmax, Vmin
+    - type: 1=PQ, 2=PV, 3=Slack
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - Gs/Bs: Shunt conductance/susceptance (MW/MVAr at V=1.0 p.u.)
+    - Vm: Voltage magnitude (p.u.)
+    - Va: Voltage angle (degrees)
+    - baseKV: Base voltage (kV)
+    - Vmax/Vmin: Voltage limits (p.u.)
+
+Units (generators) columns:
+    id, bus_id, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, mc_a, mc_b, mc_c
+    - Pg/Qg: Active/Reactive power output (MW/MVAr)
+    - Qmax/Qmin: Reactive power limits (MVAr)
+    - Vg: Voltage setpoint (p.u.)
+    - mBase: Machine base (MVA)
+    - status: >0 in-service, <=0 out-of-service
+    - Pmax/Pmin: Active power limits (MW)
+    - mc_a/mc_b/mc_c: Marginal cost coefficients (quadratic)
+
+Lines (branches) columns:
+    id, from, to, r, x, b, rateA, ratio, angle, status, floor, cap
+    - r/x: Resistance/Reactance (p.u.)
+    - b: Line charging susceptance (p.u.)
+    - rateA: Long-term rating (MVA)
+    - ratio: Transformer tap ratio (0 for line)
+    - angle: Transformer phase shift (degrees)
+    - status: 1=in-service, 0=out-of-service
+    - floor/cap: Power flow limits (MW)
+
+Loads columns:
+    id, bus_id, Pd, Qd, mc_a, mc_b, mc_c, d_max, d_min
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - mc_a/mc_b/mc_c: Marginal utility coefficients
+    - d_max/d_min: Demand limits (MW)
+"""
+from math import inf
+
+from powerzoo.case.CaseBase import ClearCase, DataFrame
+
+
+class Case118zh(ClearCase):
+    GRID_TYPE = "distribution"
+    BUS_COUNT = 118
+    VOLTAGE_LEVEL = "MV"
+    SOURCE = "MATPOWER"
+    DESCRIPTION = "118-bus Zhang distribution system"
+
+    def __init__(self, *args, **kwargs):
+        # System base values
+        self.baseMVA = 10.0
+        self.baseKV = 11.0
+        
+        # Node (bus) data
+        self.nodes = DataFrame(
+            ['id', 'type', 'Pd', 'Qd', 'Gs', 'Bs', 'Vm', 'Va', 'baseKV', 'Vmax', 'Vmin'],
+            [[1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 11.0, 1.0, 1.0],
+             [2.0, 1.0, 0.13384000000000001, 0.10114, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [3.0, 1.0, 0.016214, 0.011292, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [4.0, 1.0, 0.034315, 0.021845, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [5.0, 1.0, 0.07301600000000001, 0.06360199999999999, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [6.0, 1.0, 0.1442, 0.068604, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [7.0, 1.0, 0.10447, 0.061725, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [8.0, 1.0, 0.028547, 0.011503, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [9.0, 1.0, 0.08756, 0.051073, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [10.0, 1.0, 0.1982, 0.10676999999999999, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [11.0, 1.0, 0.1468, 0.07599500000000001, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [12.0, 1.0, 0.02604, 0.018687000000000002, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [13.0, 1.0, 0.0521, 0.023219999999999998, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [14.0, 1.0, 0.1419, 0.1175, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [15.0, 1.0, 0.02187, 0.02879, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [16.0, 1.0, 0.03337, 0.026449999999999998, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [17.0, 1.0, 0.03243, 0.02523, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [18.0, 1.0, 0.020234000000000002, 0.011906, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [19.0, 1.0, 0.15694, 0.078523, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [20.0, 1.0, 0.5462899999999999, 0.3514, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [21.0, 1.0, 0.18031, 0.16419999999999998, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [22.0, 1.0, 0.093167, 0.054594000000000004, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [23.0, 1.0, 0.08518, 0.03965, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [24.0, 1.0, 0.1681, 0.095178, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [25.0, 1.0, 0.12511, 0.15022, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [26.0, 1.0, 0.016030000000000003, 0.02462, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [27.0, 1.0, 0.02603, 0.02462, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [28.0, 1.0, 0.59456, 0.52262, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [29.0, 1.0, 0.12062, 0.059116999999999996, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [30.0, 1.0, 0.10238, 0.099554, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [31.0, 1.0, 0.5134, 0.3185, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [32.0, 1.0, 0.47525, 0.45614, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [33.0, 1.0, 0.15143, 0.13679, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [34.0, 1.0, 0.20538, 0.083302, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [35.0, 1.0, 0.1316, 0.093082, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [36.0, 1.0, 0.44839999999999997, 0.36979, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [37.0, 1.0, 0.44051999999999997, 0.32164, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [38.0, 1.0, 0.11254, 0.055134, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [39.0, 1.0, 0.053963000000000004, 0.038998, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [40.0, 1.0, 0.39305, 0.3426, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [41.0, 1.0, 0.32674000000000003, 0.27856000000000003, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [42.0, 1.0, 0.53626, 0.24024, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [43.0, 1.0, 0.076247, 0.066562, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [44.0, 1.0, 0.053520000000000005, 0.03976, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [45.0, 1.0, 0.040328, 0.031964, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [46.0, 1.0, 0.039653, 0.020758, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [47.0, 1.0, 0.06619499999999999, 0.042360999999999996, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [48.0, 1.0, 0.073904, 0.051653, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [49.0, 1.0, 0.11477, 0.057965, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [50.0, 1.0, 0.91837, 1.2050999999999998, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [51.0, 1.0, 0.21030000000000001, 0.14665999999999998, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [52.0, 1.0, 0.06668, 0.056608, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [53.0, 1.0, 0.042207, 0.040184, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [54.0, 1.0, 0.43374, 0.28341000000000005, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [55.0, 1.0, 0.0621, 0.02686, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [56.0, 1.0, 0.09246, 0.08838, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [57.0, 1.0, 0.085188, 0.055436, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [58.0, 1.0, 0.3453, 0.3324, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [59.0, 1.0, 0.0225, 0.016829999999999998, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [60.0, 1.0, 0.080551, 0.049156, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [61.0, 1.0, 0.09586, 0.09075799999999999, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [62.0, 1.0, 0.06292, 0.047700000000000006, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [63.0, 1.0, 0.4788, 0.46374, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [64.0, 1.0, 0.12093999999999999, 0.052006000000000004, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [65.0, 1.0, 0.13911, 0.10034, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [66.0, 1.0, 0.39177999999999996, 0.1935, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [67.0, 1.0, 0.027741, 0.026713, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [68.0, 1.0, 0.052814, 0.025257, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [69.0, 1.0, 0.06689, 0.038713000000000004, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [70.0, 1.0, 0.4675, 0.39514, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [71.0, 1.0, 0.59485, 0.23974, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [72.0, 1.0, 0.1325, 0.084363, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [73.0, 1.0, 0.052698999999999996, 0.022482, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [74.0, 1.0, 0.86979, 0.614775, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [75.0, 1.0, 0.031349, 0.029817, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [76.0, 1.0, 0.19238999999999998, 0.12243000000000001, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [77.0, 1.0, 0.06575, 0.04537, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [78.0, 1.0, 0.23815, 0.22322, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [79.0, 1.0, 0.29455000000000003, 0.16247, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [80.0, 1.0, 0.48557, 0.43792000000000003, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [81.0, 1.0, 0.24353, 0.18303, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [82.0, 1.0, 0.24353, 0.18303, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [83.0, 1.0, 0.13425, 0.11929000000000001, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [84.0, 1.0, 0.02271, 0.027960000000000002, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [85.0, 1.0, 0.049513, 0.026515, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [86.0, 1.0, 0.38377999999999995, 0.25716, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [87.0, 1.0, 0.049640000000000004, 0.0206, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [88.0, 1.0, 0.022473, 0.011805999999999999, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [89.0, 1.0, 0.06293, 0.04296, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [90.0, 1.0, 0.030670000000000003, 0.03493, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [91.0, 1.0, 0.06253, 0.06679, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [92.0, 1.0, 0.11456999999999999, 0.081748, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [93.0, 1.0, 0.081292, 0.066526, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [94.0, 1.0, 0.031733, 0.015960000000000002, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [95.0, 1.0, 0.03332, 0.06048, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [96.0, 1.0, 0.53128, 0.22485, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [97.0, 1.0, 0.50703, 0.36742, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [98.0, 1.0, 0.02639, 0.011699999999999999, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [99.0, 1.0, 0.04599, 0.030392, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [100.0, 1.0, 0.10066, 0.047572, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [101.0, 1.0, 0.45648, 0.3503, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [102.0, 1.0, 0.5225599999999999, 0.44929, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [103.0, 1.0, 0.40843, 0.16846, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [104.0, 1.0, 0.14148, 0.13425, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [105.0, 1.0, 0.10443000000000001, 0.066024, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [106.0, 1.0, 0.096793, 0.083647, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [107.0, 1.0, 0.49392, 0.41934, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [108.0, 1.0, 0.22538, 0.13588, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [109.0, 1.0, 0.5092099999999999, 0.38721, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [110.0, 1.0, 0.1885, 0.17346, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [111.0, 1.0, 0.91803, 0.89855, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [112.0, 1.0, 0.30507999999999996, 0.21537, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [113.0, 1.0, 0.054380000000000005, 0.04097, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [114.0, 1.0, 0.21114, 0.19290000000000002, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [115.0, 1.0, 0.067009, 0.053336, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [116.0, 1.0, 0.16207, 0.090321, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [117.0, 1.0, 0.048784999999999995, 0.029155999999999998, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9],
+             [118.0, 1.0, 0.0339, 0.01898, 0.0, 0.0, 1.0, 0.0, 11.0, 1.1, 0.9]])
+
+        # Generator (unit) data
+        self.units = DataFrame(
+            ['id', 'bus_id', 'Pg', 'Qg', 'Qmax', 'Qmin', 'Vg', 'mBase', 'status', 'Pmax', 'Pmin', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min'],
+            [[1.0, 1.0, 0.0, 0.0, 10.0, -10.0, 1.0, 100.0, 1.0, 10.0, 0.0, 0.0, 0.0, 20.0, 10.0, 0.0]])
+
+        # Branch (line) data
+        self.lines = DataFrame(
+            ['id', 'from', 'to', 'r', 'x', 'b', 'rateA', 'ratio', 'angle', 'status', 'floor', 'cap'],
+            [[1.0, 1.0, 2.0, 0.0029752066115702478, 0.0010710743801652892, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [2.0, 2.0, 3.0, 0.0027272727272727275, 0.0009818181818181818, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [3.0, 2.0, 4.0, 0.00371900826446281, 0.0013388429752066115, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [4.0, 4.0, 5.0, 0.0012396694214876034, 0.004462809917355372, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [5.0, 5.0, 6.0, 0.0012396694214876034, 0.004462809917355372, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [6.0, 6.0, 7.0, 0.0012396694214876034, 0.0010330578512396695, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [7.0, 7.0, 8.0, 0.0014876033057851239, 0.0011570247933884298, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [8.0, 8.0, 9.0, 0.0017355371900826448, 0.005206611570247934, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [9.0, 2.0, 10.0, 0.01371900826446281, 0.011107438016528925, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [10.0, 10.0, 11.0, 0.009256198347107438, 0.006520661157024793, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [11.0, 11.0, 12.0, 0.015454545454545455, 0.025867768595041325, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [12.0, 12.0, 13.0, 0.011735537190082643, 0.012495867768595043, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [13.0, 13.0, 14.0, 0.01487603305785124, 0.009752066115702479, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [14.0, 14.0, 15.0, 0.012396694214876033, 0.00371900826446281, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [15.0, 15.0, 16.0, 0.01322314049586777, 0.01487603305785124, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [16.0, 16.0, 17.0, 0.012975206611570249, 0.01413223140495868, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [17.0, 11.0, 18.0, 0.018016528925619835, 0.023553719008264463, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [18.0, 18.0, 19.0, 0.009752066115702479, 0.015289256198347107, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [19.0, 19.0, 20.0, 0.01322314049586777, 0.01619834710743802, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [20.0, 20.0, 21.0, 0.009917355371900827, 0.015619834710743801, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [21.0, 21.0, 22.0, 0.009917355371900827, 0.006520661157024793, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [22.0, 22.0, 23.0, 0.1165289256198347, 0.05975206611570248, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [23.0, 23.0, 24.0, 0.02421487603305785, 0.011140495867768595, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [24.0, 24.0, 25.0, 0.010991735537190083, 0.00859504132231405, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [25.0, 25.0, 26.0, 0.014710743801652892, 0.011074380165289258, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [26.0, 26.0, 27.0, 0.014710743801652892, 0.011074380165289258, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [27.0, 4.0, 28.0, 0.0012396694214876034, 0.0024462809917355373, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [28.0, 28.0, 29.0, 0.0009917355371900827, 0.00228099173553719, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [29.0, 29.0, 30.0, 0.009917355371900827, 0.022859504132231406, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [30.0, 30.0, 31.0, 0.017355371900826446, 0.020082644628099174, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [31.0, 31.0, 32.0, 0.009917355371900827, 0.004462809917355372, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [32.0, 32.0, 33.0, 0.014710743801652892, 0.019338842975206612, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [33.0, 33.0, 34.0, 0.014710743801652892, 0.019338842975206612, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [34.0, 34.0, 35.0, 0.012727272727272728, 0.013388429752066116, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [35.0, 30.0, 36.0, 0.015454545454545455, 0.0215702479338843, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [36.0, 36.0, 37.0, 0.010991735537190083, 0.008181818181818182, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [37.0, 29.0, 38.0, 0.027272727272727275, 0.01603305785123967, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [38.0, 38.0, 39.0, 0.0256198347107438, 0.01603305785123967, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [39.0, 39.0, 40.0, 0.010743801652892562, 0.01603305785123967, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [40.0, 40.0, 41.0, 0.023140495867768597, 0.012396694214876033, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [41.0, 41.0, 42.0, 0.09752066115702479, 0.07024793388429752, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [42.0, 42.0, 43.0, 0.03471074380165289, 0.020132231404958678, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [43.0, 43.0, 44.0, 0.022314049586776862, 0.008033057851239669, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [44.0, 44.0, 45.0, 0.028016528925619837, 0.010090909090909091, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [45.0, 45.0, 46.0, 0.022314049586776862, 0.014702479338842975, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [46.0, 35.0, 47.0, 0.017355371900826446, 0.011429752066115703, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [47.0, 47.0, 48.0, 0.009917355371900827, 0.006520661157024793, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [48.0, 48.0, 49.0, 0.012396694214876033, 0.00815702479338843, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [49.0, 49.0, 50.0, 0.012396694214876033, 0.00815702479338843, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [50.0, 50.0, 51.0, 0.019834710743801654, 0.013066115702479338, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [51.0, 51.0, 52.0, 0.009917355371900827, 0.006520661157024793, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [52.0, 52.0, 53.0, 0.03347107438016529, 0.012049586776859505, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [53.0, 53.0, 54.0, 0.03347107438016529, 0.012049586776859505, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [54.0, 29.0, 55.0, 0.03231404958677686, 0.01165289256198347, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [55.0, 55.0, 56.0, 0.03355371900826447, 0.012074380165289257, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [56.0, 56.0, 57.0, 0.03355371900826447, 0.012074380165289257, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [57.0, 57.0, 58.0, 0.05834710743801653, 0.04513223140495868, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [58.0, 58.0, 59.0, 0.027933884297520663, 0.010066115702479339, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [59.0, 59.0, 60.0, 0.027933884297520663, 0.010066115702479339, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [60.0, 60.0, 61.0, 0.017107438016528927, 0.006173553719008265, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [61.0, 61.0, 62.0, 0.02041322314049587, 0.07373553719008265, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [62.0, 1.0, 63.0, 0.0023140495867768596, 0.0034545454545454545, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [63.0, 63.0, 64.0, 0.009669421487603306, 0.01666115702479339, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [64.0, 64.0, 65.0, 0.021074380165289258, 0.007586776859504133, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [65.0, 65.0, 66.0, 0.017355371900826446, 0.006272727272727273, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [66.0, 66.0, 67.0, 0.031652892561983476, 0.011404958677685952, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [67.0, 67.0, 68.0, 0.04165289256198347, 0.027297520661157024, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [68.0, 68.0, 69.0, 0.03355371900826447, 0.012074380165289257, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [69.0, 69.0, 70.0, 0.07950413223140496, 0.06289256198347108, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [70.0, 70.0, 71.0, 0.013636363636363637, 0.0049586776859504135, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [71.0, 71.0, 72.0, 0.025041322314049586, 0.009024793388429753, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [72.0, 72.0, 73.0, 0.025041322314049586, 0.009024793388429753, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [73.0, 73.0, 74.0, 0.01702479338842975, 0.011900826446280991, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [74.0, 74.0, 75.0, 0.01925619834710744, 0.006942148760330579, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [75.0, 75.0, 76.0, 0.04884297520661157, 0.014652892561983473, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [76.0, 76.0, 77.0, 0.010413223140495868, 0.0037438016528925622, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [77.0, 64.0, 78.0, 0.046198347107438024, 0.030471074380165292, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [78.0, 78.0, 79.0, 0.015371900826446282, 0.010140495867768596, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [79.0, 79.0, 80.0, 0.015371900826446282, 0.010140495867768596, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [80.0, 80.0, 81.0, 0.021487603305785124, 0.011487603305785125, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [81.0, 81.0, 82.0, 0.012727272727272728, 0.012231404958677685, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [82.0, 82.0, 83.0, 0.01900826446280992, 0.010578512396694216, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [83.0, 83.0, 84.0, 0.020826446280991735, 0.008760330578512396, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [84.0, 84.0, 85.0, 0.01487603305785124, 0.012231404958677685, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [85.0, 79.0, 86.0, 0.01322314049586777, 0.015041322314049586, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [86.0, 86.0, 87.0, 0.01652892561983471, 0.01900826446280992, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [87.0, 87.0, 88.0, 0.01322314049586777, 0.03247933884297521, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [88.0, 65.0, 89.0, 0.05528925619834711, 0.01993388429752066, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [89.0, 89.0, 90.0, 0.021983471074380166, 0.010140495867768596, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [90.0, 90.0, 91.0, 0.021983471074380166, 0.010140495867768596, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [91.0, 91.0, 92.0, 0.021983471074380166, 0.010140495867768596, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [92.0, 92.0, 93.0, 0.021983471074380166, 0.010140495867768596, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [93.0, 93.0, 94.0, 0.01925619834710744, 0.00950413223140496, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [94.0, 94.0, 95.0, 0.040991735537190085, 0.011404958677685952, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [95.0, 91.0, 96.0, 0.01619834710743802, 0.01487603305785124, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [96.0, 96.0, 97.0, 0.01619834710743802, 0.01487603305785124, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [97.0, 97.0, 98.0, 0.015421487603305785, 0.010082644628099173, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [98.0, 98.0, 99.0, 0.006165289256198347, 0.02628099173553719, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [99.0, 1.0, 100.0, 0.005165289256198347, 0.002190082644628099, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [100.0, 100.0, 101.0, 0.012404958677685951, 0.019338842975206612, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [101.0, 101.0, 102.0, 0.011132231404958677, 0.0073388429752066124, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [102.0, 102.0, 103.0, 0.01906611570247934, 0.00994214876033058, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [103.0, 103.0, 104.0, 0.03694214876033058, 0.013289256198347107, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [104.0, 104.0, 105.0, 0.013487603305785125, 0.004859504132231405, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [105.0, 105.0, 106.0, 0.027272727272727275, 0.008181818181818182, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [106.0, 106.0, 107.0, 0.012892561983471074, 0.004636363636363636, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [107.0, 107.0, 108.0, 0.03156198347107438, 0.011355371900826446, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [108.0, 108.0, 109.0, 0.013438016528925619, 0.004834710743801653, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [109.0, 109.0, 110.0, 0.03156198347107438, 0.011355371900826446, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [110.0, 110.0, 111.0, 0.020206611570247935, 0.0072644628099173564, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [111.0, 110.0, 112.0, 0.01725619834710744, 0.006223140495867769, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [112.0, 112.0, 113.0, 0.019016528925619836, 0.00684297520661157, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [113.0, 100.0, 114.0, 0.050429752066115704, 0.018148760330578512, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [114.0, 114.0, 115.0, 0.015421487603305785, 0.010495867768595043, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [115.0, 115.0, 116.0, 0.03084297520661157, 0.020330578512396693, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [116.0, 116.0, 117.0, 0.03347107438016529, 0.030330578512396695, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [117.0, 117.0, 118.0, 0.04041322314049587, 0.036198347107438016, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [118.0, 46.0, 27.0, 0.04345454545454546, 0.024173553719008263, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [119.0, 17.0, 27.0, 0.04345454545454546, 0.02409917355371901, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [120.0, 8.0, 24.0, 0.03530578512396695, 0.012719008264462811, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [121.0, 54.0, 43.0, 0.03966942148760331, 0.014280991735537192, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [122.0, 62.0, 49.0, 0.02975206611570248, 0.010710743801652892, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [123.0, 37.0, 62.0, 0.047107438016528926, 0.04727272727272727, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [124.0, 9.0, 40.0, 0.043801652892561986, 0.027669421487603305, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [125.0, 58.0, 96.0, 0.032702479338842974, 0.011776859504132231, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [126.0, 73.0, 91.0, 0.05619834710743802, 0.053553719008264465, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [127.0, 88.0, 75.0, 0.0335702479338843, 0.01209917355371901, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [128.0, 99.0, 77.0, 0.03823140495867769, 0.013834710743801652, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [129.0, 108.0, 83.0, 0.05380165289256199, 0.019338842975206612, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [130.0, 105.0, 86.0, 0.06714876033057851, 0.024173553719008263, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [131.0, 110.0, 118.0, 0.058586776859504135, 0.02109917355371901, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [132.0, 25.0, 35.0, 0.04132231404958678, 0.04132231404958678, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
+
+        # Load data
+        self.loads = DataFrame(
+            ['id', 'bus_id', 'Pd', 'Qd', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'],
+            [[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [2.0, 2.0, 0.13384000000000001, 0.10114, 0.0, 0.0, 0.0, 0.13384000000000001, 0.13384000000000001],
+             [3.0, 3.0, 0.016214, 0.011292, 0.0, 0.0, 0.0, 0.016214, 0.016214],
+             [4.0, 4.0, 0.034315, 0.021845, 0.0, 0.0, 0.0, 0.034315, 0.034315],
+             [5.0, 5.0, 0.07301600000000001, 0.06360199999999999, 0.0, 0.0, 0.0, 0.07301600000000001, 0.07301600000000001],
+             [6.0, 6.0, 0.1442, 0.068604, 0.0, 0.0, 0.0, 0.1442, 0.1442],
+             [7.0, 7.0, 0.10447, 0.061725, 0.0, 0.0, 0.0, 0.10447, 0.10447],
+             [8.0, 8.0, 0.028547, 0.011503, 0.0, 0.0, 0.0, 0.028547, 0.028547],
+             [9.0, 9.0, 0.08756, 0.051073, 0.0, 0.0, 0.0, 0.08756, 0.08756],
+             [10.0, 10.0, 0.1982, 0.10676999999999999, 0.0, 0.0, 0.0, 0.1982, 0.1982],
+             [11.0, 11.0, 0.1468, 0.07599500000000001, 0.0, 0.0, 0.0, 0.1468, 0.1468],
+             [12.0, 12.0, 0.02604, 0.018687000000000002, 0.0, 0.0, 0.0, 0.02604, 0.02604],
+             [13.0, 13.0, 0.0521, 0.023219999999999998, 0.0, 0.0, 0.0, 0.0521, 0.0521],
+             [14.0, 14.0, 0.1419, 0.1175, 0.0, 0.0, 0.0, 0.1419, 0.1419],
+             [15.0, 15.0, 0.02187, 0.02879, 0.0, 0.0, 0.0, 0.02187, 0.02187],
+             [16.0, 16.0, 0.03337, 0.026449999999999998, 0.0, 0.0, 0.0, 0.03337, 0.03337],
+             [17.0, 17.0, 0.03243, 0.02523, 0.0, 0.0, 0.0, 0.03243, 0.03243],
+             [18.0, 18.0, 0.020234000000000002, 0.011906, 0.0, 0.0, 0.0, 0.020234000000000002, 0.020234000000000002],
+             [19.0, 19.0, 0.15694, 0.078523, 0.0, 0.0, 0.0, 0.15694, 0.15694],
+             [20.0, 20.0, 0.5462899999999999, 0.3514, 0.0, 0.0, 0.0, 0.5462899999999999, 0.5462899999999999],
+             [21.0, 21.0, 0.18031, 0.16419999999999998, 0.0, 0.0, 0.0, 0.18031, 0.18031],
+             [22.0, 22.0, 0.093167, 0.054594000000000004, 0.0, 0.0, 0.0, 0.093167, 0.093167],
+             [23.0, 23.0, 0.08518, 0.03965, 0.0, 0.0, 0.0, 0.08518, 0.08518],
+             [24.0, 24.0, 0.1681, 0.095178, 0.0, 0.0, 0.0, 0.1681, 0.1681],
+             [25.0, 25.0, 0.12511, 0.15022, 0.0, 0.0, 0.0, 0.12511, 0.12511],
+             [26.0, 26.0, 0.016030000000000003, 0.02462, 0.0, 0.0, 0.0, 0.016030000000000003, 0.016030000000000003],
+             [27.0, 27.0, 0.02603, 0.02462, 0.0, 0.0, 0.0, 0.02603, 0.02603],
+             [28.0, 28.0, 0.59456, 0.52262, 0.0, 0.0, 0.0, 0.59456, 0.59456],
+             [29.0, 29.0, 0.12062, 0.059116999999999996, 0.0, 0.0, 0.0, 0.12062, 0.12062],
+             [30.0, 30.0, 0.10238, 0.099554, 0.0, 0.0, 0.0, 0.10238, 0.10238],
+             [31.0, 31.0, 0.5134, 0.3185, 0.0, 0.0, 0.0, 0.5134, 0.5134],
+             [32.0, 32.0, 0.47525, 0.45614, 0.0, 0.0, 0.0, 0.47525, 0.47525],
+             [33.0, 33.0, 0.15143, 0.13679, 0.0, 0.0, 0.0, 0.15143, 0.15143],
+             [34.0, 34.0, 0.20538, 0.083302, 0.0, 0.0, 0.0, 0.20538, 0.20538],
+             [35.0, 35.0, 0.1316, 0.093082, 0.0, 0.0, 0.0, 0.1316, 0.1316],
+             [36.0, 36.0, 0.44839999999999997, 0.36979, 0.0, 0.0, 0.0, 0.44839999999999997, 0.44839999999999997],
+             [37.0, 37.0, 0.44051999999999997, 0.32164, 0.0, 0.0, 0.0, 0.44051999999999997, 0.44051999999999997],
+             [38.0, 38.0, 0.11254, 0.055134, 0.0, 0.0, 0.0, 0.11254, 0.11254],
+             [39.0, 39.0, 0.053963000000000004, 0.038998, 0.0, 0.0, 0.0, 0.053963000000000004, 0.053963000000000004],
+             [40.0, 40.0, 0.39305, 0.3426, 0.0, 0.0, 0.0, 0.39305, 0.39305],
+             [41.0, 41.0, 0.32674000000000003, 0.27856000000000003, 0.0, 0.0, 0.0, 0.32674000000000003, 0.32674000000000003],
+             [42.0, 42.0, 0.53626, 0.24024, 0.0, 0.0, 0.0, 0.53626, 0.53626],
+             [43.0, 43.0, 0.076247, 0.066562, 0.0, 0.0, 0.0, 0.076247, 0.076247],
+             [44.0, 44.0, 0.053520000000000005, 0.03976, 0.0, 0.0, 0.0, 0.053520000000000005, 0.053520000000000005],
+             [45.0, 45.0, 0.040328, 0.031964, 0.0, 0.0, 0.0, 0.040328, 0.040328],
+             [46.0, 46.0, 0.039653, 0.020758, 0.0, 0.0, 0.0, 0.039653, 0.039653],
+             [47.0, 47.0, 0.06619499999999999, 0.042360999999999996, 0.0, 0.0, 0.0, 0.06619499999999999, 0.06619499999999999],
+             [48.0, 48.0, 0.073904, 0.051653, 0.0, 0.0, 0.0, 0.073904, 0.073904],
+             [49.0, 49.0, 0.11477, 0.057965, 0.0, 0.0, 0.0, 0.11477, 0.11477],
+             [50.0, 50.0, 0.91837, 1.2050999999999998, 0.0, 0.0, 0.0, 0.91837, 0.91837],
+             [51.0, 51.0, 0.21030000000000001, 0.14665999999999998, 0.0, 0.0, 0.0, 0.21030000000000001, 0.21030000000000001],
+             [52.0, 52.0, 0.06668, 0.056608, 0.0, 0.0, 0.0, 0.06668, 0.06668],
+             [53.0, 53.0, 0.042207, 0.040184, 0.0, 0.0, 0.0, 0.042207, 0.042207],
+             [54.0, 54.0, 0.43374, 0.28341000000000005, 0.0, 0.0, 0.0, 0.43374, 0.43374],
+             [55.0, 55.0, 0.0621, 0.02686, 0.0, 0.0, 0.0, 0.0621, 0.0621],
+             [56.0, 56.0, 0.09246, 0.08838, 0.0, 0.0, 0.0, 0.09246, 0.09246],
+             [57.0, 57.0, 0.085188, 0.055436, 0.0, 0.0, 0.0, 0.085188, 0.085188],
+             [58.0, 58.0, 0.3453, 0.3324, 0.0, 0.0, 0.0, 0.3453, 0.3453],
+             [59.0, 59.0, 0.0225, 0.016829999999999998, 0.0, 0.0, 0.0, 0.0225, 0.0225],
+             [60.0, 60.0, 0.080551, 0.049156, 0.0, 0.0, 0.0, 0.080551, 0.080551],
+             [61.0, 61.0, 0.09586, 0.09075799999999999, 0.0, 0.0, 0.0, 0.09586, 0.09586],
+             [62.0, 62.0, 0.06292, 0.047700000000000006, 0.0, 0.0, 0.0, 0.06292, 0.06292],
+             [63.0, 63.0, 0.4788, 0.46374, 0.0, 0.0, 0.0, 0.4788, 0.4788],
+             [64.0, 64.0, 0.12093999999999999, 0.052006000000000004, 0.0, 0.0, 0.0, 0.12093999999999999, 0.12093999999999999],
+             [65.0, 65.0, 0.13911, 0.10034, 0.0, 0.0, 0.0, 0.13911, 0.13911],
+             [66.0, 66.0, 0.39177999999999996, 0.1935, 0.0, 0.0, 0.0, 0.39177999999999996, 0.39177999999999996],
+             [67.0, 67.0, 0.027741, 0.026713, 0.0, 0.0, 0.0, 0.027741, 0.027741],
+             [68.0, 68.0, 0.052814, 0.025257, 0.0, 0.0, 0.0, 0.052814, 0.052814],
+             [69.0, 69.0, 0.06689, 0.038713000000000004, 0.0, 0.0, 0.0, 0.06689, 0.06689],
+             [70.0, 70.0, 0.4675, 0.39514, 0.0, 0.0, 0.0, 0.4675, 0.4675],
+             [71.0, 71.0, 0.59485, 0.23974, 0.0, 0.0, 0.0, 0.59485, 0.59485],
+             [72.0, 72.0, 0.1325, 0.084363, 0.0, 0.0, 0.0, 0.1325, 0.1325],
+             [73.0, 73.0, 0.052698999999999996, 0.022482, 0.0, 0.0, 0.0, 0.052698999999999996, 0.052698999999999996],
+             [74.0, 74.0, 0.86979, 0.614775, 0.0, 0.0, 0.0, 0.86979, 0.86979],
+             [75.0, 75.0, 0.031349, 0.029817, 0.0, 0.0, 0.0, 0.031349, 0.031349],
+             [76.0, 76.0, 0.19238999999999998, 0.12243000000000001, 0.0, 0.0, 0.0, 0.19238999999999998, 0.19238999999999998],
+             [77.0, 77.0, 0.06575, 0.04537, 0.0, 0.0, 0.0, 0.06575, 0.06575],
+             [78.0, 78.0, 0.23815, 0.22322, 0.0, 0.0, 0.0, 0.23815, 0.23815],
+             [79.0, 79.0, 0.29455000000000003, 0.16247, 0.0, 0.0, 0.0, 0.29455000000000003, 0.29455000000000003],
+             [80.0, 80.0, 0.48557, 0.43792000000000003, 0.0, 0.0, 0.0, 0.48557, 0.48557],
+             [81.0, 81.0, 0.24353, 0.18303, 0.0, 0.0, 0.0, 0.24353, 0.24353],
+             [82.0, 82.0, 0.24353, 0.18303, 0.0, 0.0, 0.0, 0.24353, 0.24353],
+             [83.0, 83.0, 0.13425, 0.11929000000000001, 0.0, 0.0, 0.0, 0.13425, 0.13425],
+             [84.0, 84.0, 0.02271, 0.027960000000000002, 0.0, 0.0, 0.0, 0.02271, 0.02271],
+             [85.0, 85.0, 0.049513, 0.026515, 0.0, 0.0, 0.0, 0.049513, 0.049513],
+             [86.0, 86.0, 0.38377999999999995, 0.25716, 0.0, 0.0, 0.0, 0.38377999999999995, 0.38377999999999995],
+             [87.0, 87.0, 0.049640000000000004, 0.0206, 0.0, 0.0, 0.0, 0.049640000000000004, 0.049640000000000004],
+             [88.0, 88.0, 0.022473, 0.011805999999999999, 0.0, 0.0, 0.0, 0.022473, 0.022473],
+             [89.0, 89.0, 0.06293, 0.04296, 0.0, 0.0, 0.0, 0.06293, 0.06293],
+             [90.0, 90.0, 0.030670000000000003, 0.03493, 0.0, 0.0, 0.0, 0.030670000000000003, 0.030670000000000003],
+             [91.0, 91.0, 0.06253, 0.06679, 0.0, 0.0, 0.0, 0.06253, 0.06253],
+             [92.0, 92.0, 0.11456999999999999, 0.081748, 0.0, 0.0, 0.0, 0.11456999999999999, 0.11456999999999999],
+             [93.0, 93.0, 0.081292, 0.066526, 0.0, 0.0, 0.0, 0.081292, 0.081292],
+             [94.0, 94.0, 0.031733, 0.015960000000000002, 0.0, 0.0, 0.0, 0.031733, 0.031733],
+             [95.0, 95.0, 0.03332, 0.06048, 0.0, 0.0, 0.0, 0.03332, 0.03332],
+             [96.0, 96.0, 0.53128, 0.22485, 0.0, 0.0, 0.0, 0.53128, 0.53128],
+             [97.0, 97.0, 0.50703, 0.36742, 0.0, 0.0, 0.0, 0.50703, 0.50703],
+             [98.0, 98.0, 0.02639, 0.011699999999999999, 0.0, 0.0, 0.0, 0.02639, 0.02639],
+             [99.0, 99.0, 0.04599, 0.030392, 0.0, 0.0, 0.0, 0.04599, 0.04599],
+             [100.0, 100.0, 0.10066, 0.047572, 0.0, 0.0, 0.0, 0.10066, 0.10066],
+             [101.0, 101.0, 0.45648, 0.3503, 0.0, 0.0, 0.0, 0.45648, 0.45648],
+             [102.0, 102.0, 0.5225599999999999, 0.44929, 0.0, 0.0, 0.0, 0.5225599999999999, 0.5225599999999999],
+             [103.0, 103.0, 0.40843, 0.16846, 0.0, 0.0, 0.0, 0.40843, 0.40843],
+             [104.0, 104.0, 0.14148, 0.13425, 0.0, 0.0, 0.0, 0.14148, 0.14148],
+             [105.0, 105.0, 0.10443000000000001, 0.066024, 0.0, 0.0, 0.0, 0.10443000000000001, 0.10443000000000001],
+             [106.0, 106.0, 0.096793, 0.083647, 0.0, 0.0, 0.0, 0.096793, 0.096793],
+             [107.0, 107.0, 0.49392, 0.41934, 0.0, 0.0, 0.0, 0.49392, 0.49392],
+             [108.0, 108.0, 0.22538, 0.13588, 0.0, 0.0, 0.0, 0.22538, 0.22538],
+             [109.0, 109.0, 0.5092099999999999, 0.38721, 0.0, 0.0, 0.0, 0.5092099999999999, 0.5092099999999999],
+             [110.0, 110.0, 0.1885, 0.17346, 0.0, 0.0, 0.0, 0.1885, 0.1885],
+             [111.0, 111.0, 0.91803, 0.89855, 0.0, 0.0, 0.0, 0.91803, 0.91803],
+             [112.0, 112.0, 0.30507999999999996, 0.21537, 0.0, 0.0, 0.0, 0.30507999999999996, 0.30507999999999996],
+             [113.0, 113.0, 0.054380000000000005, 0.04097, 0.0, 0.0, 0.0, 0.054380000000000005, 0.054380000000000005],
+             [114.0, 114.0, 0.21114, 0.19290000000000002, 0.0, 0.0, 0.0, 0.21114, 0.21114],
+             [115.0, 115.0, 0.067009, 0.053336, 0.0, 0.0, 0.0, 0.067009, 0.067009],
+             [116.0, 116.0, 0.16207, 0.090321, 0.0, 0.0, 0.0, 0.16207, 0.16207],
+             [117.0, 117.0, 0.048784999999999995, 0.029155999999999998, 0.0, 0.0, 0.0, 0.048784999999999995, 0.048784999999999995],
+             [118.0, 118.0, 0.0339, 0.01898, 0.0, 0.0, 0.0, 0.0339, 0.0339]])
+        
+        self.real_params = True
+        super().__init__(*args, **kwargs)

