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Add optimized states to optimization result API #1290

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184e199
add optimized states in OptimizationResult and LinearOptimizationResult
bqth29 Feb 26, 2025
e0fe8d5
handle 2P
bqth29 Feb 26, 2025
00690c8
add tests
bqth29 Feb 26, 2025
d79ad6e
Merge branch 'main' into feature/add-optimized-states-to-optimization…
bqth29 Mar 17, 2025
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2 changes: 1 addition & 1 deletion ...n/java/com/powsybl/openrao/searchtreerao/castor/algorithm/CastorContingencyScenarios.java
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Expand Up @@ -207,7 +207,7 @@ private OptimizationResult optimizeCurativePerimeter(Perimeter curativePerimeter
boolean stopCriterionReached = isStopCriterionChecked(objectiveFunctionResult, curativeTreeParameters);
if (stopCriterionReached) {
NetworkActionsResult networkActionsResult = new NetworkActionsResultImpl(Collections.emptySet());
return new OptimizationResultImpl(objectiveFunctionResult, prePerimeterSensitivityOutput, prePerimeterSensitivityOutput, networkActionsResult, rangeActionsResult);
return new OptimizationResultImpl(objectiveFunctionResult, prePerimeterSensitivityOutput, prePerimeterSensitivityOutput, networkActionsResult, rangeActionsResult, Set.of(curativeState));
}

OptimizationPerimeter optPerimeter = CurativeOptimizationPerimeter.buildForStates(curativeState, curativePerimeter.getAllStates(), crac, network, raoParameters, prePerimeterSensitivityOutput);
Expand Down
87 changes: 46 additions & 41 deletions ...powsybl/openrao/searchtreerao/linearoptimisation/algorithms/IteratingLinearOptimizer.java
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Expand Up @@ -7,6 +7,7 @@

package com.powsybl.openrao.searchtreerao.linearoptimisation.algorithms;

import com.powsybl.openrao.data.crac.api.State;
import com.powsybl.openrao.data.crac.api.rangeaction.PstRangeAction;
import com.powsybl.openrao.data.raoresult.api.ComputationStatus;
import com.powsybl.openrao.raoapi.parameters.extensions.SearchTreeRaoRangeActionsOptimizationParameters.PstModel;
Expand All @@ -27,6 +28,7 @@
import org.apache.commons.lang3.tuple.Pair;

import java.util.Locale;
import java.util.Set;

import static com.powsybl.openrao.commons.logs.OpenRaoLoggerProvider.*;
import static com.powsybl.openrao.raoapi.parameters.extensions.SearchTreeRaoRangeActionsOptimizationParameters.getPstModel;
Expand All @@ -43,19 +45,20 @@ private IteratingLinearOptimizer() {
public static LinearOptimizationResult optimize(IteratingLinearOptimizerInput input, IteratingLinearOptimizerParameters parameters) {

IteratingLinearOptimizationResultImpl bestResult = createResult(
input.preOptimizationFlowResult(),
input.preOptimizationSensitivityResult(),
input.raActivationFromParentLeaf(),
input.appliedNetworkActionsInPrimaryState(),
0,
input.objectiveFunction());
input.preOptimizationFlowResult(),
input.preOptimizationSensitivityResult(),
input.raActivationFromParentLeaf(),
input.appliedNetworkActionsInPrimaryState(),
0,
input.objectiveFunction(),
input.optimizationPerimeter().getRangeActionOptimizationStates());

IteratingLinearOptimizationResultImpl previousResult = bestResult;

SensitivityComputer sensitivityComputer = null;

LinearProblem linearProblem = LinearProblem.create()
.buildFromInputsAndParameters(input, parameters);
.buildFromInputsAndParameters(input, parameters);

linearProblem.fill(input.preOptimizationFlowResult(), input.preOptimizationSensitivityResult());

Expand Down Expand Up @@ -92,12 +95,13 @@ public static LinearOptimizationResult optimize(IteratingLinearOptimizerInput in
}

IteratingLinearOptimizationResultImpl currentResult = createResult(
sensitivityComputer.getBranchResult(input.network()),
sensitivityComputer.getSensitivityResult(),
currentRangeActionActivationResult,
input.appliedNetworkActionsInPrimaryState(),
iteration,
input.objectiveFunction()
sensitivityComputer.getBranchResult(input.network()),
sensitivityComputer.getSensitivityResult(),
currentRangeActionActivationResult,
input.appliedNetworkActionsInPrimaryState(),
iteration,
input.objectiveFunction(),
input.optimizationPerimeter().getRangeActionOptimizationStates()
);
previousResult = currentResult;

