| 引用本文: | 顾博,李凤婷,张增强,辛超山,余中平.基于GA-PSO的电动汽车换电站时空双层充电优化策略[J].电力系统保护与控制,2019,47(14):116-124.[点击复制] |
| GU Bo,LI Fengting,ZHANG Zengqiang,XIN Chaoshan,YU Zhongping.Optimization strategy of electric vehicle battery swapping station space-time bi-level charging based on GA-PSO[J].Power System Protection and Control,2019,47(14):116-124[点击复制] |
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| 摘要: |
| 针对电动汽车(Electric Vehicle, EV)用户换电体验不佳、换电站备用电池组空闲、充电成本过高及配电网负荷特性恶化的问题，建立了兼顾EV用户、换电站和电网公司三方利益的时空双层充电优化模型。该模型采用双层时空解耦结构，上层模型以满足EV用户个性化需求为目标，重点解决空间尺度上换电站的选择问题。下层模型在时间尺度上采用一种两阶段优化策略，第一阶段以充电成本最小为目标重点关注电池组充电方案的制定问题，第二阶段虑及电网激励以配电网负荷波动最小和峰谷差最小为目标重点关注充电方案的优化问题。最后，采用Monte Carlo法模拟EV用户的换电需求，采用GA-PSO(遗传-粒子群算法)对提出的时空双层优化模型进行迭代求解。以某典型城区为例，仿真验证了所提模型与方法的正确性。 |
| 关键词: 电动汽车换电站 双层优化 两阶段优化 Monte Carlo GA-PSO 电网激励 |
| DOI：10.7667/PSPC20191415 |
| 投稿时间：2018-08-05 |
| 基金项目:国家电网公司科技项目资助(SGGSKY00FJJS 1700318);新疆维吾尔自治区科技支疆项目(2017E0277) |
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| Optimization strategy of electric vehicle battery swapping station space-time bi-level charging based on GA-PSO |
| GU Bo,LI Fengting,ZHANG Zengqiang,XIN Chaoshan,YU Zhongping |
| (Engineering Research Center for Renewable Energy Power Generation and Grid Technology Xinjiang University, Ministry of Education, Urumqi 830047, China;Economics and Technology Research Institute, State Grid Xinjiang Electric Power Company, Urumqi 830047, China) |
| Abstract: |
| To solve the existing problems, which include inconvenience of battery swapping for Electric Vehicle (EV) users, low utilization of battery packs in Battery Swapping Station (BSS), high charging cost, and deterioration of loading characteristics of distribution network, a space-time bi-level charging optimization model, which gives consideration to the tripartite benefits of EV users, BSS and power grid corporation, is established. The model adopts double space-time decoupling structure. The upper model, which aims at meeting the individualized needs of EV users, focuses on solving the problem of BSS selection in spatial scale; the lower model adopts a two-stage optimization strategy in time scale. The first-stage, which takes the minimized charging cost as the objective, focuses on the establishment of the battery packs charging scheme; the second-stage, which gives consideration to the incentive given by grid corporation and aims at the minimum load fluctuation and peak-valley difference of the distribution network, focuses on the optimization of the charging scheme. Finally, the Monte Carlo method is used to simulate the battery swapping demand of EV users, and a Genetic Algorithm (GA) - Particle Swarm Optimization (PSO) method is used to solve the proposed space-time bi-level optimization model. Taking a typical urban area as an example, the validity of the proposed model and method is verified by simulation. This work is supported by Science and Technology Project of State Grid Corporation of China (No. SGGSKY00FJJS1700318) andScience and Technology Support Project of Xinjiang Uygur Autonomous Region (No. 2017E0277). |
| Key words: electric vehicle battery swapping station (EVBSS) bi-level optimization two-stage optimization Monte Carlo GA-PSO power grid incentive |
