S3TS is a stochastic scenario-structured tree search algorithm for planning under uncertainty with non-linear models, evaluated on a demand response problem with near-optimal results in linear cases and superior performance in non-linear ones.
Model predictive control-guided reinforce- ment learning for implicit balancing
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Simulations using 2023 Belgian data show implicit balancing via batteries cuts TSO costs at moderate participation but risks overshooting when capacity grows large, with BRPs still profiting.
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S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty
S3TS is a stochastic scenario-structured tree search algorithm for planning under uncertainty with non-linear models, evaluated on a demand response problem with near-optimal results in linear cases and superior performance in non-linear ones.