Expand Down Expand Up @@ -157,8 +161,8 @@ private static LinearProblemStatus solveLinearProblem(LinearProblem linearProble

private static boolean hasAnyRangeActionChanged(RangeActionActivationResult newRangeActionActivationResult, RangeActionActivationResult oldRangeActionActivationResult, OptimizationPerimeter optimizationContext) {
return optimizationContext.getRangeActionsPerState().entrySet().stream()
.anyMatch(e -> e.getValue().stream()
.anyMatch(ra -> Math.abs(newRangeActionActivationResult.getOptimizedSetpoint(ra, e.getKey()) - oldRangeActionActivationResult.getOptimizedSetpoint(ra, e.getKey())) >= 1e-6));
.anyMatch(e -> e.getValue().stream()
.anyMatch(ra -> Math.abs(newRangeActionActivationResult.getOptimizedSetpoint(ra, e.getKey()) - oldRangeActionActivationResult.getOptimizedSetpoint(ra, e.getKey())) >= 1e-6));
}

public static AppliedRemedialActions applyRangeActions(RangeActionActivationResult rangeActionActivationResult, IteratingLinearOptimizerInput input) {
Expand All @@ -167,15 +171,15 @@ public static AppliedRemedialActions applyRangeActions(RangeActionActivationResu

// apply RangeAction from first optimization state
optimizationContext.getRangeActionsPerState().get(optimizationContext.getMainOptimizationState())
.forEach(ra -> ra.apply(input.network(), rangeActionActivationResult.getOptimizedSetpoint(ra, optimizationContext.getMainOptimizationState())));
.forEach(ra -> ra.apply(input.network(), rangeActionActivationResult.getOptimizedSetpoint(ra, optimizationContext.getMainOptimizationState())));

// add RangeAction activated in the following states
if (optimizationContext instanceof GlobalOptimizationPerimeter) {
AppliedRemedialActions appliedRemedialActions = input.preOptimizationAppliedRemedialActions().copyNetworkActionsAndAutomaticRangeActions();

optimizationContext.getRangeActionsPerState().entrySet().stream()
.filter(e -> !e.getKey().equals(optimizationContext.getMainOptimizationState())) // remove preventive state
.forEach(e -> e.getValue().forEach(ra -> appliedRemedialActions.addAppliedRangeAction(e.getKey(), ra, rangeActionActivationResult.getOptimizedSetpoint(ra, e.getKey()))));
.filter(e -> !e.getKey().equals(optimizationContext.getMainOptimizationState())) // remove preventive state
.forEach(e -> e.getValue().forEach(ra -> appliedRemedialActions.addAppliedRangeAction(e.getKey(), ra, rangeActionActivationResult.getOptimizedSetpoint(ra, e.getKey()))));
return appliedRemedialActions;
} else {
return null;
Expand All @@ -185,11 +189,11 @@ public static AppliedRemedialActions applyRangeActions(RangeActionActivationResu
private static SensitivityComputer createSensitivityComputer(AppliedRemedialActions appliedRemedialActions, IteratingLinearOptimizerInput input, IteratingLinearOptimizerParameters parameters) {

SensitivityComputer.SensitivityComputerBuilder builder = SensitivityComputer.create()
.withCnecs(input.optimizationPerimeter().getFlowCnecs())
.withRangeActions(input.optimizationPerimeter().getRangeActions())
.withAppliedRemedialActions(appliedRemedialActions)
.withToolProvider(input.toolProvider())
.withOutageInstant(input.outageInstant());
.withCnecs(input.optimizationPerimeter().getFlowCnecs())
.withRangeActions(input.optimizationPerimeter().getRangeActions())
.withAppliedRemedialActions(appliedRemedialActions)
.withToolProvider(input.toolProvider())
.withOutageInstant(input.outageInstant());

if (parameters.isRaoWithLoopFlowLimitation() && parameters.getLoopFlowParametersExtension().getPtdfApproximation().shouldUpdatePtdfWithPstChange()) {
builder.withCommercialFlowsResults(input.toolProvider().getLoopFlowComputation(), input.optimizationPerimeter().getLoopFlowCnecs());
Expand All @@ -215,13 +219,14 @@ private static void runSensitivityAnalysis(SensitivityComputer sensitivityComput
}

private static IteratingLinearOptimizationResultImpl createResult(FlowResult flowResult,
SensitivityResult sensitivityResult,
RangeActionActivationResult rangeActionActivation,
NetworkActionsResult networkActionsResult,
int nbOfIterations,
ObjectiveFunction objectiveFunction) {
SensitivityResult sensitivityResult,
RangeActionActivationResult rangeActionActivation,
NetworkActionsResult networkActionsResult,
int nbOfIterations,
ObjectiveFunction objectiveFunction,
Set<State> optimizedStates) {
return new IteratingLinearOptimizationResultImpl(LinearProblemStatus.OPTIMAL, nbOfIterations, rangeActionActivation, flowResult,
objectiveFunction.evaluate(flowResult, new RemedialActionActivationResultImpl(rangeActionActivation, networkActionsResult)), sensitivityResult);
objectiveFunction.evaluate(flowResult, new RemedialActionActivationResultImpl(rangeActionActivation, networkActionsResult)), sensitivityResult, optimizedStates);
}

