首页|基于改进多目标粒子群算法的储能式充电桩优化运行策略

基于改进多目标粒子群算法的储能式充电桩优化运行策略

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针对小区接入充电桩无序充放电加大负荷峰谷差率和用户成本的问题,提出一种储能式充电桩有序充放电优化运行策略.该优化策略在降低峰谷差率的前提下,以用户充电成本最低和充电桩收益最高为优化目标.选取典型日建立储能充电桩优化充放电调度模型,采用改进多目标粒子群优化算法进行求解,结合分时电价调整储能式充电桩的充放电功率和时间.通过优化惯性权重和学习因子并自适应改变位置分裂因子来改进多目标粒子群优化算法.实验结果表明:该算法可有效提高收敛速度,避免陷入局部最优,能更好地处理多目标问题,在求解储能调度模型中降低了典型负荷峰谷差率55%,比原有算法优化了 36%,能合理分配充电桩在谷时段储存电力资源,降低用户充电费用20%-30%,提高充电桩收益,达到电网、用户和充电桩三方共赢的目的.
Optimized operation strategy for energy storage charging piles based on improved multi-objective particle swarm optimization
To address the increased load peak-to-trough ratio and user costs caused by disorderly charging and dis-charging of electric vehicle charging piles in residential communities,an optimized operation strategy is proposed for energy storage charging piles to achieve orderly charging and discharging.While reducing the peak-to-trough ratio,the strategy aims to minimize users'charging costs and maximize charging pile profits.A typical day is selected to es-tablish an optimized charging and discharging scheduling model for the energy storage charging piles,which is solved by an Improved Multi-Objective Particle Swarm Optimization(IMOPSO)algorithm,and the charging and discharging power and time of the energy storage charging pile is adjusted in combination with the time-of-use elec-tricity price.The MOPSO algorithm is improved by optimizing the inertial weights,learning factors and adaptively changing the position splitting factor.Experimental data results show that the algorithm can effectively improve the convergence speed,avoid falling into local optimum,and better handle multi-objective problem.In the energy storage scheduling model,it reduces the typical load peak-to-trough ratio by 55%,optimized by 36%compared to the origi-nal algorithm,rationally allocates charging piles to store power resources during low-demand period,effectively re-duces charging costs by 20%to 30%,and improves charging pile profits,thus achieving a win-win situation for the power grid,users and charging piles.

orderly charging and dischargingelectric vehicleenergy storagepeak shaving and valley fillingim-proved multi-objective particle swarm optimization(IMOPSO)

李鹏、俞天杨、俞斌、周成伟、孟伟

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南京信息工程大学自动化学院,南京,210044

无锡学院自动化学院,无锡,214105

有序充放电 电动汽车 储能 削峰填谷 改进多目标粒子群算法

2024

南京信息工程大学学报
南京信息工程大学

南京信息工程大学学报

CSTPCD北大核心
影响因子:0.737
ISSN:1674-7070
年,卷(期):2024.16(6)