A Two-Step Multi-objective Optimization Frame-work for Microgrid Scheduling Problem Based on Cloud-edge Computing

The application of smart metering terminals (SMTs) can collect online data of electricity customers, which would help microgrid system to obtain more accurate information of electricity load demand by edge-computing. Hence reasonable dispatch schemes can be made. In this paper, a two-step multi-objective optimization framework for microgrid scheduling problem is proposed based on cloud-edge computing. A typical microgrid structure with smart metering terminals (SMTs) is introduced and the cloud-edge computing framework is designed, in which the central energy management system (CEMS) is utilized to compute the day-ahead microgrid economical/environmental scheduling problem on the cloud side by forecast data, and the edge-computing servers are applied to re-compute the local optimum schemes on the edge-side during the operation process. Thereafter, the two-step multi-objective optimization strategy is proposed taking advantages of the global computing in cloud-side and the distributed computing in edge-side. The day-ahead global optimization model and the online local optimization model are employed to meet different goals by the two steps. Experimental results show that the proposed strategy is efficient in dealing with the microgrid scheduling problem to reduce cost and emission and can take account of the forecast data errors as well as the global optimum information.