Economic Optimal Scheduling of Wind–Photovoltaic-Storage with Electric Vehicle Microgrid Based on Quantum Mayfly Algorithm

Abstract

The effectiveness of energy management systems is a great concern for wind–photovoltaic-storage electric vehicle systems, which coordinate operation optimization and flexible scheduling with the power grid. In order to save system operation cost and reduce the energy waste caused by wind and light abandonment, a time-sharing scheduling strategy based on the state of charge (SOC) and flexible equipment is proposed, and a quantum mayfly algorithm (QMA) is innovatively designed to implement the strategy. Firstly, a scheduling strategy is produced according to the SOC of the battery and electric vehicle (EV), as well as the output power of wind–photovoltaic generation. In addition, the minimum objective function of the comprehensive operation cost is established by considering the cost of each unit’s operation and electricity market sale price. Secondly, QMA is creatively developed, including its optimization rule, whose performance evaluation is further carried out by comparisons with other typical bionics algorithms. The advantages of QMA in solving the low-power multivariable functions established in this paper are verified in the optimization results. Finally, using the empirical value of the power generation and loads collected in enterprise as the initial data, the mayfly algorithm (MA) and QMA are executed in MATLAB to solve the objective function. The scheduling results show that the time-sharing scheduling strategy can reduce the system’s cost by 60%, and the method decreases energy waste compared with ordinary scheduling methods, especially when using QMA to solve the function.