Multi-timescale scheduling for wind-solar-hydrogen-storage systems under renewable generation and electricity price uncertainties in electricity markets

Grid-connected wind-solar-storage-hydrogen systems face significant challenges in scheduling, including uncertain electricity market prices and deviation settlement, renewable power forecasting errors and renewable-electrolyzer mismatch. To address these issues, a closed-loop framework integrating a day-ahead scheduling and an intra-day rolling optimization is developed to maximize profits considering renewable generation and market price uncertainties. The day-ahead scheduling provides a trading baseline for the intra-day stage. During the intra-day stage, a chance-constrained programming (CCP) model with dynamic safety margins (DSM) is developed to account for renewable uncertainty, while a scenario-based conditional value-at-risk (CVaR) model is employed to mitigate price risk. Validation using real data from Inner Mongolia, China, shows that the method increases daily profits and reduces operational costs by more than 15% compared to conventional methods, decreases non-compliant electrical energy from 20.25% to 1.50%, and exhibits robustness against price uncertainties. The results demonstrate effectiveness in enhancing economic performance and market compliance under uncertainties.