private static Pair<IteratingLinearOptimizationResultImpl, Boolean> updateBestResultAndCheckStopCondition(boolean raRangeShrinking, LinearProblem linearProblem, IteratingLinearOptimizerInput input, int iteration, IteratingLinearOptimizationResultImpl currentResult, IteratingLinearOptimizationResultImpl bestResult) {
Expand Down Expand Up @@ -264,29 +269,29 @@ private static RangeActionActivationResultImpl roundPsts(RangeActionActivationRe
}

static void roundOtherRas(RangeActionActivationResult linearProblemResult,
OptimizationPerimeter optimizationContext,
RangeActionActivationResultImpl roundedResult) {
OptimizationPerimeter optimizationContext,
RangeActionActivationResultImpl roundedResult) {
optimizationContext.getRangeActionsPerState().keySet().forEach(state -> linearProblemResult.getActivatedRangeActions(state).stream()
.filter(ra -> !(ra instanceof PstRangeAction))
.forEach(ra -> roundedResult.putResult(ra, state, Math.round(linearProblemResult.getOptimizedSetpoint(ra, state)))));
}

private static void logBetterResult(int iteration, ObjectiveFunctionResult currentObjectiveFunctionResult) {
TECHNICAL_LOGS.info(
"Iteration {}: better solution found with a cost of {} (functional: {})",
iteration,
formatDouble(currentObjectiveFunctionResult.getCost()),
formatDouble(currentObjectiveFunctionResult.getFunctionalCost()));
"Iteration {}: better solution found with a cost of {} (functional: {})",
iteration,
formatDouble(currentObjectiveFunctionResult.getCost()),
formatDouble(currentObjectiveFunctionResult.getFunctionalCost()));
}

private static void logWorseResult(int iteration, ObjectiveFunctionResult bestResult, ObjectiveFunctionResult currentResult) {
TECHNICAL_LOGS.info(
"Iteration {}: linear optimization found a worse result than best iteration, with a cost increasing from {} to {} (functional: from {} to {})",
iteration,
formatDouble(bestResult.getCost()),
formatDouble(currentResult.getCost()),
formatDouble(bestResult.getFunctionalCost()),
formatDouble(currentResult.getFunctionalCost()));
"Iteration {}: linear optimization found a worse result than best iteration, with a cost increasing from {} to {} (functional: from {} to {})",
iteration,
formatDouble(bestResult.getCost()),
formatDouble(currentResult.getCost()),
formatDouble(bestResult.getFunctionalCost()),
formatDouble(currentResult.getFunctionalCost()));
}

private static String formatDouble(double value) {
Expand Down
16 changes: 10 additions & 6 deletions .../java/com/powsybl/openrao/searchtreerao/marmot/InterTemporalIteratingLinearOptimizer.java
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Expand Up @@ -38,6 +38,7 @@

import java.time.OffsetDateTime;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Locale;
import java.util.Map;
Expand All @@ -58,12 +59,14 @@ private InterTemporalIteratingLinearOptimizer() {

public static LinearOptimizationResult optimize(InterTemporalIteratingLinearOptimizerInput input, IteratingLinearOptimizerParameters parameters) {
// 1. Initialize best result using input data
Set<State> optimizedStates = new HashSet<>(input.iteratingLinearOptimizerInputs().map(individualInput -> individualInput.optimizationPerimeter().getMainOptimizationState()).getDataPerTimestamp().values());
GlobalLinearOptimizationResult bestResult = createInitialResult(
input.iteratingLinearOptimizerInputs().map(IteratingLinearOptimizerInput::prePerimeterFlowResult),
input.iteratingLinearOptimizerInputs().map(IteratingLinearOptimizerInput::preOptimizationSensitivityResult),
input.iteratingLinearOptimizerInputs().map(IteratingLinearOptimizerInput::prePerimeterSetpoints).map(RangeActionActivationResultImpl::new),
input.iteratingLinearOptimizerInputs().map(IteratingLinearOptimizerInput::appliedNetworkActionsInPrimaryState),
input.objectiveFunction()
input.objectiveFunction(),
optimizedStates
);
GlobalLinearOptimizationResult previousResult = bestResult;

Expand Down Expand Up @@ -138,7 +141,8 @@ public static LinearOptimizationResult optimize(InterTemporalIteratingLinearOpti
input.iteratingLinearOptimizerInputs().map(IteratingLinearOptimizerInput::network),
rangeActionActivationPerTimestamp,
input.iteratingLinearOptimizerInputs().map(IteratingLinearOptimizerInput::appliedNetworkActionsInPrimaryState),
input.objectiveFunction()
input.objectiveFunction(),
optimizedStates
);
previousResult = newResult;