powergrid_case/distribution/Case123.py

@@ -0,0 +1,458 @@
+from powerzoo.case.CaseBase import ClearCase, DataFrame
+
+class Case123(ClearCase):
+    GRID_TYPE = "distribution"
+    BUS_COUNT = 123
+    PHASE = "3"
+    VOLTAGE_LEVEL = "MV"
+    SOURCE = "MATPOWER"
+    DESCRIPTION = "IEEE 123-bus three-phase distribution"
+
+    def __init__(self, *args, **kwargs):
+        # System base values
+        self.baseMVA = 10
+        self.baseKV = 4.16
+        
+        # Node (bus) data
+        self.nodes = DataFrame(
+            ['id', 'type', 'Pd_A', 'Qd_A', 'Pd_B', 'Qd_B', 'Pd_C', 'Qd_C', 'Gs', 'Bs', 'Vm', 'Va', 'baseKV', 'Vmax', 'Vmin'],
+            [[1, 3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.0, 1.0],
+             [2, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [3, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [4, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [5, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [6, 1, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [7, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [8, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [9, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [10, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [11, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [12, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [13, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [14, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [15, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [16, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [17, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [18, 1, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [19, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [20, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [21, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [22, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [23, 1, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [24, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [25, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [26, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [27, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [28, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [29, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [30, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [31, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [32, 1, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [33, 1, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [34, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [35, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [36, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [37, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [38, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [39, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [40, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [41, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [42, 1, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [43, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [44, 1, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [45, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [46, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [47, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [48, 1, 0.0035, 0.0025, 0.0035, 0.0025, 0.0035, 0.0025, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [49, 1, 0.007, 0.005, 0.007, 0.005, 0.007, 0.005, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [50, 1, 0.0035, 0.0025, 0.007, 0.005, 0.0035, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [51, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [52, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [53, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [54, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [55, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [56, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [57, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [58, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [59, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [60, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [61, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [62, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [63, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [64, 1, 0.0, 0.0, 0.0075, 0.0035, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [65, 1, 0.0035, 0.0025, 0.0035, 0.0025, 0.007, 0.005, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [66, 1, 0.0, 0.0, 0.0, 0.0, 0.0075, 0.0035, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [67, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [68, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [69, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [70, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [71, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [72, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [73, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [74, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [75, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [76, 1, 0.0105, 0.008, 0.007, 0.005, 0.007, 0.005, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [77, 1, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [78, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [79, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [80, 1, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [81, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [82, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [83, 1, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [84, 1, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [85, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [86, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [87, 1, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [88, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [89, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [90, 1, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [91, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [92, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [93, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [94, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [95, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [96, 1, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [97, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [98, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [99, 1, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [100, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [101, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [102, 1, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [103, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [104, 1, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [105, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [106, 1, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [107, 1, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [108, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [109, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [110, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [111, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [112, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [113, 1, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8],
+             [114, 1, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 4.16, 1.2, 0.8]])
+
+        # Generator (unit) data
+        self.units = DataFrame(
+            ['id', 'bus_id', 'Pg', 'Qg', 'Qmax', 'Qmin', 'Vg', 'mBase', 'status', 'Pmax', 'Pmin', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min'],
+            [[1, 114, 0.0, 0.0, 200.0, -200.0, 1.0, 1.0, 1, 200.0, -200.0, 0.0, 0.0, 0.0, 200.0, -200.0]])
+
+        # Line (branch) data
+        self.lines = DataFrame(
+            ['id', 'from', 'to', 'config_name', 'length', 'type', 'rateA', 'ratio', 'angle', 'status', 'floor', 'cap'],
+            [[1, 1, 2, 1, 0.075758, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [2, 2, 3, 10, 0.033144, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [3, 2, 4, 11, 0.047348, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [4, 4, 5, 11, 0.037879, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [5, 4, 6, 11, 0.061553, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [6, 6, 7, 11, 0.047348, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [7, 2, 8, 1, 0.056818, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [8, 8, 9, 1, 0.037879, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [9, 9, 10, 9, 0.042614, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [10, 15, 11, 9, 0.047348, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [11, 15, 12, 9, 0.047348, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [12, 9, 13, 10, 0.042614, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [13, 9, 14, 1, 0.056818, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [14, 10, 15, 9, 0.080492, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [15, 35, 16, 11, 0.018939, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [16, 16, 17, 11, 0.071023, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [17, 16, 18, 11, 0.066288, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [18, 14, 19, 2, 0.15625, 'CABN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [19, 19, 20, 9, 0.047348, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [20, 20, 21, 9, 0.061553, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [21, 19, 22, 2, 0.056818, 'CABN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [22, 22, 23, 10, 0.099432, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [23, 22, 24, 2, 0.047348, 'CABN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [24, 24, 25, 11, 0.104167, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [25, 24, 26, 2, 0.052083, 'CABN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [26, 26, 27, 7, 0.066288, 'ACN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [27, 27, 28, 7, 0.052083, 'ACN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [28, 26, 29, 2, 0.037879, 'CABN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [29, 29, 30, 2, 0.056818, 'CABN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [30, 30, 31, 2, 0.066288, 'CABN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [31, 27, 32, 11, 0.042614, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [32, 32, 33, 11, 0.056818, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [33, 28, 34, 9, 0.094697, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [34, 14, 35, 11, 0.028409, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [35, 19, 36, 4, 0.071023, 'CBAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [36, 36, 37, 8, 0.123106, 'ABN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [37, 37, 38, 9, 0.056818, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [38, 37, 39, 10, 0.047348, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [39, 39, 40, 10, 0.061553, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [40, 36, 41, 1, 0.047348, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [41, 41, 42, 11, 0.061553, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [42, 41, 43, 1, 0.047348, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [43, 43, 44, 10, 0.094697, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [44, 43, 45, 1, 0.037879, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [45, 45, 46, 9, 0.037879, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [46, 46, 47, 9, 0.056818, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [47, 45, 48, 1, 0.047348, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [48, 48, 49, 4, 0.028409, 'CBAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [49, 48, 50, 4, 0.047348, 'CBAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [50, 50, 51, 4, 0.047348, 'CBAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [51, 51, 52, 4, 0.047348, 'CBAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [52, 14, 53, 1, 0.075758, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [53, 53, 54, 1, 0.037879, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [54, 54, 55, 1, 0.023674, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [55, 55, 56, 1, 0.052083, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [56, 56, 57, 1, 0.052083, 'ABCN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [57, 55, 58, 3, 0.066288, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [58, 58, 59, 10, 0.047348, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [59, 59, 60, 10, 0.047348, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [60, 58, 61, 3, 0.142045, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [61, 61, 62, 12, 0.047348, 'nan', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [62, 62, 63, 12, 0.033144, 'nan', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [63, 63, 64, 12, 0.066288, 'nan', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [64, 64, 65, 12, 0.080492, 'nan', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [65, 65, 66, 12, 0.061553, 'nan', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [66, 61, 67, 6, 0.066288, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [67, 67, 68, 9, 0.037879, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [68, 68, 69, 9, 0.052083, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [69, 69, 70, 9, 0.061553, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [70, 70, 71, 9, 0.052083, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [71, 67, 72, 3, 0.052083, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [72, 72, 73, 11, 0.052083, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [73, 73, 74, 11, 0.066288, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [74, 74, 75, 11, 0.075758, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [75, 72, 76, 3, 0.037879, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [76, 76, 77, 6, 0.075758, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [77, 77, 78, 6, 0.018939, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [78, 78, 79, 6, 0.042614, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [79, 78, 80, 6, 0.089962, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [80, 80, 81, 6, 0.089962, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [81, 81, 82, 6, 0.047348, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [82, 82, 83, 6, 0.047348, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [83, 81, 84, 11, 0.127841, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [84, 84, 85, 11, 0.089962, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [85, 76, 86, 3, 0.132576, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [86, 86, 87, 6, 0.085227, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [87, 87, 88, 9, 0.033144, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [88, 87, 89, 6, 0.052083, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [89, 89, 90, 10, 0.042614, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [90, 89, 91, 6, 0.042614, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [91, 91, 92, 11, 0.056818, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [92, 91, 93, 6, 0.042614, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [93, 93, 94, 9, 0.052083, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [94, 93, 95, 6, 0.056818, 'ACBN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [95, 95, 96, 10, 0.037879, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [96, 67, 97, 3, 0.047348, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [97, 97, 98, 3, 0.052083, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [98, 98, 99, 3, 0.104167, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [99, 99, 100, 3, 0.056818, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [100, 97, 101, 3, 0.047348, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [101, 101, 102, 11, 0.042614, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [102, 102, 103, 11, 0.061553, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [103, 103, 104, 11, 0.132576, 'CN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [104, 101, 105, 3, 0.052083, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [105, 105, 106, 10, 0.042614, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [106, 106, 107, 10, 0.108902, 'BN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [107, 105, 108, 3, 0.061553, 'BCAN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [108, 108, 109, 9, 0.085227, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [109, 109, 110, 9, 0.056818, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [110, 110, 111, 9, 0.108902, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [111, 110, 112, 9, 0.023674, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [112, 112, 113, 9, 0.099432, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [113, 113, 114, 9, 0.061553, 'AN', 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]])
+
+        # Line configuration data (Z in ohms per mile, B in micro Siemens per mile)
+        self.line_config = DataFrame(
+            ['id', 'Z11', 'Z12', 'Z13', 'Z21', 'Z22', 'Z23', 'Z31', 'Z32', 'Z33', 'B11', 'B12', 'B13', 'B21', 'B22', 'B23', 'B31', 'B32', 'B33'],
+            [[1, (0.4576+1.078j), (0.156+0.5017j), (0.1535+0.3849j), (0.156+0.5017j), (0.4666+1.0482j), (0.158+0.4236j), (0.1535+0.3849j), (0.158+0.4236j), (0.4615+1.0651j), 5.6765, -1.8319, -0.6982, -1.8319, 5.9809, -1.1645, -0.6982, -1.1645, 5.3971],
+             [2, (0.4666+1.0482j), (0.158+0.4236j), (0.156+0.5017j), (0.158+0.4236j), (0.4615+1.0651j), (0.1535+0.3849j), (0.156+0.5017j), (0.1535+0.3849j), (0.4576+1.078j), 5.9809, -1.1645, -1.8319, -1.1645, 5.3971, -0.6982, -1.8319, -0.6982, 5.6765],
+             [3, (0.4615+1.0651j), (0.1535+0.3849j), (0.158+0.4236j), (0.1535+0.3849j), (0.4576+1.078j), (0.156+0.5017j), (0.158+0.4236j), (0.156+0.5017j), (0.4666+1.0482j), 5.3971, -0.6982, -1.1645, -0.6982, 5.6765, -1.8319, -1.1645, -1.8319, 5.9809],
+             [4, (0.4615+1.0651j), (0.158+0.4236j), (0.1535+0.3849j), (0.158+0.4236j), (0.4666+1.0482j), (0.156+0.5017j), (0.1535+0.3849j), (0.156+0.5017j), (0.4576+1.078j), 5.3971, -1.1645, -0.6982, -1.1645, 5.9809, -1.8319, -0.6982, -1.8319, 5.6765],
+             [5, (0.4666+1.0482j), (0.156+0.5017j), (0.158+0.4236j), (0.156+0.5017j), (0.4576+1.078j), (0.1535+0.3849j), (0.158+0.4236j), (0.1535+0.3849j), (0.4615+1.0651j), 5.9809, -1.8319, -1.1645, -1.8319, 5.6765, -0.6982, -1.1645, -0.6982, 5.3971],
+             [6, (0.4576+1.078j), (0.1535+0.3849j), (0.156+0.5017j), (0.1535+0.3849j), (0.4615+1.0651j), (0.158+0.4236j), (0.156+0.5017j), (0.158+0.4236j), (0.4666+1.0482j), 5.6765, -0.6982, -1.8319, -0.6982, 5.3971, -1.1645, -1.8319, -1.1645, 5.9809],
+             [7, (0.4576+1.078j), 0j, (0.1535+0.3849j), 0j, 0j, 0j, (0.1535+0.3849j), 0j, (0.4615+1.0651j), 5.1154, 0.0, -1.0549, 0.0, 0.0, 0.0, -1.0549, 0.0, 5.1704],
+             [8, (0.4576+1.078j), (0.1535+0.3849j), 0j, (0.1535+0.3849j), (0.4615+1.0651j), 0j, 0j, 0j, 0j, 5.1154, -1.0549, 0.0, -1.0549, 5.1704, 0.0, 0.0, 0.0, 0.0],
+             [9, (1.3292+1.3475j), 0j, 0j, 0j, 0j, 0j, 0j, 0j, 0j, 4.5193, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [10, 0j, 0j, 0j, 0j, (1.3292+1.3475j), 0j, 0j, 0j, 0j, 0.0, 0.0, 0.0, 0.0, 4.5193, 0.0, 0.0, 0.0, 0.0],
+             [11, 0j, 0j, 0j, 0j, 0j, 0j, 0j, 0j, (1.3292+1.3475j), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.5193],
+             [12, (1.5209+0.7521j), (0.5198+0.2775j), (0.4924+0.2157j), (0.5198+0.2775j), (1.5329+0.7162j), (0.5198+0.2775j), (0.4924+0.2157j), (0.5198+0.2775j), (1.5209+0.7521j), 67.2242, 0.0, 0.0, 0.0, 67.2242, 0.0, 0.0, 0.0, 67.2242]])
+
+        # Load data
+        self.loads = DataFrame(
+            ['id', 'bus_id', 'Pd_A', 'Qd_A', 'Pd_B', 'Qd_B', 'Pd_C', 'Qd_C', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'],
+            [[1, 1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [2, 2, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [3, 3, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [4, 4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [5, 5, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [6, 6, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [7, 7, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [8, 8, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [9, 9, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [10, 10, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [11, 11, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [12, 12, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [13, 13, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [14, 14, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [15, 15, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [16, 16, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [17, 17, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [18, 18, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [19, 19, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [20, 20, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [21, 21, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [22, 22, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [23, 23, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [24, 24, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [25, 25, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [26, 26, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [27, 27, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [28, 28, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [29, 29, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [30, 30, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [31, 31, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [32, 32, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [33, 33, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [34, 34, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [35, 35, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [36, 36, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [37, 37, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [38, 38, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [39, 39, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [40, 40, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [41, 41, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [42, 42, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [43, 43, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [44, 44, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [45, 45, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [46, 46, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [47, 47, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [48, 48, 0.0035, 0.0025, 0.0035, 0.0025, 0.0035, 0.0025, 0.0, 0.0, 0.0, 0.0105, 0.0105],
+             [49, 49, 0.007, 0.005, 0.007, 0.005, 0.007, 0.005, 0.0, 0.0, 0.0, 0.021, 0.021],
+             [50, 50, 0.0035, 0.0025, 0.007, 0.005, 0.0035, 0.002, 0.0, 0.0, 0.0, 0.014, 0.014],
+             [51, 51, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [52, 52, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [53, 53, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [54, 54, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [55, 55, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [56, 56, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [57, 57, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [58, 58, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [59, 59, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [60, 60, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [61, 61, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [62, 62, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [63, 63, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [64, 64, 0.0, 0.0, 0.0075, 0.0035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0075, 0.0075],
+             [65, 65, 0.0035, 0.0025, 0.0035, 0.0025, 0.007, 0.005, 0.0, 0.0, 0.0, 0.014, 0.014],
+             [66, 66, 0.0, 0.0, 0.0, 0.0, 0.0075, 0.0035, 0.0, 0.0, 0.0, 0.0075, 0.0075],
+             [67, 67, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [68, 68, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [69, 69, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [70, 70, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [71, 71, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [72, 72, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [73, 73, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [74, 74, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [75, 75, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [76, 76, 0.0105, 0.008, 0.007, 0.005, 0.007, 0.005, 0.0, 0.0, 0.0, 0.0245, 0.0245],
+             [77, 77, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [78, 78, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [79, 79, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [80, 80, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [81, 81, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [82, 82, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [83, 83, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [84, 84, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [85, 85, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [86, 86, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [87, 87, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [88, 88, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [89, 89, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [90, 90, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [91, 91, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [92, 92, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [93, 93, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [94, 94, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [95, 95, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [96, 96, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [97, 97, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [98, 98, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [99, 99, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [100, 100, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [101, 101, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [102, 102, 0.0, 0.0, 0.0, 0.0, 0.002, 0.001, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [103, 103, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [104, 104, 0.0, 0.0, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [105, 105, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [106, 106, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [107, 107, 0.0, 0.0, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [108, 108, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [109, 109, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [110, 110, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [111, 111, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [112, 112, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002],
+             [113, 113, 0.004, 0.002, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004, 0.004],
+             [114, 114, 0.002, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.002, 0.002]])
+        
+        self.real_params = True
+        super().__init__(*args, **kwargs)
+
+if __name__ == '__main__':
+    print("=" * 60)
+    print("Testing IEEE 123-bus System - Graph Connectivity")
+    print("=" * 60)
+    
+    # Create case instance
+    case = Casegrid_IEEE123()
+    
+    # Basic information
+    print(f"\nSystem Information:")
+    print(f"  Base MVA: {case.baseMVA}")
+    print(f"  Base KV: {case.baseKV}")
+    print(f"  Number of nodes: {len(case.nodes)}")
+    print(f"  Number of lines: {len(case.lines)}")
+    print(f"  Number of units: {len(case.units)}")
+    print(f"  Number of loads: {len(case.loads)}")
+    print(f"  Number of line configs: {len(case.line_config)}")
+    
+    # Test graph connectivity
+    print(f"\n{'=' * 60}")
+    print("Graph Connectivity Analysis")
+    print("=" * 60)
+    
+    is_connected = case.is_connected_graph()
+    print(f"\nIs the graph connected? {'✓ YES' if is_connected else '✗ NO'}")
+    
+    # Get connected components
+    components = case.get_graph_components()
+    print(f"\nNumber of connected components: {len(components)}")
+    
+    if len(components) == 1:
+        print(f"  ✓ All {len(case.nodes)} nodes form a single connected component")
+    else:
+        print(f"  ✗ The graph is disconnected with {len(components)} separate components:")
+        for i, component in enumerate(components):
+            print(f"    Component {i+1}: {len(component)} nodes")
+            if len(component) <= 10:
+                print(f"      Nodes: {sorted(component)}")
+            else:
+                print(f"      Nodes: {sorted(component[:5])} ... {sorted(component[-5:])}")
+    
+    # Additional statistics
+    print(f"\n{'=' * 60}")
+    print("Network Statistics")
+    print("=" * 60)
+    
+    active_lines = case.lines[case.lines['status'] == 1]
+    print(f"\nActive lines: {len(active_lines)} / {len(case.lines)}")
+    
+    # Count lines per configuration
+    print(f"\nLines per configuration:")
+    config_counts = case.lines.groupby('config_name').size().sort_index()
+    for config_id, count in config_counts.items():
+        config_name = int(config_id)
+        print(f"  Config {config_name:2d}: {count:3d} lines")
+    
+    # Load statistics
+    total_pd = case.loads['Pd'].sum()
+    total_qd = case.loads['Qd'].sum()
+    print(f"\nTotal system load:")
+    print(f"  Active power (P):   {total_pd:.4f} p.u. ({total_pd * case.baseMVA:.2f} MW)")
+    print(f"  Reactive power (Q): {total_qd:.4f} p.u. ({total_qd * case.baseMVA:.2f} MVar)")
+    
+    print(f"\n{'=' * 60}")
+    print("Test completed successfully!")
+    print("=" * 60)

