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Subhourly
Module for converting UC instances from 40-minute time periods (36 periods/day)
to 20-minute time periods (72 periods/day).
"""
module Subhourly
using Dates
import ..UnitCommitment
export convert_to_subhourly, interpolate_values, repeat_values
"""
convert_to_subhourly(instance_path::AbstractString, next_day_path::AbstractString)
Convert a UC instance from 36 time periods (40 minutes each) to 72 time periods (20 minutes each).
# Arguments
- `instance_path::AbstractString`: Path to the current day's instance
- `next_day_path::AbstractString`: Path to the next day's instance (needed for interpolation at boundaries)
# Returns
- Modified `UnitCommitmentInstance` with 72 time periods
# Details
The function performs the following transformations:
1. **Interpolated quantities** (demands, profiled unit outputs): Linear interpolation using next day's first period
2. **Repeated quantities** (max_power, min_power, flow limits): Each value repeated twice
3. **Ramping capacities**: Halved (since time periods are half the duration)
4. **Time period counts** (min_uptime, min_downtime, startup delay): Doubled
5. **Power production costs (not the fixed startup costs)**: Halved
# Example
```julia
current_instance = convert_to_subhourly(
"matpower/case14/2017-01-01",
"matpower/case14/2017-01-02"
)
println("Total time periods: ", current_instance.time) # Should be 72
```
"""
function convert_to_subhourly(instance_path::AbstractString, next_day_path::AbstractString)
# Read instances
instance = read_instance(instance_path)
next_instance = read_instance(next_day_path)
return convert_to_subhourly(instance, next_instance)
end
"""
convert_to_subhourly(instance::UnitCommitment.UnitCommitmentInstance,
next_instance::UnitCommitment.UnitCommitmentInstance)
Convert a UC instance from 36 time periods to 72 time periods using instance objects.
"""
function convert_to_subhourly(instance, next_instance)
sc_current = instance.scenarios[1]
sc_next = next_instance.scenarios[1]
# Process buses - interpolate loads
for i in 1:length(sc_current.buses)
load_current = sc_current.buses[i].load
load_next_first = sc_next.buses[i].load[1]
sc_current.buses[i].load = interpolate_values(load_current, load_next_first)
end
# Process thermal units
for i in 1:length(sc_current.thermal_units)
unit = sc_current.thermal_units[i]
unit_next = sc_next.thermal_units[i]
# Repeat time-dependent vector quantities
unit.max_power = repeat_values(unit.max_power)
unit.min_power = repeat_values(unit.min_power)
unit.must_run = repeat_values(unit.must_run)
unit.min_power_cost = repeat_values(unit.min_power_cost)
# Process cost segments
for j in 1:length(unit.cost_segments)
# cost should be repeated and then halved
unit.cost_segments[j].cost = interpolate_values(unit.cost_segments[j].cost, unit.cost_segments[j].cost[1]) ./ 2.0
unit.cost_segments[j].mw = interpolate_values(unit.cost_segments[j].mw, unit.cost_segments[j].mw[1])
end
# Repeat commitment status
unit.commitment_status = repeat_values(unit.commitment_status)
# Halve ramping capacities per time period (since time periods are half the duration)
unit.ramp_up_limit = unit.ramp_up_limit / 2.0
unit.ramp_down_limit = unit.ramp_down_limit / 2.0
# Note: startup_limit and shutdown_limit are NOT modified (they are power limits, not per-period rates)
# Double time period counts
unit.min_uptime = unit.min_uptime * 2
unit.min_downtime = unit.min_downtime * 2
# Double startup delays in startup categories
for startup_cat in unit.startup_categories
startup_cat.delay = startup_cat.delay * 2
end
end
# Process transmission lines
for i in 1:length(sc_current.lines)
line = sc_current.lines[i]
# Repeat flow limits
line.normal_flow_limit = repeat_values(line.normal_flow_limit)
line.emergency_flow_limit = repeat_values(line.emergency_flow_limit)
line.flow_limit_penalty = repeat_values(line.flow_limit_penalty)
end
# Process reserves - interpolate
for i in 1:length(sc_current.reserves)
reserve = sc_current.reserves[i]
reserve_next = sc_next.reserves[i]
# Interpolate reserve requirements
reserve.amount = interpolate_values(reserve.amount, reserve_next.amount[1])
