Optimal Scheduling of Electric Vehicle Integrated Energy Station Using a Novel Many-Objective Stochastic Competitive Optimization Algorithm

Abstract

The construction of the Electric Vehicle Integrated Energy Station (EV-IES) is a prerequisite for the rapid development of the EV industry. However, how to optimize the operation of the EV-IES is a problem worthy of study. Therefore, this paper designs an EV-IES model with PV and Energy Storage System (ESS). Fully consider the peak-valley time-of-use electricity price, user traffic flow, PV output, and other factors. On this basis, the three objectives of the maximum daily revenue of the EV-IES, the minimum exchanged energy between the EV-IES and the Regional Power System (RPS), and the minimum pollutant emission are optimized at the same time. Secondly, this paper proposes a MOSCO algorithm, which is utilized to assess the DTLZ1-7 benchmark functions and the optimization scheduling problem of EV-IES. By comparing its simulation results with those of five other optimization algorithms, it is evident that the MOSCO algorithm outperforms the other five in terms of IGD, GD, HV, and Spread values. This indicates the effectiveness of the MOSCO algorithm in addressing many-objective optimization problems. Finally, in order to illustrate the feasibility of designing the EV-IES model, three comparative cases were designed. The

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“…As shown in Figure 1, China's electricity consumption increases by 100 billion kilowatt-hours every 2-3 years, and it is expected to reach 90 trillion kilowatt-hours in 2023, making it the world's largest electricity consumer. On the other hand, the types of electricity loads in China are also continuously expanding, including highly fluctuating loads such as high-speed rail (HR) traction stations, spatially distributed loads like electric vehicle charging stations, high-power loads like aluminum smelting and chip manufacturing [1], [2].…”

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