powergrid_case/distribution/Case141.py

@@ -0,0 +1,505 @@
+"""Power System Case: 141
+
+Data Source: MATPOWER case file
+Base MVA: 10.0 MVA
+Base kV: 12.47 kV
+
+Unit conversions applied:
+  - Pd converted from kVA to MW/MVAr (divided by 1000, power factor=0.85)
+  - r/x converted from Ohms to p.u. (Z_pu = Z_ohm * Sbase / Vbase^2)
+
+
+Nodes (bus) columns:
+    id, type, Pd, Qd, Gs, Bs, Vm, Va, baseKV, Vmax, Vmin
+    - type: 1=PQ, 2=PV, 3=Slack
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - Gs/Bs: Shunt conductance/susceptance (MW/MVAr at V=1.0 p.u.)
+    - Vm: Voltage magnitude (p.u.)
+    - Va: Voltage angle (degrees)
+    - baseKV: Base voltage (kV)
+    - Vmax/Vmin: Voltage limits (p.u.)
+
+Units (generators) columns:
+    id, bus_id, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, mc_a, mc_b, mc_c
+    - Pg/Qg: Active/Reactive power output (MW/MVAr)
+    - Qmax/Qmin: Reactive power limits (MVAr)
+    - Vg: Voltage setpoint (p.u.)
+    - mBase: Machine base (MVA)
+    - status: >0 in-service, <=0 out-of-service
+    - Pmax/Pmin: Active power limits (MW)
+    - mc_a/mc_b/mc_c: Marginal cost coefficients (quadratic)
+
+Lines (branches) columns:
+    id, from, to, r, x, b, rateA, ratio, angle, status, floor, cap
+    - r/x: Resistance/Reactance (p.u.)
+    - b: Line charging susceptance (p.u.)
+    - rateA: Long-term rating (MVA)
+    - ratio: Transformer tap ratio (0 for line)
+    - angle: Transformer phase shift (degrees)
+    - status: 1=in-service, 0=out-of-service
+    - floor/cap: Power flow limits (MW)
+
+Loads columns:
+    id, bus_id, Pd, Qd, mc_a, mc_b, mc_c, d_max, d_min
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - mc_a/mc_b/mc_c: Marginal utility coefficients
+    - d_max/d_min: Demand limits (MW)
+"""
+from math import inf
+
+from powerzoo.case.CaseBase import ClearCase, DataFrame
+
+
+class Case141(ClearCase):
+    GRID_TYPE = "distribution"
+    BUS_COUNT = 141
+    VOLTAGE_LEVEL = "MV"
+    SOURCE = "MATPOWER"
+    DESCRIPTION = "141-bus Caracas distribution system (Khodr et al., EPSR 2008)"
+
+    def __init__(self, *args, **kwargs):
+        # System base values
+        self.baseMVA = 10.0
+        self.baseKV = 12.47
+        
+        # Node (bus) data
+        self.nodes = DataFrame(
+            ['id', 'type', 'Pd', 'Qd', 'Gs', 'Bs', 'Vm', 'Va', 'baseKV', 'Vmax', 'Vmin'],
+            [[1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.0, 1.0],
+             [2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [3.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [4.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [5.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [6.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [7.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [8.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [9.0, 1.0, 0.0085, 0.00526782687642637, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [10.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [11.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [12.0, 1.0, 0.02125, 0.013169567191065924, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [13.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [14.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [15.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [16.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [17.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [18.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [19.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [20.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [21.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [22.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [23.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [24.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [25.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [26.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [27.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [28.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [29.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [30.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [31.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [32.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [33.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [34.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [35.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [36.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [37.0, 1.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [38.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [39.0, 1.0, 0.017, 0.01053565375285274, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [40.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [41.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [42.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [43.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [44.0, 1.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [45.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [46.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [47.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [48.0, 1.0, 0.10625, 0.06584783595532961, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [49.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [50.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [51.0, 1.0, 0.10625, 0.06584783595532961, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [52.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [53.0, 1.0, 0.085, 0.052678268764263694, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [54.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [55.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [56.0, 1.0, 0.02125, 0.013169567191065924, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [57.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [58.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [59.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [60.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [61.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [62.0, 1.0, 0.17, 0.10535653752852739, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [63.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [64.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [65.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [66.0, 1.0, 0.19125, 0.11852610471959331, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [67.0, 1.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [68.0, 1.0, 0.085, 0.052678268764263694, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [69.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [70.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [71.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [72.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [73.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [74.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [75.0, 1.0, 0.03825, 0.02370522094391866, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [76.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [77.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [78.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [79.0, 1.0, 0.4271249999999999, 0.26470830054042505, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [80.0, 1.0, 0.6375, 0.3950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [81.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [82.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [83.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [84.0, 1.0, 0.19125, 0.11852610471959331, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [85.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [86.0, 1.0, 0.425, 0.26339134382131846, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [87.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [88.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [89.0, 1.0, 0.05525, 0.0342408746967714, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [90.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [91.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [92.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [93.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [94.0, 1.0, 0.0935, 0.05794609564069006, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [95.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [96.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [97.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [98.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [99.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [100.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [101.0, 1.0, 0.01275, 0.007901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [102.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [103.0, 1.0, 0.10625, 0.06584783595532961, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [104.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [105.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [106.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [107.0, 1.0, 0.4271249999999999, 0.26470830054042505, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [108.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [109.0, 1.0, 0.6375, 0.3950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [110.0, 1.0, 0.6375, 0.3950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [111.0, 1.0, 0.02125, 0.013169567191065924, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [112.0, 1.0, 0.425, 0.26339134382131846, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [113.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [114.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [115.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [116.0, 1.0, 0.255, 0.15803480629279107, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [117.0, 1.0, 0.05525, 0.0342408746967714, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [118.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [119.0, 1.0, 0.0935, 0.05794609564069006, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [120.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [121.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [122.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [123.0, 1.0, 0.085, 0.052678268764263694, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [124.0, 1.0, 0.10625, 0.06584783595532961, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [125.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [126.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [127.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [128.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [129.0, 1.0, 0.0935, 0.05794609564069006, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [130.0, 1.0, 0.095625, 0.059263052359796654, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [131.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [132.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [133.0, 1.0, 0.03825, 0.02370522094391866, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [134.0, 1.0, 0.029750000000000002, 0.018437394067492292, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [135.0, 1.0, 0.02125, 0.013169567191065924, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [136.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [137.0, 1.0, 0.04675, 0.02897304782034503, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [138.0, 1.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [139.0, 1.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [140.0, 1.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9],
+             [141.0, 1.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 1.0, 0.0, 12.47, 1.1, 0.9]])
+
+        # Generator (unit) data
+        self.units = DataFrame(
+            ['id', 'bus_id', 'Pg', 'Qg', 'Qmax', 'Qmin', 'Vg', 'mBase', 'status', 'Pmax', 'Pmin', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min'],
+            [[1.0, 1.0, 0.0, 0.0, 100.0, -100.0, 1.0, 100.0, 1.0, 100.0, 0.0, 0.0, 0.0, 20.0, 100.0, 0.0]])
+
+        # Branch (line) data
+        self.lines = DataFrame(
+            ['id', 'from', 'to', 'r', 'x', 'b', 'rateA', 'ratio', 'angle', 'status', 'floor', 'cap'],
+            [[1.0, 1.0, 2.0, 0.003710589456395429, 0.002630209857306292, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [2.0, 2.0, 3.0, 0.011093183383504531, 0.00786490624813104, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [3.0, 3.0, 4.0, 5.787747852263234e-05, 3.8584985681754894e-05, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [4.0, 4.0, 5.0, 0.0005916364471202417, 0.00041800401155234467, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [5.0, 5.0, 6.0, 0.0004372965043932221, 0.0003151107164009983, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [6.0, 6.0, 7.0, 0.0030160597141238405, 0.004019269341849468, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [7.0, 7.0, 8.0, 0.004733091576961934, 0.0063086451589669254, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [8.0, 8.0, 9.0, 0.004173609284576487, 0.0029517514046542496, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [9.0, 9.0, 10.0, 0.003260431290108289, 0.0023086683099583344, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [10.0, 10.0, 11.0, 0.0007459763898472612, 0.0005273281376506503, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [11.0, 11.0, 12.0, 0.008302202752524262, 0.005871348654573704, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [12.0, 12.0, 13.0, 0.007890629571918877, 0.005569099600066623, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [13.0, 13.0, 14.0, 0.003138245502116065, 0.0022186366767009066, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [14.0, 14.0, 15.0, 0.006154305216239905, 0.004353672551091344, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [15.0, 15.0, 16.0, 0.005530514614384868, 0.003916376046698122, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [16.0, 16.0, 17.0, 0.0025594707168897415, 0.00181349432704248, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [17.0, 17.0, 18.0, 0.005324728024082175, 0.003639850315978878, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [18.0, 18.0, 19.0, 0.0011961345561344017, 0.0008488696849986077, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [19.0, 19.0, 20.0, 0.0035948344993501644, 0.002540178224048864, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [20.0, 20.0, 21.0, 0.0023472532956400893, 0.0015819844129519507, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [21.0, 21.0, 22.0, 0.003684866132607592, 0.001974265100716459, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [22.0, 22.0, 23.0, 0.0016913085390502563, 0.0012282887108691974, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [23.0, 23.0, 24.0, 0.004392257536773099, 0.003196122980638697, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [24.0, 24.0, 25.0, 0.0025594707168897415, 0.00181349432704248, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [25.0, 25.0, 26.0, 0.00468807576033322, 0.0034083404018883487, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [26.0, 26.0, 27.0, 0.002154328367231315, 0.0015691227510580324, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [27.0, 27.0, 28.0, 0.003755605273024143, 0.0026623640120410877, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [28.0, 28.0, 29.0, 0.004212194270258243, 0.002977474728442086, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [29.0, 61.0, 62.0, 0.00264307151920021, 0.0018713718055651124, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [30.0, 60.0, 63.0, 0.0022700833242765795, 0.0016077077367397873, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [31.0, 63.0, 64.0, 0.006733080001466229, 0.00476524573169673, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [32.0, 64.0, 65.0, 0.004334380058250467, 0.003067506361699514, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [33.0, 65.0, 66.0, 0.0019421109459816632, 0.0013761978226492578, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [34.0, 66.0, 67.0, 0.002932458911813372, 0.0020771583958678055, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [35.0, 67.0, 68.0, 0.0014019211464370944, 0.000990347965831709, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [36.0, 70.0, 72.0, 0.004501581662871405, 0.003183261318744779, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [37.0, 42.0, 73.0, 0.0014855219487475634, 0.0010546562753013006, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [38.0, 73.0, 74.0, 0.00019292492840877447, 0.00041157318060538556, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [39.0, 43.0, 75.0, 0.0024372849288975176, 0.001723462693785052, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [40.0, 44.0, 76.0, 0.0035498186827214503, 0.0025144549002610274, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [41.0, 46.0, 77.0, 0.003318308768630921, 0.002803842292874189, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [42.0, 76.0, 78.0, 0.0010739487681421778, 0.0007073914041655063, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [43.0, 78.0, 79.0, 0.002668794842988047, 0.000649513925642874, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [44.0, 79.0, 80.0, 0.006450123439800027, 0.0015691227510580324, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [45.0, 79.0, 81.0, 0.009729847222749192, 0.002379407450374885, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [46.0, 81.0, 82.0, 0.00021221742124965192, 5.1446647575673195e-05, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [47.0, 47.0, 83.0, 0.0005466206304915277, 0.0003987115187114672, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [48.0, 49.0, 84.0, 0.00332473959957788, 0.0028874430951846583, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [49.0, 50.0, 85.0, 0.0009453321492029949, 0.00023150991409052937, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [50.0, 85.0, 86.0, 0.00023794074503748853, 0.00010289329515134639, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [51.0, 86.0, 87.0, 0.0, 6.43083094695915e-07, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [52.0, 7.0, 88.0, 0.001118964584770892, 0.0014855219487475634, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [53.0, 88.0, 89.0, 0.0030160597141238405, 0.004019269341849468, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [54.0, 89.0, 90.0, 0.0019228184531407855, 0.0025594707168897415, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [55.0, 90.0, 91.0, 0.0013633361607553395, 0.001819925157989439, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [56.0, 91.0, 92.0, 0.002025711748292132, 0.0027009489977228426, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [57.0, 92.0, 93.0, 0.0018006326651485618, 0.0023986999432157627, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [58.0, 93.0, 94.0, 0.0013247511750735846, 0.001762047679466807, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [59.0, 94.0, 95.0, 0.0013247511750735846, 0.001762047679466807, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [60.0, 89.0, 96.0, 0.004417980860560935, 0.0031253838402221464, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [61.0, 96.0, 97.0, 0.0062379060185503744, 0.0044115500296139755, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [62.0, 97.0, 98.0, 0.005800609514157153, 0.0012604428656039933, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [63.0, 97.0, 99.0, 0.00021221742124965192, 5.1446647575673195e-05, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [64.0, 131.0, 132.0, 0.0022314983385948246, 0.0015755535820049917, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [65.0, 131.0, 133.0, 0.005916364471202417, 0.004302225903515671, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [66.0, 121.0, 134.0, 0.005408328826392644, 0.003935668539538999, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [67.0, 16.0, 135.0, 0.0033890479090474715, 0.0023986999432157627, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [68.0, 16.0, 136.0, 0.0019421109459816632, 0.0013761978226492578, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [69.0, 18.0, 137.0, 0.003755605273024143, 0.0026623640120410877, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [70.0, 23.0, 138.0, 0.004945308998211585, 0.0035948344993501644, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [71.0, 29.0, 30.0, 0.002199344183860029, 0.001594846074845869, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [72.0, 30.0, 31.0, 0.0008231463612107711, 0.0005852056161732826, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [73.0, 31.0, 32.0, 0.0022314983385948246, 0.0015755535820049917, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [74.0, 2.0, 33.0, 0.002848858109502903, 0.0020192809173451725, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [75.0, 33.0, 34.0, 0.000128616618939183, 5.787747852263234e-05, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [76.0, 5.0, 35.0, 0.014623709573385104, 0.0035626803446153683, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [77.0, 5.0, 36.0, 0.008135001147903324, 0.010064250431991068, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [78.0, 6.0, 37.0, 0.00035369570208275316, 0.0004694506591280179, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [79.0, 37.0, 38.0, 0.013093171808008827, 0.009260396563621175, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [80.0, 38.0, 39.0, 0.006032119428247681, 0.004263640917833916, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [81.0, 39.0, 40.0, 0.0022314983385948246, 0.0015755535820049917, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [82.0, 40.0, 41.0, 0.005903502809308499, 0.004180040115523447, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [83.0, 41.0, 42.0, 0.014906666135051308, 0.010546562753013005, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [84.0, 42.0, 43.0, 0.007762012952979693, 0.0054919296287031134, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [85.0, 43.0, 44.0, 0.002848858109502903, 0.0020192809173451725, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [86.0, 44.0, 45.0, 0.0026044865335184556, 0.001852079312724235, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [87.0, 45.0, 46.0, 0.001028932951513464, 0.0008167155302638119, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [88.0, 46.0, 47.0, 0.004090008482266019, 0.002893873926131617, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [89.0, 47.0, 48.0, 0.0026816565048819654, 0.0018970951293529488, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [90.0, 48.0, 49.0, 0.004707368253174097, 0.003279723782949166, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [91.0, 49.0, 50.0, 0.005324728024082175, 0.0035755420065092867, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [92.0, 50.0, 51.0, 0.0025594707168897415, 0.00181349432704248, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [93.0, 51.0, 52.0, 0.0014469369630658085, 0.0010225021205665047, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [94.0, 38.0, 53.0, 0.005408328826392644, 0.0038263444134406933, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [95.0, 42.0, 54.0, 0.001035363782460423, 0.000733114727953343, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [96.0, 54.0, 55.0, 0.0033890479090474715, 0.0023986999432157627, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [97.0, 55.0, 56.0, 0.00574273203563452, 0.004064285158478183, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [98.0, 56.0, 57.0, 0.005575530431013582, 0.003942099370485958, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [99.0, 57.0, 58.0, 0.004334380058250467, 0.003067506361699514, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [100.0, 58.0, 59.0, 0.0030160597141238405, 0.0021350358743904376, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [101.0, 55.0, 60.0, 0.0021478975362843556, 0.001517676103482359, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [102.0, 60.0, 61.0, 0.002102881719655642, 0.0014919527796945224, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [103.0, 63.0, 69.0, 0.0023536841265870486, 0.0016655852152624197, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [104.0, 55.0, 70.0, 0.0014855219487475634, 0.0010546562753013006, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [105.0, 70.0, 71.0, 0.0007716997136350979, 0.0001864940974618153, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [106.0, 99.0, 100.0, 0.00021221742124965192, 5.1446647575673195e-05, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [107.0, 91.0, 101.0, 0.0014855219487475634, 0.0010546562753013006, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [108.0, 101.0, 102.0, 0.003717020287342388, 0.002630209857306292, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [109.0, 102.0, 103.0, 0.005717008711846684, 0.0013954903154901354, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [110.0, 103.0, 104.0, 0.004044992665637305, 0.0009839171348847498, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [111.0, 104.0, 105.0, 0.007524072207942205, 0.0018327868198833575, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [112.0, 104.0, 106.0, 0.000733114727953343, 0.00016720160462093786, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [113.0, 92.0, 107.0, 0.005459775473968318, 0.0013311820060205439, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [114.0, 94.0, 108.0, 0.003935668539538999, 0.0016720160462093787, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [115.0, 108.0, 109.0, 0.002906735588025535, 0.0012347195418161566, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [116.0, 94.0, 110.0, 0.00021221742124965192, 5.1446647575673195e-05, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [117.0, 7.0, 111.0, 0.0046237674508636285, 0.003273292952002207, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [118.0, 10.0, 112.0, 0.00688098911324629, 0.0016784468771563381, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [119.0, 11.0, 113.0, 0.0022314983385948246, 0.0015755535820049917, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [120.0, 13.0, 114.0, 0.00400640767995555, 0.002835996447608985, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [121.0, 114.0, 115.0, 0.004295795072568711, 0.0030417830379116774, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [122.0, 115.0, 116.0, 0.000257233237878366, 6.43083094695915e-05, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [123.0, 14.0, 117.0, 0.0032540004591613296, 0.0023536841265870486, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [124.0, 15.0, 118.0, 0.001035363782460423, 0.000733114727953343, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [125.0, 118.0, 119.0, 0.002971043897495127, 0.002102881719655642, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [126.0, 119.0, 120.0, 0.002726672321510679, 0.0019292492840877447, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [127.0, 120.0, 121.0, 0.003260431290108289, 0.0023086683099583344, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [128.0, 121.0, 122.0, 0.004707368253174097, 0.0033311704305248394, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [129.0, 122.0, 123.0, 0.003755605273024143, 0.0026623640120410877, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [130.0, 123.0, 124.0, 0.003922806877645081, 0.0027781189690863524, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [131.0, 124.0, 125.0, 0.0050353406314690135, 0.0035626803446153683, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [132.0, 125.0, 126.0, 0.005363313009763931, 0.003903514384804203, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [133.0, 126.0, 127.0, 0.0022314983385948246, 0.0015755535820049917, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [134.0, 127.0, 128.0, 0.003665573639766715, 0.0027009489977228426, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [135.0, 128.0, 129.0, 0.0037620361039711024, 0.0027331031524576387, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [136.0, 129.0, 130.0, 0.0006623755875367923, 0.0004694506591280179, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [137.0, 119.0, 131.0, 0.0022829449861704976, 0.0016270002295806648, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [138.0, 25.0, 139.0, 0.006109289399611192, 0.004327949227303507, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [139.0, 30.0, 140.0, 0.0033376012614717986, 0.002424423267003599, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [140.0, 31.0, 141.0, 0.003755605273024143, 0.0026623640120410877, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]])
+
+        # Load data
+        self.loads = DataFrame(
+            ['id', 'bus_id', 'Pd', 'Qd', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'],
+            [[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [2.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [3.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [6.0, 6.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [7.0, 7.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [8.0, 8.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [9.0, 9.0, 0.0085, 0.00526782687642637, 0.0, 0.0, 0.0, 0.0085, 0.0085],
+             [10.0, 10.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [11.0, 11.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [12.0, 12.0, 0.02125, 0.013169567191065924, 0.0, 0.0, 0.0, 0.02125, 0.02125],
+             [13.0, 13.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [14.0, 14.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [15.0, 15.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [16.0, 16.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [17.0, 17.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [18.0, 18.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [19.0, 19.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [20.0, 20.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [21.0, 21.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [22.0, 22.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [23.0, 23.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [24.0, 24.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [25.0, 25.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [26.0, 26.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [27.0, 27.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [28.0, 28.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [29.0, 29.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [30.0, 30.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [31.0, 31.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [32.0, 32.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [33.0, 33.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [34.0, 34.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [35.0, 35.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [36.0, 36.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [37.0, 37.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 0.0, 0.0425, 0.0425],
+             [38.0, 38.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [39.0, 39.0, 0.017, 0.01053565375285274, 0.0, 0.0, 0.0, 0.017, 0.017],
+             [40.0, 40.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [41.0, 41.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [42.0, 42.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [43.0, 43.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [44.0, 44.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 0.0, 0.0425, 0.0425],
+             [45.0, 45.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [46.0, 46.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [47.0, 47.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [48.0, 48.0, 0.10625, 0.06584783595532961, 0.0, 0.0, 0.0, 0.10625, 0.10625],
+             [49.0, 49.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [50.0, 50.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [51.0, 51.0, 0.10625, 0.06584783595532961, 0.0, 0.0, 0.0, 0.10625, 0.10625],
+             [52.0, 52.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [53.0, 53.0, 0.085, 0.052678268764263694, 0.0, 0.0, 0.0, 0.085, 0.085],
+             [54.0, 54.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [55.0, 55.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [56.0, 56.0, 0.02125, 0.013169567191065924, 0.0, 0.0, 0.0, 0.02125, 0.02125],
+             [57.0, 57.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [58.0, 58.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [59.0, 59.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [60.0, 60.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [61.0, 61.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [62.0, 62.0, 0.17, 0.10535653752852739, 0.0, 0.0, 0.0, 0.17, 0.17],
+             [63.0, 63.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [64.0, 64.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [65.0, 65.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [66.0, 66.0, 0.19125, 0.11852610471959331, 0.0, 0.0, 0.0, 0.19125, 0.19125],
+             [67.0, 67.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 0.0, 0.0425, 0.0425],
+             [68.0, 68.0, 0.085, 0.052678268764263694, 0.0, 0.0, 0.0, 0.085, 0.085],
+             [69.0, 69.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [70.0, 70.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [71.0, 71.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [72.0, 72.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [73.0, 73.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [74.0, 74.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [75.0, 75.0, 0.03825, 0.02370522094391866, 0.0, 0.0, 0.0, 0.03825, 0.03825],
+             [76.0, 76.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [77.0, 77.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [78.0, 78.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [79.0, 79.0, 0.4271249999999999, 0.26470830054042505, 0.0, 0.0, 0.0, 0.4271249999999999, 0.4271249999999999],
+             [80.0, 80.0, 0.6375, 0.3950870157319777, 0.0, 0.0, 0.0, 0.6375, 0.6375],
+             [81.0, 81.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [82.0, 82.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [83.0, 83.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [84.0, 84.0, 0.19125, 0.11852610471959331, 0.0, 0.0, 0.0, 0.19125, 0.19125],
+             [85.0, 85.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [86.0, 86.0, 0.425, 0.26339134382131846, 0.0, 0.0, 0.0, 0.425, 0.425],
+             [87.0, 87.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [88.0, 88.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [89.0, 89.0, 0.05525, 0.0342408746967714, 0.0, 0.0, 0.0, 0.05525, 0.05525],
+             [90.0, 90.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [91.0, 91.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [92.0, 92.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [93.0, 93.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [94.0, 94.0, 0.0935, 0.05794609564069006, 0.0, 0.0, 0.0, 0.0935, 0.0935],
+             [95.0, 95.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [96.0, 96.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [97.0, 97.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [98.0, 98.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [99.0, 99.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [100.0, 100.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [101.0, 101.0, 0.01275, 0.007901740314639553, 0.0, 0.0, 0.0, 0.01275, 0.01275],
+             [102.0, 102.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [103.0, 103.0, 0.10625, 0.06584783595532961, 0.0, 0.0, 0.0, 0.10625, 0.10625],
+             [104.0, 104.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [105.0, 105.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [106.0, 106.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [107.0, 107.0, 0.4271249999999999, 0.26470830054042505, 0.0, 0.0, 0.0, 0.4271249999999999, 0.4271249999999999],
+             [108.0, 108.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [109.0, 109.0, 0.6375, 0.3950870157319777, 0.0, 0.0, 0.0, 0.6375, 0.6375],
+             [110.0, 110.0, 0.6375, 0.3950870157319777, 0.0, 0.0, 0.0, 0.6375, 0.6375],
+             [111.0, 111.0, 0.02125, 0.013169567191065924, 0.0, 0.0, 0.0, 0.02125, 0.02125],
+             [112.0, 112.0, 0.425, 0.26339134382131846, 0.0, 0.0, 0.0, 0.425, 0.425],
+             [113.0, 113.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [114.0, 114.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [115.0, 115.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [116.0, 116.0, 0.255, 0.15803480629279107, 0.0, 0.0, 0.0, 0.255, 0.255],
+             [117.0, 117.0, 0.05525, 0.0342408746967714, 0.0, 0.0, 0.0, 0.05525, 0.05525],
+             [118.0, 118.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [119.0, 119.0, 0.0935, 0.05794609564069006, 0.0, 0.0, 0.0, 0.0935, 0.0935],
+             [120.0, 120.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [121.0, 121.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [122.0, 122.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [123.0, 123.0, 0.085, 0.052678268764263694, 0.0, 0.0, 0.0, 0.085, 0.085],
+             [124.0, 124.0, 0.10625, 0.06584783595532961, 0.0, 0.0, 0.0, 0.10625, 0.10625],
+             [125.0, 125.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [126.0, 126.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [127.0, 127.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [128.0, 128.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [129.0, 129.0, 0.0935, 0.05794609564069006, 0.0, 0.0, 0.0, 0.0935, 0.0935],
+             [130.0, 130.0, 0.095625, 0.059263052359796654, 0.0, 0.0, 0.0, 0.095625, 0.095625],
+             [131.0, 131.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [132.0, 132.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [133.0, 133.0, 0.03825, 0.02370522094391866, 0.0, 0.0, 0.0, 0.03825, 0.03825],
+             [134.0, 134.0, 0.029750000000000002, 0.018437394067492292, 0.0, 0.0, 0.0, 0.029750000000000002, 0.029750000000000002],
+             [135.0, 135.0, 0.02125, 0.013169567191065924, 0.0, 0.0, 0.0, 0.02125, 0.02125],
+             [136.0, 136.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375],
+             [137.0, 137.0, 0.04675, 0.02897304782034503, 0.0, 0.0, 0.0, 0.04675, 0.04675],
+             [138.0, 138.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 0.0, 0.0425, 0.0425],
+             [139.0, 139.0, 0.0425, 0.026339134382131847, 0.0, 0.0, 0.0, 0.0425, 0.0425],
+             [140.0, 140.0, 0.1275, 0.07901740314639553, 0.0, 0.0, 0.0, 0.1275, 0.1275],
+             [141.0, 141.0, 0.06375, 0.03950870157319777, 0.0, 0.0, 0.0, 0.06375, 0.06375]])
+        
+        self.real_params = True
+        super().__init__(*args, **kwargs)

powergrid_case/distribution/Case33bw.py

@@ -0,0 +1,184 @@
+"""Power System Case: 33bw
+
+Data Source: MATPOWER case file
+Base MVA: 100.0 MVA
+Base kV: 12.66 kV
+
+Unit conversions applied:
+  - Pd/Qd converted from kW/kVar to MW/MVAr (divided by 1000)
+  - r/x converted from Ohms to p.u. (Z_pu = Z_ohm * Sbase / Vbase^2)
+
+
+Nodes (bus) columns:
+    id, type, Pd, Qd, Gs, Bs, Vm, Va, baseKV, Vmax, Vmin
+    - type: 1=PQ, 2=PV, 3=Slack
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - Gs/Bs: Shunt conductance/susceptance (MW/MVAr at V=1.0 p.u.)
+    - Vm: Voltage magnitude (p.u.)
+    - Va: Voltage angle (degrees)
+    - baseKV: Base voltage (kV)
+    - Vmax/Vmin: Voltage limits (p.u.)
+
+Units (generators) columns:
+    id, bus_id, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, mc_a, mc_b, mc_c
+    - Pg/Qg: Active/Reactive power output (MW/MVAr)
+    - Qmax/Qmin: Reactive power limits (MVAr)
+    - Vg: Voltage setpoint (p.u.)
+    - mBase: Machine base (MVA)
+    - status: >0 in-service, <=0 out-of-service
+    - Pmax/Pmin: Active power limits (MW)
+    - mc_a/mc_b/mc_c: Marginal cost coefficients (quadratic)
+
+Lines (branches) columns:
+    id, from, to, r, x, b, rateA, ratio, angle, status, floor, cap
+    - r/x: Resistance/Reactance (p.u.)
+    - b: Line charging susceptance (p.u.)
+    - rateA: Long-term rating (MVA)
+    - ratio: Transformer tap ratio (0 for line)
+    - angle: Transformer phase shift (degrees)
+    - status: 1=in-service, 0=out-of-service
+    - floor/cap: Power flow limits (MW)
+
+Loads columns:
+    id, bus_id, Pd, Qd, mc_a, mc_b, mc_c, d_max, d_min
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - mc_a/mc_b/mc_c: Marginal utility coefficients
+    - d_max/d_min: Demand limits (MW)
+"""
+from powerzoo.case.CaseBase import ClearCase, DataFrame
+
+
+class Case33bw(ClearCase):
+    GRID_TYPE = "distribution"
+    BUS_COUNT = 33
+    VOLTAGE_LEVEL = "MV"
+    SOURCE = "MATPOWER"
+    DESCRIPTION = "IEEE 33-bus Baran & Wu radial distribution"
+
+    def __init__(self, *args, **kwargs):
+        # System base values
+        self.baseMVA = 100.0
+        self.baseKV = 12.66
+        
+        # Node (bus) data
+        self.nodes = DataFrame(
+            ['id', 'type', 'Pd', 'Qd', 'Gs', 'Bs', 'Vm', 'Va', 'baseKV', 'Vmax', 'Vmin'],
+            [[1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 12.66, 1.0, 1.0],
+             [2.0, 1.0, 0.1, 0.06, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [3.0, 1.0, 0.09, 0.04, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [4.0, 1.0, 0.12, 0.08, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [5.0, 1.0, 0.06, 0.03, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [6.0, 1.0, 0.06, 0.02, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [7.0, 1.0, 0.2, 0.1, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [8.0, 1.0, 0.2, 0.1, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [9.0, 1.0, 0.06, 0.02, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [10.0, 1.0, 0.06, 0.02, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [11.0, 1.0, 0.045, 0.03, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [12.0, 1.0, 0.06, 0.035, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [13.0, 1.0, 0.06, 0.035, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [14.0, 1.0, 0.12, 0.08, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [15.0, 1.0, 0.06, 0.01, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [16.0, 1.0, 0.06, 0.02, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [17.0, 1.0, 0.06, 0.02, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [18.0, 1.0, 0.09, 0.04, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [19.0, 1.0, 0.09, 0.04, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [20.0, 1.0, 0.09, 0.04, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [21.0, 1.0, 0.09, 0.04, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [22.0, 1.0, 0.09, 0.04, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [23.0, 1.0, 0.09, 0.05, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [24.0, 1.0, 0.42, 0.2, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [25.0, 1.0, 0.42, 0.2, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [26.0, 1.0, 0.06, 0.025, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [27.0, 1.0, 0.06, 0.025, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [28.0, 1.0, 0.06, 0.02, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [29.0, 1.0, 0.12, 0.07, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [30.0, 1.0, 0.2, 0.6, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [31.0, 1.0, 0.15, 0.07, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [32.0, 1.0, 0.21, 0.1, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9],
+             [33.0, 1.0, 0.06, 0.04, 0.0, 0.0, 1.0, 0.0, 12.66, 1.1, 0.9]])
+
+        # Generator (unit) data
+        self.units = DataFrame(
+            ['id', 'bus_id', 'Pg', 'Qg', 'Qmax', 'Qmin', 'Vg', 'mBase', 'status', 'Pmax', 'Pmin', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min'],
+            [[1.0, 1.0, 0.0, 0.0, 10.0, -10.0, 1.0, 100.0, 1.0, 10.0, 0.0, 0.0, 0.0, 20.0, 10.0, 0.0]])
+
+        # Branch (line) data
+        self.lines = DataFrame(
+            ['id', 'from', 'to', 'r', 'x', 'b', 'rateA', 'ratio', 'angle', 'status', 'floor', 'cap'],
+            [[1.0, 1.0, 2.0, 0.05752591161723931, 0.02932448856844086, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [2.0, 2.0, 3.0, 0.3075951673242839, 0.156667639990117, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [3.0, 3.0, 4.0, 0.22835665566062455, 0.11629967381185907, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [4.0, 4.0, 5.0, 0.23777792751984705, 0.12110389853477384, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [5.0, 5.0, 6.0, 0.5109948114372992, 0.44111517910399334, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [6.0, 6.0, 7.0, 0.11679881404281126, 0.386084968641515, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [7.0, 7.0, 8.0, 0.4438604503742304, 0.14668483537107332, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [8.0, 8.0, 9.0, 0.642643047350938, 0.461704713630771, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [9.0, 9.0, 10.0, 0.6513780013926013, 0.461704713630771, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [10.0, 10.0, 11.0, 0.12266371175649943, 0.04055514376486502, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [11.0, 11.0, 12.0, 0.2335976280856225, 0.0772419507398506, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [12.0, 12.0, 13.0, 0.9159223237972591, 0.7206337084372169, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [13.0, 13.0, 14.0, 0.33791793635462913, 0.4447963383072657, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [14.0, 14.0, 15.0, 0.36873984561592654, 0.3281847018510615, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [15.0, 15.0, 16.0, 0.4656354429495194, 0.340039282336176, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [16.0, 16.0, 17.0, 0.8042396971217077, 1.0737754218358877, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [17.0, 17.0, 18.0, 0.4567133113212491, 0.3581331157081926, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [18.0, 2.0, 19.0, 0.10232374734519789, 0.09764430768002116, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [19.0, 19.0, 20.0, 0.9385084192478456, 0.8456683362907391, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [20.0, 20.0, 21.0, 0.2554974057186496, 0.29848585810940653, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [21.0, 21.0, 22.0, 0.4423006371525048, 0.5848051730893535, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [22.0, 3.0, 23.0, 0.28151509025703225, 0.19235616650319826, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [23.0, 23.0, 24.0, 0.5602849092438275, 0.4424254222102428, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [24.0, 24.0, 25.0, 0.559037058666447, 0.43743401990072095, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [25.0, 6.0, 26.0, 0.12665683360411692, 0.06451387485056989, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [26.0, 26.0, 27.0, 0.17731956704576368, 0.09028198927347643, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [27.0, 27.0, 28.0, 0.6607368807229547, 0.5825590420500687, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [28.0, 28.0, 29.0, 0.5017607171646838, 0.4371220572563759, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [29.0, 29.0, 30.0, 0.3166420840102922, 0.16128468712642474, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [30.0, 30.0, 31.0, 0.6079528012997611, 0.6008400530086925, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [31.0, 31.0, 32.0, 0.19372880213831675, 0.2257985619769946, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [32.0, 32.0, 33.0, 0.2127585234433688, 0.3308051880635605, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [33.0, 21.0, 8.0, 1.247850577380462, 1.247850577380462, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [34.0, 9.0, 15.0, 1.247850577380462, 1.247850577380462, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [35.0, 12.0, 22.0, 1.247850577380462, 1.247850577380462, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [36.0, 18.0, 33.0, 0.3119626443451155, 0.3119626443451155, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [37.0, 25.0, 29.0, 0.3119626443451155, 0.3119626443451155, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
+
+        # Load data
+        self.loads = DataFrame(
+            ['id', 'bus_id', 'Pd', 'Qd', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'],
+            [[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [2.0, 2.0, 0.1, 0.06, 0.0, 0.0, 0.0, 0.1, 0.1],
+             [3.0, 3.0, 0.09, 0.04, 0.0, 0.0, 0.0, 0.09, 0.09],
+             [4.0, 4.0, 0.12, 0.08, 0.0, 0.0, 0.0, 0.12, 0.12],
+             [5.0, 5.0, 0.06, 0.03, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [6.0, 6.0, 0.06, 0.02, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [7.0, 7.0, 0.2, 0.1, 0.0, 0.0, 0.0, 0.2, 0.2],
+             [8.0, 8.0, 0.2, 0.1, 0.0, 0.0, 0.0, 0.2, 0.2],
+             [9.0, 9.0, 0.06, 0.02, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [10.0, 10.0, 0.06, 0.02, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [11.0, 11.0, 0.045, 0.03, 0.0, 0.0, 0.0, 0.045, 0.045],
+             [12.0, 12.0, 0.06, 0.035, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [13.0, 13.0, 0.06, 0.035, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [14.0, 14.0, 0.12, 0.08, 0.0, 0.0, 0.0, 0.12, 0.12],
+             [15.0, 15.0, 0.06, 0.01, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [16.0, 16.0, 0.06, 0.02, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [17.0, 17.0, 0.06, 0.02, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [18.0, 18.0, 0.09, 0.04, 0.0, 0.0, 0.0, 0.09, 0.09],
+             [19.0, 19.0, 0.09, 0.04, 0.0, 0.0, 0.0, 0.09, 0.09],
+             [20.0, 20.0, 0.09, 0.04, 0.0, 0.0, 0.0, 0.09, 0.09],
+             [21.0, 21.0, 0.09, 0.04, 0.0, 0.0, 0.0, 0.09, 0.09],
+             [22.0, 22.0, 0.09, 0.04, 0.0, 0.0, 0.0, 0.09, 0.09],
+             [23.0, 23.0, 0.09, 0.05, 0.0, 0.0, 0.0, 0.09, 0.09],
+             [24.0, 24.0, 0.42, 0.2, 0.0, 0.0, 0.0, 0.42, 0.42],
+             [25.0, 25.0, 0.42, 0.2, 0.0, 0.0, 0.0, 0.42, 0.42],
+             [26.0, 26.0, 0.06, 0.025, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [27.0, 27.0, 0.06, 0.025, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [28.0, 28.0, 0.06, 0.02, 0.0, 0.0, 0.0, 0.06, 0.06],
+             [29.0, 29.0, 0.12, 0.07, 0.0, 0.0, 0.0, 0.12, 0.12],
+             [30.0, 30.0, 0.2, 0.6, 0.0, 0.0, 0.0, 0.2, 0.2],
+             [31.0, 31.0, 0.15, 0.07, 0.0, 0.0, 0.0, 0.15, 0.15],
+             [32.0, 32.0, 0.21, 0.1, 0.0, 0.0, 0.0, 0.21, 0.21],
+             [33.0, 33.0, 0.06, 0.04, 0.0, 0.0, 0.0, 0.06, 0.06]])
+        
+        self.real_params = True
+        super().__init__(*args, **kwargs)

powergrid_case/distribution/__init__.py

@@ -0,0 +1,17 @@
+"""Distribution grid case data."""
+
+from .Case33bw import Case33bw
+from .Case118zh import Case118zh
+from .Case123 import Case123
+from .Case141 import Case141
+from .Case533mt_hi import Case533mt_hi
+from .Case533mt_lo import Case533mt_lo
+
+__all__ = [
+    "Case33bw",
+    "Case118zh",
+    "Case123",
+    "Case141",
+    "Case533mt_hi",
+    "Case533mt_lo",
+]

