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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
"""
solve_market(
da_path::Union{String, Vector{String}},
rt_paths::Vector{String},
settings::MarketSettings;
optimizer,
lp_optimizer = nothing,
after_build_da = nothing,
after_optimize_da = nothing,
after_build_rt = nothing,
after_optimize_rt = nothing,
)::OrderedDict
Solve the day-ahead and the real-time markets by the means of commitment status mapping.
The method firstly acquires the commitment status outcomes through the resolution of the day-ahead market;
and secondly resolves each real-time market based on the corresponding results obtained previously.
Arguments
---------
- `da_path`:
the data file path of the day-ahead market, can be stochastic.
- `rt_paths`:
the list of data file paths of the real-time markets, must be deterministic for each market.
- `settings`:
the MarketSettings which include the problem formulation, the solving method, and LMP method.
- `optimizer`:
the optimizer for solving the problem.
- `lp_optimizer`:
the linear programming optimizer for solving the LMP problem, defaults to `nothing`.
If not specified by the user, the program uses `optimizer` instead.
- `after_build_da`:
a user-defined function that allows modifying the DA model after building,
must have 2 arguments `model` and `instance` in order.
- `after_optimize_da`:
a user-defined function that allows handling additional steps after optimizing the DA model,
must have 3 arguments `solution`, `model` and `instance` in order.
- `after_build_rt`:
a user-defined function that allows modifying each RT model after building,
must have 2 arguments `model` and `instance` in order.
- `after_optimize_rt`:
a user-defined function that allows handling additional steps after optimizing each RT model,
must have 3 arguments `solution`, `model` and `instance` in order.
Examples
--------
```julia
using UnitCommitment, Cbc, HiGHS
import UnitCommitment:
MarketSettings,
XavQiuWanThi2019,
ConventionalLMP,
Formulation
solution = UnitCommitment.solve_market(
"da_instance.json",
["rt_instance_1.json", "rt_instance_2.json", "rt_instance_3.json"],
MarketSettings(
inner_method = XavQiuWanThi2019.Method(),
lmp_method = ConventionalLMP(),
formulation = Formulation(),
),
optimizer = Cbc.Optimizer,
lp_optimizer = HiGHS.Optimizer,
)
"""
function solve_market(
da_path::Union{String,Vector{String}},
rt_paths::Vector{String};
settings::MarketSettings = MarketSettings(),
optimizer,
lp_optimizer = nothing,
after_build_da = nothing,
after_optimize_da = nothing,
after_build_rt = nothing,
after_optimize_rt = nothing,
)::OrderedDict
# solve da instance as usual
@info "Solving the day-ahead market with file $da_path..."
instance_da = UnitCommitment.read(da_path)
# LP optimizer is optional: if not specified, use optimizer
lp_optimizer = lp_optimizer === nothing ? optimizer : lp_optimizer
# build and optimize the DA market
model_da, solution_da = _build_and_optimize(
instance_da,
settings,
optimizer = optimizer,
lp_optimizer = lp_optimizer,
after_build = after_build_da,
after_optimize = after_optimize_da,
)
# prepare the final solution
solution = OrderedDict()
solution["DA"] = solution_da
solution["RT"] = []
# count the time, sc.time = n-slots, sc.time_step = slot-interval
# sufficient to look at only one scenario
sc = instance_da.scenarios[1]
# max time (min) of the DA market
max_time = sc.time * sc.time_step
# current time increments through the RT market list
current_time = 0
# DA market time slots in (min)
da_time_intervals = [sc.time_step * ts for ts in 1:sc.time]
# get the uc status and set each uc fixed
solution_rt = OrderedDict()
prev_initial_status = OrderedDict()
for rt_path in rt_paths
@info "Solving the real-time market with file $rt_path..."
instance_rt = UnitCommitment.read(rt_path)
# check instance time
sc = instance_rt.scenarios[1]
# check each time slot in the RT model
for ts in 1:sc.time
slot_t_end = current_time + ts * sc.time_step
# ensure this RT's slot time ub never exceeds max time of DA
slot_t_end <= max_time || error(
"The time of the real-time market cannot exceed the time of the day-ahead market.",
)
# get the slot start time to determine commitment status
slot_t_start = slot_t_end - sc.time_step
# find the index of the first DA time slot that covers slot_t_start
da_time_slot = findfirst(ti -> slot_t_start < ti, da_time_intervals)
# update thermal unit commitment status
for g in sc.thermal_units
g.commitment_status[ts] =
value(model_da[:is_on][g.name, da_time_slot]) == 1.0
end
end
# update current time by ONE slot only
current_time += sc.time_step
# set initial status for all generators in all scenarios
if !isempty(solution_rt) && !isempty(prev_initial_status)
for g in sc.thermal_units
g.initial_power =
solution_rt["Thermal production (MW)"][g.name][1]
g.initial_status = UnitCommitment._determine_initial_status(
prev_initial_status[g.name],
[solution_rt["Is on"][g.name][1]],
)
end
end
# build and optimize the RT market
_, solution_rt = _build_and_optimize(
instance_rt,
settings,
optimizer = optimizer,
lp_optimizer = lp_optimizer,
after_build = after_build_rt,
after_optimize = after_optimize_rt,
)
prev_initial_status =
OrderedDict(g.name => g.initial_status for g in sc.thermal_units)
push!(solution["RT"], solution_rt)
end # end of for-loop that checks each RT market
return solution
end
function _build_and_optimize(
instance::UnitCommitmentInstance,
settings::MarketSettings;
optimizer,
lp_optimizer,
after_build = nothing,
after_optimize = nothing,
)::Tuple{JuMP.Model,OrderedDict}
# build model with after build
model = UnitCommitment.build_model(
instance = instance,
optimizer = optimizer,
formulation = settings.formulation,
)
if after_build !== nothing
after_build(model, instance)
end
# optimize model
UnitCommitment.optimize!(model, settings.inner_method)
solution = UnitCommitment.solution(model)
# compute lmp and add to solution
if settings.lmp_method !== nothing
lmp = UnitCommitment.compute_lmp(
model,
settings.lmp_method,
optimizer = lp_optimizer,
)
if length(instance.scenarios) == 1
solution["LMP (\$/MW)"] = lmp
else
for sc in instance.scenarios
solution[sc.name]["LMP (\$/MW)"] = OrderedDict(
key => val for (key, val) in lmp if key[1] == sc.name
)
end
end
end
# run after optimize with solution
if after_optimize !== nothing
after_optimize(solution, model, instance)
end
return model, solution
end
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