Edge‐cloud collaborative architecture based multi‐time scales rolling optimization of regional integrated electrical and natural gas energy system considering wind power uncertainty

Abstract

Abstract With the serious energy crisis and speedy development of natural gas utilization, regional integrated electrical and gas energy system is an efficient way to improve energy potency. This paper studies the energy scheduling of integrated electrical and gas energy system with bi‐directional energy conversion at distribution network level, considering wind power uncertainty. To realize independent autonomy of energy networks, meantime adjusting scheduling strategies of integrated energy system from system‐level perspective, innovative edge‐cloud collaborative operation architecture is proposed. In edge servers, electrical and gas networks are optimized severally for scheduling strategies. In cloud server, the uncertainty of wind power are processed with robust optimization method, strategies of energy conversion units are updated for re‐optimization. Cooperated with edge‐cloud architecture, a novel multi‐time scales rolling optimization framework is proposed, electrical and gas networks are optimized under day‐ahead time scale in corresponding edge servers, and electrical network is further adjusted under intraday time scale in electrical edge server. Case studies show that edge‐cloud collaborative operation architecture can ensure the practical feasibility of optimization, which is more suitable for energy scheduling of integrated electrical and gas energy system. Multi‐time scales rolling optimization framework have significant efficiency on reducing the impact of wind power uncertainty.

Keywords

• Wind power plants
• Optimisation techniques
• Optimisation techniques
• Power system management, operation and economics
• Distribution networks
• Power engineering computing