powergrid_case/transmission/Case118.py

@@ -0,0 +1,535 @@
+"""IEEE 118-bus system (MATPOWER case118)
+
+Topology and power-flow data (nodes / lines / generator Pg–Qg–Vg) follow
+MATPOWER `data/case118.m` (bundled as `source_mfile/case118.m`).
+
+UC-style economic columns (`type`, `mc_*`, `p_max`/`p_min`, ramps, …) are
+preserved from the previous PowerZoo Case118 SCUC-style table, keyed by
+`bus_id` (54 generators; unique buses).
+
+Nodes / lines include full MATPOWER fields so `cal_pf_trans.run_acpf` and
+`run_dcpf` work on `Case118().init()`.
+
+Base MVA: 100 MVA
+"""
+from powerzoo.case.CaseBase import ClearCase, DataFrame
+
+
+class Case118(ClearCase):
+    GRID_TYPE = "transmission"
+    BUS_COUNT = 118
+    VOLTAGE_LEVEL = "HV"
+    SOURCE = "MATPOWER"
+    DESCRIPTION = "IEEE 118-bus test system"
+
+    def __init__(self, *args, **kwargs):
+        self.baseMVA = 100
+        self.baseKV = 138.0
+
+        self.nodes = DataFrame(
+            ['id', 'type', 'Pd', 'Qd', 'Gs', 'Bs', 'Vm', 'Va', 'baseKV', 'Vmax', 'Vmin'],
+            [
+    [1, 2, 51, 27, 0, 0, 0.955, 10.67, 138, 1.06, 0.94],
+    [2, 1, 20, 9, 0, 0, 0.971, 11.22, 138, 1.06, 0.94],
+    [3, 1, 39, 10, 0, 0, 0.968, 11.56, 138, 1.06, 0.94],
+    [4, 2, 39, 12, 0, 0, 0.998, 15.28, 138, 1.06, 0.94],
+    [5, 1, 0, 0, 0, -40, 1.002, 15.73, 138, 1.06, 0.94],
+    [6, 2, 52, 22, 0, 0, 0.99, 13, 138, 1.06, 0.94],
+    [7, 1, 19, 2, 0, 0, 0.989, 12.56, 138, 1.06, 0.94],
+    [8, 2, 28, 0, 0, 0, 1.015, 20.77, 345, 1.06, 0.94],
+    [9, 1, 0, 0, 0, 0, 1.043, 28.02, 345, 1.06, 0.94],
+    [10, 2, 0, 0, 0, 0, 1.05, 35.61, 345, 1.06, 0.94],
+    [11, 1, 70, 23, 0, 0, 0.985, 12.72, 138, 1.06, 0.94],
+    [12, 2, 47, 10, 0, 0, 0.99, 12.2, 138, 1.06, 0.94],
+    [13, 1, 34, 16, 0, 0, 0.968, 11.35, 138, 1.06, 0.94],
+    [14, 1, 14, 1, 0, 0, 0.984, 11.5, 138, 1.06, 0.94],
+    [15, 2, 90, 30, 0, 0, 0.97, 11.23, 138, 1.06, 0.94],
+    [16, 1, 25, 10, 0, 0, 0.984, 11.91, 138, 1.06, 0.94],
+    [17, 1, 11, 3, 0, 0, 0.995, 13.74, 138, 1.06, 0.94],
+    [18, 2, 60, 34, 0, 0, 0.973, 11.53, 138, 1.06, 0.94],
+    [19, 2, 45, 25, 0, 0, 0.963, 11.05, 138, 1.06, 0.94],
+    [20, 1, 18, 3, 0, 0, 0.958, 11.93, 138, 1.06, 0.94],
+    [21, 1, 14, 8, 0, 0, 0.959, 13.52, 138, 1.06, 0.94],
+    [22, 1, 10, 5, 0, 0, 0.97, 16.08, 138, 1.06, 0.94],
+    [23, 1, 7, 3, 0, 0, 1, 21, 138, 1.06, 0.94],
+    [24, 2, 13, 0, 0, 0, 0.992, 20.89, 138, 1.06, 0.94],
+    [25, 2, 0, 0, 0, 0, 1.05, 27.93, 138, 1.06, 0.94],
+    [26, 2, 0, 0, 0, 0, 1.015, 29.71, 345, 1.06, 0.94],
+    [27, 2, 71, 13, 0, 0, 0.968, 15.35, 138, 1.06, 0.94],
+    [28, 1, 17, 7, 0, 0, 0.962, 13.62, 138, 1.06, 0.94],
+    [29, 1, 24, 4, 0, 0, 0.963, 12.63, 138, 1.06, 0.94],
+    [30, 1, 0, 0, 0, 0, 0.968, 18.79, 345, 1.06, 0.94],
+    [31, 2, 43, 27, 0, 0, 0.967, 12.75, 138, 1.06, 0.94],
+    [32, 2, 59, 23, 0, 0, 0.964, 14.8, 138, 1.06, 0.94],
+    [33, 1, 23, 9, 0, 0, 0.972, 10.63, 138, 1.06, 0.94],
+    [34, 2, 59, 26, 0, 14, 0.986, 11.3, 138, 1.06, 0.94],
+    [35, 1, 33, 9, 0, 0, 0.981, 10.87, 138, 1.06, 0.94],
+    [36, 2, 31, 17, 0, 0, 0.98, 10.87, 138, 1.06, 0.94],
+    [37, 1, 0, 0, 0, -25, 0.992, 11.77, 138, 1.06, 0.94],
+    [38, 1, 0, 0, 0, 0, 0.962, 16.91, 345, 1.06, 0.94],
+    [39, 1, 27, 11, 0, 0, 0.97, 8.41, 138, 1.06, 0.94],
+    [40, 2, 66, 23, 0, 0, 0.97, 7.35, 138, 1.06, 0.94],
+    [41, 1, 37, 10, 0, 0, 0.967, 6.92, 138, 1.06, 0.94],
+    [42, 2, 96, 23, 0, 0, 0.985, 8.53, 138, 1.06, 0.94],
+    [43, 1, 18, 7, 0, 0, 0.978, 11.28, 138, 1.06, 0.94],
+    [44, 1, 16, 8, 0, 10, 0.985, 13.82, 138, 1.06, 0.94],
+    [45, 1, 53, 22, 0, 10, 0.987, 15.67, 138, 1.06, 0.94],
+    [46, 2, 28, 10, 0, 10, 1.005, 18.49, 138, 1.06, 0.94],
+    [47, 1, 34, 0, 0, 0, 1.017, 20.73, 138, 1.06, 0.94],
+    [48, 1, 20, 11, 0, 15, 1.021, 19.93, 138, 1.06, 0.94],
+    [49, 2, 87, 30, 0, 0, 1.025, 20.94, 138, 1.06, 0.94],
+    [50, 1, 17, 4, 0, 0, 1.001, 18.9, 138, 1.06, 0.94],
+    [51, 1, 17, 8, 0, 0, 0.967, 16.28, 138, 1.06, 0.94],
+    [52, 1, 18, 5, 0, 0, 0.957, 15.32, 138, 1.06, 0.94],
+    [53, 1, 23, 11, 0, 0, 0.946, 14.35, 138, 1.06, 0.94],
+    [54, 2, 113, 32, 0, 0, 0.955, 15.26, 138, 1.06, 0.94],
+    [55, 2, 63, 22, 0, 0, 0.952, 14.97, 138, 1.06, 0.94],
+    [56, 2, 84, 18, 0, 0, 0.954, 15.16, 138, 1.06, 0.94],
+    [57, 1, 12, 3, 0, 0, 0.971, 16.36, 138, 1.06, 0.94],
+    [58, 1, 12, 3, 0, 0, 0.959, 15.51, 138, 1.06, 0.94],
+    [59, 2, 277, 113, 0, 0, 0.985, 19.37, 138, 1.06, 0.94],
+    [60, 1, 78, 3, 0, 0, 0.993, 23.15, 138, 1.06, 0.94],
+    [61, 2, 0, 0, 0, 0, 0.995, 24.04, 138, 1.06, 0.94],
+    [62, 2, 77, 14, 0, 0, 0.998, 23.43, 138, 1.06, 0.94],
+    [63, 1, 0, 0, 0, 0, 0.969, 22.75, 345, 1.06, 0.94],
+    [64, 1, 0, 0, 0, 0, 0.984, 24.52, 345, 1.06, 0.94],
+    [65, 2, 0, 0, 0, 0, 1.005, 27.65, 345, 1.06, 0.94],
+    [66, 2, 39, 18, 0, 0, 1.05, 27.48, 138, 1.06, 0.94],
+    [67, 1, 28, 7, 0, 0, 1.02, 24.84, 138, 1.06, 0.94],
+    [68, 1, 0, 0, 0, 0, 1.003, 27.55, 345, 1.06, 0.94],
+    [69, 3, 0, 0, 0, 0, 1.035, 30, 138, 1.06, 0.94],
+    [70, 2, 66, 20, 0, 0, 0.984, 22.58, 138, 1.06, 0.94],
+    [71, 1, 0, 0, 0, 0, 0.987, 22.15, 138, 1.06, 0.94],
+    [72, 2, 12, 0, 0, 0, 0.98, 20.98, 138, 1.06, 0.94],
+    [73, 2, 6, 0, 0, 0, 0.991, 21.94, 138, 1.06, 0.94],
+    [74, 2, 68, 27, 0, 12, 0.958, 21.64, 138, 1.06, 0.94],
+    [75, 1, 47, 11, 0, 0, 0.967, 22.91, 138, 1.06, 0.94],
+    [76, 2, 68, 36, 0, 0, 0.943, 21.77, 138, 1.06, 0.94],
+    [77, 2, 61, 28, 0, 0, 1.006, 26.72, 138, 1.06, 0.94],
+    [78, 1, 71, 26, 0, 0, 1.003, 26.42, 138, 1.06, 0.94],
+    [79, 1, 39, 32, 0, 20, 1.009, 26.72, 138, 1.06, 0.94],
+    [80, 2, 130, 26, 0, 0, 1.04, 28.96, 138, 1.06, 0.94],
+    [81, 1, 0, 0, 0, 0, 0.997, 28.1, 345, 1.06, 0.94],
+    [82, 1, 54, 27, 0, 20, 0.989, 27.24, 138, 1.06, 0.94],
+    [83, 1, 20, 10, 0, 10, 0.985, 28.42, 138, 1.06, 0.94],
+    [84, 1, 11, 7, 0, 0, 0.98, 30.95, 138, 1.06, 0.94],
+    [85, 2, 24, 15, 0, 0, 0.985, 32.51, 138, 1.06, 0.94],
+    [86, 1, 21, 10, 0, 0, 0.987, 31.14, 138, 1.06, 0.94],
+    [87, 2, 0, 0, 0, 0, 1.015, 31.4, 161, 1.06, 0.94],
+    [88, 1, 48, 10, 0, 0, 0.987, 35.64, 138, 1.06, 0.94],
+    [89, 2, 0, 0, 0, 0, 1.005, 39.69, 138, 1.06, 0.94],
+    [90, 2, 163, 42, 0, 0, 0.985, 33.29, 138, 1.06, 0.94],
+    [91, 2, 10, 0, 0, 0, 0.98, 33.31, 138, 1.06, 0.94],
+    [92, 2, 65, 10, 0, 0, 0.993, 33.8, 138, 1.06, 0.94],
+    [93, 1, 12, 7, 0, 0, 0.987, 30.79, 138, 1.06, 0.94],
+    [94, 1, 30, 16, 0, 0, 0.991, 28.64, 138, 1.06, 0.94],
+    [95, 1, 42, 31, 0, 0, 0.981, 27.67, 138, 1.06, 0.94],
+    [96, 1, 38, 15, 0, 0, 0.993, 27.51, 138, 1.06, 0.94],
+    [97, 1, 15, 9, 0, 0, 1.011, 27.88, 138, 1.06, 0.94],
+    [98, 1, 34, 8, 0, 0, 1.024, 27.4, 138, 1.06, 0.94],
+    [99, 2, 42, 0, 0, 0, 1.01, 27.04, 138, 1.06, 0.94],
+    [100, 2, 37, 18, 0, 0, 1.017, 28.03, 138, 1.06, 0.94],
+    [101, 1, 22, 15, 0, 0, 0.993, 29.61, 138, 1.06, 0.94],
+    [102, 1, 5, 3, 0, 0, 0.991, 32.3, 138, 1.06, 0.94],
+    [103, 2, 23, 16, 0, 0, 1.001, 24.44, 138, 1.06, 0.94],
+    [104, 2, 38, 25, 0, 0, 0.971, 21.69, 138, 1.06, 0.94],
+    [105, 2, 31, 26, 0, 20, 0.965, 20.57, 138, 1.06, 0.94],
+    [106, 1, 43, 16, 0, 0, 0.962, 20.32, 138, 1.06, 0.94],
+    [107, 2, 50, 12, 0, 6, 0.952, 17.53, 138, 1.06, 0.94],
+    [108, 1, 2, 1, 0, 0, 0.967, 19.38, 138, 1.06, 0.94],
+    [109, 1, 8, 3, 0, 0, 0.967, 18.93, 138, 1.06, 0.94],
+    [110, 2, 39, 30, 0, 6, 0.973, 18.09, 138, 1.06, 0.94],
+    [111, 2, 0, 0, 0, 0, 0.98, 19.74, 138, 1.06, 0.94],
+    [112, 2, 68, 13, 0, 0, 0.975, 14.99, 138, 1.06, 0.94],
+    [113, 2, 6, 0, 0, 0, 0.993, 13.74, 138, 1.06, 0.94],
+    [114, 1, 8, 3, 0, 0, 0.96, 14.46, 138, 1.06, 0.94],
+    [115, 1, 22, 7, 0, 0, 0.96, 14.46, 138, 1.06, 0.94],
+    [116, 2, 184, 0, 0, 0, 1.005, 27.12, 138, 1.06, 0.94],
+    [117, 1, 20, 8, 0, 0, 0.974, 10.67, 138, 1.06, 0.94],
+    [118, 1, 33, 15, 0, 0, 0.949, 21.92, 138, 1.06, 0.94]
+            ])
+
+        self.units = DataFrame(
+            ['id', 'bus_id', 'Pg', 'Qg', 'Qmax', 'Qmin', 'Vg', 'mBase', 'status',
+             'Pmax', 'Pmin', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min',
+             'type', 'ramp_up', 'ramp_down', 'init_start_up_cost', 'keep_time',
+             'init_power', 'init_state', 'min_up_time', 'min_down_time', 'init_no_load_cost'],
+            [
+    [1, 1, 0, 0, 15, -5, 0.955, 100, 1, 100, 0, 0.030864, -2.335, 70, 90, 18, 'coal', 0.7, 0.7, 14167, -96, 0, 0, 4, 4, 941.73],
+    [2, 4, 0, 0, 300, -300, 0.998, 100, 1, 100, 0, 0.029537, -2.284239, 70, 92, 18, 'coal', 0.7, 0.7, 11377, -96, 0, 0, 4, 4, 962.66],
+    [3, 6, 0, 0, 50, -13, 0.99, 100, 1, 100, 0, 0.043824, -4.543579, 138, 95, 19, 'gas', 0.7, 0.7, 9153, -96, 0, 0, 1, 1, 1902.08],
+    [4, 8, 0, 0, 300, -300, 1.015, 100, 1, 100, 0, 0.042916, -4.49625, 138, 96, 19, 'gas', 0.7, 0.7, 8777, -96, 0, 0, 1, 1, 1922.1],
+    [5, 10, 450, 0, 200, -147, 1.05, 100, 1, 550, 0, 0.000826, -0.382091, 64, 550, 110, 'coal', 0.7, 0.7, 11948, 96, 110, 1, 4, 4, 5095.02],
+    [6, 12, 85, 0, 120, -35, 0.99, 100, 1, 185, 0, 0.00303, -0.629467, 33, 169, 34, 'nuclear', 0.7, 0.7, 25094, 96, 169, 1, 96, 96, 794.84],
+    [7, 15, 0, 0, 30, -10, 0.97, 100, 1, 100, 0, 0.046728, -4.691739, 139, 92, 18, 'gas', 0.7, 0.7, 9745, -96, 0, 0, 1, 1, 1860.41],
+    [8, 18, 0, 0, 50, -16, 0.973, 100, 1, 100, 0, 0.028905, -2.259677, 70, 93, 19, 'coal', 0.6999, 0.6999, 11402, 96, 93, 1, 4, 4, 973.12],
+    [9, 19, 0, 0, 24, -8, 0.962, 100, 1, 100, 0, 0.032283, -2.388068, 70, 88, 18, 'coal', 0.7, 0.7, 14277, 96, 88, 1, 4, 4, 920.8],
+    [10, 24, 0, 0, 300, -300, 0.992, 100, 1, 100, 0, 0.029537, -2.284239, 70, 92, 18, 'coal', 0.7, 0.7, 15093, 96, 92, 1, 4, 4, 962.66],
+    [11, 25, 220, 0, 140, -47, 1.05, 100, 1, 320, 0, 0.000883, -0.339872, 32, 313, 63, 'nuclear', 0.7, 0.7, 49408, 96, 313, 1, 96, 96, 1409.49],
+    [12, 26, 314, 0, 1000, -1000, 1.015, 100, 1, 414, 0, 0.002319, -1.045133, 130, 413, 83, 'gas', 0.7, 0.7, 8867, -96, 0, 0, 1, 1, 7608.24],
+    [13, 27, 0, 0, 300, -300, 0.968, 100, 1, 100, 0, 0.032283, -2.388068, 70, 88, 18, 'coal', 0.7, 0.7, 13902, 96, 88, 1, 4, 4, 920.8],
+    [14, 31, 7, 0, 300, -300, 0.967, 100, 1, 107, 0, 0.0352, -4.072075, 138, 106, 21, 'gas', 0.7, 0.7, 9954, -96, 0, 0, 1, 1, 2122.32],
+    [15, 32, 0, 0, 42, -14, 0.963, 100, 1, 100, 0, 0.032283, -2.388068, 70, 88, 18, 'coal', 0.7001, 0.7001, 11115, 96, 88, 1, 4, 4, 920.8],
+    [16, 34, 0, 0, 24, -8, 0.984, 100, 1, 100, 0, 0.029537, -2.284239, 70, 92, 18, 'coal', 0.7, 0.7, 11897, -96, 0, 0, 4, 4, 962.66],
+    [17, 36, 0, 0, 24, -8, 0.98, 100, 1, 100, 0, 0.046728, -4.691739, 139, 92, 18, 'gas', 0.7001, 0.7001, 9925, 96, 92, 1, 1, 1, 1860.41],
+    [18, 40, 0, 0, 300, -300, 0.97, 100, 1, 100, 0, 0.044761, -4.591915, 138, 94, 19, 'gas', 0.7, 0.7, 8680, -96, 0, 0, 1, 1, 1882.06],
+    [19, 42, 0, 0, 300, -300, 0.985, 100, 1, 100, 0, 0.043824, -4.543579, 138, 95, 19, 'gas', 0.7, 0.7, 7309, 96, 95, 1, 1, 1, 1902.08],
+    [20, 46, 19, 0, 100, -100, 1.005, 100, 1, 119, 0, 0.017955, -1.780932, 69, 118, 24, 'coal', 0.7, 0.7, 17685, 96, 24, 1, 4, 4, 1211.11],
+    [21, 49, 204, 0, 210, -85, 1.025, 100, 1, 304, 0, 0.000949, -0.352252, 32, 302, 60, 'nuclear', 0.7, 0.7, 53638, 96, 302, 1, 96, 96, 1359.96],
+    [22, 54, 48, 0, 300, -300, 0.955, 100, 1, 148, 0, 0.012056, -1.459375, 69, 144, 29, 'coal', 0.7, 0.7, 12008, -96, 0, 0, 4, 4, 1477.97],
+    [23, 55, 0, 0, 23, -8, 0.952, 100, 1, 100, 0, 0.042916, -4.49625, 138, 96, 19, 'gas', 0.7, 0.7, 7920, -96, 0, 0, 1, 1, 1922.1],
+    [24, 56, 0, 0, 15, -8, 0.954, 100, 1, 100, 0, 0.029537, -2.284239, 70, 92, 18, 'coal', 0.7, 0.7, 14251, -96, 0, 0, 4, 4, 962.66],
+    [25, 59, 155, 0, 180, -60, 0.985, 100, 1, 255, 0, 0.004032, -0.843976, 68, 249, 50, 'coal', 0.7, 0.7, 18429, 96, 249, 1, 4, 4, 2505.85],
+    [26, 61, 160, 0, 300, -100, 0.995, 100, 1, 260, 0, 0.003815, -0.820898, 68, 256, 51, 'coal', 0.7, 0.7, 18041, 96, 256, 1, 4, 4, 2576.3],
+    [27, 62, 0, 0, 20, -20, 0.998, 100, 1, 100, 0, 0.042916, -4.49625, 138, 96, 19, 'gas', 0.7, 0.7, 5163, -96, 0, 0, 1, 1, 1922.1],
+    [28, 65, 391, 0, 200, -67, 1.005, 100, 1, 491, 0, 0.001641, -0.879104, 128, 491, 98, 'gas', 0.7, 0.7, 8340, 96, 98, 1, 1, 1, 8848.75],
+    [29, 66, 392, 0, 200, -67, 1.05, 100, 1, 492, 0, 0.001033, -0.427134, 65, 492, 98, 'coal', 0.7, 0.7, 10019, 96, 98, 1, 4, 4, 4656.12],
+    [30, 69, 516.4, 0, 300, -300, 1.035, 100, 1, 805.2, 0, 0.000134, -0.132149, 30, 805, 161, 'nuclear', 0.7, 0.7, 105237, -96, 0, 0, 96, 96, 3303.05],
+    [31, 70, 0, 0, 32, -10, 0.984, 100, 1, 100, 0, 0.028905, -2.259677, 70, 93, 19, 'coal', 0.7, 0.7, 14265, -96, 0, 0, 4, 4, 973.12],
+    [32, 72, 0, 0, 100, -100, 0.98, 100, 1, 100, 0, 0.028293, -2.235638, 70, 94, 19, 'coal', 0.7, 0.7, 11281, 96, 94, 1, 4, 4, 983.58],
+    [33, 73, 0, 0, 100, -100, 0.991, 100, 1, 100, 0, 0.035431, -2.501786, 70, 84, 17, 'coal', 0.7, 0.7, 12609, -96, 0, 0, 4, 4, 878.95],
+    [34, 74, 0, 0, 9, -6, 0.958, 100, 1, 100, 0, 0.047761, -4.743297, 139, 91, 18, 'gas', 0.7, 0.7, 7543, 96, 85, 1, 1, 1, 1840.19],
+    [35, 76, 0, 0, 23, -8, 0.943, 100, 1, 100, 0, 0.028293, -2.235638, 70, 94, 19, 'coal', 0.7, 0.7, 12383, -96, 0, 0, 4, 4, 983.58],
+    [36, 77, 0, 0, 70, -20, 1.006, 100, 1, 100, 0, 0.029537, -2.284239, 70, 92, 18, 'coal', 0.7, 0.7, 11580, -96, 0, 0, 4, 4, 962.66],
+    [37, 80, 477, 0, 280, -165, 1.04, 100, 1, 577, 0, 0.000751, -0.364211, 64, 577, 115, 'coal', 0.7, 0.7, 11367, 96, 115, 1, 4, 4, 5345.14],
+    [38, 85, 0, 0, 23, -8, 0.985, 100, 1, 100, 0, 0.029537, -2.284239, 70, 92, 18, 'coal', 0.7, 0.7, 11322, 96, 92, 1, 4, 4, 962.66],
+    [39, 87, 4, 0, 1000, -100, 1.015, 100, 1, 104, 0, 0.025508, -2.122727, 70, 99, 20, 'coal', 0.7, 0.7, 10239, -96, 0, 0, 4, 4, 1035.9],
+    [40, 89, 607, 0, 300, -210, 1.005, 100, 1, 707, 0, 0.000518, -0.302374, 62, 695, 139, 'coal', 0.7, 0.7, 18192, 96, 139, 1, 4, 4, 6160.25],
+    [41, 90, 0, 0, 300, -300, 0.985, 100, 1, 100, 0, 0.045729, -4.64129, 139, 93, 19, 'gas', 0.6999, 0.6999, 7559, -96, 0, 0, 1, 1, 1880.64],
+    [42, 91, 0, 0, 100, -100, 0.98, 100, 1, 100, 0, 0.028905, -2.259677, 70, 93, 19, 'coal', 0.7, 0.7, 13464, 96, 93, 1, 4, 4, 973.12],
+    [43, 92, 0, 0, 9, -3, 0.99, 100, 1, 100, 0, 0.028293, -2.235638, 70, 94, 19, 'coal', 0.7, 0.7, 13218, 96, 94, 1, 4, 4, 983.58],
+    [44, 99, 0, 0, 100, -100, 1.01, 100, 1, 100, 0, 0.028293, -2.235638, 70, 94, 19, 'coal', 0.7, 0.7, 10277, -96, 0, 0, 4, 4, 983.58],
+    [45, 100, 252, 0, 155, -50, 1.017, 100, 1, 352, 0, 0.000698, -0.302216, 32, 352, 70, 'nuclear', 0.7, 0.7, 33872, -96, 0, 0, 96, 96, 1585.12],
+    [46, 103, 40, 0, 40, -15, 1.01, 100, 1, 140, 0, 0.013717, -1.556667, 69, 135, 27, 'coal', 0.7, 0.7, 16123, -96, 0, 0, 4, 4, 1385.6],
+    [47, 104, 0, 0, 23, -8, 0.971, 100, 1, 100, 0, 0.043824, -4.543579, 138, 95, 19, 'gas', 0.7, 0.7, 7948, 96, 95, 1, 1, 1, 1902.08],
+    [48, 105, 0, 0, 23, -8, 0.965, 100, 1, 100, 0, 0.048828, -4.796, 139, 90, 18, 'gas', 0.7, 0.7, 9611, 96, 90, 1, 1, 1, 1819.97],
+    [49, 107, 0, 0, 200, -200, 0.952, 100, 1, 100, 0, 0.046728, -4.691739, 139, 92, 18, 'gas', 0.7, 0.7, 7920, 96, 92, 1, 1, 1, 1860.41],
+    [50, 110, 0, 0, 23, -8, 0.973, 100, 1, 100, 0, 0.029537, -2.284239, 70, 92, 18, 'coal', 0.7, 0.7, 11775, 96, 92, 1, 4, 4, 962.66],
+    [51, 111, 36, 0, 1000, -100, 0.98, 100, 1, 136, 0, 0.014348, -1.592045, 69, 132, 26, 'coal', 0.7, 0.7, 19880, 96, 132, 1, 4, 4, 1354.8],
+    [52, 112, 0, 0, 1000, -100, 0.975, 100, 1, 100, 0, 0.028293, -2.235638, 70, 94, 19, 'coal', 0.7, 0.7, 10897, -96, 0, 0, 4, 4, 983.58],
+    [53, 113, 0, 0, 200, -100, 0.993, 100, 1, 100, 0, 0.027701, -2.212105, 70, 95, 19, 'coal', 0.7, 0.7, 11855, 96, 95, 1, 4, 4, 994.05],
+    [54, 116, 0, 0, 1000, -1000, 1.005, 100, 1, 100, 0, 0.045729, -4.64129, 139, 93, 19, 'gas', 0.7, 0.7, 7749, -96, 0, 0, 1, 1, 1880.64]
+            ])
+
+        self.lines = DataFrame(
+            ['id', 'from', 'to', 'r', 'x', 'b', 'rateA', 'ratio', 'angle', 'status', 'floor', 'cap'],
+            [
+    [1, 1, 2, 0.0303, 0.0999, 0.0254, 0, 0, 0, 1, 0, 0],
+    [2, 1, 3, 0.0129, 0.0424, 0.01082, 0, 0, 0, 1, 0, 0],
+    [3, 4, 5, 0.00176, 0.00798, 0.0021, 0, 0, 0, 1, 0, 0],
+    [4, 3, 5, 0.0241, 0.108, 0.0284, 0, 0, 0, 1, 0, 0],
+    [5, 5, 6, 0.0119, 0.054, 0.01426, 0, 0, 0, 1, 0, 0],
+    [6, 6, 7, 0.00459, 0.0208, 0.0055, 0, 0, 0, 1, 0, 0],
+    [7, 8, 9, 0.00244, 0.0305, 1.162, 0, 0, 0, 1, 0, 0],
+    [8, 8, 5, 0, 0.0267, 0, 0, 0.985, 0, 1, 0, 0],
+    [9, 9, 10, 0.00258, 0.0322, 1.23, 0, 0, 0, 1, 0, 0],
+    [10, 4, 11, 0.0209, 0.0688, 0.01748, 0, 0, 0, 1, 0, 0],
+    [11, 5, 11, 0.0203, 0.0682, 0.01738, 0, 0, 0, 1, 0, 0],
+    [12, 11, 12, 0.00595, 0.0196, 0.00502, 0, 0, 0, 1, 0, 0],
+    [13, 2, 12, 0.0187, 0.0616, 0.01572, 0, 0, 0, 1, 0, 0],
+    [14, 3, 12, 0.0484, 0.16, 0.0406, 0, 0, 0, 1, 0, 0],
+    [15, 7, 12, 0.00862, 0.034, 0.00874, 0, 0, 0, 1, 0, 0],
+    [16, 11, 13, 0.02225, 0.0731, 0.01876, 0, 0, 0, 1, 0, 0],
+    [17, 12, 14, 0.0215, 0.0707, 0.01816, 0, 0, 0, 1, 0, 0],
+    [18, 13, 15, 0.0744, 0.2444, 0.06268, 0, 0, 0, 1, 0, 0],
+    [19, 14, 15, 0.0595, 0.195, 0.0502, 0, 0, 0, 1, 0, 0],
+    [20, 12, 16, 0.0212, 0.0834, 0.0214, 0, 0, 0, 1, 0, 0],
+    [21, 15, 17, 0.0132, 0.0437, 0.0444, 0, 0, 0, 1, 0, 0],
+    [22, 16, 17, 0.0454, 0.1801, 0.0466, 0, 0, 0, 1, 0, 0],
+    [23, 17, 18, 0.0123, 0.0505, 0.01298, 0, 0, 0, 1, 0, 0],
+    [24, 18, 19, 0.01119, 0.0493, 0.01142, 0, 0, 0, 1, 0, 0],
+    [25, 19, 20, 0.0252, 0.117, 0.0298, 0, 0, 0, 1, 0, 0],
+    [26, 15, 19, 0.012, 0.0394, 0.0101, 0, 0, 0, 1, 0, 0],
+    [27, 20, 21, 0.0183, 0.0849, 0.0216, 0, 0, 0, 1, 0, 0],
+    [28, 21, 22, 0.0209, 0.097, 0.0246, 0, 0, 0, 1, 0, 0],
+    [29, 22, 23, 0.0342, 0.159, 0.0404, 0, 0, 0, 1, 0, 0],
+    [30, 23, 24, 0.0135, 0.0492, 0.0498, 0, 0, 0, 1, 0, 0],
+    [31, 23, 25, 0.0156, 0.08, 0.0864, 0, 0, 0, 1, 0, 0],
+    [32, 26, 25, 0, 0.0382, 0, 0, 0.96, 0, 1, 0, 0],
+    [33, 25, 27, 0.0318, 0.163, 0.1764, 0, 0, 0, 1, 0, 0],
+    [34, 27, 28, 0.01913, 0.0855, 0.0216, 0, 0, 0, 1, 0, 0],
+    [35, 28, 29, 0.0237, 0.0943, 0.0238, 0, 0, 0, 1, 0, 0],
+    [36, 30, 17, 0, 0.0388, 0, 0, 0.96, 0, 1, 0, 0],
+    [37, 8, 30, 0.00431, 0.0504, 0.514, 0, 0, 0, 1, 0, 0],
+    [38, 26, 30, 0.00799, 0.086, 0.908, 0, 0, 0, 1, 0, 0],
+    [39, 17, 31, 0.0474, 0.1563, 0.0399, 0, 0, 0, 1, 0, 0],
+    [40, 29, 31, 0.0108, 0.0331, 0.0083, 0, 0, 0, 1, 0, 0],
+    [41, 23, 32, 0.0317, 0.1153, 0.1173, 0, 0, 0, 1, 0, 0],
+    [42, 31, 32, 0.0298, 0.0985, 0.0251, 0, 0, 0, 1, 0, 0],
+    [43, 27, 32, 0.0229, 0.0755, 0.01926, 0, 0, 0, 1, 0, 0],
+    [44, 15, 33, 0.038, 0.1244, 0.03194, 0, 0, 0, 1, 0, 0],
+    [45, 19, 34, 0.0752, 0.247, 0.0632, 0, 0, 0, 1, 0, 0],
+    [46, 35, 36, 0.00224, 0.0102, 0.00268, 0, 0, 0, 1, 0, 0],
+    [47, 35, 37, 0.011, 0.0497, 0.01318, 0, 0, 0, 1, 0, 0],
+    [48, 33, 37, 0.0415, 0.142, 0.0366, 0, 0, 0, 1, 0, 0],
+    [49, 34, 36, 0.00871, 0.0268, 0.00568, 0, 0, 0, 1, 0, 0],
+    [50, 34, 37, 0.00256, 0.0094, 0.00984, 0, 0, 0, 1, 0, 0],
+    [51, 38, 37, 0, 0.0375, 0, 0, 0.935, 0, 1, 0, 0],
+    [52, 37, 39, 0.0321, 0.106, 0.027, 0, 0, 0, 1, 0, 0],
+    [53, 37, 40, 0.0593, 0.168, 0.042, 0, 0, 0, 1, 0, 0],
+    [54, 30, 38, 0.00464, 0.054, 0.422, 0, 0, 0, 1, 0, 0],
+    [55, 39, 40, 0.0184, 0.0605, 0.01552, 0, 0, 0, 1, 0, 0],
+    [56, 40, 41, 0.0145, 0.0487, 0.01222, 0, 0, 0, 1, 0, 0],
+    [57, 40, 42, 0.0555, 0.183, 0.0466, 0, 0, 0, 1, 0, 0],
+    [58, 41, 42, 0.041, 0.135, 0.0344, 0, 0, 0, 1, 0, 0],
+    [59, 43, 44, 0.0608, 0.2454, 0.06068, 0, 0, 0, 1, 0, 0],
+    [60, 34, 43, 0.0413, 0.1681, 0.04226, 0, 0, 0, 1, 0, 0],
+    [61, 44, 45, 0.0224, 0.0901, 0.0224, 0, 0, 0, 1, 0, 0],
+    [62, 45, 46, 0.04, 0.1356, 0.0332, 0, 0, 0, 1, 0, 0],
+    [63, 46, 47, 0.038, 0.127, 0.0316, 0, 0, 0, 1, 0, 0],
+    [64, 46, 48, 0.0601, 0.189, 0.0472, 0, 0, 0, 1, 0, 0],
+    [65, 47, 49, 0.0191, 0.0625, 0.01604, 0, 0, 0, 1, 0, 0],
+    [66, 42, 49, 0.0715, 0.323, 0.086, 0, 0, 0, 1, 0, 0],
+    [67, 42, 49, 0.0715, 0.323, 0.086, 0, 0, 0, 1, 0, 0],
+    [68, 45, 49, 0.0684, 0.186, 0.0444, 0, 0, 0, 1, 0, 0],
+    [69, 48, 49, 0.0179, 0.0505, 0.01258, 0, 0, 0, 1, 0, 0],
+    [70, 49, 50, 0.0267, 0.0752, 0.01874, 0, 0, 0, 1, 0, 0],
+    [71, 49, 51, 0.0486, 0.137, 0.0342, 0, 0, 0, 1, 0, 0],
+    [72, 51, 52, 0.0203, 0.0588, 0.01396, 0, 0, 0, 1, 0, 0],
+    [73, 52, 53, 0.0405, 0.1635, 0.04058, 0, 0, 0, 1, 0, 0],
+    [74, 53, 54, 0.0263, 0.122, 0.031, 0, 0, 0, 1, 0, 0],
+    [75, 49, 54, 0.073, 0.289, 0.0738, 0, 0, 0, 1, 0, 0],
+    [76, 49, 54, 0.0869, 0.291, 0.073, 0, 0, 0, 1, 0, 0],
+    [77, 54, 55, 0.0169, 0.0707, 0.0202, 0, 0, 0, 1, 0, 0],
+    [78, 54, 56, 0.00275, 0.00955, 0.00732, 0, 0, 0, 1, 0, 0],
+    [79, 55, 56, 0.00488, 0.0151, 0.00374, 0, 0, 0, 1, 0, 0],
+    [80, 56, 57, 0.0343, 0.0966, 0.0242, 0, 0, 0, 1, 0, 0],
+    [81, 50, 57, 0.0474, 0.134, 0.0332, 0, 0, 0, 1, 0, 0],
+    [82, 56, 58, 0.0343, 0.0966, 0.0242, 0, 0, 0, 1, 0, 0],
+    [83, 51, 58, 0.0255, 0.0719, 0.01788, 0, 0, 0, 1, 0, 0],
+    [84, 54, 59, 0.0503, 0.2293, 0.0598, 0, 0, 0, 1, 0, 0],
+    [85, 56, 59, 0.0825, 0.251, 0.0569, 0, 0, 0, 1, 0, 0],
+    [86, 56, 59, 0.0803, 0.239, 0.0536, 0, 0, 0, 1, 0, 0],
+    [87, 55, 59, 0.04739, 0.2158, 0.05646, 0, 0, 0, 1, 0, 0],
+    [88, 59, 60, 0.0317, 0.145, 0.0376, 0, 0, 0, 1, 0, 0],
+    [89, 59, 61, 0.0328, 0.15, 0.0388, 0, 0, 0, 1, 0, 0],
+    [90, 60, 61, 0.00264, 0.0135, 0.01456, 0, 0, 0, 1, 0, 0],
+    [91, 60, 62, 0.0123, 0.0561, 0.01468, 0, 0, 0, 1, 0, 0],
+    [92, 61, 62, 0.00824, 0.0376, 0.0098, 0, 0, 0, 1, 0, 0],
+    [93, 63, 59, 0, 0.0386, 0, 0, 0.96, 0, 1, 0, 0],
+    [94, 63, 64, 0.00172, 0.02, 0.216, 0, 0, 0, 1, 0, 0],
+    [95, 64, 61, 0, 0.0268, 0, 0, 0.985, 0, 1, 0, 0],
+    [96, 38, 65, 0.00901, 0.0986, 1.046, 0, 0, 0, 1, 0, 0],
+    [97, 64, 65, 0.00269, 0.0302, 0.38, 0, 0, 0, 1, 0, 0],
+    [98, 49, 66, 0.018, 0.0919, 0.0248, 0, 0, 0, 1, 0, 0],
+    [99, 49, 66, 0.018, 0.0919, 0.0248, 0, 0, 0, 1, 0, 0],
+    [100, 62, 66, 0.0482, 0.218, 0.0578, 0, 0, 0, 1, 0, 0],
+    [101, 62, 67, 0.0258, 0.117, 0.031, 0, 0, 0, 1, 0, 0],
+    [102, 65, 66, 0, 0.037, 0, 0, 0.935, 0, 1, 0, 0],
+    [103, 66, 67, 0.0224, 0.1015, 0.02682, 0, 0, 0, 1, 0, 0],
+    [104, 65, 68, 0.00138, 0.016, 0.638, 0, 0, 0, 1, 0, 0],
+    [105, 47, 69, 0.0844, 0.2778, 0.07092, 0, 0, 0, 1, 0, 0],
+    [106, 49, 69, 0.0985, 0.324, 0.0828, 0, 0, 0, 1, 0, 0],
+    [107, 68, 69, 0, 0.037, 0, 0, 0.935, 0, 1, 0, 0],
+    [108, 69, 70, 0.03, 0.127, 0.122, 0, 0, 0, 1, 0, 0],
+    [109, 24, 70, 0.00221, 0.4115, 0.10198, 0, 0, 0, 1, 0, 0],
+    [110, 70, 71, 0.00882, 0.0355, 0.00878, 0, 0, 0, 1, 0, 0],
+    [111, 24, 72, 0.0488, 0.196, 0.0488, 0, 0, 0, 1, 0, 0],
+    [112, 71, 72, 0.0446, 0.18, 0.04444, 0, 0, 0, 1, 0, 0],
+    [113, 71, 73, 0.00866, 0.0454, 0.01178, 0, 0, 0, 1, 0, 0],
+    [114, 70, 74, 0.0401, 0.1323, 0.03368, 0, 0, 0, 1, 0, 0],
+    [115, 70, 75, 0.0428, 0.141, 0.036, 0, 0, 0, 1, 0, 0],
+    [116, 69, 75, 0.0405, 0.122, 0.124, 0, 0, 0, 1, 0, 0],
+    [117, 74, 75, 0.0123, 0.0406, 0.01034, 0, 0, 0, 1, 0, 0],
+    [118, 76, 77, 0.0444, 0.148, 0.0368, 0, 0, 0, 1, 0, 0],
+    [119, 69, 77, 0.0309, 0.101, 0.1038, 0, 0, 0, 1, 0, 0],
+    [120, 75, 77, 0.0601, 0.1999, 0.04978, 0, 0, 0, 1, 0, 0],
+    [121, 77, 78, 0.00376, 0.0124, 0.01264, 0, 0, 0, 1, 0, 0],
+    [122, 78, 79, 0.00546, 0.0244, 0.00648, 0, 0, 0, 1, 0, 0],
+    [123, 77, 80, 0.017, 0.0485, 0.0472, 0, 0, 0, 1, 0, 0],
+    [124, 77, 80, 0.0294, 0.105, 0.0228, 0, 0, 0, 1, 0, 0],
+    [125, 79, 80, 0.0156, 0.0704, 0.0187, 0, 0, 0, 1, 0, 0],
+    [126, 68, 81, 0.00175, 0.0202, 0.808, 0, 0, 0, 1, 0, 0],
+    [127, 81, 80, 0, 0.037, 0, 0, 0.935, 0, 1, 0, 0],
+    [128, 77, 82, 0.0298, 0.0853, 0.08174, 0, 0, 0, 1, 0, 0],
+    [129, 82, 83, 0.0112, 0.03665, 0.03796, 0, 0, 0, 1, 0, 0],
+    [130, 83, 84, 0.0625, 0.132, 0.0258, 0, 0, 0, 1, 0, 0],
+    [131, 83, 85, 0.043, 0.148, 0.0348, 0, 0, 0, 1, 0, 0],
+    [132, 84, 85, 0.0302, 0.0641, 0.01234, 0, 0, 0, 1, 0, 0],
+    [133, 85, 86, 0.035, 0.123, 0.0276, 0, 0, 0, 1, 0, 0],
+    [134, 86, 87, 0.02828, 0.2074, 0.0445, 0, 1, 0, 1, 0, 0],
+    [135, 85, 88, 0.02, 0.102, 0.0276, 0, 0, 0, 1, 0, 0],
+    [136, 85, 89, 0.0239, 0.173, 0.047, 0, 0, 0, 1, 0, 0],
+    [137, 88, 89, 0.0139, 0.0712, 0.01934, 0, 0, 0, 1, 0, 0],
+    [138, 89, 90, 0.0518, 0.188, 0.0528, 0, 0, 0, 1, 0, 0],
+    [139, 89, 90, 0.0238, 0.0997, 0.106, 0, 0, 0, 1, 0, 0],
+    [140, 90, 91, 0.0254, 0.0836, 0.0214, 0, 0, 0, 1, 0, 0],
+    [141, 89, 92, 0.0099, 0.0505, 0.0548, 0, 0, 0, 1, 0, 0],
+    [142, 89, 92, 0.0393, 0.1581, 0.0414, 0, 0, 0, 1, 0, 0],
+    [143, 91, 92, 0.0387, 0.1272, 0.03268, 0, 0, 0, 1, 0, 0],
+    [144, 92, 93, 0.0258, 0.0848, 0.0218, 0, 0, 0, 1, 0, 0],
+    [145, 92, 94, 0.0481, 0.158, 0.0406, 0, 0, 0, 1, 0, 0],
+    [146, 93, 94, 0.0223, 0.0732, 0.01876, 0, 0, 0, 1, 0, 0],
+    [147, 94, 95, 0.0132, 0.0434, 0.0111, 0, 0, 0, 1, 0, 0],
+    [148, 80, 96, 0.0356, 0.182, 0.0494, 0, 0, 0, 1, 0, 0],
+    [149, 82, 96, 0.0162, 0.053, 0.0544, 0, 0, 0, 1, 0, 0],
+    [150, 94, 96, 0.0269, 0.0869, 0.023, 0, 0, 0, 1, 0, 0],
+    [151, 80, 97, 0.0183, 0.0934, 0.0254, 0, 0, 0, 1, 0, 0],
+    [152, 80, 98, 0.0238, 0.108, 0.0286, 0, 0, 0, 1, 0, 0],
+    [153, 80, 99, 0.0454, 0.206, 0.0546, 0, 0, 0, 1, 0, 0],
+    [154, 92, 100, 0.0648, 0.295, 0.0472, 0, 0, 0, 1, 0, 0],
+    [155, 94, 100, 0.0178, 0.058, 0.0604, 0, 0, 0, 1, 0, 0],
+    [156, 95, 96, 0.0171, 0.0547, 0.01474, 0, 0, 0, 1, 0, 0],
+    [157, 96, 97, 0.0173, 0.0885, 0.024, 0, 0, 0, 1, 0, 0],
+    [158, 98, 100, 0.0397, 0.179, 0.0476, 0, 0, 0, 1, 0, 0],
+    [159, 99, 100, 0.018, 0.0813, 0.0216, 0, 0, 0, 1, 0, 0],
+    [160, 100, 101, 0.0277, 0.1262, 0.0328, 0, 0, 0, 1, 0, 0],
+    [161, 92, 102, 0.0123, 0.0559, 0.01464, 0, 0, 0, 1, 0, 0],
+    [162, 101, 102, 0.0246, 0.112, 0.0294, 0, 0, 0, 1, 0, 0],
+    [163, 100, 103, 0.016, 0.0525, 0.0536, 0, 0, 0, 1, 0, 0],
+    [164, 100, 104, 0.0451, 0.204, 0.0541, 0, 0, 0, 1, 0, 0],
+    [165, 103, 104, 0.0466, 0.1584, 0.0407, 0, 0, 0, 1, 0, 0],
+    [166, 103, 105, 0.0535, 0.1625, 0.0408, 0, 0, 0, 1, 0, 0],
+    [167, 100, 106, 0.0605, 0.229, 0.062, 0, 0, 0, 1, 0, 0],
+    [168, 104, 105, 0.00994, 0.0378, 0.00986, 0, 0, 0, 1, 0, 0],
+    [169, 105, 106, 0.014, 0.0547, 0.01434, 0, 0, 0, 1, 0, 0],
+    [170, 105, 107, 0.053, 0.183, 0.0472, 0, 0, 0, 1, 0, 0],
+    [171, 105, 108, 0.0261, 0.0703, 0.01844, 0, 0, 0, 1, 0, 0],
+    [172, 106, 107, 0.053, 0.183, 0.0472, 0, 0, 0, 1, 0, 0],
+    [173, 108, 109, 0.0105, 0.0288, 0.0076, 0, 0, 0, 1, 0, 0],
+    [174, 103, 110, 0.03906, 0.1813, 0.0461, 0, 0, 0, 1, 0, 0],
+    [175, 109, 110, 0.0278, 0.0762, 0.0202, 0, 0, 0, 1, 0, 0],
+    [176, 110, 111, 0.022, 0.0755, 0.02, 0, 0, 0, 1, 0, 0],
+    [177, 110, 112, 0.0247, 0.064, 0.062, 0, 0, 0, 1, 0, 0],
+    [178, 17, 113, 0.00913, 0.0301, 0.00768, 0, 0, 0, 1, 0, 0],
+    [179, 32, 113, 0.0615, 0.203, 0.0518, 0, 0, 0, 1, 0, 0],
+    [180, 32, 114, 0.0135, 0.0612, 0.01628, 0, 0, 0, 1, 0, 0],
+    [181, 27, 115, 0.0164, 0.0741, 0.01972, 0, 0, 0, 1, 0, 0],
+    [182, 114, 115, 0.0023, 0.0104, 0.00276, 0, 0, 0, 1, 0, 0],
+    [183, 68, 116, 0.00034, 0.00405, 0.164, 0, 1, 0, 1, 0, 0],
+    [184, 12, 117, 0.0329, 0.14, 0.0358, 0, 0, 0, 1, 0, 0],
+    [185, 75, 118, 0.0145, 0.0481, 0.01198, 0, 0, 0, 1, 0, 0],
+    [186, 76, 118, 0.0164, 0.0544, 0.01356, 0, 0, 0, 1, 0, 0]
+            ])
+
+        self.loads = DataFrame(
+            ['id', 'bus_id', 'Pd', 'Qd', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'],
+            [
+    [1, 1, 51, 27, 0, 0, 0, 51, 0],
+    [2, 2, 20, 9, 0, 0, 0, 20, 0],
+    [3, 3, 39, 10, 0, 0, 0, 39, 0],
+    [4, 4, 39, 12, 0, 0, 0, 39, 0],
+    [5, 5, 0, 0, 0, 0, 0, 0, 0],
+    [6, 6, 52, 22, 0, 0, 0, 52, 0],
+    [7, 7, 19, 2, 0, 0, 0, 19, 0],
+    [8, 8, 28, 0, 0, 0, 0, 28, 0],
+    [9, 9, 0, 0, 0, 0, 0, 0, 0],
+    [10, 10, 0, 0, 0, 0, 0, 0, 0],
+    [11, 11, 70, 23, 0, 0, 0, 70, 0],
+    [12, 12, 47, 10, 0, 0, 0, 47, 0],
+    [13, 13, 34, 16, 0, 0, 0, 34, 0],
+    [14, 14, 14, 1, 0, 0, 0, 14, 0],
+    [15, 15, 90, 30, 0, 0, 0, 90, 0],
+    [16, 16, 25, 10, 0, 0, 0, 25, 0],
+    [17, 17, 11, 3, 0, 0, 0, 11, 0],
+    [18, 18, 60, 34, 0, 0, 0, 60, 0],
+    [19, 19, 45, 25, 0, 0, 0, 45, 0],
+    [20, 20, 18, 3, 0, 0, 0, 18, 0],
+    [21, 21, 14, 8, 0, 0, 0, 14, 0],
+    [22, 22, 10, 5, 0, 0, 0, 10, 0],
+    [23, 23, 7, 3, 0, 0, 0, 7, 0],
+    [24, 24, 13, 0, 0, 0, 0, 13, 0],
+    [25, 25, 0, 0, 0, 0, 0, 0, 0],
+    [26, 26, 0, 0, 0, 0, 0, 0, 0],
+    [27, 27, 71, 13, 0, 0, 0, 71, 0],
+    [28, 28, 17, 7, 0, 0, 0, 17, 0],
+    [29, 29, 24, 4, 0, 0, 0, 24, 0],
+    [30, 30, 0, 0, 0, 0, 0, 0, 0],
+    [31, 31, 43, 27, 0, 0, 0, 43, 0],
+    [32, 32, 59, 23, 0, 0, 0, 59, 0],
+    [33, 33, 23, 9, 0, 0, 0, 23, 0],
+    [34, 34, 59, 26, 0, 0, 0, 59, 0],
+    [35, 35, 33, 9, 0, 0, 0, 33, 0],
+    [36, 36, 31, 17, 0, 0, 0, 31, 0],
+    [37, 37, 0, 0, 0, 0, 0, 0, 0],
+    [38, 38, 0, 0, 0, 0, 0, 0, 0],
+    [39, 39, 27, 11, 0, 0, 0, 27, 0],
+    [40, 40, 66, 23, 0, 0, 0, 66, 0],
+    [41, 41, 37, 10, 0, 0, 0, 37, 0],
+    [42, 42, 96, 23, 0, 0, 0, 96, 0],
+    [43, 43, 18, 7, 0, 0, 0, 18, 0],
+    [44, 44, 16, 8, 0, 0, 0, 16, 0],
+    [45, 45, 53, 22, 0, 0, 0, 53, 0],
+    [46, 46, 28, 10, 0, 0, 0, 28, 0],
+    [47, 47, 34, 0, 0, 0, 0, 34, 0],
+    [48, 48, 20, 11, 0, 0, 0, 20, 0],
+    [49, 49, 87, 30, 0, 0, 0, 87, 0],
+    [50, 50, 17, 4, 0, 0, 0, 17, 0],
+    [51, 51, 17, 8, 0, 0, 0, 17, 0],
+    [52, 52, 18, 5, 0, 0, 0, 18, 0],
+    [53, 53, 23, 11, 0, 0, 0, 23, 0],
+    [54, 54, 113, 32, 0, 0, 0, 113, 0],
+    [55, 55, 63, 22, 0, 0, 0, 63, 0],
+    [56, 56, 84, 18, 0, 0, 0, 84, 0],
+    [57, 57, 12, 3, 0, 0, 0, 12, 0],
+    [58, 58, 12, 3, 0, 0, 0, 12, 0],
+    [59, 59, 277, 113, 0, 0, 0, 277, 0],
+    [60, 60, 78, 3, 0, 0, 0, 78, 0],
+    [61, 61, 0, 0, 0, 0, 0, 0, 0],
+    [62, 62, 77, 14, 0, 0, 0, 77, 0],
+    [63, 63, 0, 0, 0, 0, 0, 0, 0],
+    [64, 64, 0, 0, 0, 0, 0, 0, 0],
+    [65, 65, 0, 0, 0, 0, 0, 0, 0],
+    [66, 66, 39, 18, 0, 0, 0, 39, 0],
+    [67, 67, 28, 7, 0, 0, 0, 28, 0],
+    [68, 68, 0, 0, 0, 0, 0, 0, 0],
+    [69, 69, 0, 0, 0, 0, 0, 0, 0],
+    [70, 70, 66, 20, 0, 0, 0, 66, 0],
+    [71, 71, 0, 0, 0, 0, 0, 0, 0],
+    [72, 72, 12, 0, 0, 0, 0, 12, 0],
+    [73, 73, 6, 0, 0, 0, 0, 6, 0],
+    [74, 74, 68, 27, 0, 0, 0, 68, 0],
+    [75, 75, 47, 11, 0, 0, 0, 47, 0],
+    [76, 76, 68, 36, 0, 0, 0, 68, 0],
+    [77, 77, 61, 28, 0, 0, 0, 61, 0],
+    [78, 78, 71, 26, 0, 0, 0, 71, 0],
+    [79, 79, 39, 32, 0, 0, 0, 39, 0],
+    [80, 80, 130, 26, 0, 0, 0, 130, 0],
+    [81, 81, 0, 0, 0, 0, 0, 0, 0],
+    [82, 82, 54, 27, 0, 0, 0, 54, 0],
+    [83, 83, 20, 10, 0, 0, 0, 20, 0],
+    [84, 84, 11, 7, 0, 0, 0, 11, 0],
+    [85, 85, 24, 15, 0, 0, 0, 24, 0],
+    [86, 86, 21, 10, 0, 0, 0, 21, 0],
+    [87, 87, 0, 0, 0, 0, 0, 0, 0],
+    [88, 88, 48, 10, 0, 0, 0, 48, 0],
+    [89, 89, 0, 0, 0, 0, 0, 0, 0],
+    [90, 90, 163, 42, 0, 0, 0, 163, 0],
+    [91, 91, 10, 0, 0, 0, 0, 10, 0],
+    [92, 92, 65, 10, 0, 0, 0, 65, 0],
+    [93, 93, 12, 7, 0, 0, 0, 12, 0],
+    [94, 94, 30, 16, 0, 0, 0, 30, 0],
+    [95, 95, 42, 31, 0, 0, 0, 42, 0],
+    [96, 96, 38, 15, 0, 0, 0, 38, 0],
+    [97, 97, 15, 9, 0, 0, 0, 15, 0],
+    [98, 98, 34, 8, 0, 0, 0, 34, 0],
+    [99, 99, 42, 0, 0, 0, 0, 42, 0],
+    [100, 100, 37, 18, 0, 0, 0, 37, 0],
+    [101, 101, 22, 15, 0, 0, 0, 22, 0],
+    [102, 102, 5, 3, 0, 0, 0, 5, 0],
+    [103, 103, 23, 16, 0, 0, 0, 23, 0],
+    [104, 104, 38, 25, 0, 0, 0, 38, 0],
+    [105, 105, 31, 26, 0, 0, 0, 31, 0],
+    [106, 106, 43, 16, 0, 0, 0, 43, 0],
+    [107, 107, 50, 12, 0, 0, 0, 50, 0],
+    [108, 108, 2, 1, 0, 0, 0, 2, 0],
+    [109, 109, 8, 3, 0, 0, 0, 8, 0],
+    [110, 110, 39, 30, 0, 0, 0, 39, 0],
+    [111, 111, 0, 0, 0, 0, 0, 0, 0],
+    [112, 112, 68, 13, 0, 0, 0, 68, 0],
+    [113, 113, 6, 0, 0, 0, 0, 6, 0],
+    [114, 114, 8, 3, 0, 0, 0, 8, 0],
+    [115, 115, 22, 7, 0, 0, 0, 22, 0],
+    [116, 116, 184, 0, 0, 0, 0, 184, 0],
+    [117, 117, 20, 8, 0, 0, 0, 20, 0],
+    [118, 118, 33, 15, 0, 0, 0, 33, 0]
+            ])
+
+        self.real_params = True
+        super().__init__(*args, **kwargs)
+
+
+if __name__ == '__main__':
+    c = Case118()
+    c.check()
+    print(c.get_node_ptdf())

