Joint Optimization of Multienergy Virtual Power Plant Configuration and Operation Considering Electric Vehicle Access

Abstract

The problems of energy shortage and environmental pollution can no longer be ignored. How to make the best of energy and improve energy efficiency has always been a concern of researchers. The rapid development of electric vehicles (EVs) has made them an energy load that cannot be ignored. On this basis, an optimal configuration model of a multienergy virtual power plant (MEVPP) considering EV access is constructed to meet the multiple energy needs. To better consider EV users’ willingness to respond, this paper combines price demand response (PDR) with incentive demand response (IDR), establishes a fuzzy response model for EV charging and discharging under the joint response strategy (JRS), and analyzes the influence of user responsiveness and large-scale EV access on MEVPP planning and operation under different incentive levels. Meanwhile, to realize the low carbon, a stepped carbon trading mechanism (SCTM) is introduced. Based on the gazelle algorithm and mixed integer linear programming (MILP), the capacity and output of the system energy equipment are jointly optimized, and the running curve of MEVPP in a typical quarter is analyzed. The example analysis shows that the joint response strategy proposed reduces the operating cost by 7.1%, and the introduction of SCTM reduces the carbon emission by 13.7%, realizing the low-carbon and economic running of MEVPP.