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Capacity coordination planning of isolated microgrid and battery swapping station based on the quantum behavior particle swarm optimization algorithm

机译:基于量子行为粒子群优化算法的孤立微电网和电池交换站的能力协调规划

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摘要

In battery swapping station (BSS), the battery swapping of electric vehicle (EV) is not synchronous with the centralized charging of BSS. Based on new energy electricity generation and battery swapping demand information of EVs, how to formulate charging-discharging strategies of centralized charging stations is essential in BSS energy management system. Different from traditional BSS scheduling issues, considering that the supply-demand balance of power in isolated microgrid (IMG) could be regulated by the discharging of backup batteries, a service model of EV BSS is proposed with strong constraints, coupling relationships and randomness. Because the microgrid-BSS capacity configuration optimization issue is nonlinear and multivariable, if traditional optimization algorithms are utilized, it would take a long time and the optimal solution may not be obtained. Moreover, there is no guarantee of complete convergence even with the standard particle swarm optimization (PSO) algorithm. In this paper, the quantum behavior particle swarm optimization (QPSO) algorithm is used to handle operation control issues of BSS. As shown in case results, with the QPSO algorithm proposed, both the reliability of battery swapping service and the maximum profit of BSSs could be implemented.
机译:在电池交换站(BSS)中,电动车辆(EV)的电池交换与BSS的集中充电不同步。基于EVS的新能源发电和电池交换需求信息,如何在BSS能源管理系统中制定集中充电站的充电放电策略。与传统的BSS调度问题不同,考虑到备用电池的电力供需平衡,可以通过备用电池放电来调节,提出了强制,耦合关系和随机性的EV BSS的服务模型。由于MicroGrid-BSS容量配置优化问题是非线性和多变量,因为如果使用传统优化算法,则需要很长时间并且可能无法获得最佳解决方案。此外,即使使用标准粒子群优化(PSO)算法,也无法保证完全收敛。本文使用量子行为粒子群优化(QPSO)算法用于处理BSS的操作控制问题。如图所示,随着QPSO算法所提出的,可以实现电池交换服务的可靠性和BSSS的最大利润。

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