powergrid_case/transmission/Case14.py

@@ -0,0 +1,142 @@
+"""IEEE 14-bus system (MATPOWER case14)
+
+Data Source: MATPOWER case file `source_mfile/case14.m`
+Base MVA: 100.0 MVA
+Base kV: 69.0 kV (nominal; MATPOWER bus table uses 0 for baseKV)
+
+UC / economic columns on ``self.units`` match PowerZooJax ``create_case14()`` and are
+regenerated from ``powerzoo/case/raw_cases/case14.json`` via
+``python -m powerzoo.case.raw_cases.scuc_json_to_case_units`` (keep that script and JSON
+in sync with the sibling ``powerzoojax/case/raw_cases/`` tree).
+
+All values are in standard MATPOWER units (MW, MVAr, p.u.)
+
+
+Nodes (bus) columns:
+    id, type, Pd, Qd, Gs, Bs, Vm, Va, baseKV, Vmax, Vmin
+    - type: 1=PQ, 2=PV, 3=Slack
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - Gs/Bs: Shunt conductance/susceptance (MW/MVAr at V=1.0 p.u.)
+    - Vm: Voltage magnitude (p.u.)
+    - Va: Voltage angle (degrees)
+    - baseKV: Base voltage (kV)
+    - Vmax/Vmin: Voltage limits (p.u.)
+
+Units (generators) columns:
+    id, bus_id, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, mc_a, mc_b, mc_c,
+    p_max, p_min, type, ramp_up, ramp_down, init_start_up_cost, keep_time,
+    init_power, init_state, min_up_time, min_down_time, init_no_load_cost
+    - Pg/Qg: Active/Reactive power output (MW/MVAr)
+    - Qmax/Qmin: Reactive power limits (MVAr)
+    - Vg: Voltage setpoint (p.u.)
+    - mBase: Machine base (MVA)
+    - status: >0 in-service, <=0 out-of-service
+    - Pmax/Pmin: Active power limits (MW)
+    - mc_a/mc_b/mc_c: Marginal cost coefficients (quadratic)
+
+Lines (branches) columns:
+    id, from, to, r, x, b, rateA, ratio, angle, status, floor, cap
+    - r/x: Resistance/Reactance (p.u.)
+    - b: Line charging susceptance (p.u.)
+    - rateA: Long-term rating (MVA)
+    - ratio: Transformer tap ratio (0 for line)
+    - angle: Transformer phase shift (degrees)
+    - status: 1=in-service, 0=out-of-service
+    - floor/cap: Power flow limits (MW)
+
+Loads columns:
+    id, bus_id, Pd, Qd, mc_a, mc_b, mc_c, d_max, d_min
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - mc_a/mc_b/mc_c: Marginal utility coefficients
+    - d_max/d_min: Demand limits (MW)
+"""
+from powerzoo.case.CaseBase import ClearCase, DataFrame
+
+
+class Case14(ClearCase):
+    GRID_TYPE = "transmission"
+    BUS_COUNT = 14
+    VOLTAGE_LEVEL = "HV"
+    SOURCE = "MATPOWER"
+    DESCRIPTION = "IEEE 14-bus test system"
+
+    def __init__(self, *args, **kwargs):
+        # System base values
+        self.baseMVA = 100.0
+        # MATPOWER case14 has baseKV=0 on all buses; use a nominal kV for metadata.
+        self.baseKV = 69.0
+        
+        # Node (bus) data
+        self.nodes = DataFrame(
+            ['id', 'type', 'Pd', 'Qd', 'Gs', 'Bs', 'Vm', 'Va', 'baseKV', 'Vmax', 'Vmin'],
+            [[1.0, 3.0, 0.0, 0.0, 0.0, 0.0, 1.06, 0.0, 0.0, 1.06, 0.94],
+             [2.0, 2.0, 21.7, 12.7, 0.0, 0.0, 1.045, -4.98, 0.0, 1.06, 0.94],
+             [3.0, 2.0, 94.2, 19.0, 0.0, 0.0, 1.01, -12.72, 0.0, 1.06, 0.94],
+             [4.0, 1.0, 47.8, -3.9, 0.0, 0.0, 1.019, -10.33, 0.0, 1.06, 0.94],
+             [5.0, 1.0, 7.6, 1.6, 0.0, 0.0, 1.02, -8.78, 0.0, 1.06, 0.94],
+             [6.0, 2.0, 11.2, 7.5, 0.0, 0.0, 1.07, -14.22, 0.0, 1.06, 0.94],
+             [7.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.062, -13.37, 0.0, 1.06, 0.94],
+             [8.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.09, -13.36, 0.0, 1.06, 0.94],
+             [9.0, 1.0, 29.5, 16.6, 0.0, 19.0, 1.056, -14.94, 0.0, 1.06, 0.94],
+             [10.0, 1.0, 9.0, 5.8, 0.0, 0.0, 1.051, -15.1, 0.0, 1.06, 0.94],
+             [11.0, 1.0, 3.5, 1.8, 0.0, 0.0, 1.057, -14.79, 0.0, 1.06, 0.94],
+             [12.0, 1.0, 6.1, 1.6, 0.0, 0.0, 1.055, -15.07, 0.0, 1.06, 0.94],
+             [13.0, 1.0, 13.5, 5.8, 0.0, 0.0, 1.05, -15.16, 0.0, 1.06, 0.94],
+             [14.0, 1.0, 14.9, 5.0, 0.0, 0.0, 1.036, -16.04, 0.0, 1.06, 0.94]])
+
+        # Generator (unit) data — UC columns aligned with Case118 / PowerZooJax case14
+        self.units = DataFrame(
+            ['id', 'bus_id', 'Pg', 'Qg', 'Qmax', 'Qmin', 'Vg', 'mBase', 'status',
+             'Pmax', 'Pmin', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min',
+             'type', 'ramp_up', 'ramp_down', 'init_start_up_cost', 'keep_time',
+             'init_power', 'init_state', 'min_up_time', 'min_down_time', 'init_no_load_cost'],
+            [[1.0, 1.0, 232.4, -16.9, 10.0, 0.0, 1.06, 100.0, 1.0, 332.4, 0.0, 0.0, 0.05541301, 34.216168, 332.4, 0.0, 'gas', 0.6953069, 0.6953069, 34238.45, 48.0, 230.74888, 1.0, 2.0, 2.0, 0.0],
+             [2.0, 2.0, 40.0, 42.4, 50.0, -40.0, 1.045, 100.0, 1.0, 140.0, 0.0, 0.00897295, -1.143378, 67.97416, 140.0, 0.0, 'gas', 0.66157144, 0.66157144, 11382.84, 48.0, 0.0, 0.0, 2.0, 2.0, 0.0],
+             [3.0, 3.0, 0.0, 23.4, 40.0, 0.0, 1.01, 100.0, 1.0, 100.0, 0.0, 0.0357393, -2.380081, 72.64597, 100.0, 0.0, 'gas', 0.6621, 0.6621, 5235.41, 48.0, 0.0, 0.0, 2.0, 2.0, 0.0],
+             [4.0, 6.0, 0.0, 12.2, 24.0, -6.0, 1.07, 100.0, 1.0, 100.0, 0.0, 0.03130176, -2.3915932, 78.5705, 100.0, 0.0, 'gas', 0.6694, 0.6694, 5272.29, 48.0, 0.0, 0.0, 8.0, 8.0, 0.0],
+             [5.0, 8.0, 0.0, 17.4, 24.0, -6.0, 1.09, 100.0, 1.0, 100.0, 0.0, 0.03348683, -2.4855613, 76.65385, 100.0, 0.0, 'gas', 0.6504, 0.6504, 7950.56, 48.0, 0.0, 0.0, 2.0, 2.0, 0.0]])
+
+        # Branch (line) data
+        self.lines = DataFrame(
+            ['id', 'from', 'to', 'r', 'x', 'b', 'rateA', 'ratio', 'angle', 'status', 'floor', 'cap'],
+            [[1.0, 1.0, 2.0, 0.01938, 0.05917, 0.0528, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [2.0, 1.0, 5.0, 0.05403, 0.22304, 0.0492, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [3.0, 2.0, 3.0, 0.04699, 0.19797, 0.0438, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [4.0, 2.0, 4.0, 0.05811, 0.17632, 0.034, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [5.0, 2.0, 5.0, 0.05695, 0.17388, 0.0346, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [6.0, 3.0, 4.0, 0.06701, 0.17103, 0.0128, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [7.0, 4.0, 5.0, 0.01335, 0.04211, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [8.0, 4.0, 7.0, 0.0, 0.20912, 0.0, 0.0, 0.978, 0.0, 1.0, 0.0, 0.0],
+             [9.0, 4.0, 9.0, 0.0, 0.55618, 0.0, 0.0, 0.969, 0.0, 1.0, 0.0, 0.0],
+             [10.0, 5.0, 6.0, 0.0, 0.25202, 0.0, 0.0, 0.932, 0.0, 1.0, 0.0, 0.0],
+             [11.0, 6.0, 11.0, 0.09498, 0.1989, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [12.0, 6.0, 12.0, 0.12291, 0.25581, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [13.0, 6.0, 13.0, 0.06615, 0.13027, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [14.0, 7.0, 8.0, 0.0, 0.17615, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [15.0, 7.0, 9.0, 0.0, 0.11001, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [16.0, 9.0, 10.0, 0.03181, 0.0845, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [17.0, 9.0, 14.0, 0.12711, 0.27038, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [18.0, 10.0, 11.0, 0.08205, 0.19207, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [19.0, 12.0, 13.0, 0.22092, 0.19988, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [20.0, 13.0, 14.0, 0.17093, 0.34802, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]])
+
+        # Load data
+        self.loads = DataFrame(
+            ['id', 'bus_id', 'Pd', 'Qd', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'],
+            [[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [2.0, 2.0, 21.7, 12.7, 0.0, 0.0, 0.0, 21.7, 21.7],
+             [3.0, 3.0, 94.2, 19.0, 0.0, 0.0, 0.0, 94.2, 94.2],
+             [4.0, 4.0, 47.8, -3.9, 0.0, 0.0, 0.0, 47.8, 47.8],
+             [5.0, 5.0, 7.6, 1.6, 0.0, 0.0, 0.0, 7.6, 7.6],
+             [6.0, 6.0, 11.2, 7.5, 0.0, 0.0, 0.0, 11.2, 11.2],
+             [7.0, 7.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [8.0, 8.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [9.0, 9.0, 29.5, 16.6, 0.0, 0.0, 0.0, 29.5, 29.5],
+             [10.0, 10.0, 9.0, 5.8, 0.0, 0.0, 0.0, 9.0, 9.0],
+             [11.0, 11.0, 3.5, 1.8, 0.0, 0.0, 0.0, 3.5, 3.5],
+             [12.0, 12.0, 6.1, 1.6, 0.0, 0.0, 0.0, 6.1, 6.1],
+             [13.0, 13.0, 13.5, 5.8, 0.0, 0.0, 0.0, 13.5, 13.5],
+             [14.0, 14.0, 14.9, 5.0, 0.0, 0.0, 0.0, 14.9, 14.9]])
+        
+        self.real_params = True
+        super().__init__(*args, **kwargs)

