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arXiv · 2508.08520

Multi-timescale Stochastic Programming with Applications in Power Systems

Yihang Zhang·Suvrajeet Sen
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Abstract

This paper introduces a multi-timescale stochastic programming framework designed to address decision-making challenges in power systems, particularly those with high renewable energy penetration. The framework models interactions across different timescales using aggregated state variables to coordinate decisions. In addition to Multi-timescale uncertainty modeled via multihorizon trees, we also introduce a "synchronized state approximation," which periodically aligns states across timescales to maintain consistency and tractability. Using this approximation, we propose two instantiation methods: a scenario-based approach and a value function-based approach specialized for this setup. Our framework is very generic, and covers a wide-spectrum of applications.

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Yihang Zhang, Suvrajeet Sen. 2025-08-11. Multi-timescale Stochastic Programming with Applications in Power Systems. https://arxiv.org/abs/2508.08520

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