考虑电动汽车充电负荷及储能寿命的充电站储能容量配置优化
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引用本文:马永翔1,韩子悦1,闫群民1,万佳鹏2,淡文国3.考虑电动汽车充电负荷及储能寿命的充电站储能容量配置优化[J].电网与清洁能源,2024,40(4):92~101
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作者单位
马永翔1 1. 陕西理工大学电气工程学院 
韩子悦1 1. 陕西理工大学电气工程学院 
闫群民1 1. 陕西理工大学电气工程学院 
万佳鹏2 2. 陕西省电力有限公司咸阳供电公司 
淡文国3 3. 乌兰察布电业局 
基金项目:国家自然科学基金(E0704)
中文摘要:提出了一种优化电动汽车充电站储能容量配置的方法。该方法考虑了季节性电动汽车充电负荷波动与光伏出力之间的关系,并且考虑了储能寿命。论文利用蒙特卡罗法考虑了不同类型电动汽车的多种影响因素,对整体负荷进行预测。以每日运行成本最低为优化目标,在考虑四季光伏出力和储能寿命的影响下,采用了3种算法对目标函数进行优化,以得到最佳的光储充电站储能配置方案。研究以西北某地区为例。结果表明:冬季下综合成本为3.043 2×106元,相比于其余3个季节综合成本最低;采用遗传算法时,在综合成本相差不多时,获得的储能配置最优,储能容量为22.82 MWh,储能功率为7.31 MW,从而得到光储充电站最优的储能容量配置。
中文关键词:光储充电站  电动汽车  储能寿命  储能容量优化  遗传算法  粒子群算法  蚁群算法
 
Optimization of Storage Capacity Allocation at Charging Stations Considering EV Charging Load and Storage Lifetime
Abstract:This paper presents a method to optimize the energy storage capacity allocation for electric vehicle charging stations. The method takes into account the relationship between seasonal EV charging load fluctuations and PV output, and the energy storage lifetime as well. Firstly, the operation model of the photovoltaic storage charging station system is introduced. Secondly, the models of the PV system, the energy storage system and the genset system are described. The paper uses the Monte Carlo method to predict the overall load by considering multiple influencing factors of different types of electric vehicles. With the optimization objective of minimum daily operating cost, three algorithms are used to optimize the objective function to obtain the best energy storage configuration scheme for the photovoltaic and storage charging station, taking into account the influence of the PV output and the storage lifetime in all seasons. Taking a certain region in the northwest as an example, the study results show that the comprehensive cost in winter is 3.043 2×106 Yuan, which is the lowest compared to the other three seasons. With the genetic algorithm used, and with similar overall cost, the obtained energy storage configuration is optimal, with a storage capacity of 22.82 MW·h and a storage power of 7.31 MW, thus the optimal energy storage capacity configuration for the optical storage charging station is obtained.
keywords:optical storage charging station  electric vehicle  energy storage life  energy storage capacity optimization  genetic algorithm  particle swarm algorithm  ant colony algorithm
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