powergrid_case/transmission/Case29GB.py

@@ -0,0 +1,235 @@
+"""Power System Case: Case29GB (GB reduced network)
+
+Data Source: MATPOWER GBreducednetwork.m
+Description: Reduced model of GB (Great Britain) network
+            29 buses, 66 generators, 99 transmission lines
+            Data provided by Manolis Belivanis from Strathclyde
+
+Generator cost parameters are simulated using the same methodology as Case118.
+"""
+from powerzoo.case.CaseBase import ClearCase, DataFrame
+
+
+class Case29GB(ClearCase):
+    GRID_TYPE = "transmission"
+    BUS_COUNT = 29
+    VOLTAGE_LEVEL = "HV"
+    SOURCE = "custom"
+    DESCRIPTION = "GB reduced 29-bus transmission network"
+
+    def __init__(self, *args, **kwargs):
+        self.nodes = DataFrame(['id'], range(1, 30))
+        
+        self.units = DataFrame(
+            ['id', 'bus_id', 'type', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min', 'ramp_up', 'ramp_down', 'init_start_up_cost', 'keep_time', 'init_power', 'init_state', 'min_up_time', 'min_down_time', 'init_no_load_cost'],
+            [
+            [1, 1, 'nuclear', 0.000194, -0.159251, 61.0, 668, 134, 0.7, 0.7, 22500, 96, 494, 1, 96, 96, 10323.78],
+            [2, 1, 'coal', 0.000815, -0.379332, 128.0, 554, 111, 0.7, 0.7, 9000, 96, 550, 1, 4, 4, 18334.91],
+            [3, 1, 'coal', 0.002778, -0.7005, 134.0, 300, 60, 0.7, 0.7, 9000, -96, 0, 0, 4, 4, 10468.65],
+            [4, 1, 'nuclear', 0.239723, -5.598947, 69.0, 19, 4, 0.7, 0.7, 22500, 96, 19, 1, 96, 96, 339.24],
+            [5, 2, 'nuclear', 0.033272, -2.085882, 68.0, 51, 10, 0.7, 0.7, 22500, 96, 36, 1, 96, 96, 895.29],
+            [6, 2, 'gas', 0.00017, -0.283228, 216.0, 1524, 305, 0.7, 0.7, 4500, 96, 1524, 1, 1, 1, 81431.65],
+            [7, 2, 'gas', 2.746597, -35.97, 282.0, 12, 2, 0.7, 0.7, 4500, 96, 12, 1, 1, 1, 878.79],
+            [8, 3, 'nuclear', 0.000285, -0.193067, 62.0, 551, 110, 0.7, 0.7, 22500, 96, 388, 1, 96, 96, 8680.87],
+            [9, 3, 'nuclear', 0.239723, -5.598947, 69.0, 19, 4, 0.7, 0.7, 22500, 96, 14, 1, 96, 96, 339.24],
+            [10, 3, 'coal', 0.000818, -0.380018, 128.0, 553, 111, 0.7, 0.7, 9000, 96, 553, 1, 4, 4, 18301.81],
+            [11, 3, 'nuclear', 0.384622, -7.092, 69.0, 15, 3, 0.7, 0.7, 22500, 96, 15, 1, 96, 96, 267.82],
+            [12, 4, 'nuclear', 0.070645, -3.039429, 68.0, 35, 7, 0.7, 0.7, 22500, 96, 25, 1, 96, 96, 614.42],
+            [13, 4, 'coal', 4.8e-05, -0.09201, 97.0, 2284, 457, 0.7, 0.7, 9000, 96, 2284, 1, 4, 4, 54348.92],
+            [14, 4, 'coal', 0.001291, -0.477614, 131.0, 440, 88, 0.7, 0.7, 9000, 96, 340, 1, 4, 4, 14958.02],
+            [15, 4, 'gas', 0.005896, -1.666564, 268.0, 259, 52, 0.7, 0.7, 4500, 96, 259, 1, 1, 1, 17879.51],
+            [16, 5, 'gas', 0.000343, -0.401899, 232.0, 1074, 215, 0.7, 0.7, 4500, 96, 1074, 1, 1, 1, 62542.07],
+            [17, 5, 'gas', 0.747656, -18.766957, 281.0, 23, 5, 0.7, 0.7, 4500, 96, 23, 1, 1, 1, 1677.46],
+            [18, 6, 'nuclear', 4.5e-05, -0.076477, 54.0, 1391, 278, 0.7, 0.7, 22500, 96, 1002, 1, 96, 96, 18576.47],
+            [19, 6, 'coal', 0.229568, -6.368182, 141.0, 33, 7, 0.7, 0.7, 9000, 96, 33, 1, 4, 4, 1220.85],
+            [20, 6, 'nuclear', 0.036043, -2.17102, 68.0, 49, 10, 0.7, 0.7, 22500, 96, 49, 1, 96, 96, 860.18],
+            [21, 7, 'nuclear', 0.000878, -0.33879, 65.0, 314, 63, 0.7, 0.7, 22500, 96, 227, 1, 96, 96, 5229.59],
+            [22, 7, 'gas', 0.000268, -0.355259, 227.0, 1215, 243, 0.7, 0.7, 4500, 96, 1216, 1, 1, 1, 68930.4],
+            [23, 7, 'coal', 0.000206, -0.190699, 116.0, 1102, 220, 0.7, 0.7, 9000, 96, 1104, 1, 4, 4, 32504.04],
+            [24, 10, 'gas', 0.000271, -0.357614, 227.0, 1207, 241, 0.7, 0.7, 4500, 96, 1208, 1, 1, 1, 68476.54],
+            [25, 10, 'gas', 0.000113, -0.230208, 205.0, 1875, 375, 0.7, 0.7, 4500, 96, 1875, 1, 1, 1, 93999.08],
+            [26, 10, 'coal', 0.001417, -0.500357, 131.0, 420, 84, 0.7, 0.7, 9000, 96, 420, 1, 4, 4, 14278.11],
+            [27, 11, 'nuclear', 0.002671, -0.591, 66.0, 180, 36, 0.7, 0.7, 22500, 96, 130, 1, 96, 96, 3051.86],
+            [28, 11, 'gas', 6.8e-05, -0.179401, 191.0, 2406, 481, 0.7, 0.7, 4500, 96, 2406, 1, 1, 1, 110514.41],
+            [29, 11, 'coal', 6.3e-05, -0.105762, 102.0, 1987, 397, 0.7, 0.7, 9000, 96, 1961, 1, 4, 4, 50262.16],
+            [30, 11, 'gas', 0.016462, -2.784774, 274.0, 155, 31, 0.7, 0.7, 4500, 96, 155, 1, 1, 1, 10979.09],
+            [31, 11, 'gas', 0.000366, -0.415438, 234.0, 1039, 208, 0.7, 0.7, 4500, 96, 1039, 1, 1, 1, 61127.32],
+            [32, 12, 'coal', 0.00026, -0.214439, 119.0, 980, 196, 0.7, 0.7, 9000, 96, 980, 1, 4, 4, 29787.59],
+            [33, 12, 'gas', 0.000111, -0.228987, 205.0, 1885, 377, 0.7, 0.7, 4500, 96, 1885, 1, 1, 1, 94500.4],
+            [34, 12, 'gas', 0.008968, -2.055429, 271.0, 210, 42, 0.7, 0.7, 4500, 96, 210, 1, 1, 1, 14685.9],
+            [35, 12, 'coal', 6.2e-05, -0.104865, 101.0, 2004, 401, 0.7, 0.7, 9000, 96, 1096, 1, 4, 4, 50090.98],
+            [36, 15, 'coal', 4e-06, -0.026832, 55.0, 7832, 1566, 0.7, 0.7, 9000, 96, 7800, 1, 4, 4, 87683.16],
+            [37, 16, 'coal', 1.6e-05, -0.052709, 79.0, 3987, 797, 0.7, 0.7, 9000, 96, 3972, 1, 4, 4, 73342.86],
+            [38, 16, 'gas', 8e-06, -0.061312, 117.0, 7040, 1408, 0.7, 0.7, 4500, 96, 7041, 1, 1, 1, 167079.19],
+            [39, 16, 'gas', 0.000267, -0.354384, 227.0, 1218, 244, 0.7, 0.7, 4500, 96, 1218, 1, 1, 1, 69100.6],
+            [40, 17, 'coal', 6.1e-05, -0.103983, 101.0, 2021, 404, 0.7, 0.7, 9000, 96, 2000, 1, 4, 4, 50515.91],
+            [41, 18, 'coal', 6.4e-05, -0.106029, 102.0, 1982, 396, 0.7, 0.7, 9000, 96, 1960, 1, 4, 4, 50135.68],
+            [42, 18, 'gas', 0.007608, -1.893158, 270.0, 228, 46, 0.7, 0.7, 4500, 96, 228, 1, 1, 1, 15876.29],
+            [43, 19, 'gas', 4.9e-05, -0.151719, 180.0, 2845, 569, 0.7, 0.7, 4500, 96, 2845, 1, 1, 1, 121290.43],
+            [44, 19, 'nuclear', 0.000522, -0.261376, 64.0, 407, 81, 0.7, 0.7, 22500, 96, 407, 1, 96, 96, 6656.39],
+            [45, 20, 'coal', 0.000174, -0.175125, 115.0, 1200, 240, 0.7, 0.7, 9000, 96, 1200, 1, 4, 4, 35034.6],
+            [46, 20, 'nuclear', 0.000668, -0.2955, 64.0, 360, 72, 0.7, 0.7, 22500, 96, 360, 1, 96, 96, 5887.71],
+            [47, 21, 'gas', 0.000894, -0.649083, 249.0, 665, 133, 0.7, 0.7, 4500, 96, 665, 1, 1, 1, 42116.34],
+            [48, 22, 'gas', 0.00246, -1.076409, 261.0, 401, 80, 0.7, 0.7, 4500, 96, 401, 1, 1, 1, 26840.07],
+            [49, 23, 'coal', 1.7e-05, -0.055288, 81.0, 3801, 760, 0.7, 0.7, 9000, 96, 3772, 1, 4, 4, 72201.9],
+            [50, 23, 'coal', 0.001897, -0.578926, 132.0, 363, 73, 0.7, 0.7, 9000, 96, 363, 1, 4, 4, 12449.27],
+            [51, 23, 'coal', 0.001132, -0.447128, 130.0, 470, 94, 0.7, 0.7, 9000, 96, 470, 1, 4, 4, 15836.88],
+            [52, 23, 'gas', 2e-05, -0.097414, 150.0, 4431, 886, 0.7, 0.7, 4500, 96, 3081, 1, 1, 1, 149027.11],
+            [53, 23, 'gas', 0.039551, -4.3164, 277.0, 100, 20, 0.7, 0.7, 4500, -96, 0, 0, 1, 1, 7173.28],
+            [54, 25, 'gas', 8.8e-05, -0.203316, 198.0, 2123, 425, 0.7, 0.7, 4500, 96, 2123, 1, 1, 1, 101973.72],
+            [55, 25, 'coal', 0.000161, -0.168795, 114.0, 1245, 249, 0.7, 0.7, 9000, -96, 0, 0, 4, 4, 35974.9],
+            [56, 26, 'coal', 6.5e-05, -0.106892, 102.0, 1966, 393, 0.7, 0.7, 9000, 96, 1940, 1, 4, 4, 49730.95],
+            [57, 26, 'coal', 0.000195, -0.185809, 116.0, 1131, 226, 0.7, 0.7, 9000, 96, 1104, 1, 4, 4, 33359.41],
+            [58, 26, 'gas', 3.9e-05, -0.136379, 173.0, 3165, 633, 0.7, 0.7, 4500, 96, 2465, 1, 1, 1, 128286.44],
+            [59, 26, 'coal', 0.000136, -0.155092, 112.0, 1355, 271, 0.7, 0.7, 9000, -96, 0, 0, 4, 4, 38340.4],
+            [60, 27, 'gas', 0.000338, -0.399297, 232.0, 1081, 216, 0.7, 0.7, 4500, 96, 1082, 1, 1, 1, 62949.7],
+            [61, 27, 'nuclear', 0.000962, -0.3546, 65.0, 300, 60, 0.7, 0.7, 22500, 96, 216, 1, 96, 96, 4996.43],
+            [62, 28, 'gas', 0.000227, -0.327, 223.0, 1320, 264, 0.7, 0.7, 4500, 96, 1320, 1, 1, 1, 73303.35],
+            [63, 28, 'gas', 0.009142, -2.075192, 271.0, 208, 42, 0.7, 0.7, 4500, 96, 208, 1, 1, 1, 14546.03],
+            [64, 29, 'gas', 0.000249, -0.3423, 225.0, 1261, 252, 0.7, 0.7, 4500, 96, 1261, 1, 1, 1, 70783.51],
+            [65, 29, 'gas', 0.000483, -0.47695, 239.0, 905, 181, 0.7, 0.7, 4500, 96, 905, 1, 1, 1, 54601.22],
+            [66, 29, 'gas', 0.020179, -3.083143, 275.0, 140, 28, 0.7, 0.7, 4500, -96, 0, 0, 1, 1, 9958.6]])
+        
+        self.lines = DataFrame(
+            ['id', 'from', 'to', 'x', 's', 'floor', 'cap'],
+            [
+            [1, 1, 2, 0.02, 50.0, -525.0, 525.0],
+            [2, 1, 3, 0.15, 6.66667, -132.0, 132.0],
+            [3, 1, 2, 0.02, 50.0, -525.0, 525.0],
+            [4, 1, 3, 0.15, 6.66667, -132.0, 132.0],
+            [5, 2, 4, 0.065, 15.38462, -760.0, 760.0],
+            [6, 2, 4, 0.065, 15.38462, -760.0, 760.0],
+            [7, 4, 7, 0.0135, 74.07407, -1090.0, 1090.0],
+            [8, 4, 6, 0.023, 43.47826, -1500.0, 1500.0],
+            [9, 4, 6, 0.023, 43.47826, -1120.0, 1120.0],
+            [10, 4, 5, 0.024, 41.66667, -1000.0, 1000.0],
+            [11, 4, 5, 0.024, 41.66667, -1000.0, 1000.0],
+            [12, 4, 7, 0.0135, 74.07407, -1090.0, 1090.0],
+            [13, 5, 6, 0.01051, 95.14748, -1390.0, 1390.0],
+            [14, 5, 6, 0.01613, 61.99628, -1390.0, 1390.0],
+            [15, 6, 9, 0.00852, 117.37089, -2100.0, 2100.0],
+            [16, 6, 9, 0.00852, 117.37089, -2100.0, 2100.0],
+            [17, 7, 8, 0.0001, 10000.0, -2180.0, 2180.0],
+            [18, 7, 8, 0.0001, 10000.0, -2500.0, 2500.0],
+            [19, 7, 6, 0.2, 5.0, -950.0, 950.0],
+            [20, 7, 6, 0.2, 5.0, -950.0, 950.0],
+            [21, 8, 10, 0.0175, 57.14286, -3070.0, 3070.0],
+            [22, 8, 10, 0.0175, 57.14286, -3070.0, 3070.0],
+            [23, 9, 11, 0.0163, 61.34969, -1390.0, 1390.0],
+            [24, 9, 11, 0.0163, 61.34969, -1390.0, 1390.0],
+            [25, 9, 10, 0.02453, 40.76641, -855.0, 855.0],
+            [26, 9, 10, 0.0343, 29.15452, -775.0, 775.0],
+            [27, 10, 15, 0.00835, 119.76048, -4840.0, 4840.0],
+            [28, 10, 15, 0.0063, 158.73016, -4020.0, 4020.0],
+            [29, 11, 15, 0.042, 23.80952, -2520.0, 2520.0],
+            [30, 11, 15, 0.042, 23.80952, -2520.0, 2520.0],
+            [31, 11, 13, 0.0052, 192.30769, -2170.0, 2170.0],
+            [32, 11, 13, 0.0052, 192.30769, -2210.0, 2210.0],
+            [33, 11, 12, 0.0085, 117.64706, -3320.0, 3320.0],
+            [34, 11, 12, 0.0085, 117.64706, -3320.0, 3320.0],
+            [35, 12, 13, 0.01078, 92.76438, -3100.0, 3100.0],
+            [36, 12, 18, 0.009, 111.11111, -2400.0, 2400.0],
+            [37, 12, 18, 0.009, 111.11111, -2400.0, 2400.0],
+            [38, 12, 13, 0.01078, 92.76438, -3100.0, 3100.0],
+            [39, 13, 18, 0.007, 142.85714, -2400.0, 2400.0],
+            [40, 13, 18, 0.007, 142.85714, -2400.0, 2400.0],
+            [41, 13, 15, 0.023, 43.47826, -1240.0, 1240.0],
+            [42, 13, 15, 0.023, 43.47826, -955.0, 955.0],
+            [43, 13, 14, 0.01163, 85.98452, -1040.0, 1040.0],
+            [44, 13, 14, 0.01201, 83.26395, -1040.0, 1040.0],
+            [45, 14, 16, 0.016, 62.5, -2580.0, 2580.0],
+            [46, 14, 16, 0.018, 55.55556, -625.0, 625.0],
+            [47, 15, 16, 0.0052, 192.30769, -2770.0, 2770.0],
+            [48, 15, 16, 0.00172, 581.39535, -5540.0, 5540.0],
+            [49, 15, 14, 0.00222, 450.45045, -5000.0, 5000.0],
+            [50, 15, 14, 0.00222, 450.45045, -5000.0, 5000.0],
+            [51, 16, 19, 0.0141, 70.92199, -2780.0, 2780.0],
+            [52, 16, 19, 0.0141, 70.92199, -3820.0, 3820.0],
+            [53, 17, 16, 0.01072, 93.28358, -2150.0, 2150.0],
+            [54, 17, 16, 0.01072, 93.28358, -1890.0, 1890.0],
+            [55, 17, 22, 0.0097, 103.09278, -2100.0, 2100.0],
+            [56, 17, 22, 0.0097, 103.09278, -2100.0, 2100.0],
+            [57, 18, 17, 0.0018, 555.55556, -3100.0, 3100.0],
+            [58, 18, 17, 0.0018, 555.55556, -3460.0, 3460.0],
+            [59, 18, 23, 0.0096, 104.16667, -1970.0, 1970.0],
+            [60, 18, 23, 0.0096, 104.16667, -1970.0, 1970.0],
+            [61, 20, 26, 0.0023, 434.78261, -2780.0, 2780.0],
+            [62, 20, 26, 0.0023, 434.78261, -2780.0, 2780.0],
+            [63, 20, 19, 0.0213, 46.94836, -1590.0, 1590.0],
+            [64, 20, 19, 0.0143, 69.93007, -1590.0, 1590.0],
+            [65, 21, 16, 0.01824, 54.82456, -2780.0, 2780.0],
+            [66, 21, 16, 0.01824, 54.82456, -2780.0, 2780.0],
+            [67, 21, 25, 0.01, 100.0, -2780.0, 2780.0],
+            [68, 21, 25, 0.01, 100.0, -2780.0, 2780.0],
+            [69, 21, 20, 0.0048, 208.33333, -2780.0, 2780.0],
+            [70, 21, 20, 0.0048, 208.33333, -2780.0, 2780.0],
+            [71, 21, 19, 0.0059, 169.49153, -3030.0, 3030.0],
+            [72, 21, 19, 0.0059, 169.49153, -2780.0, 2780.0],
+            [73, 22, 16, 0.0172, 58.13953, -2010.0, 2010.0],
+            [74, 22, 16, 0.0172, 58.13953, -2010.0, 2010.0],
+            [75, 22, 25, 0.0041, 243.90244, -3275.0, 3275.0],
+            [76, 22, 25, 0.0041, 243.90244, -3275.0, 3275.0],
+            [77, 22, 21, 0.00111, 900.9009, -2780.0, 2780.0],
+            [78, 22, 21, 0.0061, 163.93443, -2780.0, 2780.0],
+            [79, 23, 29, 0.0182, 54.94505, -2010.0, 2010.0],
+            [80, 23, 24, 0.0008, 1250.0, -2780.0, 2780.0],
+            [81, 23, 24, 0.0007, 1428.57143, -4400.0, 4400.0],
+            [82, 23, 22, 0.003, 333.33333, -2780.0, 2780.0],
+            [83, 23, 22, 0.003, 333.33333, -2770.0, 2770.0],
+            [84, 23, 29, 0.0182, 54.94505, -2010.0, 2010.0],
+            [85, 24, 28, 0.007, 142.85714, -2210.0, 2210.0],
+            [86, 24, 25, 0.0091, 109.89011, -1390.0, 1390.0],
+            [87, 24, 25, 0.0091, 109.89011, -1390.0, 1390.0],
+            [88, 24, 28, 0.007, 142.85714, -2210.0, 2210.0],
+            [89, 25, 26, 0.0057, 175.4386, -6960.0, 6960.0],
+            [90, 25, 26, 0.0057, 175.4386, -5540.0, 5540.0],
+            [91, 27, 26, 0.00503, 198.80716, -3100.0, 3100.0],
+            [92, 27, 26, 0.00503, 198.80716, -3100.0, 3100.0],
+            [93, 28, 27, 0.00711, 140.64698, -3070.0, 3070.0],
+            [94, 28, 27, 0.00711, 140.64698, -3070.0, 3070.0],
+            [95, 29, 28, 0.00796, 125.62814, -2780.0, 2780.0],
+            [96, 29, 28, 0.00796, 125.62814, -2780.0, 2780.0],
+            [97, 3, 4, 0.041, 24.39024, -648.0, 648.0],
+            [98, 3, 4, 0.041, 24.39024, -648.0, 648.0],
+            [99, 3, 2, 0.077, 12.98701, -652.0, 652.0]])
+        
+        self.loads = DataFrame(
+            ['id', 'bus_id', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'],
+            [
+            [1, 1, 0.0, 0.0, 0.0, 0.008309, 0.008309],
+            [2, 2, 0.0, 0.0, 0.0, 0.009108, 0.009108],
+            [3, 3, 0.0, 0.0, 0.0, 0.009853, 0.009853],
+            [4, 4, 0.0, 0.0, 0.0, 0.023222, 0.023222],
+            [5, 5, 0.0, 0.0, 0.0, 0.008912, 0.008912],
+            [6, 6, 0.0, 0.0, 0.0, 0.020879, 0.020879],
+            [7, 7, 0.0, 0.0, 0.0, 0.013227, 0.013227],
+            [8, 8, 0.0, 0.0, 0.0, 0.002086, 0.002086],
+            [9, 9, 0.0, 0.0, 0.0, 0.002308, 0.002308],
+            [10, 10, 0.0, 0.0, 0.0, 0.045468, 0.045468],
+            [11, 11, 0.0, 0.0, 0.0, 0.059653, 0.059653],
+            [12, 12, 0.0, 0.0, 0.0, 0.021109, 0.021109],
+            [13, 13, 0.0, 0.0, 0.0, 0.044811, 0.044811],
+            [14, 14, 0.0, 0.0, 0.0, 0.032507, 0.032507],
+            [15, 15, 0.0, 0.0, 0.0, 0.046746, 0.046746],
+            [16, 16, 0.0, 0.0, 0.0, 0.02853, 0.02853],
+            [17, 17, 0.0, 0.0, 0.0, 0.019192, 0.019192],
+            [18, 18, 0.0, 0.0, 0.0, 0.095196, 0.095196],
+            [19, 19, 0.0, 0.0, 0.0, 0.035845, 0.035845],
+            [20, 20, 0.0, 0.0, 0.0, 0.01824, 0.01824],
+            [21, 21, 0.0, 0.0, 0.0, 0.012463, 0.012463],
+            [22, 22, 0.0, 0.0, 0.0, 0.032312, 0.032312],
+            [23, 23, 0.0, 0.0, 0.0, 0.084047, 0.084047],
+            [24, 24, 0.0, 0.0, 0.0, 0.025175, 0.025175],
+            [25, 25, 0.0, 0.0, 0.0, 0.172816, 0.172816],
+            [26, 26, 0.0, 0.0, 0.0, 0.025281, 0.025281],
+            [27, 27, 0.0, 0.0, 0.0, 0.008114, 0.008114],
+            [28, 28, 0.0, 0.0, 0.0, 0.048841, 0.048841],
+            [29, 29, 0.0, 0.0, 0.0, 0.045752, 0.045752]])
+        
+        self.real_params = True
+        super().__init__(*args, **kwargs)
+
+
+if __name__ == '__main__':
+    c = Case29GB()
+    c.check()

