Robust Optimization for Energy Transactions in Microgrid Cluster (MC) Considering Electric Vehicle (EV)

Abstract

In order to cope with the many challenges brought by the large-scale electric vehicles (EVs) access to the microgrid, in view of the insufficient consideration of the existing EV disptaching strategy for the charging and discharging requirements of EVs, a two-stage microgrid cluster (MC) collaborative optimization was proposed, which uses the parking generation rate to realize the orderly charging of electric vehicles. In this paper, a two-stage adaptive robust optimization cooperative operation method in MC is proposed. The first stage is mainly to use the parking generation rate parameter to achieve orderly charging and discharging of EVs, and the second stage is to describe wind, photovoltaic (PV) and EVs for the improved polyhedral uncertain set. By transforming the KKT optimality conditions, the column constraint generation algorithm (C&CG) is used to solve the optimization model effectively. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy trading in MC. The simulation results show that the orderly charging strategy of EVs based on the parking generation rate can effectively avoid the problem of load spikes caused by a large number of EVs connected to the microgrid, provide the grid with peak-shaving and valley-filling services, and achieve a win-win situation for users and microgrids. At the same time, the use of multiple-microgrid can effectively reduce the transactions between microgrids and the power grid.