Expand Down Expand Up @@ -275,17 +279,17 @@ private static void runSensitivityAnalysis(SensitivityComputer sensitivityComput
}

// Result management
private static GlobalLinearOptimizationResult createInitialResult(TemporalData<FlowResult> flowResults, TemporalData<SensitivityResult> sensitivityResults, TemporalData<RangeActionActivationResult> rangeActionActivations, TemporalData<NetworkActionsResult> preventiveTopologicalActions, ObjectiveFunction objectiveFunction) {
return new GlobalLinearOptimizationResult(flowResults, sensitivityResults, rangeActionActivations, preventiveTopologicalActions, objectiveFunction, LinearProblemStatus.OPTIMAL);
private static GlobalLinearOptimizationResult createInitialResult(TemporalData<FlowResult> flowResults, TemporalData<SensitivityResult> sensitivityResults, TemporalData<RangeActionActivationResult> rangeActionActivations, TemporalData<NetworkActionsResult> preventiveTopologicalActions, ObjectiveFunction objectiveFunction, Set<State> optimizedStates) {
return new GlobalLinearOptimizationResult(flowResults, sensitivityResults, rangeActionActivations, preventiveTopologicalActions, objectiveFunction, LinearProblemStatus.OPTIMAL, optimizedStates);
}

private static GlobalLinearOptimizationResult createResultFromData(TemporalData<SensitivityComputer> sensitivityComputers, TemporalData<Network> networks, TemporalData<RangeActionActivationResult> rangeActionActivation, TemporalData<NetworkActionsResult> preventiveTopologicalActions, ObjectiveFunction objectiveFunction) {
private static GlobalLinearOptimizationResult createResultFromData(TemporalData<SensitivityComputer> sensitivityComputers, TemporalData<Network> networks, TemporalData<RangeActionActivationResult> rangeActionActivation, TemporalData<NetworkActionsResult> preventiveTopologicalActions, ObjectiveFunction objectiveFunction, Set<State> optimizedStates) {
Map<OffsetDateTime, FlowResult> flowResults = new HashMap<>();
for (OffsetDateTime timestamp : sensitivityComputers.getTimestamps()) {
FlowResult flowResult = sensitivityComputers.getData(timestamp).orElseThrow().getBranchResult(networks.getData(timestamp).orElseThrow());
flowResults.put(timestamp, flowResult);
}
return new GlobalLinearOptimizationResult(new TemporalDataImpl<>(flowResults), sensitivityComputers.map(SensitivityComputer::getSensitivityResult), rangeActionActivation, preventiveTopologicalActions, objectiveFunction, LinearProblemStatus.OPTIMAL);
return new GlobalLinearOptimizationResult(new TemporalDataImpl<>(flowResults), sensitivityComputers.map(SensitivityComputer::getSensitivityResult), rangeActionActivation, preventiveTopologicalActions, objectiveFunction, LinearProblemStatus.OPTIMAL, optimizedStates);
}

// Set-point rounding
Expand Down
4 changes: 3 additions & 1 deletion ...ee-rao/src/main/java/com/powsybl/openrao/searchtreerao/marmot/PostOptimizationResult.java
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Expand Up @@ -18,6 +18,8 @@
import com.powsybl.openrao.searchtreerao.result.impl.OneStateOnlyRaoResultImpl;
import com.powsybl.openrao.searchtreerao.result.impl.OptimizationResultImpl;

import java.util.Set;

import static com.powsybl.openrao.searchtreerao.marmot.MarmotUtils.getPreventivePerimeterCnecs;

/** This class concatenates all data around one individual timestamp from running Marmot:
Expand All @@ -33,7 +35,7 @@ public record PostOptimizationResult(RaoInput raoInput, PrePerimeterResult initi
public RaoResult merge() {
Crac crac = raoInput.getCrac();
State preventiveState = crac.getPreventiveState();
OptimizationResult mergedOptimizationResult = new OptimizationResultImpl(linearOptimizationResult, linearOptimizationResult, linearOptimizationResult, new NetworkActionsResultImpl(topologicalOptimizationResult.getActivatedNetworkActionsDuringState(preventiveState)), linearOptimizationResult);
OptimizationResult mergedOptimizationResult = new OptimizationResultImpl(linearOptimizationResult, linearOptimizationResult, linearOptimizationResult, new NetworkActionsResultImpl(topologicalOptimizationResult.getActivatedNetworkActionsDuringState(preventiveState)), linearOptimizationResult, Set.of(preventiveState));
return new OneStateOnlyRaoResultImpl(preventiveState, initialResult, mergedOptimizationResult, getPreventivePerimeterCnecs(crac));
}
}
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