powergrid_case/transmission/Case300.py

@@ -0,0 +1,1160 @@
+"""Power System Case: 300
+
+Data Source: MATPOWER case file
+Base MVA: 100.0 MVA
+Base kV: 115.0 kV
+
+All values are in standard MATPOWER units (MW, MVAr, p.u.)
+
+
+Nodes (bus) columns:
+    id, type, Pd, Qd, Gs, Bs, Vm, Va, baseKV, Vmax, Vmin
+    - type: 1=PQ, 2=PV, 3=Slack
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - Gs/Bs: Shunt conductance/susceptance (MW/MVAr at V=1.0 p.u.)
+    - Vm: Voltage magnitude (p.u.)
+    - Va: Voltage angle (degrees)
+    - baseKV: Base voltage (kV)
+    - Vmax/Vmin: Voltage limits (p.u.)
+
+Units (generators) columns:
+    id, bus_id, Pg, Qg, Qmax, Qmin, Vg, mBase, status, Pmax, Pmin, mc_a, mc_b, mc_c
+    - Pg/Qg: Active/Reactive power output (MW/MVAr)
+    - Qmax/Qmin: Reactive power limits (MVAr)
+    - Vg: Voltage setpoint (p.u.)
+    - mBase: Machine base (MVA)
+    - status: >0 in-service, <=0 out-of-service
+    - Pmax/Pmin: Active power limits (MW)
+    - mc_a/mc_b/mc_c: Marginal cost coefficients (quadratic)
+
+Lines (branches) columns:
+    id, from, to, r, x, b, rateA, ratio, angle, status, floor, cap
+    - r/x: Resistance/Reactance (p.u.)
+    - b: Line charging susceptance (p.u.)
+    - rateA: Long-term rating (MVA)
+    - ratio: Transformer tap ratio (0 for line)
+    - angle: Transformer phase shift (degrees)
+    - status: 1=in-service, 0=out-of-service
+    - floor/cap: Power flow limits (MW)
+
+Loads columns:
+    id, bus_id, Pd, Qd, mc_a, mc_b, mc_c, d_max, d_min
+    - Pd/Qd: Active/Reactive power demand (MW/MVAr)
+    - mc_a/mc_b/mc_c: Marginal utility coefficients
+    - d_max/d_min: Demand limits (MW)
+"""
+from math import inf
+
+from powerzoo.case.CaseBase import ClearCase, DataFrame
+
+
+class Case300(ClearCase):
+    GRID_TYPE = "transmission"
+    BUS_COUNT = 300
+    VOLTAGE_LEVEL = "HV"
+    SOURCE = "MATPOWER"
+    DESCRIPTION = "IEEE 300-bus test system"
+
+    def __init__(self, *args, **kwargs):
+        # System base values
+        self.baseMVA = 100.0
+        self.baseKV = 115.0
+        
+        # Node (bus) data
+        self.nodes = DataFrame(
+            ['id', 'type', 'Pd', 'Qd', 'Gs', 'Bs', 'Vm', 'Va', 'baseKV', 'Vmax', 'Vmin'],
+            [[1.0, 1.0, 90.0, 49.0, 0.0, 0.0, 1.0284, 5.95, 115.0, 1.06, 0.94],
+             [2.0, 1.0, 56.0, 15.0, 0.0, 0.0, 1.0354, 7.74, 115.0, 1.06, 0.94],
+             [3.0, 1.0, 20.0, 0.0, 0.0, 0.0, 0.9971, 6.64, 230.0, 1.06, 0.94],
+             [4.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0308, 4.71, 345.0, 1.06, 0.94],
+             [5.0, 1.0, 353.0, 130.0, 0.0, 0.0, 1.0191, 4.68, 115.0, 1.06, 0.94],
+             [6.0, 1.0, 120.0, 41.0, 0.0, 0.0, 1.0312, 6.99, 115.0, 1.06, 0.94],
+             [7.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9934, 6.19, 230.0, 1.06, 0.94],
+             [8.0, 2.0, 63.0, 14.0, 0.0, 0.0, 1.0153, 2.4, 115.0, 1.06, 0.94],
+             [9.0, 1.0, 96.0, 43.0, 0.0, 0.0, 1.0034, 2.85, 115.0, 1.06, 0.94],
+             [10.0, 2.0, 153.0, 33.0, 0.0, 0.0, 1.0205, 1.35, 230.0, 1.06, 0.94],
+             [11.0, 1.0, 83.0, 21.0, 0.0, 0.0, 1.0057, 2.46, 115.0, 1.06, 0.94],
+             [12.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9974, 5.21, 230.0, 1.06, 0.94],
+             [13.0, 1.0, 58.0, 10.0, 0.0, 0.0, 0.9977, -0.55, 115.0, 1.06, 0.94],
+             [14.0, 1.0, 160.0, 60.0, 0.0, 0.0, 0.9991, -4.81, 115.0, 1.06, 0.94],
+             [15.0, 1.0, 126.7, 23.0, 0.0, 0.0, 1.0343, -8.59, 115.0, 1.06, 0.94],
+             [16.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0315, -2.65, 345.0, 1.06, 0.94],
+             [17.0, 1.0, 561.0, 220.0, 0.0, 0.0, 1.0649, -13.1, 115.0, 1.06, 0.94],
+             [19.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.982, 1.08, 230.0, 1.06, 0.94],
+             [20.0, 2.0, 605.0, 120.0, 0.0, 0.0, 1.001, -2.46, 115.0, 1.06, 0.94],
+             [21.0, 1.0, 77.0, 1.0, 0.0, 0.0, 0.9752, 1.62, 230.0, 1.06, 0.94],
+             [22.0, 1.0, 81.0, 23.0, 0.0, 0.0, 0.9963, -1.97, 115.0, 1.06, 0.94],
+             [23.0, 1.0, 21.0, 7.0, 0.0, 0.0, 1.0501, 3.94, 115.0, 1.06, 0.94],
+             [24.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0057, 6.02, 230.0, 1.06, 0.94],
+             [25.0, 1.0, 45.0, 12.0, 0.0, 0.0, 1.0234, 1.44, 115.0, 1.06, 0.94],
+             [26.0, 1.0, 28.0, 9.0, 0.0, 0.0, 0.9986, -1.73, 115.0, 1.06, 0.94],
+             [27.0, 1.0, 69.0, 13.0, 0.0, 0.0, 0.975, -4.9, 115.0, 1.06, 0.94],
+             [33.0, 1.0, 55.0, 6.0, 0.0, 0.0, 1.0244, -12.02, 115.0, 1.06, 0.94],
+             [34.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0414, -7.94, 345.0, 1.06, 0.94],
+             [35.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9757, -25.72, 115.0, 1.06, 0.94],
+             [36.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0011, -22.59, 230.0, 1.06, 0.94],
+             [37.0, 1.0, 85.0, 32.0, 0.0, 0.0, 1.0201, -11.23, 115.0, 1.06, 0.94],
+             [38.0, 1.0, 155.0, 18.0, 0.0, 0.0, 1.0202, -12.56, 115.0, 1.06, 0.94],
+             [39.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0535, -5.81, 345.0, 1.06, 0.94],
+             [40.0, 1.0, 46.0, -21.0, 0.0, 0.0, 1.0216, -12.78, 115.0, 1.06, 0.94],
+             [41.0, 1.0, 86.0, 0.0, 0.0, 0.0, 1.0292, -10.45, 115.0, 1.06, 0.94],
+             [42.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0448, -7.44, 345.0, 1.06, 0.94],
+             [43.0, 1.0, 39.0, 9.0, 0.0, 0.0, 1.0006, -16.79, 115.0, 1.06, 0.94],
+             [44.0, 1.0, 195.0, 29.0, 0.0, 0.0, 1.0086, -17.47, 115.0, 1.06, 0.94],
+             [45.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0215, -14.74, 230.0, 1.06, 0.94],
+             [46.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0344, -11.75, 345.0, 1.06, 0.94],
+             [47.0, 1.0, 58.0, 11.8, 0.0, 0.0, 0.9777, -23.17, 115.0, 1.06, 0.94],
+             [48.0, 1.0, 41.0, 19.0, 0.0, 0.0, 1.0019, -16.09, 115.0, 1.06, 0.94],
+             [49.0, 1.0, 92.0, 26.0, 0.0, 0.0, 1.0475, -2.95, 115.0, 1.06, 0.94],
+             [51.0, 1.0, -5.0, 5.0, 0.0, 0.0, 1.0253, -8.15, 115.0, 1.06, 0.94],
+             [52.0, 1.0, 61.0, 28.0, 0.0, 0.0, 0.9979, -11.86, 115.0, 1.06, 0.94],
+             [53.0, 1.0, 69.0, 3.0, 0.0, 0.0, 0.9959, -17.6, 115.0, 1.06, 0.94],
+             [54.0, 1.0, 10.0, 1.0, 0.0, 0.0, 1.005, -16.25, 115.0, 1.06, 0.94],
+             [55.0, 1.0, 22.0, 10.0, 0.0, 0.0, 1.015, -12.21, 115.0, 1.06, 0.94],
+             [57.0, 1.0, 98.0, 20.0, 0.0, 0.0, 1.0335, -8.0, 115.0, 1.06, 0.94],
+             [58.0, 1.0, 14.0, 1.0, 0.0, 0.0, 0.9918, -5.99, 115.0, 1.06, 0.94],
+             [59.0, 1.0, 218.0, 106.0, 0.0, 0.0, 0.9789, -5.29, 115.0, 1.06, 0.94],
+             [60.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0246, -9.56, 230.0, 1.06, 0.94],
+             [61.0, 1.0, 227.0, 110.0, 0.0, 0.0, 0.9906, -3.47, 115.0, 1.06, 0.94],
+             [62.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.016, -1.1, 230.0, 1.06, 0.94],
+             [63.0, 2.0, 70.0, 30.0, 0.0, 0.0, 0.9583, -17.62, 115.0, 1.06, 0.94],
+             [64.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.948, -12.97, 230.0, 1.06, 0.94],
+             [69.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.963, -25.66, 115.0, 1.06, 0.94],
+             [70.0, 1.0, 56.0, 20.0, 0.0, 0.0, 0.9513, -35.16, 115.0, 1.06, 0.94],
+             [71.0, 1.0, 116.0, 38.0, 0.0, 0.0, 0.9793, -29.88, 115.0, 1.06, 0.94],
+             [72.0, 1.0, 57.0, 19.0, 0.0, 0.0, 0.9696, -27.48, 115.0, 1.06, 0.94],
+             [73.0, 1.0, 224.0, 71.0, 0.0, 0.0, 0.9775, -25.77, 115.0, 1.06, 0.94],
+             [74.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9964, -22.0, 230.0, 1.06, 0.94],
+             [76.0, 2.0, 208.0, 107.0, 0.0, 0.0, 0.9632, -26.54, 115.0, 1.06, 0.94],
+             [77.0, 1.0, 74.0, 28.0, 0.0, 0.0, 0.9837, -24.94, 115.0, 1.06, 0.94],
+             [78.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.99, -24.05, 115.0, 1.06, 0.94],
+             [79.0, 1.0, 48.0, 14.0, 0.0, 0.0, 0.982, -24.97, 115.0, 1.06, 0.94],
+             [80.0, 1.0, 28.0, 7.0, 0.0, 0.0, 0.9872, -24.97, 115.0, 1.06, 0.94],
+             [81.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.034, -18.89, 345.0, 1.06, 0.94],
+             [84.0, 2.0, 37.0, 13.0, 0.0, 0.0, 1.025, -17.16, 115.0, 1.06, 0.94],
+             [85.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9872, -17.68, 230.0, 1.06, 0.94],
+             [86.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9909, -14.19, 230.0, 1.06, 0.94],
+             [87.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9921, -7.77, 230.0, 1.06, 0.94],
+             [88.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0151, -20.96, 230.0, 1.06, 0.94],
+             [89.0, 1.0, 44.2, 0.0, 0.0, 0.0, 1.0317, -11.13, 115.0, 1.06, 0.94],
+             [90.0, 1.0, 66.0, 0.0, 0.0, 0.0, 1.0272, -11.23, 115.0, 1.06, 0.94],
+             [91.0, 2.0, 17.4, 0.0, 0.0, 0.0, 1.052, -9.4, 115.0, 1.06, 0.94],
+             [92.0, 2.0, 15.8, 0.0, 0.0, 0.0, 1.052, -6.2, 115.0, 1.06, 0.94],
+             [94.0, 1.0, 60.3, 0.0, 0.0, 0.0, 0.993, -9.42, 115.0, 1.06, 0.94],
+             [97.0, 1.0, 39.9, 0.0, 0.0, 0.0, 1.0183, -13.24, 115.0, 1.06, 0.94],
+             [98.0, 2.0, 66.7, 0.0, 0.0, 0.0, 1.0, -14.6, 115.0, 1.06, 0.94],
+             [99.0, 1.0, 83.5, 0.0, 0.0, 0.0, 0.9894, -20.27, 115.0, 1.06, 0.94],
+             [100.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.006, -14.45, 115.0, 1.06, 0.94],
+             [102.0, 1.0, 77.8, 0.0, 0.0, 0.0, 1.0008, -15.23, 115.0, 1.06, 0.94],
+             [103.0, 1.0, 32.0, 0.0, 0.0, 0.0, 1.0288, -12.06, 115.0, 1.06, 0.94],
+             [104.0, 1.0, 8.6, 0.0, 0.0, 0.0, 0.9958, -17.33, 115.0, 1.06, 0.94],
+             [105.0, 1.0, 49.6, 0.0, 0.0, 0.0, 1.0223, -12.94, 115.0, 1.06, 0.94],
+             [107.0, 1.0, 4.6, 0.0, 0.0, 0.0, 1.0095, -16.03, 115.0, 1.06, 0.94],
+             [108.0, 2.0, 112.1, 0.0, 0.0, 0.0, 0.99, -20.26, 115.0, 1.06, 0.94],
+             [109.0, 1.0, 30.7, 0.0, 0.0, 0.0, 0.9749, -26.06, 115.0, 1.06, 0.94],
+             [110.0, 1.0, 63.0, 0.0, 0.0, 0.0, 0.973, -24.72, 115.0, 1.06, 0.94],
+             [112.0, 1.0, 19.6, 0.0, 0.0, 0.0, 0.9725, -28.69, 115.0, 1.06, 0.94],
+             [113.0, 1.0, 26.2, 0.0, 0.0, 0.0, 0.97, -25.38, 115.0, 1.06, 0.94],
+             [114.0, 1.0, 18.2, 0.0, 0.0, 0.0, 0.9747, -28.59, 115.0, 1.06, 0.94],
+             [115.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9603, -13.57, 115.0, 1.06, 0.94],
+             [116.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0249, -12.69, 115.0, 1.06, 0.94],
+             [117.0, 1.0, 0.0, 0.0, 0.0, 325.0, 0.9348, -4.72, 115.0, 1.06, 0.94],
+             [118.0, 1.0, 14.1, 650.0, 0.0, 0.0, 0.9298, -4.12, 115.0, 1.06, 0.94],
+             [119.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0435, 5.17, 115.0, 1.06, 0.94],
+             [120.0, 1.0, 777.0, 215.0, 0.0, 55.0, 0.9584, -8.77, 115.0, 1.06, 0.94],
+             [121.0, 1.0, 535.0, 55.0, 0.0, 0.0, 0.9871, -12.64, 115.0, 1.06, 0.94],
+             [122.0, 1.0, 229.1, 11.8, 0.0, 0.0, 0.9728, -14.36, 115.0, 1.06, 0.94],
+             [123.0, 1.0, 78.0, 1.4, 0.0, 0.0, 1.0006, -17.64, 115.0, 1.06, 0.94],
+             [124.0, 2.0, 276.4, 59.3, 0.0, 0.0, 1.0233, -13.49, 115.0, 1.06, 0.94],
+             [125.0, 2.0, 514.8, 82.7, 0.0, 0.0, 1.0103, -18.43, 115.0, 1.06, 0.94],
+             [126.0, 1.0, 57.9, 5.1, 0.0, 0.0, 0.9978, -12.86, 115.0, 1.06, 0.94],
+             [127.0, 1.0, 380.8, 37.0, 0.0, 0.0, 1.0001, -10.52, 230.0, 1.06, 0.94],
+             [128.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0024, -4.78, 230.0, 1.06, 0.94],
+             [129.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0028, -4.4, 230.0, 1.06, 0.94],
+             [130.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0191, 5.56, 230.0, 1.06, 0.94],
+             [131.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9861, 6.06, 230.0, 1.06, 0.94],
+             [132.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0045, 3.04, 230.0, 1.06, 0.94],
+             [133.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.002, -5.46, 230.0, 1.06, 0.94],
+             [134.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.022, -8.04, 230.0, 1.06, 0.94],
+             [135.0, 1.0, 169.2, 41.6, 0.0, 0.0, 1.0193, -6.76, 230.0, 1.06, 0.94],
+             [136.0, 1.0, 55.2, 18.2, 0.0, 0.0, 1.0476, 1.54, 230.0, 1.06, 0.94],
+             [137.0, 1.0, 273.6, 99.8, 0.0, 0.0, 1.0471, -1.45, 230.0, 1.06, 0.94],
+             [138.0, 2.0, 1019.2, 135.2, 0.0, 0.0, 1.055, -6.35, 230.0, 1.06, 0.94],
+             [139.0, 1.0, 595.0, 83.3, 0.0, 0.0, 1.0117, -3.57, 115.0, 1.06, 0.94],
+             [140.0, 1.0, 387.7, 114.7, 0.0, 0.0, 1.043, -3.44, 230.0, 1.06, 0.94],
+             [141.0, 2.0, 145.0, 58.0, 0.0, 0.0, 1.051, 0.05, 230.0, 1.06, 0.94],
+             [142.0, 1.0, 56.5, 24.5, 0.0, 0.0, 1.0155, -2.77, 230.0, 1.06, 0.94],
+             [143.0, 2.0, 89.5, 35.5, 0.0, 0.0, 1.0435, 4.03, 230.0, 1.06, 0.94],
+             [144.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.016, -0.7, 230.0, 1.06, 0.94],
+             [145.0, 1.0, 24.0, 14.0, 0.0, 0.0, 1.0081, -0.16, 230.0, 1.06, 0.94],
+             [146.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0528, 4.32, 230.0, 1.06, 0.94],
+             [147.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0528, 8.36, 230.0, 1.06, 0.94],
+             [148.0, 1.0, 63.0, 25.0, 0.0, 0.0, 1.0577, 0.28, 230.0, 1.06, 0.94],
+             [149.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0735, 5.23, 230.0, 1.06, 0.94],
+             [150.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9869, 6.34, 230.0, 1.06, 0.94],
+             [151.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0048, 4.13, 230.0, 1.06, 0.94],
+             [152.0, 2.0, 17.0, 9.0, 0.0, 0.0, 1.0535, 9.24, 230.0, 1.06, 0.94],
+             [153.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0435, 10.46, 230.0, 1.06, 0.94],
+             [154.0, 1.0, 70.0, 5.0, 0.0, 34.5, 0.9663, -1.8, 115.0, 1.06, 0.94],
+             [155.0, 1.0, 200.0, 50.0, 0.0, 0.0, 1.0177, 6.75, 230.0, 1.06, 0.94],
+             [156.0, 2.0, 75.0, 50.0, 0.0, 0.0, 0.963, 5.15, 115.0, 1.06, 0.94],
+             [157.0, 1.0, 123.5, -24.3, 0.0, 0.0, 0.9845, -11.93, 230.0, 1.06, 0.94],
+             [158.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9987, -11.4, 230.0, 1.06, 0.94],
+             [159.0, 1.0, 33.0, 16.5, 0.0, 0.0, 0.9867, -9.82, 230.0, 1.06, 0.94],
+             [160.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9998, -12.55, 230.0, 1.06, 0.94],
+             [161.0, 1.0, 35.0, 15.0, 0.0, 0.0, 1.036, 8.85, 230.0, 1.06, 0.94],
+             [162.0, 1.0, 85.0, 24.0, 0.0, 0.0, 0.9918, 18.5, 230.0, 1.06, 0.94],
+             [163.0, 1.0, 0.0, 0.4, 0.0, 0.0, 1.041, 2.91, 230.0, 1.06, 0.94],
+             [164.0, 1.0, 0.0, 0.0, 0.0, -212.0, 0.9839, 9.66, 230.0, 1.06, 0.94],
+             [165.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0002, 26.31, 230.0, 1.06, 0.94],
+             [166.0, 1.0, 0.0, 0.0, 0.0, -103.0, 0.9973, 30.22, 230.0, 1.06, 0.94],
+             [167.0, 1.0, 299.9, 95.7, 0.0, 0.0, 0.9715, -6.91, 230.0, 1.06, 0.94],
+             [168.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0024, -4.8, 230.0, 1.06, 0.94],
+             [169.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9879, -6.68, 230.0, 1.06, 0.94],
+             [170.0, 2.0, 481.8, 205.0, 0.0, 0.0, 0.929, 0.09, 115.0, 1.06, 0.94],
+             [171.0, 2.0, 763.6, 291.1, 0.0, 0.0, 0.9829, -9.94, 115.0, 1.06, 0.94],
+             [172.0, 1.0, 26.5, 0.0, 0.0, 0.0, 1.0244, -6.22, 115.0, 1.06, 0.94],
+             [173.0, 1.0, 163.5, 43.0, 0.0, 53.0, 0.9837, -12.75, 115.0, 1.06, 0.94],
+             [174.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0622, -2.69, 115.0, 1.06, 0.94],
+             [175.0, 1.0, 176.0, 83.0, 0.0, 0.0, 0.973, -7.21, 115.0, 1.06, 0.94],
+             [176.0, 2.0, 5.0, 4.0, 0.0, 0.0, 1.0522, 4.67, 115.0, 1.06, 0.94],
+             [177.0, 2.0, 28.0, 12.0, 0.0, 0.0, 1.0077, 0.62, 115.0, 1.06, 0.94],
+             [178.0, 1.0, 427.4, 173.6, 0.0, 0.0, 0.9397, -6.56, 115.0, 1.06, 0.94],
+             [179.0, 1.0, 74.0, 29.0, 0.0, 45.0, 0.9699, -9.37, 115.0, 1.06, 0.94],
+             [180.0, 1.0, 69.5, 49.3, 0.0, 0.0, 0.9793, -3.09, 115.0, 1.06, 0.94],
+             [181.0, 1.0, 73.4, 0.0, 0.0, 0.0, 1.0518, -1.33, 230.0, 1.06, 0.94],
+             [182.0, 1.0, 240.7, 89.0, 0.0, 0.0, 1.0447, -4.19, 230.0, 1.06, 0.94],
+             [183.0, 1.0, 40.0, 4.0, 0.0, 0.0, 0.9717, 7.12, 115.0, 1.06, 0.94],
+             [184.0, 1.0, 136.8, 16.6, 0.0, 0.0, 1.0386, -6.85, 230.0, 1.06, 0.94],
+             [185.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0522, -4.33, 230.0, 1.06, 0.94],
+             [186.0, 2.0, 59.8, 24.3, 0.0, 0.0, 1.065, 2.17, 230.0, 1.06, 0.94],
+             [187.0, 2.0, 59.8, 24.3, 0.0, 0.0, 1.065, 1.4, 230.0, 1.06, 0.94],
+             [188.0, 1.0, 182.6, 43.6, 0.0, 0.0, 1.0533, -0.72, 230.0, 1.06, 0.94],
+             [189.0, 1.0, 7.0, 2.0, 0.0, 0.0, 0.9975, -25.84, 66.0, 1.06, 0.94],
+             [190.0, 2.0, 0.0, 0.0, 0.0, -150.0, 1.0551, -20.62, 345.0, 1.06, 0.94],
+             [191.0, 2.0, 489.0, 53.0, 0.0, 0.0, 1.0435, 12.25, 230.0, 1.06, 0.94],
+             [192.0, 1.0, 800.0, 72.0, 0.0, 0.0, 0.9374, -11.18, 230.0, 1.06, 0.94],
+             [193.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9897, -26.09, 66.0, 1.06, 0.94],
+             [194.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0489, -19.21, 345.0, 1.06, 0.94],
+             [195.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0357, -20.79, 345.0, 1.06, 0.94],
+             [196.0, 1.0, 10.0, 3.0, 0.0, 0.0, 0.9695, -25.32, 115.0, 1.06, 0.94],
+             [197.0, 1.0, 43.0, 14.0, 0.0, 0.0, 0.9907, -23.72, 115.0, 1.06, 0.94],
+             [198.0, 2.0, 64.0, 21.0, 0.0, 0.0, 1.015, -20.58, 115.0, 1.06, 0.94],
+             [199.0, 1.0, 35.0, 12.0, 0.0, 0.0, 0.9528, -26.05, 115.0, 1.06, 0.94],
+             [200.0, 1.0, 27.0, 12.0, 0.0, 0.0, 0.955, -25.93, 115.0, 1.06, 0.94],
+             [201.0, 1.0, 41.0, 14.0, 0.0, 0.0, 0.9692, -27.49, 66.0, 1.06, 0.94],
+             [202.0, 1.0, 38.0, 13.0, 0.0, 0.0, 0.9908, -25.33, 66.0, 1.06, 0.94],
+             [203.0, 1.0, 42.0, 14.0, 0.0, 0.0, 1.0033, -22.35, 115.0, 1.06, 0.94],
+             [204.0, 1.0, 72.0, 24.0, 0.0, 0.0, 0.9718, -25.7, 66.0, 1.06, 0.94],
+             [205.0, 1.0, 0.0, -5.0, 0.0, 0.0, 0.9838, -26.07, 66.0, 1.06, 0.94],
+             [206.0, 1.0, 12.0, 2.0, 0.0, 0.0, 0.9992, -27.41, 66.0, 1.06, 0.94],
+             [207.0, 1.0, -21.0, -14.2, 0.0, 0.0, 1.0137, -27.44, 66.0, 1.06, 0.94],
+             [208.0, 1.0, 7.0, 2.0, 0.0, 0.0, 0.9929, -26.28, 66.0, 1.06, 0.94],
+             [209.0, 1.0, 38.0, 13.0, 0.0, 0.0, 0.9999, -25.66, 66.0, 1.06, 0.94],
+             [210.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9788, -24.22, 115.0, 1.06, 0.94],
+             [211.0, 1.0, 96.0, 7.0, 0.0, 0.0, 1.0017, -23.31, 115.0, 1.06, 0.94],
+             [212.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0132, -22.51, 138.0, 1.06, 0.94],
+             [213.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.01, -11.67, 16.5, 1.06, 0.94],
+             [214.0, 1.0, 22.0, 16.0, 0.0, 0.0, 0.9919, -17.53, 138.0, 1.06, 0.94],
+             [215.0, 1.0, 47.0, 26.0, 0.0, 0.0, 0.9866, -20.23, 138.0, 1.06, 0.94],
+             [216.0, 1.0, 176.0, 105.0, 0.0, 0.0, 0.9751, -22.53, 138.0, 1.06, 0.94],
+             [217.0, 1.0, 100.0, 75.0, 0.0, 0.0, 1.0215, -22.2, 138.0, 1.06, 0.94],
+             [218.0, 1.0, 131.0, 96.0, 0.0, 0.0, 1.0075, -22.63, 138.0, 1.06, 0.94],
+             [219.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0554, -21.15, 345.0, 1.06, 0.94],
+             [220.0, 2.0, 285.0, 100.0, 0.0, 0.0, 1.008, -21.73, 138.0, 1.06, 0.94],
+             [221.0, 2.0, 171.0, 70.0, 0.0, 0.0, 1.0, -22.49, 138.0, 1.06, 0.94],
+             [222.0, 2.0, 328.0, 188.0, 0.0, 0.0, 1.05, -23.17, 20.0, 1.06, 0.94],
+             [223.0, 1.0, 428.0, 232.0, 0.0, 0.0, 0.9965, -22.7, 138.0, 1.06, 0.94],
+             [224.0, 1.0, 173.0, 99.0, 0.0, 0.0, 1.0002, -21.55, 230.0, 1.06, 0.94],
+             [225.0, 1.0, 410.0, 40.0, 0.0, 0.0, 0.9453, -11.34, 230.0, 1.06, 0.94],
+             [226.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.018, -21.61, 230.0, 1.06, 0.94],
+             [227.0, 2.0, 538.0, 369.0, 0.0, 0.0, 1.0, -27.22, 27.0, 1.06, 0.94],
+             [228.0, 1.0, 223.0, 148.0, 0.0, 0.0, 1.0423, -20.94, 138.0, 1.06, 0.94],
+             [229.0, 1.0, 96.0, 46.0, 0.0, 0.0, 1.0496, -19.94, 138.0, 1.06, 0.94],
+             [230.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.04, -13.82, 20.0, 1.06, 0.94],
+             [231.0, 1.0, 159.0, 107.0, 0.0, -300.0, 1.0535, -21.22, 345.0, 1.06, 0.94],
+             [232.0, 1.0, 448.0, 143.0, 0.0, 0.0, 1.0414, -23.19, 138.0, 1.06, 0.94],
+             [233.0, 2.0, 404.0, 212.0, 0.0, 0.0, 1.0, -25.9, 66.0, 1.06, 0.94],
+             [234.0, 1.0, 572.0, 244.0, 0.0, 0.0, 1.0387, -20.89, 138.0, 1.06, 0.94],
+             [235.0, 1.0, 269.0, 157.0, 0.0, 0.0, 1.0095, -21.03, 138.0, 1.06, 0.94],
+             [236.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0165, -15.4, 20.0, 1.06, 0.94],
+             [237.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0558, -21.1, 345.0, 1.06, 0.94],
+             [238.0, 2.0, 255.0, 149.0, 0.0, -150.0, 1.01, -20.94, 138.0, 1.06, 0.94],
+             [239.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0, -15.86, 138.0, 1.06, 0.94],
+             [240.0, 1.0, 0.0, 0.0, 0.0, -140.0, 1.0237, -20.14, 230.0, 1.06, 0.94],
+             [241.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.05, -16.5, 20.0, 1.06, 0.94],
+             [242.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.993, -17.53, 138.0, 1.06, 0.94],
+             [243.0, 2.0, 8.0, 3.0, 0.0, 0.0, 1.01, -19.27, 66.0, 1.06, 0.94],
+             [244.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9921, -20.21, 66.0, 1.06, 0.94],
+             [245.0, 1.0, 61.0, 30.0, 0.0, 0.0, 0.9711, -20.9, 66.0, 1.06, 0.94],
+             [246.0, 1.0, 77.0, 33.0, 0.0, 0.0, 0.9651, -21.74, 66.0, 1.06, 0.94],
+             [247.0, 1.0, 61.0, 30.0, 0.0, 0.0, 0.9688, -21.67, 66.0, 1.06, 0.94],
+             [248.0, 1.0, 29.0, 14.0, 0.0, 45.6, 0.976, -25.23, 66.0, 1.06, 0.94],
+             [249.0, 1.0, 29.0, 14.0, 0.0, 0.0, 0.9752, -25.65, 66.0, 1.06, 0.94],
+             [250.0, 1.0, -23.0, -17.0, 0.0, 0.0, 1.0196, -23.8, 66.0, 1.06, 0.94],
+             [281.0, 1.0, -33.1, -29.4, 0.0, 0.0, 1.0251, -20.06, 230.0, 1.06, 0.94],
+             [319.0, 1.0, 115.8, -24.0, 0.0, 0.0, 1.0152, 1.48, 230.0, 1.06, 0.94],
+             [320.0, 1.0, 2.4, -12.6, 0.0, 0.0, 1.0146, -2.23, 115.0, 1.06, 0.94],
+             [322.0, 1.0, 2.4, -3.9, 0.0, 0.0, 1.0005, -17.61, 115.0, 1.06, 0.94],
+             [323.0, 1.0, -14.9, 26.5, 0.0, 0.0, 0.981, -13.69, 230.0, 1.06, 0.94],
+             [324.0, 1.0, 24.7, -1.2, 0.0, 0.0, 0.975, -23.42, 115.0, 1.06, 0.94],
+             [526.0, 1.0, 145.3, -34.9, 0.0, 0.0, 0.9429, -34.31, 115.0, 1.06, 0.94],
+             [528.0, 1.0, 28.1, -20.5, 0.0, 0.0, 0.9723, -37.58, 115.0, 1.06, 0.94],
+             [531.0, 1.0, 14.0, 2.5, 0.0, 0.0, 0.9604, -29.1, 115.0, 1.06, 0.94],
+             [552.0, 1.0, -11.1, -1.4, 0.0, 0.0, 1.0009, -23.36, 115.0, 1.06, 0.94],
+             [562.0, 1.0, 50.5, 17.4, 0.0, 0.0, 0.9777, -28.0, 230.0, 1.06, 0.94],
+             [609.0, 1.0, 29.6, 0.6, 0.0, 0.0, 0.9583, -28.79, 115.0, 1.06, 0.94],
+             [664.0, 1.0, -113.7, 76.7, 0.0, 0.0, 1.0309, -17.0, 345.0, 1.06, 0.94],
+             [1190.0, 1.0, 100.31, 29.17, 0.0, 0.0, 1.0128, 3.9, 86.0, 1.06, 0.94],
+             [1200.0, 1.0, -100.0, 34.17, 0.0, 0.0, 1.0244, -7.52, 86.0, 1.06, 0.94],
+             [1201.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0122, -15.18, 115.0, 1.06, 0.94],
+             [2040.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9653, -14.94, 115.0, 1.06, 0.94],
+             [7001.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0507, 10.79, 13.8, 1.06, 0.94],
+             [7002.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0507, 12.48, 13.8, 1.06, 0.94],
+             [7003.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0323, 13.76, 13.8, 1.06, 0.94],
+             [7011.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0145, 4.99, 13.8, 1.06, 0.94],
+             [7012.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0507, 11.57, 13.8, 1.06, 0.94],
+             [7017.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0507, -10.47, 13.8, 1.06, 0.94],
+             [7023.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0507, 6.15, 13.8, 1.06, 0.94],
+             [7024.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.029, 12.6, 13.8, 1.06, 0.94],
+             [7039.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.05, 2.11, 20.0, 1.06, 0.94],
+             [7044.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0145, -13.92, 13.8, 1.06, 0.94],
+             [7049.0, 3.0, 0.0, 0.0, 0.0, 0.0, 1.0507, 0.0, 13.8, 1.06, 0.94],
+             [7055.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.9967, -7.5, 13.8, 1.06, 0.94],
+             [7057.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0212, -3.44, 13.8, 1.06, 0.94],
+             [7061.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0145, 1.97, 13.8, 1.06, 0.94],
+             [7062.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0017, 5.8, 13.8, 1.06, 0.94],
+             [7071.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.9893, -25.35, 13.8, 1.06, 0.94],
+             [7130.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0507, 19.02, 13.8, 1.06, 0.94],
+             [7139.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0507, 2.75, 13.8, 1.06, 0.94],
+             [7166.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0145, 35.05, 13.8, 1.06, 0.94],
+             [9001.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0117, -11.25, 115.0, 1.06, 0.94],
+             [9002.0, 2.0, 4.2, 0.0, 0.0, 0.0, 0.9945, -18.86, 6.6, 1.06, 0.94],
+             [9003.0, 1.0, 2.71, 0.94, 0.14, 2.4, 0.9833, -19.68, 6.6, 1.06, 0.94],
+             [9004.0, 1.0, 0.86, 0.28, 0.0, 0.0, 0.9768, -19.82, 6.6, 1.06, 0.94],
+             [9005.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0117, -11.32, 115.0, 1.06, 0.94],
+             [9006.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0029, -17.42, 6.6, 1.06, 0.94],
+             [9007.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9913, -18.69, 6.6, 1.06, 0.94],
+             [9012.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0023, -17.27, 6.6, 1.06, 0.94],
+             [9021.0, 1.0, 4.75, 1.56, 0.0, 0.0, 0.9887, -19.09, 6.6, 1.06, 0.94],
+             [9022.0, 1.0, 1.53, 0.53, 0.08, 0.0, 0.9648, -21.67, 0.6, 1.06, 0.94],
+             [9023.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.9747, -19.41, 6.6, 1.06, 0.94],
+             [9024.0, 1.0, 1.35, 0.47, 0.07, 0.0, 0.9706, -21.43, 0.6, 1.06, 0.94],
+             [9025.0, 1.0, 0.45, 0.16, 0.02, 0.0, 0.9649, -20.48, 0.6, 1.06, 0.94],
+             [9026.0, 1.0, 0.45, 0.16, 0.02, 0.0, 0.9657, -20.39, 0.6, 1.06, 0.94],
+             [9031.0, 1.0, 1.84, 0.64, 0.1, 0.0, 0.9318, -25.03, 0.6, 1.06, 0.94],
+             [9032.0, 1.0, 1.39, 0.48, 0.07, 0.0, 0.9441, -23.84, 0.6, 1.06, 0.94],
+             [9033.0, 1.0, 1.89, 0.65, 0.1, 0.0, 0.9286, -25.33, 0.6, 1.06, 0.94],
+             [9034.0, 1.0, 1.55, 0.54, 0.08, 1.72, 0.9973, -21.1, 0.6, 1.06, 0.94],
+             [9035.0, 1.0, 1.66, 0.58, 0.09, 0.0, 0.9506, -23.19, 0.6, 1.06, 0.94],
+             [9036.0, 1.0, 3.03, 1.0, 0.0, 0.0, 0.9598, -22.67, 2.3, 1.06, 0.94],
+             [9037.0, 1.0, 1.86, 0.64, 0.1, 0.0, 0.957, -22.58, 0.6, 1.06, 0.94],
+             [9038.0, 1.0, 2.58, 0.89, 0.14, 0.0, 0.9391, -24.41, 0.6, 1.06, 0.94],
+             [9041.0, 1.0, 1.01, 0.35, 0.05, 0.0, 0.9636, -21.33, 0.6, 1.06, 0.94],
+             [9042.0, 1.0, 0.81, 0.28, 0.04, 0.0, 0.9501, -22.5, 0.6, 1.06, 0.94],
+             [9043.0, 1.0, 1.6, 0.52, 0.0, 0.0, 0.9646, -21.42, 2.3, 1.06, 0.94],
+             [9044.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.979, -19.78, 6.6, 1.06, 0.94],
+             [9051.0, 2.0, 35.81, 0.0, 0.0, 0.0, 1.0, -19.4, 13.8, 1.06, 0.94],
+             [9052.0, 1.0, 30.0, 23.0, 0.0, 0.0, 0.9786, -17.25, 13.8, 1.06, 0.94],
+             [9053.0, 2.0, 26.48, 0.0, 0.0, 0.0, 1.0, -17.68, 13.8, 1.06, 0.94],
+             [9054.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0, -6.83, 13.8, 1.06, 0.94],
+             [9055.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0, -7.54, 13.8, 1.06, 0.94],
+             [9071.0, 1.0, 1.02, 0.35, 0.05, 0.0, 0.9752, -20.48, 0.6, 1.06, 0.94],
+             [9072.0, 1.0, 1.02, 0.35, 0.05, 0.0, 0.9803, -19.92, 0.6, 1.06, 0.94],
+             [9121.0, 1.0, 3.8, 1.25, 0.0, 0.0, 0.9799, -19.3, 6.6, 1.06, 0.94],
+             [9533.0, 1.0, 1.19, 0.41, 0.1, 0.0, 1.0402, -18.24, 2.3, 1.06, 0.94]])
+
+        # Generator (unit) data
+        self.units = DataFrame(
+            ['id', 'bus_id', 'Pg', 'Qg', 'Qmax', 'Qmin', 'Vg', 'mBase', 'status', 'Pmax', 'Pmin', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min'],
+            [[1.0, 8.0, 0.0, 0.0, 10.0, -10.0, 1.0153, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [2.0, 10.0, 0.0, 0.0, 20.0, -20.0, 1.0205, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [3.0, 20.0, 0.0, 0.0, 20.0, -20.0, 1.001, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [4.0, 63.0, 0.0, 0.0, 25.0, -25.0, 0.9583, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [5.0, 76.0, 0.0, 0.0, 35.0, 12.0, 0.9632, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [6.0, 84.0, 375.0, 0.0, 240.0, -240.0, 1.025, 100.0, 1.0, 475.0, 0.0, 0.0, 0.0533333334, 20.0, 475.0, 0.0],
+             [7.0, 91.0, 155.0, 0.0, 96.0, -11.0, 1.052, 100.0, 1.0, 255.0, 0.0, 0.0, 0.129032258, 20.0, 255.0, 0.0],
+             [8.0, 92.0, 290.0, 0.0, 153.0, -153.0, 1.052, 100.0, 1.0, 390.0, 0.0, 0.0, 0.0689655172, 20.0, 390.0, 0.0],
+             [9.0, 98.0, 68.0, 0.0, 56.0, -30.0, 1.0, 100.0, 1.0, 168.0, 0.0, 0.0, 0.294117648, 20.0, 168.0, 0.0],
+             [10.0, 108.0, 117.0, 0.0, 77.0, -24.0, 0.99, 100.0, 1.0, 217.0, 0.0, 0.0, 0.170940171, 20.0, 217.0, 0.0],
+             [11.0, 119.0, 1930.0, 0.0, 1500.0, -500.0, 1.0435, 100.0, 1.0, 2030.0, 0.0, 0.0, 0.0103626943, 20.0, 2030.0, 0.0],
+             [12.0, 124.0, 240.0, 0.0, 120.0, -60.0, 1.0233, 100.0, 1.0, 340.0, 0.0, 0.0, 0.0833333334, 20.0, 340.0, 0.0],
+             [13.0, 125.0, 0.0, 0.0, 200.0, -25.0, 1.0103, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [14.0, 138.0, 0.0, 0.0, 350.0, -125.0, 1.055, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [15.0, 141.0, 281.0, 0.0, 75.0, -50.0, 1.051, 100.0, 1.0, 381.0, 0.0, 0.0, 0.0711743772, 20.0, 381.0, 0.0],
+             [16.0, 143.0, 696.0, 0.0, 300.0, -100.0, 1.0435, 100.0, 1.0, 796.0, 0.0, 0.0, 0.0287356322, 20.0, 796.0, 0.0],
+             [17.0, 146.0, 84.0, 0.0, 35.0, -15.0, 1.0528, 100.0, 1.0, 184.0, 0.0, 0.0, 0.238095238, 20.0, 184.0, 0.0],
+             [18.0, 147.0, 217.0, 0.0, 100.0, -50.0, 1.0528, 100.0, 1.0, 317.0, 0.0, 0.0, 0.0921658986, 20.0, 317.0, 0.0],
+             [19.0, 149.0, 103.0, 0.0, 50.0, -25.0, 1.0735, 100.0, 1.0, 203.0, 0.0, 0.0, 0.1941747572, 20.0, 203.0, 0.0],
+             [20.0, 152.0, 372.0, 0.0, 175.0, -50.0, 1.0535, 100.0, 1.0, 472.0, 0.0, 0.0, 0.0537634408, 20.0, 472.0, 0.0],
+             [21.0, 153.0, 216.0, 0.0, 90.0, -50.0, 1.0435, 100.0, 1.0, 316.0, 0.0, 0.0, 0.0925925926, 20.0, 316.0, 0.0],
+             [22.0, 156.0, 0.0, 0.0, 15.0, -10.0, 0.963, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [23.0, 170.0, 205.0, 0.0, 90.0, -40.0, 0.929, 100.0, 1.0, 305.0, 0.0, 0.0, 0.0975609756, 20.0, 305.0, 0.0],
+             [24.0, 171.0, 0.0, 0.0, 150.0, -50.0, 0.9829, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [25.0, 176.0, 228.0, 0.0, 90.0, -45.0, 1.0522, 100.0, 1.0, 328.0, 0.0, 0.0, 0.0877192982, 20.0, 328.0, 0.0],
+             [26.0, 177.0, 84.0, 0.0, 35.0, -15.0, 1.0077, 100.0, 1.0, 184.0, 0.0, 0.0, 0.238095238, 20.0, 184.0, 0.0],
+             [27.0, 185.0, 200.0, 0.0, 80.0, -50.0, 1.0522, 100.0, 1.0, 300.0, 0.0, 0.0, 0.1, 20.0, 300.0, 0.0],
+             [28.0, 186.0, 1200.0, 0.0, 400.0, -100.0, 1.065, 100.0, 1.0, 1300.0, 0.0, 0.0, 0.01666666666, 20.0, 1300.0, 0.0],
+             [29.0, 187.0, 1200.0, 0.0, 400.0, -100.0, 1.065, 100.0, 1.0, 1300.0, 0.0, 0.0, 0.01666666666, 20.0, 1300.0, 0.0],
+             [30.0, 190.0, 475.0, 0.0, 300.0, -300.0, 1.0551, 100.0, 1.0, 575.0, 0.0, 0.0, 0.0421052632, 20.0, 575.0, 0.0],
+             [31.0, 191.0, 1973.0, 0.0, 1000.0, -1000.0, 1.0435, 100.0, 1.0, 2073.0, 0.0, 0.0, 0.01013684744, 20.0, 2073.0, 0.0],
+             [32.0, 198.0, 424.0, 0.0, 260.0, -260.0, 1.015, 100.0, 1.0, 524.0, 0.0, 0.0, 0.0471698114, 20.0, 524.0, 0.0],
+             [33.0, 213.0, 272.0, 0.0, 150.0, -150.0, 1.01, 100.0, 1.0, 372.0, 0.0, 0.0, 0.0735294118, 20.0, 372.0, 0.0],
+             [34.0, 220.0, 100.0, 0.0, 60.0, -60.0, 1.008, 100.0, 1.0, 200.0, 0.0, 0.0, 0.2, 20.0, 200.0, 0.0],
+             [35.0, 221.0, 450.0, 0.0, 320.0, -320.0, 1.0, 100.0, 1.0, 550.0, 0.0, 0.0, 0.0444444444, 20.0, 550.0, 0.0],
+             [36.0, 222.0, 250.0, 0.0, 300.0, -300.0, 1.05, 100.0, 1.0, 350.0, 0.0, 0.0, 0.08, 20.0, 350.0, 0.0],
+             [37.0, 227.0, 303.0, 0.0, 300.0, -300.0, 1.0, 100.0, 1.0, 403.0, 0.0, 0.0, 0.0660066006, 20.0, 403.0, 0.0],
+             [38.0, 230.0, 345.0, 0.0, 250.0, -250.0, 1.04, 100.0, 1.0, 445.0, 0.0, 0.0, 0.0579710144, 20.0, 445.0, 0.0],
+             [39.0, 233.0, 300.0, 0.0, 500.0, -500.0, 1.0, 100.0, 1.0, 400.0, 0.0, 0.0, 0.0666666666, 20.0, 400.0, 0.0],
+             [40.0, 236.0, 600.0, 0.0, 300.0, -300.0, 1.0165, 100.0, 1.0, 700.0, 0.0, 0.0, 0.0333333334, 20.0, 700.0, 0.0],
+             [41.0, 238.0, 250.0, 0.0, 200.0, -200.0, 1.01, 100.0, 1.0, 350.0, 0.0, 0.0, 0.08, 20.0, 350.0, 0.0],
+             [42.0, 239.0, 550.0, 0.0, 400.0, -400.0, 1.0, 100.0, 1.0, 650.0, 0.0, 0.0, 0.0363636364, 20.0, 650.0, 0.0],
+             [43.0, 241.0, 575.43, 0.0, 600.0, -600.0, 1.05, 100.0, 1.0, 675.43, 0.0, 0.0, 0.0347566168, 20.0, 675.43, 0.0],
+             [44.0, 242.0, 170.0, 0.0, 100.0, 40.0, 0.993, 100.0, 1.0, 270.0, 0.0, 0.0, 0.1176470588, 20.0, 270.0, 0.0],
+             [45.0, 243.0, 84.0, 0.0, 80.0, 40.0, 1.01, 100.0, 1.0, 184.0, 0.0, 0.0, 0.238095238, 20.0, 184.0, 0.0],
+             [46.0, 7001.0, 467.0, 0.0, 210.0, -210.0, 1.0507, 100.0, 1.0, 567.0, 0.0, 0.0, 0.0428265524, 20.0, 567.0, 0.0],
+             [47.0, 7002.0, 623.0, 0.0, 280.0, -280.0, 1.0507, 100.0, 1.0, 723.0, 0.0, 0.0, 0.0321027288, 20.0, 723.0, 0.0],
+             [48.0, 7003.0, 1210.0, 0.0, 420.0, -420.0, 1.0323, 100.0, 1.0, 1310.0, 0.0, 0.0, 0.01652892562, 20.0, 1310.0, 0.0],
+             [49.0, 7011.0, 234.0, 0.0, 100.0, -100.0, 1.0145, 100.0, 1.0, 334.0, 0.0, 0.0, 0.0854700854, 20.0, 334.0, 0.0],
+             [50.0, 7012.0, 372.0, 0.0, 224.0, -224.0, 1.0507, 100.0, 1.0, 472.0, 0.0, 0.0, 0.0537634408, 20.0, 472.0, 0.0],
+             [51.0, 7017.0, 330.0, 0.0, 350.0, 0.0, 1.0507, 100.0, 1.0, 430.0, 0.0, 0.0, 0.0606060606, 20.0, 430.0, 0.0],
+             [52.0, 7023.0, 185.0, 0.0, 120.0, 0.0, 1.0507, 100.0, 1.0, 285.0, 0.0, 0.0, 0.1081081082, 20.0, 285.0, 0.0],
+             [53.0, 7024.0, 410.0, 0.0, 224.0, -224.0, 1.029, 100.0, 1.0, 510.0, 0.0, 0.0, 0.0487804878, 20.0, 510.0, 0.0],
+             [54.0, 7039.0, 500.0, 0.0, 200.0, -200.0, 1.05, 100.0, 1.0, 600.0, 0.0, 0.0, 0.04, 20.0, 600.0, 0.0],
+             [55.0, 7044.0, 37.0, 0.0, 42.0, 0.0, 1.0145, 100.0, 1.0, 137.0, 0.0, 0.0, 0.54054054, 20.0, 137.0, 0.0],
+             [56.0, 7049.0, 0.0, 0.0, 10.0, 0.0, 1.0507, 100.0, 1.0, 2399.005, 0.0, 0.0, 0.02, 40.0, 2399.005, 0.0],
+             [57.0, 7055.0, 45.0, 0.0, 25.0, 0.0, 0.9967, 100.0, 1.0, 145.0, 0.0, 0.0, 0.444444444, 20.0, 145.0, 0.0],
+             [58.0, 7057.0, 165.0, 0.0, 90.0, -90.0, 1.0212, 100.0, 1.0, 265.0, 0.0, 0.0, 0.1212121212, 20.0, 265.0, 0.0],
+             [59.0, 7061.0, 400.0, 0.0, 150.0, -150.0, 1.0145, 100.0, 1.0, 500.0, 0.0, 0.0, 0.05, 20.0, 500.0, 0.0],
+             [60.0, 7062.0, 400.0, 0.0, 150.0, 0.0, 1.0017, 100.0, 1.0, 500.0, 0.0, 0.0, 0.05, 20.0, 500.0, 0.0],
+             [61.0, 7071.0, 116.0, 0.0, 87.0, 0.0, 0.9893, 100.0, 1.0, 216.0, 0.0, 0.0, 0.1724137932, 20.0, 216.0, 0.0],
+             [62.0, 7130.0, 1292.0, 0.0, 600.0, -100.0, 1.0507, 100.0, 1.0, 1392.0, 0.0, 0.0, 0.01547987616, 20.0, 1392.0, 0.0],
+             [63.0, 7139.0, 700.0, 0.0, 325.0, -125.0, 1.0507, 100.0, 1.0, 800.0, 0.0, 0.0, 0.0285714286, 20.0, 800.0, 0.0],
+             [64.0, 7166.0, 553.0, 0.0, 300.0, -200.0, 1.0145, 100.0, 1.0, 653.0, 0.0, 0.0, 0.0361663652, 20.0, 653.0, 0.0],
+             [65.0, 9002.0, 0.0, 0.0, 2.0, -2.0, 0.9945, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [66.0, 9051.0, 0.0, 0.0, 17.35, -17.35, 1.0, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [67.0, 9053.0, 0.0, 0.0, 12.83, -12.8, 1.0, 100.0, 1.0, 100.0, 0.0, 0.0, 0.02, 40.0, 100.0, 0.0],
+             [68.0, 9054.0, 50.0, 0.0, 38.0, -38.0, 1.0, 100.0, 1.0, 150.0, 0.0, 0.0, 0.4, 20.0, 150.0, 0.0],
+             [69.0, 9055.0, 8.0, 0.0, 6.0, -6.0, 1.0, 100.0, 1.0, 108.0, 0.0, 0.0, 2.5, 20.0, 108.0, 0.0]])
+
+        # Branch (line) data
+        self.lines = DataFrame(
+            ['id', 'from', 'to', 'r', 'x', 'b', 'rateA', 'ratio', 'angle', 'status', 'floor', 'cap'],
+            [[1.0, 37.0, 9001.0, 6e-05, 0.00046, 0.0, 0.0, 1.0082, 0.0, 1.0, 0.0, 0.0],
+             [2.0, 9001.0, 9005.0, 0.0008, 0.00348, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [3.0, 9001.0, 9006.0, 0.02439, 0.43682, 0.0, 0.0, 0.9668, 0.0, 1.0, 0.0, 0.0],
+             [4.0, 9001.0, 9012.0, 0.03624, 0.64898, 0.0, 0.0, 0.9796, 0.0, 1.0, 0.0, 0.0],
+             [5.0, 9005.0, 9051.0, 0.01578, 0.37486, 0.0, 0.0, 1.0435, 0.0, 1.0, 0.0, 0.0],
+             [6.0, 9005.0, 9052.0, 0.01578, 0.37486, 0.0, 0.0, 0.9391, 0.0, 1.0, 0.0, 0.0],
+             [7.0, 9005.0, 9053.0, 0.01602, 0.38046, 0.0, 0.0, 1.0435, 0.0, 1.0, 0.0, 0.0],
+             [8.0, 9005.0, 9054.0, 0.0, 0.152, 0.0, 0.0, 1.0435, 0.0, 1.0, 0.0, 0.0],
+             [9.0, 9005.0, 9055.0, 0.0, 0.8, 0.0, 0.0, 1.0435, 0.0, 1.0, 0.0, 0.0],
+             [10.0, 9006.0, 9007.0, 0.05558, 0.24666, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [11.0, 9006.0, 9003.0, 0.11118, 0.49332, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [12.0, 9006.0, 9003.0, 0.11118, 0.49332, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [13.0, 9012.0, 9002.0, 0.07622, 0.43286, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [14.0, 9012.0, 9002.0, 0.07622, 0.43286, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [15.0, 9002.0, 9021.0, 0.0537, 0.07026, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [16.0, 9021.0, 9023.0, 1.1068, 0.95278, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [17.0, 9021.0, 9022.0, 0.44364, 2.8152, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [18.0, 9002.0, 9024.0, 0.50748, 3.2202, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [19.0, 9023.0, 9025.0, 0.66688, 3.944, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [20.0, 9023.0, 9026.0, 0.6113, 3.6152, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [21.0, 9007.0, 9071.0, 0.4412, 2.9668, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [22.0, 9007.0, 9072.0, 0.30792, 2.057, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [23.0, 9007.0, 9003.0, 0.0558, 0.24666, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [24.0, 9003.0, 9031.0, 0.73633, 4.6724, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [25.0, 9003.0, 9032.0, 0.76978, 4.8846, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [26.0, 9003.0, 9033.0, 0.75732, 4.8056, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [27.0, 9003.0, 9044.0, 0.07378, 0.06352, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [28.0, 9044.0, 9004.0, 0.03832, 0.02894, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [29.0, 9004.0, 9041.0, 0.36614, 2.456, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [30.0, 9004.0, 9042.0, 1.0593, 5.4536, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [31.0, 9004.0, 9043.0, 0.1567, 1.6994, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [32.0, 9003.0, 9034.0, 0.13006, 1.3912, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [33.0, 9003.0, 9035.0, 0.54484, 3.4572, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [34.0, 9003.0, 9036.0, 0.15426, 1.6729, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [35.0, 9003.0, 9037.0, 0.3849, 2.5712, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [36.0, 9003.0, 9038.0, 0.4412, 2.9668, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [37.0, 9012.0, 9121.0, 0.23552, 0.99036, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [38.0, 9053.0, 9533.0, 0.0, 0.75, 0.0, 0.0, 0.9583, 0.0, 1.0, 0.0, 0.0],
+             [39.0, 1.0, 5.0, 0.001, 0.006, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [40.0, 2.0, 6.0, 0.001, 0.009, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [41.0, 2.0, 8.0, 0.006, 0.027, 0.054, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [42.0, 3.0, 7.0, 0.0, 0.003, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [43.0, 3.0, 19.0, 0.008, 0.069, 0.139, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [44.0, 3.0, 150.0, 0.001, 0.007, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [45.0, 4.0, 16.0, 0.002, 0.019, 1.127, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [46.0, 5.0, 9.0, 0.006, 0.029, 0.018, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [47.0, 7.0, 12.0, 0.001, 0.009, 0.07, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [48.0, 7.0, 131.0, 0.001, 0.007, 0.014, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [49.0, 8.0, 11.0, 0.013, 0.0595, 0.033, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [50.0, 8.0, 14.0, 0.013, 0.042, 0.081, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [51.0, 9.0, 11.0, 0.006, 0.027, 0.013, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [52.0, 11.0, 13.0, 0.008, 0.034, 0.018, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [53.0, 12.0, 21.0, 0.002, 0.015, 0.118, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [54.0, 13.0, 20.0, 0.006, 0.034, 0.016, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [55.0, 14.0, 15.0, 0.014, 0.042, 0.097, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [56.0, 15.0, 37.0, 0.065, 0.248, 0.121, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [57.0, 15.0, 89.0, 0.099, 0.248, 0.035, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [58.0, 15.0, 90.0, 0.096, 0.363, 0.048, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [59.0, 16.0, 42.0, 0.002, 0.022, 1.28, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [60.0, 19.0, 21.0, 0.002, 0.018, 0.036, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [61.0, 19.0, 87.0, 0.013, 0.08, 0.151, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [62.0, 20.0, 22.0, 0.016, 0.033, 0.015, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [63.0, 20.0, 27.0, 0.069, 0.186, 0.098, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [64.0, 21.0, 24.0, 0.004, 0.034, 0.28, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [65.0, 22.0, 23.0, 0.052, 0.111, 0.05, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [66.0, 23.0, 25.0, 0.019, 0.039, 0.018, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+             [67.0, 24.0, 319.0, 0.007, 0.068, 0.134, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
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+             [366.0, 143.0, 148.0, 0.0013, 0.0371, 0.0, 0.0, 0.9565, 0.0, 1.0, 0.0, 0.0],
+             [367.0, 145.0, 180.0, 0.0005, 0.0182, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [368.0, 151.0, 170.0, 0.001, 0.0392, 0.0, 0.0, 1.05, 0.0, 1.0, 0.0, 0.0],
+             [369.0, 153.0, 183.0, 0.0027, 0.0639, 0.0, 0.0, 1.073, 0.0, 1.0, 0.0, 0.0],
+             [370.0, 155.0, 156.0, 0.0008, 0.0256, 0.0, 0.0, 1.05, 0.0, 1.0, 0.0, 0.0],
+             [371.0, 159.0, 117.0, 0.0, 0.016, 0.0, 0.0, 1.0506, 0.0, 1.0, 0.0, 0.0],
+             [372.0, 160.0, 124.0, 0.0012, 0.0396, 0.0, 0.0, 0.975, 0.0, 1.0, 0.0, 0.0],
+             [373.0, 163.0, 137.0, 0.0013, 0.0384, -0.057, 0.0, 0.98, 0.0, 1.0, 0.0, 0.0],
+             [374.0, 164.0, 155.0, 0.0009, 0.0231, -0.033, 0.0, 0.956, 0.0, 1.0, 0.0, 0.0],
+             [375.0, 182.0, 139.0, 0.0003, 0.0131, 0.0, 0.0, 1.05, 0.0, 1.0, 0.0, 0.0],
+             [376.0, 189.0, 210.0, 0.0, 0.252, 0.0, 0.0, 1.03, 0.0, 1.0, 0.0, 0.0],
+             [377.0, 193.0, 196.0, 0.0, 0.237, 0.0, 0.0, 1.03, 0.0, 1.0, 0.0, 0.0],
+             [378.0, 195.0, 212.0, 0.0008, 0.0366, 0.0, 0.0, 0.985, 0.0, 1.0, 0.0, 0.0],
+             [379.0, 200.0, 248.0, 0.0, 0.22, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [380.0, 201.0, 69.0, 0.0, 0.098, 0.0, 0.0, 1.03, 0.0, 1.0, 0.0, 0.0],
+             [381.0, 202.0, 211.0, 0.0, 0.128, 0.0, 0.0, 1.01, 0.0, 1.0, 0.0, 0.0],
+             [382.0, 204.0, 2040.0, 0.02, 0.204, -0.012, 0.0, 1.05, 0.0, 1.0, 0.0, 0.0],
+             [383.0, 209.0, 198.0, 0.026, 0.211, 0.0, 0.0, 1.03, 0.0, 1.0, 0.0, 0.0],
+             [384.0, 211.0, 212.0, 0.003, 0.0122, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [385.0, 218.0, 219.0, 0.001, 0.0354, -0.01, 0.0, 0.97, 0.0, 1.0, 0.0, 0.0],
+             [386.0, 223.0, 224.0, 0.0012, 0.0195, -0.364, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [387.0, 229.0, 230.0, 0.001, 0.0332, 0.0, 0.0, 1.02, 0.0, 1.0, 0.0, 0.0],
+             [388.0, 234.0, 236.0, 0.0005, 0.016, 0.0, 0.0, 1.07, 0.0, 1.0, 0.0, 0.0],
+             [389.0, 238.0, 239.0, 0.0005, 0.016, 0.0, 0.0, 1.02, 0.0, 1.0, 0.0, 0.0],
+             [390.0, 196.0, 2040.0, 0.0001, 0.02, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [391.0, 119.0, 1190.0, 0.001, 0.023, 0.0, 0.0, 1.0223, 0.0, 1.0, 0.0, 0.0],
+             [392.0, 120.0, 1200.0, 0.0, 0.023, 0.0, 0.0, 0.9284, 0.0, 1.0, 0.0, 0.0],
+             [393.0, 7002.0, 2.0, 0.001, 0.0146, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [394.0, 7003.0, 3.0, 0.0, 0.01054, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [395.0, 7061.0, 61.0, 0.0, 0.0238, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [396.0, 7062.0, 62.0, 0.0, 0.03214, 0.0, 0.0, 0.95, 0.0, 1.0, 0.0, 0.0],
+             [397.0, 7166.0, 166.0, 0.0, 0.0154, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [398.0, 7024.0, 24.0, 0.0, 0.0289, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [399.0, 7001.0, 1.0, 0.0, 0.01953, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [400.0, 7130.0, 130.0, 0.0, 0.0193, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [401.0, 7011.0, 11.0, 0.0, 0.01923, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [402.0, 7023.0, 23.0, 0.0, 0.023, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [403.0, 7049.0, 49.0, 0.0, 0.0124, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [404.0, 7139.0, 139.0, 0.0, 0.0167, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [405.0, 7012.0, 12.0, 0.0, 0.0312, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0],
+             [406.0, 7017.0, 17.0, 0.0, 0.01654, 0.0, 0.0, 0.942, 0.0, 1.0, 0.0, 0.0],
+             [407.0, 7039.0, 39.0, 0.0, 0.03159, 0.0, 0.0, 0.965, 0.0, 1.0, 0.0, 0.0],
+             [408.0, 7057.0, 57.0, 0.0, 0.05347, 0.0, 0.0, 0.95, 0.0, 1.0, 0.0, 0.0],
+             [409.0, 7044.0, 44.0, 0.0, 0.18181, 0.0, 0.0, 0.942, 0.0, 1.0, 0.0, 0.0],
+             [410.0, 7055.0, 55.0, 0.0, 0.19607, 0.0, 0.0, 0.942, 0.0, 1.0, 0.0, 0.0],
+             [411.0, 7071.0, 71.0, 0.0, 0.06896, 0.0, 0.0, 0.9565, 0.0, 1.0, 0.0, 0.0]])
+
+        # Load data
+        self.loads = DataFrame(
+            ['id', 'bus_id', 'Pd', 'Qd', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'],
+            [[1.0, 1.0, 90.0, 49.0, 0.0, 0.0, 0.0, 90.0, 90.0],
+             [2.0, 2.0, 56.0, 15.0, 0.0, 0.0, 0.0, 56.0, 56.0],
+             [3.0, 3.0, 20.0, 0.0, 0.0, 0.0, 0.0, 20.0, 20.0],
+             [4.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [5.0, 5.0, 353.0, 130.0, 0.0, 0.0, 0.0, 353.0, 353.0],
+             [6.0, 6.0, 120.0, 41.0, 0.0, 0.0, 0.0, 120.0, 120.0],
+             [7.0, 7.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [8.0, 8.0, 63.0, 14.0, 0.0, 0.0, 0.0, 63.0, 63.0],
+             [9.0, 9.0, 96.0, 43.0, 0.0, 0.0, 0.0, 96.0, 96.0],
+             [10.0, 10.0, 153.0, 33.0, 0.0, 0.0, 0.0, 153.0, 153.0],
+             [11.0, 11.0, 83.0, 21.0, 0.0, 0.0, 0.0, 83.0, 83.0],
+             [12.0, 12.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [13.0, 13.0, 58.0, 10.0, 0.0, 0.0, 0.0, 58.0, 58.0],
+             [14.0, 14.0, 160.0, 60.0, 0.0, 0.0, 0.0, 160.0, 160.0],
+             [15.0, 15.0, 126.7, 23.0, 0.0, 0.0, 0.0, 126.7, 126.7],
+             [16.0, 16.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [17.0, 17.0, 561.0, 220.0, 0.0, 0.0, 0.0, 561.0, 561.0],
+             [18.0, 19.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [19.0, 20.0, 605.0, 120.0, 0.0, 0.0, 0.0, 605.0, 605.0],
+             [20.0, 21.0, 77.0, 1.0, 0.0, 0.0, 0.0, 77.0, 77.0],
+             [21.0, 22.0, 81.0, 23.0, 0.0, 0.0, 0.0, 81.0, 81.0],
+             [22.0, 23.0, 21.0, 7.0, 0.0, 0.0, 0.0, 21.0, 21.0],
+             [23.0, 24.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [24.0, 25.0, 45.0, 12.0, 0.0, 0.0, 0.0, 45.0, 45.0],
+             [25.0, 26.0, 28.0, 9.0, 0.0, 0.0, 0.0, 28.0, 28.0],
+             [26.0, 27.0, 69.0, 13.0, 0.0, 0.0, 0.0, 69.0, 69.0],
+             [27.0, 33.0, 55.0, 6.0, 0.0, 0.0, 0.0, 55.0, 55.0],
+             [28.0, 34.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [29.0, 35.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [30.0, 36.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [31.0, 37.0, 85.0, 32.0, 0.0, 0.0, 0.0, 85.0, 85.0],
+             [32.0, 38.0, 155.0, 18.0, 0.0, 0.0, 0.0, 155.0, 155.0],
+             [33.0, 39.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [34.0, 40.0, 46.0, -21.0, 0.0, 0.0, 0.0, 46.0, 46.0],
+             [35.0, 41.0, 86.0, 0.0, 0.0, 0.0, 0.0, 86.0, 86.0],
+             [36.0, 42.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [37.0, 43.0, 39.0, 9.0, 0.0, 0.0, 0.0, 39.0, 39.0],
+             [38.0, 44.0, 195.0, 29.0, 0.0, 0.0, 0.0, 195.0, 195.0],
+             [39.0, 45.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [40.0, 46.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [41.0, 47.0, 58.0, 11.8, 0.0, 0.0, 0.0, 58.0, 58.0],
+             [42.0, 48.0, 41.0, 19.0, 0.0, 0.0, 0.0, 41.0, 41.0],
+             [43.0, 49.0, 92.0, 26.0, 0.0, 0.0, 0.0, 92.0, 92.0],
+             [44.0, 51.0, -5.0, 5.0, 0.0, 0.0, 0.0, -5.0, -5.0],
+             [45.0, 52.0, 61.0, 28.0, 0.0, 0.0, 0.0, 61.0, 61.0],
+             [46.0, 53.0, 69.0, 3.0, 0.0, 0.0, 0.0, 69.0, 69.0],
+             [47.0, 54.0, 10.0, 1.0, 0.0, 0.0, 0.0, 10.0, 10.0],
+             [48.0, 55.0, 22.0, 10.0, 0.0, 0.0, 0.0, 22.0, 22.0],
+             [49.0, 57.0, 98.0, 20.0, 0.0, 0.0, 0.0, 98.0, 98.0],
+             [50.0, 58.0, 14.0, 1.0, 0.0, 0.0, 0.0, 14.0, 14.0],
+             [51.0, 59.0, 218.0, 106.0, 0.0, 0.0, 0.0, 218.0, 218.0],
+             [52.0, 60.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [53.0, 61.0, 227.0, 110.0, 0.0, 0.0, 0.0, 227.0, 227.0],
+             [54.0, 62.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [55.0, 63.0, 70.0, 30.0, 0.0, 0.0, 0.0, 70.0, 70.0],
+             [56.0, 64.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [57.0, 69.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [58.0, 70.0, 56.0, 20.0, 0.0, 0.0, 0.0, 56.0, 56.0],
+             [59.0, 71.0, 116.0, 38.0, 0.0, 0.0, 0.0, 116.0, 116.0],
+             [60.0, 72.0, 57.0, 19.0, 0.0, 0.0, 0.0, 57.0, 57.0],
+             [61.0, 73.0, 224.0, 71.0, 0.0, 0.0, 0.0, 224.0, 224.0],
+             [62.0, 74.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [63.0, 76.0, 208.0, 107.0, 0.0, 0.0, 0.0, 208.0, 208.0],
+             [64.0, 77.0, 74.0, 28.0, 0.0, 0.0, 0.0, 74.0, 74.0],
+             [65.0, 78.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [66.0, 79.0, 48.0, 14.0, 0.0, 0.0, 0.0, 48.0, 48.0],
+             [67.0, 80.0, 28.0, 7.0, 0.0, 0.0, 0.0, 28.0, 28.0],
+             [68.0, 81.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [69.0, 84.0, 37.0, 13.0, 0.0, 0.0, 0.0, 37.0, 37.0],
+             [70.0, 85.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [71.0, 86.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [72.0, 87.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [73.0, 88.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [74.0, 89.0, 44.2, 0.0, 0.0, 0.0, 0.0, 44.2, 44.2],
+             [75.0, 90.0, 66.0, 0.0, 0.0, 0.0, 0.0, 66.0, 66.0],
+             [76.0, 91.0, 17.4, 0.0, 0.0, 0.0, 0.0, 17.4, 17.4],
+             [77.0, 92.0, 15.8, 0.0, 0.0, 0.0, 0.0, 15.8, 15.8],
+             [78.0, 94.0, 60.3, 0.0, 0.0, 0.0, 0.0, 60.3, 60.3],
+             [79.0, 97.0, 39.9, 0.0, 0.0, 0.0, 0.0, 39.9, 39.9],
+             [80.0, 98.0, 66.7, 0.0, 0.0, 0.0, 0.0, 66.7, 66.7],
+             [81.0, 99.0, 83.5, 0.0, 0.0, 0.0, 0.0, 83.5, 83.5],
+             [82.0, 100.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [83.0, 102.0, 77.8, 0.0, 0.0, 0.0, 0.0, 77.8, 77.8],
+             [84.0, 103.0, 32.0, 0.0, 0.0, 0.0, 0.0, 32.0, 32.0],
+             [85.0, 104.0, 8.6, 0.0, 0.0, 0.0, 0.0, 8.6, 8.6],
+             [86.0, 105.0, 49.6, 0.0, 0.0, 0.0, 0.0, 49.6, 49.6],
+             [87.0, 107.0, 4.6, 0.0, 0.0, 0.0, 0.0, 4.6, 4.6],
+             [88.0, 108.0, 112.1, 0.0, 0.0, 0.0, 0.0, 112.1, 112.1],
+             [89.0, 109.0, 30.7, 0.0, 0.0, 0.0, 0.0, 30.7, 30.7],
+             [90.0, 110.0, 63.0, 0.0, 0.0, 0.0, 0.0, 63.0, 63.0],
+             [91.0, 112.0, 19.6, 0.0, 0.0, 0.0, 0.0, 19.6, 19.6],
+             [92.0, 113.0, 26.2, 0.0, 0.0, 0.0, 0.0, 26.2, 26.2],
+             [93.0, 114.0, 18.2, 0.0, 0.0, 0.0, 0.0, 18.2, 18.2],
+             [94.0, 115.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [95.0, 116.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [96.0, 117.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [97.0, 118.0, 14.1, 650.0, 0.0, 0.0, 0.0, 14.1, 14.1],
+             [98.0, 119.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [99.0, 120.0, 777.0, 215.0, 0.0, 0.0, 0.0, 777.0, 777.0],
+             [100.0, 121.0, 535.0, 55.0, 0.0, 0.0, 0.0, 535.0, 535.0],
+             [101.0, 122.0, 229.1, 11.8, 0.0, 0.0, 0.0, 229.1, 229.1],
+             [102.0, 123.0, 78.0, 1.4, 0.0, 0.0, 0.0, 78.0, 78.0],
+             [103.0, 124.0, 276.4, 59.3, 0.0, 0.0, 0.0, 276.4, 276.4],
+             [104.0, 125.0, 514.8, 82.7, 0.0, 0.0, 0.0, 514.8, 514.8],
+             [105.0, 126.0, 57.9, 5.1, 0.0, 0.0, 0.0, 57.9, 57.9],
+             [106.0, 127.0, 380.8, 37.0, 0.0, 0.0, 0.0, 380.8, 380.8],
+             [107.0, 128.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [108.0, 129.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [109.0, 130.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [110.0, 131.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [111.0, 132.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [112.0, 133.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [113.0, 134.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [114.0, 135.0, 169.2, 41.6, 0.0, 0.0, 0.0, 169.2, 169.2],
+             [115.0, 136.0, 55.2, 18.2, 0.0, 0.0, 0.0, 55.2, 55.2],
+             [116.0, 137.0, 273.6, 99.8, 0.0, 0.0, 0.0, 273.6, 273.6],
+             [117.0, 138.0, 1019.2, 135.2, 0.0, 0.0, 0.0, 1019.2, 1019.2],
+             [118.0, 139.0, 595.0, 83.3, 0.0, 0.0, 0.0, 595.0, 595.0],
+             [119.0, 140.0, 387.7, 114.7, 0.0, 0.0, 0.0, 387.7, 387.7],
+             [120.0, 141.0, 145.0, 58.0, 0.0, 0.0, 0.0, 145.0, 145.0],
+             [121.0, 142.0, 56.5, 24.5, 0.0, 0.0, 0.0, 56.5, 56.5],
+             [122.0, 143.0, 89.5, 35.5, 0.0, 0.0, 0.0, 89.5, 89.5],
+             [123.0, 144.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [124.0, 145.0, 24.0, 14.0, 0.0, 0.0, 0.0, 24.0, 24.0],
+             [125.0, 146.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [126.0, 147.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [127.0, 148.0, 63.0, 25.0, 0.0, 0.0, 0.0, 63.0, 63.0],
+             [128.0, 149.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [129.0, 150.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [130.0, 151.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [131.0, 152.0, 17.0, 9.0, 0.0, 0.0, 0.0, 17.0, 17.0],
+             [132.0, 153.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [133.0, 154.0, 70.0, 5.0, 0.0, 0.0, 0.0, 70.0, 70.0],
+             [134.0, 155.0, 200.0, 50.0, 0.0, 0.0, 0.0, 200.0, 200.0],
+             [135.0, 156.0, 75.0, 50.0, 0.0, 0.0, 0.0, 75.0, 75.0],
+             [136.0, 157.0, 123.5, -24.3, 0.0, 0.0, 0.0, 123.5, 123.5],
+             [137.0, 158.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [138.0, 159.0, 33.0, 16.5, 0.0, 0.0, 0.0, 33.0, 33.0],
+             [139.0, 160.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [140.0, 161.0, 35.0, 15.0, 0.0, 0.0, 0.0, 35.0, 35.0],
+             [141.0, 162.0, 85.0, 24.0, 0.0, 0.0, 0.0, 85.0, 85.0],
+             [142.0, 163.0, 0.0, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [143.0, 164.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [144.0, 165.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [145.0, 166.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [146.0, 167.0, 299.9, 95.7, 0.0, 0.0, 0.0, 299.9, 299.9],
+             [147.0, 168.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [148.0, 169.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [149.0, 170.0, 481.8, 205.0, 0.0, 0.0, 0.0, 481.8, 481.8],
+             [150.0, 171.0, 763.6, 291.1, 0.0, 0.0, 0.0, 763.6, 763.6],
+             [151.0, 172.0, 26.5, 0.0, 0.0, 0.0, 0.0, 26.5, 26.5],
+             [152.0, 173.0, 163.5, 43.0, 0.0, 0.0, 0.0, 163.5, 163.5],
+             [153.0, 174.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [154.0, 175.0, 176.0, 83.0, 0.0, 0.0, 0.0, 176.0, 176.0],
+             [155.0, 176.0, 5.0, 4.0, 0.0, 0.0, 0.0, 5.0, 5.0],
+             [156.0, 177.0, 28.0, 12.0, 0.0, 0.0, 0.0, 28.0, 28.0],
+             [157.0, 178.0, 427.4, 173.6, 0.0, 0.0, 0.0, 427.4, 427.4],
+             [158.0, 179.0, 74.0, 29.0, 0.0, 0.0, 0.0, 74.0, 74.0],
+             [159.0, 180.0, 69.5, 49.3, 0.0, 0.0, 0.0, 69.5, 69.5],
+             [160.0, 181.0, 73.4, 0.0, 0.0, 0.0, 0.0, 73.4, 73.4],
+             [161.0, 182.0, 240.7, 89.0, 0.0, 0.0, 0.0, 240.7, 240.7],
+             [162.0, 183.0, 40.0, 4.0, 0.0, 0.0, 0.0, 40.0, 40.0],
+             [163.0, 184.0, 136.8, 16.6, 0.0, 0.0, 0.0, 136.8, 136.8],
+             [164.0, 185.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [165.0, 186.0, 59.8, 24.3, 0.0, 0.0, 0.0, 59.8, 59.8],
+             [166.0, 187.0, 59.8, 24.3, 0.0, 0.0, 0.0, 59.8, 59.8],
+             [167.0, 188.0, 182.6, 43.6, 0.0, 0.0, 0.0, 182.6, 182.6],
+             [168.0, 189.0, 7.0, 2.0, 0.0, 0.0, 0.0, 7.0, 7.0],
+             [169.0, 190.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [170.0, 191.0, 489.0, 53.0, 0.0, 0.0, 0.0, 489.0, 489.0],
+             [171.0, 192.0, 800.0, 72.0, 0.0, 0.0, 0.0, 800.0, 800.0],
+             [172.0, 193.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [173.0, 194.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [174.0, 195.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [175.0, 196.0, 10.0, 3.0, 0.0, 0.0, 0.0, 10.0, 10.0],
+             [176.0, 197.0, 43.0, 14.0, 0.0, 0.0, 0.0, 43.0, 43.0],
+             [177.0, 198.0, 64.0, 21.0, 0.0, 0.0, 0.0, 64.0, 64.0],
+             [178.0, 199.0, 35.0, 12.0, 0.0, 0.0, 0.0, 35.0, 35.0],
+             [179.0, 200.0, 27.0, 12.0, 0.0, 0.0, 0.0, 27.0, 27.0],
+             [180.0, 201.0, 41.0, 14.0, 0.0, 0.0, 0.0, 41.0, 41.0],
+             [181.0, 202.0, 38.0, 13.0, 0.0, 0.0, 0.0, 38.0, 38.0],
+             [182.0, 203.0, 42.0, 14.0, 0.0, 0.0, 0.0, 42.0, 42.0],
+             [183.0, 204.0, 72.0, 24.0, 0.0, 0.0, 0.0, 72.0, 72.0],
+             [184.0, 205.0, 0.0, -5.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [185.0, 206.0, 12.0, 2.0, 0.0, 0.0, 0.0, 12.0, 12.0],
+             [186.0, 207.0, -21.0, -14.2, 0.0, 0.0, 0.0, -21.0, -21.0],
+             [187.0, 208.0, 7.0, 2.0, 0.0, 0.0, 0.0, 7.0, 7.0],
+             [188.0, 209.0, 38.0, 13.0, 0.0, 0.0, 0.0, 38.0, 38.0],
+             [189.0, 210.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [190.0, 211.0, 96.0, 7.0, 0.0, 0.0, 0.0, 96.0, 96.0],
+             [191.0, 212.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [192.0, 213.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [193.0, 214.0, 22.0, 16.0, 0.0, 0.0, 0.0, 22.0, 22.0],
+             [194.0, 215.0, 47.0, 26.0, 0.0, 0.0, 0.0, 47.0, 47.0],
+             [195.0, 216.0, 176.0, 105.0, 0.0, 0.0, 0.0, 176.0, 176.0],
+             [196.0, 217.0, 100.0, 75.0, 0.0, 0.0, 0.0, 100.0, 100.0],
+             [197.0, 218.0, 131.0, 96.0, 0.0, 0.0, 0.0, 131.0, 131.0],
+             [198.0, 219.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [199.0, 220.0, 285.0, 100.0, 0.0, 0.0, 0.0, 285.0, 285.0],
+             [200.0, 221.0, 171.0, 70.0, 0.0, 0.0, 0.0, 171.0, 171.0],
+             [201.0, 222.0, 328.0, 188.0, 0.0, 0.0, 0.0, 328.0, 328.0],
+             [202.0, 223.0, 428.0, 232.0, 0.0, 0.0, 0.0, 428.0, 428.0],
+             [203.0, 224.0, 173.0, 99.0, 0.0, 0.0, 0.0, 173.0, 173.0],
+             [204.0, 225.0, 410.0, 40.0, 0.0, 0.0, 0.0, 410.0, 410.0],
+             [205.0, 226.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [206.0, 227.0, 538.0, 369.0, 0.0, 0.0, 0.0, 538.0, 538.0],
+             [207.0, 228.0, 223.0, 148.0, 0.0, 0.0, 0.0, 223.0, 223.0],
+             [208.0, 229.0, 96.0, 46.0, 0.0, 0.0, 0.0, 96.0, 96.0],
+             [209.0, 230.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [210.0, 231.0, 159.0, 107.0, 0.0, 0.0, 0.0, 159.0, 159.0],
+             [211.0, 232.0, 448.0, 143.0, 0.0, 0.0, 0.0, 448.0, 448.0],
+             [212.0, 233.0, 404.0, 212.0, 0.0, 0.0, 0.0, 404.0, 404.0],
+             [213.0, 234.0, 572.0, 244.0, 0.0, 0.0, 0.0, 572.0, 572.0],
+             [214.0, 235.0, 269.0, 157.0, 0.0, 0.0, 0.0, 269.0, 269.0],
+             [215.0, 236.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [216.0, 237.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [217.0, 238.0, 255.0, 149.0, 0.0, 0.0, 0.0, 255.0, 255.0],
+             [218.0, 239.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [219.0, 240.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [220.0, 241.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [221.0, 242.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [222.0, 243.0, 8.0, 3.0, 0.0, 0.0, 0.0, 8.0, 8.0],
+             [223.0, 244.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [224.0, 245.0, 61.0, 30.0, 0.0, 0.0, 0.0, 61.0, 61.0],
+             [225.0, 246.0, 77.0, 33.0, 0.0, 0.0, 0.0, 77.0, 77.0],
+             [226.0, 247.0, 61.0, 30.0, 0.0, 0.0, 0.0, 61.0, 61.0],
+             [227.0, 248.0, 29.0, 14.0, 0.0, 0.0, 0.0, 29.0, 29.0],
+             [228.0, 249.0, 29.0, 14.0, 0.0, 0.0, 0.0, 29.0, 29.0],
+             [229.0, 250.0, -23.0, -17.0, 0.0, 0.0, 0.0, -23.0, -23.0],
+             [230.0, 281.0, -33.1, -29.4, 0.0, 0.0, 0.0, -33.1, -33.1],
+             [231.0, 319.0, 115.8, -24.0, 0.0, 0.0, 0.0, 115.8, 115.8],
+             [232.0, 320.0, 2.4, -12.6, 0.0, 0.0, 0.0, 2.4, 2.4],
+             [233.0, 322.0, 2.4, -3.9, 0.0, 0.0, 0.0, 2.4, 2.4],
+             [234.0, 323.0, -14.9, 26.5, 0.0, 0.0, 0.0, -14.9, -14.9],
+             [235.0, 324.0, 24.7, -1.2, 0.0, 0.0, 0.0, 24.7, 24.7],
+             [236.0, 526.0, 145.3, -34.9, 0.0, 0.0, 0.0, 145.3, 145.3],
+             [237.0, 528.0, 28.1, -20.5, 0.0, 0.0, 0.0, 28.1, 28.1],
+             [238.0, 531.0, 14.0, 2.5, 0.0, 0.0, 0.0, 14.0, 14.0],
+             [239.0, 552.0, -11.1, -1.4, 0.0, 0.0, 0.0, -11.1, -11.1],
+             [240.0, 562.0, 50.5, 17.4, 0.0, 0.0, 0.0, 50.5, 50.5],
+             [241.0, 609.0, 29.6, 0.6, 0.0, 0.0, 0.0, 29.6, 29.6],
+             [242.0, 664.0, -113.7, 76.7, 0.0, 0.0, 0.0, -113.7, -113.7],
+             [243.0, 1190.0, 100.31, 29.17, 0.0, 0.0, 0.0, 100.31, 100.31],
+             [244.0, 1200.0, -100.0, 34.17, 0.0, 0.0, 0.0, -100.0, -100.0],
+             [245.0, 1201.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [246.0, 2040.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [247.0, 7001.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [248.0, 7002.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [249.0, 7003.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [250.0, 7011.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [251.0, 7012.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [252.0, 7017.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [253.0, 7023.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [254.0, 7024.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [255.0, 7039.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [256.0, 7044.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [257.0, 7049.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [258.0, 7055.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [259.0, 7057.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [260.0, 7061.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [261.0, 7062.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [262.0, 7071.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [263.0, 7130.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [264.0, 7139.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [265.0, 7166.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [266.0, 9001.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [267.0, 9002.0, 4.2, 0.0, 0.0, 0.0, 0.0, 4.2, 4.2],
+             [268.0, 9003.0, 2.71, 0.94, 0.0, 0.0, 0.0, 2.71, 2.71],
+             [269.0, 9004.0, 0.86, 0.28, 0.0, 0.0, 0.0, 0.86, 0.86],
+             [270.0, 9005.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [271.0, 9006.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [272.0, 9007.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [273.0, 9012.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [274.0, 9021.0, 4.75, 1.56, 0.0, 0.0, 0.0, 4.75, 4.75],
+             [275.0, 9022.0, 1.53, 0.53, 0.0, 0.0, 0.0, 1.53, 1.53],
+             [276.0, 9023.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [277.0, 9024.0, 1.35, 0.47, 0.0, 0.0, 0.0, 1.35, 1.35],
+             [278.0, 9025.0, 0.45, 0.16, 0.0, 0.0, 0.0, 0.45, 0.45],
+             [279.0, 9026.0, 0.45, 0.16, 0.0, 0.0, 0.0, 0.45, 0.45],
+             [280.0, 9031.0, 1.84, 0.64, 0.0, 0.0, 0.0, 1.84, 1.84],
+             [281.0, 9032.0, 1.39, 0.48, 0.0, 0.0, 0.0, 1.39, 1.39],
+             [282.0, 9033.0, 1.89, 0.65, 0.0, 0.0, 0.0, 1.89, 1.89],
+             [283.0, 9034.0, 1.55, 0.54, 0.0, 0.0, 0.0, 1.55, 1.55],
+             [284.0, 9035.0, 1.66, 0.58, 0.0, 0.0, 0.0, 1.66, 1.66],
+             [285.0, 9036.0, 3.03, 1.0, 0.0, 0.0, 0.0, 3.03, 3.03],
+             [286.0, 9037.0, 1.86, 0.64, 0.0, 0.0, 0.0, 1.86, 1.86],
+             [287.0, 9038.0, 2.58, 0.89, 0.0, 0.0, 0.0, 2.58, 2.58],
+             [288.0, 9041.0, 1.01, 0.35, 0.0, 0.0, 0.0, 1.01, 1.01],
+             [289.0, 9042.0, 0.81, 0.28, 0.0, 0.0, 0.0, 0.81, 0.81],
+             [290.0, 9043.0, 1.6, 0.52, 0.0, 0.0, 0.0, 1.6, 1.6],
+             [291.0, 9044.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [292.0, 9051.0, 35.81, 0.0, 0.0, 0.0, 0.0, 35.81, 35.81],
+             [293.0, 9052.0, 30.0, 23.0, 0.0, 0.0, 0.0, 30.0, 30.0],
+             [294.0, 9053.0, 26.48, 0.0, 0.0, 0.0, 0.0, 26.48, 26.48],
+             [295.0, 9054.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [296.0, 9055.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [297.0, 9071.0, 1.02, 0.35, 0.0, 0.0, 0.0, 1.02, 1.02],
+             [298.0, 9072.0, 1.02, 0.35, 0.0, 0.0, 0.0, 1.02, 1.02],
+             [299.0, 9121.0, 3.8, 1.25, 0.0, 0.0, 0.0, 3.8, 3.8],
+             [300.0, 9533.0, 1.19, 0.41, 0.0, 0.0, 0.0, 1.19, 1.19]])
+        
+        self.real_params = True
+        super().__init__(*args, **kwargs)