end
# Process price-sensitive loads - interpolate
for i in 1:length(sc_current.price_sensitive_loads)
psl = sc_current.price_sensitive_loads[i]
psl_next = sc_next.price_sensitive_loads[i]
# Interpolate demand and revenue
psl.demand = interpolate_values(psl.demand, psl_next.demand[1])
psl.revenue = interpolate_values(psl.revenue, psl_next.revenue[1])
end
# Process profiled units (renewables)
for i in 1:length(sc_current.profiled_units)
pu = sc_current.profiled_units[i]
pu_next = sc_next.profiled_units[i]
# Interpolate renewable profiles
pu.min_power = interpolate_values(pu.min_power, pu_next.min_power[1])
pu.max_power = interpolate_values(pu.max_power, pu_next.max_power[1])
pu.cost = interpolate_values(pu.cost, pu_next.cost[1])
end
# Process storage units
for i in 1:length(sc_current.storage_units)
su = sc_current.storage_units[i]
su_next = sc_next.storage_units[i]
# Interpolate storage levels
su.min_level = interpolate_values(su.min_level, su_next.min_level[1])
su.max_level = interpolate_values(su.max_level, su_next.max_level[1])
# Repeat other storage parameters
su.simultaneous_charge_and_discharge = repeat_values(su.simultaneous_charge_and_discharge)
su.charge_cost = interpolate_values(su.charge_cost, su_next.charge_cost[1])
su.discharge_cost = interpolate_values(su.discharge_cost, su_next.discharge_cost[1])
su.charge_efficiency = repeat_values(su.charge_efficiency)
su.discharge_efficiency = repeat_values(su.discharge_efficiency)
su.loss_factor = repeat_values(su.loss_factor)
# Repeat rate limits
su.min_charge_rate = repeat_values(su.min_charge_rate)
su.max_charge_rate = repeat_values(su.max_charge_rate)
su.min_discharge_rate = repeat_values(su.min_discharge_rate)
su.max_discharge_rate = repeat_values(su.max_discharge_rate)
end
# Process scenario-level fields
sc_current.power_balance_penalty = repeat_values(sc_current.power_balance_penalty)
# Update time count
sc_current.time = 72
instance.time = 72
return instance
end
"""
interpolate_values(values::Vector{T}, next_first::T) where T
Interpolate a vector of 36 values to 72 values using linear interpolation.
# Arguments
- `values::Vector{T}`: Original 36-element vector
- `next_first::T`: First value from the next day (for boundary interpolation)
# Returns
- 72-element vector with interpolated values
# Details
For each pair of consecutive values, inserts an interpolated midpoint.
Uses `next_first` to interpolate the value after the last period.
# Example
```julia
interpolate_values([10.0, 20.0, 30.0], 40.0)
# Returns: [10.0, 15.0, 20.0, 25.0, 30.0, 35.0]
```
"""
function interpolate_values(values::Vector{T}, next_first::T) where T
n = length(values)
result = Vector{T}(undef, 2 * n)
for i in 1:n-1
result[2*i-1] = values[i]
result[2*i] = (values[i] + values[i+1]) / 2
end
# Handle the last period using next day's first value
result[2*n-1] = values[n]
result[2*n] = (values[n] + next_first) / 2
return result
end
"""
repeat_values(values::Vector{T}) where T
Repeat each element of a 36-element vector to create a 72-element vector.
# Arguments
- `values::Vector{T}`: Original 36-element vector
# Returns
- 72-element vector with each value repeated twice
# Example
```julia
repeat_values([1, 2, 3])
# Returns: [1, 1, 2, 2, 3, 3]
```
"""
function repeat_values(values::Vector{T}) where T
n = length(values)
result = Vector{T}(undef, 2 * n)
for i in 1:n
result[2*i-1] = values[i]
result[2*i] = values[i]
end
return result
end
"""
read_instance(path::AbstractString)
Read a UC instance from a local file or benchmark.
# Arguments
- `path::AbstractString`: Path to instance file or benchmark name
# Returns
- `UnitCommitmentInstance` object
"""
function read_instance(path::AbstractString)
# Check if path exists locally with common extensions
if isfile(path)
return UnitCommitment.read(path)
elseif isfile(path * ".json.gz")
return UnitCommitment.read(path * ".json.gz")
elseif isfile(path * ".json")
return UnitCommitment.read(path * ".json")
else
# Try reading as benchmark
return UnitCommitment.read_benchmark(path)
end
end
end # module
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