A Multi-Time-Scale Supply-Demand Balance Optimization Method Based on a Joint Probability Distribution Model

Abstract

High renewable penetration exacerbates source-load uncertainty in power systems, with coupled stochastic variables further straining supply-demand balance. Addressing limitations in multi-source uncertainty correlation modeling, probabilistic flexibility assessment, and multi-time-scale scheduling coupling, this work proposes a joint-probability-distribution-based multi-time-scale optimization method for supply-demand balance. The method builds a copula-based joint distribution of wind, PV, and load forecast errors, develops a probabilistic flexibility model covering regulation capacity, rate, and delay, and embeds meteorology-load partitioned scenarios into a day-ahead–intraday–real-time framework for hierarchical coordinated flexibility dispatch. Tests on the modified IEEE 118bus system validate that the method accurately captures multisource uncertainty coupling, mitigates balance risks, and enhances operational economy and security, supporting optimal scheduling of high-renewable power systems.