powergrid_case/transmission/Case5.py

@@ -0,0 +1,53 @@
+from powerzoo.case.CaseBase import ClearCase, DataFrame
+
+
+class Case5(ClearCase):
+    GRID_TYPE = "transmission"
+    BUS_COUNT = 5
+    VOLTAGE_LEVEL = "HV"
+    SOURCE = "MATPOWER"
+    DESCRIPTION = "IEEE 5-bus test system"
+
+    def __init__(self, *args, **kwargs):
+        # type: 1=PQ (load), 2=PV (gen), 3=Ref (θ=0 in DCOPF)
+        # Pd/Qd: nominal load at each bus (MW / MVAr)
+        self.nodes = DataFrame(
+            ['id', 'type', 'Pd', 'Qd', 'x', 'y'],
+            [[1.0, 3,   0.0,   0.0, 0, 0],     # Reference bus (2 gens)
+             [2.0, 1, 300.0,  98.6, 3, 0],     # Load bus
+             [3.0, 2, 300.0,  98.6, 4.5, 1],   # Gen bus
+             [4.0, 2, 400.0, 131.5, 2, 2],     # Gen bus
+             [5.0, 2,   0.0,   0.0, 0, 2]])    # Gen bus
+
+        self.units = DataFrame(
+            ['id', 'bus_id', 'mc_a', 'mc_b', 'mc_c', 'p_max', 'p_min'],
+            [[1.0, 1.0, 0.0, 0.0, 14.0, 40.0, 5.0],
+             [2.0, 1.0, 0.0, 0.0, 15.0, 170.0, 10.0],
+             [3.0, 3.0, 0.0, 0.0, 30.0, 520.0, 20.0],
+             [4.0, 4.0, 0.0, 0.0, 40.0, 200.0, 10.0],
+             [5.0, 5.0, 0.0, 0.0, 10.0, 600.0, 20.0]])
+
+        self.lines = DataFrame(
+            ['id', 'from', 'to', 'x', 'floor', 'cap'],
+            [[1.0, 1.0, 2.0, 0.0281, -400.0, 400.0],
+             [2.0, 1.0, 4.0, 0.0304, 0.0, 0.0],
+             [3.0, 1.0, 5.0, 0.0064, 0.0, 0.0],
+             [4.0, 2.0, 3.0, 0.0108, 0.0, 0.0],
+             [5.0, 3.0, 4.0, 0.0297, 0.0, 0.0],
+             [6.0, 4.0, 5.0, 0.0297, -240.0, 240.0]])
+
+        self.loads = DataFrame(
+            ['id', 'bus_id', 'mc_a', 'mc_b', 'mc_c', 'd_max', 'd_min'],
+            [[1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+             [2.0, 2.0, 0.0, 0.0, 0.0, 500.0, 0.0],
+             [3.0, 3.0, 0.0, 0.0, 0.0, 600.0, 0.0],
+             [4.0, 4.0, 0.0, 0.0, 0.0, 400.0, 0.0],
+             [5.0, 5.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
+        
+        self.real_params = True
+        super().__init__(*args, **kwargs)
+
+if __name__ == '__main__':
+    c = Case5()
+    c.check()
+    print(c.get_node_ptdf())