S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty

• URL: http://arxiv.org/abs/2606.02151v1
• Date: Mon, 01 Jun 2026 12:17:41 GMT
• Title: S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty
• Abstract summary: An effective planning strategy must accommodate advanced and potentially non-linear system models.<n>Existing approaches can address either non-linearity (e.g., Monte Carlo Tree Search) or uncertainty.<n>We propose a Scenario-Structured Tree Search (S3TS) algorithm that explicitly represents uncertainty through scenario trees.
• Score: 4.2329543571238615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective scheduling in the energy sector is essential to ensure the reliable operation of electrical grids and their connected assets by, for instance, optimizing the dispatch of generation units and storage systems. An effective planning strategy must (a) accommodate advanced and potentially non-linear system models -- exploiting the increasing data availability of modern grids, and (b) explicitly handle uncertainties arising, for instance, from the integration of renewable energy sources. While existing approaches can address either non-linearity (e.g., Monte Carlo Tree Search) or uncertainty (e.g., stochastic mathematical optimization), there is a lack of planning techniques capable of addressing both challenges simultaneously. To bridge this gap, we propose a Stochastic Scenario-Structured Tree Search (S3TS) algorithm that explicitly represents uncertainty through scenario trees while enabling the integration of advanced non-linear models. We evaluate S3TS on a simulated demand response signal publication problem, largely mimicking the imbalance settlement mechanism in Belgium. The results demonstrate near-optimal performance in linear, analytically tractable settings, with costs within 14% of the mathematically optimal solution conditioned to the scenario trees. In highly non-linear scenarios, S3TS significantly outperforms baseline methods, achieving cost reductions of up to 51% and 5.4% compared to a myopic algorithm and deterministic MCTS, respectively.

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