Robust optimal scheduling method for multi-energy microgrid clusters considering wind and solar uncertainties

Abstract In the context of China’s “dual-carbon” objectives and the accelerating growth of renewable energy, weak distribution networks encounter considerable uncertainty arising from fluctuations in wind and photovoltaic generation as well as electricity price volatility. These factors pose significant difficulties for the secure and economical operation of multi-energy microgrid clusters. To address these challenges, this study develops an engineering-oriented optimal scheduling strategy for such clusters. The framework incorporates power-to-gas (P2G) conversion and carbon capture and storage (CCS) technologies, thereby enabling electricity-heat-gas coupling. Additionally, a stepped carbon trading scheme is introduced to support low-carbon operation. To handle renewable energy variability, uncertainties are described using a Wasserstein fuzzy set model, and robust optimization is applied to enhance the reliability of scheduling outcomes. The scheduling process is coordinated by a central operator and implemented in a distributed fashion through the alternating direction method of multipliers (ADMM). Simulation results indicate that the proposed approach can lower overall operating expenses, enhance wind and solar utilization, and meet carbon reduction requirements, offering a practical and applicable scheduling solution for weak distribution networks.