Originally Contributed by: Sourabh Dalvi
PowerSimulations.jl supports the construction of Operations problems in power system with three part cost bids for each time step. MarketBidCost allows the user to pass a time-series of variable cost for energy and ancillary services jointly. This example shows how to build a Operations problem with MarketBidCost and how to add the time-series data to the devices.
using SIIPExamples
using PowerSystems
using PowerSimulations
const PSI = PowerSimulations
using PowerSystemCaseBuilder
using Dates
using DataFrames
using TimeSeries
using HiGHS #solver
Create a System from RTS-GMLC data
sys = build_system(PSITestSystems, "modified_RTS_GMLC_DA_sys")
MultiDay = collect(
DateTime("2020-01-01T00:00:00"):Hour(1):(DateTime("2020-01-01T00:00:00") + Hour(8783)),
);
Here we add the energy bid
time series to the system. The TimeSeriesData that holds the energy bid data can be of any
type (i.e. SingleTimeSeries or Deterministic), but it has to be consistent with the existing
data in the sys. So, we'll first remove the existing DeterministicSingleTimeSeries, then add
the bid time series as SingleTimeSeries, then re-transform all of the time series in sys.
remove_time_series!(sys, DeterministicSingleTimeSeries)
for gen in get_components(ThermalGen, sys)
varcost = get_operation_cost(gen)
data = TimeArray(MultiDay, repeat([get_cost(get_variable(varcost))], 8784))
_time_series = SingleTimeSeries("variable_cost", data)
add_time_series!(sys, gen, _time_series)
#set_variable_cost!(sys, gen, _time_series)
end
transform_single_time_series!(sys, 24, Dates.Hour(24))
In the OperationsProblem example we defined a unit-commitment problem with a copper plate representation of the network. Here, we want do define unit-commitment problem with ThermalMultiStartUnitCommitment formulation for thermal device representation.
For now, let's just choose a standard UC formulation.
uc_template = template_unit_commitment()
And adjust the thermal generator formulation to use ThermalMultiStartUnitCommitment
set_device_model!(uc_template, ThermalMultiStart, ThermalMultiStartUnitCommitment)
Now we can build a 4-hour economic dispatch problem with the RTS data.
solver = optimizer_with_attributes(HiGHS.Optimizer, "mip_rel_gap" => 0.5)
problem = DecisionModel(uc_template, sys, horizon = 4, optimizer = solver)
build!(problem, output_dir = mktempdir())
And solve it ...
solve!(problem)
This notebook was generated using Literate.jl.