Multi-timescale rolling optimization strategy for integrated energy systems considering renewable energy consumption and source–load uncertainty.

This article focuses on a novel day-ahead stochastic-intraday robust two-phase multi-timescale rolling optimal scheduling model for integrated energy systems (IES) that addresses source–load uncertainties to enhance renewable energy utilization and reduce operational costs. The model integrates an improved adaptive Monte Carlo simulation and an enhanced k-means clustering algorithm for efficient and accurate scenario generation in the day-ahead phase, while employing a light robust optimization approach with slack variables in the intraday phase to reduce conservatism typical of traditional robust optimization. Case studies demonstrate that this combined stochastic-robust framework improves system flexibility, lowers comprehensive costs and carbon emissions, and increases renewable energy consumption compared to deterministic or solely robust optimization methods. The paper also discusses the impact of adjustable parameters on system robustness and cost, highlighting the need for further validation in practical engineering applications.