Update README.md

README.md

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----
-license: bsd-3-clause
----
+---
+license: bsd-3-clause
+task_categories:
+- reinforcement-learning
+tags:
+- power-systems
+- optimization
+- unit-commitment
+- mixed-integer-programming
+---
+
+# Unit Commitment Trajectory Dataset (UCTD)
+
+## 1. Overview
+
+This dataset is generated using a customized version of the `UnitCommitment.jl` framework, specifically designed for **Machine Learning for Optimization (ML4Opt)** research. It provides Unit Commitment (SCUC) optimization problems ranging from small IEEE test systems to large-scale national grids.
+
+**Core Innovation**: Unlike standard datasets, UCTD incorporates **Power Trajectories** for generator startup and shutdown. This provides a high-fidelity physical representation of power system operations, making it a challenging benchmark for modern optimization solvers and ML models.
+
+## 2. Case Statistics
+
+The dataset contains **464** `.mps` files across three grid models:
+
+- **Case14**: Basic test system (14-bus, 67 days).
+- **Case30**: Medium-scale system (30-bus, 45 days).
+- **Case2383wp (Challenge Set)**: Large-scale Polish national grid (2383-bus), used for testing scalability.
+
+## 3. Model Variants
+
+For each day, 4 modeling variants are provided:
+
+1. `hourly_noline`: 1-hour resolution, unit constraints only.
+2. `hourly_withline`: 1-hour resolution, including full network constraints (SCUC).
+3. `subhourly_noline`: 15-minute resolution, unit constraints only.
+4. `subhourly_withline`: 15-minute resolution, including full network constraints (SCUC).
+
+## 4. File Naming Convention
+
+Format: `{case}_{date}_{granularity}_{variant}.mps`
+Example: `case30_2017-01-01_s_withline.mps` (Case30, Sub-hourly, with network constraints).
+
+## 5. Key Features
+
+- **High-Fidelity Physics**: Includes power output trajectories during generator startup/shutdown phases.
+- **Multiple Resolutions**: Covers both traditional 1-hour scheduling and modern 15-minute sub-hourly scheduling.
+- **Standardized Format**: Uses the industry-standard `.mps` format, compatible with Gurobi, CPLEX, HiGHS, and more.
+
+## 6. Use Cases
+
+- **Supervised Learning**: Predict optimal commitment status or production levels.
+- **End-to-End Optimization**: Train Graph Neural Networks (GNN) to map problem instances directly to solutions.
+- **Solver Benchmarking**: Test the performance and scalability of modern MIP solvers on large-scale power grid problems.
+
+## 7. Citation
+
+If you use this dataset in your research, please cite the original `UnitCommitment.jl` paper and acknowledge the source of this trajectory-enhanced version.