Merge pull request #11 from psrenergy/rs/pm

Fix simulation

Project.toml

@@ -1,7 +1,7 @@
 name = "LightBenders"
 uuid = "2e9fe063-9687-4d41-bf41-4f062739391f"
 authors = ["guilhermebodin <[email protected]>"]
-version = "0.1.0"
+version = "0.1.1"
 
 [deps]
 EnumX = "4e289a0a-7415-4d19-859d-a7e5c4648b56"

src/LightBenders.jl

@@ -1,16 +1,12 @@
 module LightBenders
 
-# Standard library dependencies
-using JobQueueMPI
-using LinearAlgebra
-using Statistics
-
-# Third party dependencies
 using EnumX
+using JobQueueMPI
 using JuMP
+using LinearAlgebra
 using ProgressTables
+using Statistics
 
-# module constants
 const JQM = JobQueueMPI
 
 # Keys aspects of the algorithm

src/optimize_helpers.jl

@@ -43,9 +43,16 @@ function treat_termination_status(model::JuMP.Model, options::PolicyTrainingOpti
     return nothing
 end
 
+function treat_termination_status(model::JuMP.Model, options::SimulationOptions)
+    file_name = "model_first_stage"
+    info_msg = "Simulation model (first stage), finished with termination status: $(termination_status(model))"
+    treat_termination_status(model, info_msg, file_name, options.debugging_options)
+    return nothing
+end
+
 function treat_termination_status(model::JuMP.Model, options::SimulationOptions, t::Int, s::Int)
     file_name = "model_stage_$(t)_scenario_$(s)"
-    info_msg = "Simulation model of stage $t, scenario $s finished with termination status: $(termination_status(model))"
+    info_msg = "Simulation model stage $t, scenario $s finished with termination status: $(termination_status(model))"
     treat_termination_status(model, info_msg, file_name, options.debugging_options)
     return nothing
 end

src/simulation_strategies/benders_serial.jl

@@ -7,44 +7,58 @@ function serial_benders_simulate(;
     policy::Policy,
     simulation_options::SimulationOptions,
 )
-    stages = 2
     scenarios = simulation_options.num_scenarios
 
     simulation_total_cost = 0.0
 
-    state = Float64[]
-
     results = Dict{Tuple{String, Int}, Any}() # (variable_name, scenario) => value
 
-    for t in 1:stages
-        if t == 1 # first stage
-            state_variables_model = state_variables_builder(inputs)
-            model = first_stage_builder(state_variables_model, inputs)
-            add_all_cuts!(model, policy.pool[t], policy.policy_training_options)
-        elseif t == 2 # second stage
-            state_variables_model = state_variables_builder(inputs)
-            model = second_stage_builder(state_variables_model, inputs)
-            set_state(model, state)
-        end
-        for s in 1:scenarios
-            if t == 2
-                second_stage_modifier(model, inputs, s)
-            end
-            store_retry_data(model, simulation_options)
-            optimize_with_retry(model)
-            treat_termination_status(model, simulation_options, t, s)
-            future_cost = get_future_cost(model, policy.policy_training_options)
-            simulation_total_cost += (JuMP.objective_value(model) - future_cost) / scenarios
-            save_benders_results!(results, model, t, s, scenarios)
-            if simulation_options.state_handling == SimulationStateHandling.StatesRecalculatedInSimulation
-                state = get_state(model)
-            elseif simulation_options.state_handling == SimulationStateHandling.StatesFixedInPolicyResult
-                state = policy.states
-            else
-                error("State handling not implemented.")
-            end
-        end
+    # first stage
+    println("Simulating first stage...")
+
+    state_variables_model = state_variables_builder(inputs)
+    model = first_stage_builder(state_variables_model, inputs)
+    add_all_cuts!(model, policy.pool[1], policy.policy_training_options)
+
+    store_retry_data(model, simulation_options)
+    optimize_with_retry(model)
+    treat_termination_status(model, simulation_options)
+
+    for s in 1:scenarios
+        future_cost = get_future_cost(model, policy.policy_training_options)
+        simulation_total_cost += (JuMP.objective_value(model) - future_cost) / scenarios
+        save_benders_results!(results, model, 1, s, scenarios)
     end
+
+    # second stage
+    println("Simulating second stage...")
+
+    state = if simulation_options.state_handling == SimulationStateHandling.StatesRecalculatedInSimulation
+        get_state(model)
+    elseif simulation_options.state_handling == SimulationStateHandling.StatesFixedInPolicyResult
+        policy.states
+    else
+        error("State handling not implemented.")
+    end
+
+    state_variables_model = state_variables_builder(inputs)
+    model = second_stage_builder(state_variables_model, inputs)
+    set_state(model, state)
+
+    for s in 1:scenarios
+        second_stage_modifier(model, inputs, s)
+
+        store_retry_data(model, simulation_options)
+        optimize_with_retry(model)
+        treat_termination_status(model, simulation_options, 2, s)
+
+        future_cost = get_future_cost(model, policy.policy_training_options)
+        simulation_total_cost += (JuMP.objective_value(model) - future_cost) / scenarios
+
+        save_benders_results!(results, model, 2, s, scenarios)
+    end
+
     results["objective", 0] = simulation_total_cost
+
     return results
 end