Hybrid robust decentralized optimization of emission-aware multi-energy microgrids considering multiple uncertainties

Abstract

Multi-energy microgrid (MEMG) can effectively coordinate various energy carriers and decarbonize the power system. Several MEMGs in the same distribution network are interconnected to form a multi-microgrid system, where a coordinator exists to coordinate these MEMGs to provide more cost-effective and reliable energy services. However, in this system, protecting each MEMG's information privacy is important. Moreover, the uncertainties caused by supply-demand sides and electricity price challenge the reliable operation of MEMGs. Therefore, this study proposes a hybrid robust decentralized energy management framework for the optimal day-ahead scheduling of interconnected MEMGs. Specifically, a decomposition strategy based on the alternating direction method of multipliers (ADMM) is utilized for privacy protection. The Wasserstein metric based distributionally robust optimization (DRO) and the robust optimization (RO) are incorporated to effectively deal with multiple uncertainties. Experimental results show that the coordination of multiple energy carriers (electricity, heat, hydrogen, and gas) can significantly lower the total cost and carbon emission cost (23.39% and 41.64%, respectively). Total cost can also be decreased by 3.15% with the implementation of integrate demand response (IDR). The used ADMM owns excellent performance, which can coverage to the result of centralized manner within a few iterations. Comparative analysis further demonstrated the predictability and superiority of the proposed hybrid robust optimization (HyRO).