{"ID":2883993,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08520","arxiv_id":"2508.08520","title":"Multi-timescale Stochastic Programming with Applications in Power Systems","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.08520","url_pdf":"https://arxiv.org/pdf/2508.08520v1","authors":"[\"Yihang Zhang\",\"Suvrajeet Sen\"]","published":"2025-08-11T23:22:51Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}