A Novel Two-Stage, Dual-Layer Distributed Optimization Operational Approach for Microgrids with Electric Vehicles

Bowen Zhou (), Zhibo Zhang (), Chao Xi and Boyu Liu
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Bowen Zhou: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Zhibo Zhang: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Chao Xi: State Grid Harbin Power Supply Company, Harbin 150001, China
Boyu Liu: School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW 2052, Australia

Mathematics, 2023, vol. 11, issue 21, 1-33

Abstract: As the ownership of electric vehicles (EVs) continues to rise, EVs are becoming an integral part of urban microgrids. Incorporating the charging and discharging processes of EVs into the microgrid’s optimization scheduling process can serve to load leveling, reducing the reliance of the microgrid on external power networks. This paper proposes a novel two-stage, dual-layer distributed optimization operational approach for microgrids with EVs. The lower layer is a distributed control layer, which ensures, through consensus control methods, that every EV maintains a consistent charging/discharging and state of charge (SOC). The upper layer is the optimization scheduling layer, determining the optimal operational strategy of the microgrid using the multiagent reinforcement learning method and providing control reference signals for the lower layer. Additionally, this paper categorizes the charging process of EVs into two stages based on their SOC: the constrained scheduling stage and the free scheduling stage. By employing distinct control methods during these two stages, we ensure that EVs can participate in the microgrid scheduling while fully respecting the charging interests of the EV owners.

Keywords: microgrid; electric vehicles; consensus control; deep reinforcement learning; microgrid optimization scheduling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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