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Electric vehicles (EVs) have become an important alternative to traditional vehicles owing to their higher energy efficiency and lower emissions, especially with the intensification of the global energy crisis (Tattini et al., 2021). EVs can partially replace the role of a centralized energy storage system in peak shaving, promoting new energy consumption (Goransson et al., 2010), voltage (Kai. et al., 2022), and frequency regulation (Wang et al., 2020). They also contribute to the stable operation of the grid. However, there are still issues, such as long charging times and mileage anxiety, and EVs are uncontrollable loads (Zeng et al., 2020) that may cause issues for the grid, particularly in the event of a limited power supply (Kalla et al., 2019). A battery swapping station (BSS) is less affected by dispatchable time constraints than a charging station (i.e., a BSS is easy to realize centralized management and charging load prediction, etc.). It also can meet the demand of EV users to avoid lengthy charging times and long driving distances, and with the increase of large-scale uncontrolled charging (Hariri, A. M. et al., 2020), its dispatch optimization problem is very important.
By simulating the intelligent behavior of groups in nature, swarm intelligence algorithms are widely used as a very effective method to solve complex optimization problems. They also are widely used in optimization problems related to EV charging and switching station scheduling (Ding et al., 2022). Liu et al. (2019) proposed an opportunity-based constraint-based switching scheduling strategy that was solved by a genetic algorithm to minimize the cost of power consumption at the switching station while satisfying customer switching demand. Tirkolaee et al. (2020) proposed a hybrid strategy based on interactive fuzzy solving and an adaptive ant colony algorithm to efficiently solve the multi-objective model they developed for shop floor scheduling. Jeya Mala D. et al. (2022) proposed an improved artificial bee colony algorithm by employing some cutting-edge improvement techniques, such as “Euclidean distance” and “chaotic mapping,” and experimentally validated the algorithm’s efficacy. Sultana et al. (2018) applied a locust optimization algorithm to distributed generation and swap station layout and capacity allocation, thereby reducing grid loss and enhancing voltage stability. Li et al. (2023) proposed a fusion algorithm based on an improved genetic algorithm and dynamic windowing method to solve the path-planning problem and enhance the robot’s capacity to avoid dynamic obstacles.
Different models and compatible algorithms were used by the aforementioned scholars to solve the problems they posed. For this paper, we used a multi-objective optimization model for charging cost and grid fluctuation of the switching station to reduce the operating cost and operational risk of the switching station so as to better solve the switching station scheduling problem. Nonetheless, we discovered during the optimization process that the solution algorithm adapted to such problems can be improved in a more in-depth manner, and many completed studies in this area have not used the most recent and improved optimization-seeking algorithms to solve them. After testing the whale optimization algorithm (Yang et al., 2023), the particle swarm algorithm (Yang et al., 2023), and the gray wolf optimizer (Ren et al., 2023)—all of which have excellent capabilities—we discovered that they all are superior to the traditional optimization algorithms, but still have typical defects that lead to insufficient convergence accuracy and speed. Therefore, we need to investigate more effective optimization algorithms to solve the problem presented in this paper.