Multi-timescale Stochastic Programming with Applications in Power Systems

TL;DR

This paper presents a multi-timescale stochastic programming framework for power systems with high renewable energy, enabling coordinated decision-making across different timescales using novel approximation and instantiation methods.

Contribution

It introduces a new multi-timescale stochastic programming framework with synchronized state approximation and two instantiation methods, advancing decision-making in renewable-rich power systems.

Findings

Framework effectively models multi-timescale uncertainties.

The synchronized state approximation improves tractability.

Applicable to a wide range of power system applications.

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.