Originally Contributed by: Clayton Barrows
PowerSimulations.jl supports simulations that consist of sequential optimization problems where results from previous problems inform subsequent problems in a variety of ways. This example demonstrates some of these capabilities to represent electricity market clearing.
First, let's create Systems to represent the Day-Ahead and Real-Time market clearing
process with hourly, and 5-minute time series data, respectively.
using PowerSystems
using PowerSimulations
using PowerSystemCaseBuilder
using Dates
using DataFrames
using HiGHS # mip solver
solver = optimizer_with_attributes(HiGHS.Optimizer, "mip_rel_gap" => 0.5)
The five bus system data here includes hourly day-ahead data, 5-minute real-time market data, and 6-second actual data. We'll only use the hourly and 5-minute data for the example simulations below, but the 6-second data is included for future development.
sys_DA = build_system(SIIPExampleSystems, "5_bus_matpower_DA")
sys_RT = build_system(SIIPExampleSystems, "5_bus_matpower_RT")
ProblemTemplates¶template_uc = template_unit_commitment(use_slacks = true)
template_ed = template_economic_dispatch(
network = NetworkModel(CopperPlatePowerModel, duals = [CopperPlateBalanceConstraint]),
)
models = SimulationModels(
decision_models = [
DecisionModel(template_uc, sys_DA, name = "UC", optimizer = solver),
DecisionModel(template_ed, sys_RT, name = "ED", optimizer = solver),
],
)
feedforward = Dict(
"ED" => [
SemiContinuousFeedforward(
component_type = ThermalStandard,
source = OnVariable,
affected_values = [ActivePowerVariable],
),
],
)
DA_RT_sequence = SimulationSequence(
models = models,
ini_cond_chronology = InterProblemChronology(),
feedforwards = feedforward,
)
Simulation¶file_path = mktempdir(".", cleanup = true)
sim = Simulation(
name = "5bus-test",
steps = 1,
models = models,
sequence = DA_RT_sequence,
simulation_folder = file_path,
)
build!(sim)
execute!(sim, enable_progress_bar = false)
# Results
First we can load the result metadata
results = SimulationResults(sim);
uc_results = get_problem_results(results, "UC")
ed_results = get_problem_results(results, "ED");
Then we can read and examine the results of interest. For example, if we want to read marginal prices of the balance constraint, we can see what dual values are available:
list_dual_names(ed_results)
Then, we can read the results of the dual
prices = read_dual(ed_results, "CopperPlateBalanceConstraint__System")
or if we want to look at the realized values
read_realized_dual(ed_results, "CopperPlateBalanceConstraint__System")
note that in this simulation the prices are all equal to the balance slack penalty value of $100000/MWh because there is unserved energy in the result
This notebook was generated using Literate.jl.