| 引用本文: | 程杉,魏昭彬,赵子凯,汪业乔,赵孟雨.考虑电动汽车时空接入随机性的充储电站有序充放电分散式优化[J].电力自动化设备,2021,41(6): |
| CHENG Shan,WEI Zhaobin,ZHAO Zikai,WANG Yeqiao,ZHAO Mengyu.Decentralized optimization of ordered charging and discharging for charging-storage station considering spatial-temporal access randomness of electric vehicles[J].Electric Power Automation Equipment,2021,41(6): |
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| 摘要: |
| 针对电动汽车时空接入的随机性，提出一种基于改进拉格朗日对偶松弛法的充储电站有序充放电分散式优化调度方法。首先，根据出行链和马尔可夫决策理论，建立计及出行路径随机性的电动汽车时空接入模型和不同温度及交通路况下的电动汽车单位行驶里程能耗模型；其次，考虑电动汽车的充放电约束、充储电站和配电网的运行约束，构建以充储电站收益最大化为目标函数的充储电站侧优化数学模型；然后，基于改进拉格朗日对偶松弛法，提出该模型的分散式优化求解方法；最后，以某典型城区道路拓扑为例，对比分析不同出行路径、温度、交通路况和调度策略下各充储电站的收益、负荷曲线和计算效率。算例结果表明：所提方法综合考虑多种环境因素，使充储电站的调度结果更加全面实际，且计算效率得到了大幅提升。 |
| 关键词: 电动汽车 出行链 充储电站 马尔可夫决策理论 分散式优化 有序充放电 |
| DOI：10.16081/j.epae.202103014 |
| 分类号:U469.72 |
| 基金项目:国家自然科学基金资助项目(51607105)；三峡大学硕士学位论文培优基金资助项目(2020SSPY059) |
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| Decentralized optimization of ordered charging and discharging for charging-storage station considering spatial-temporal access randomness of electric vehicles |
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CHENG Shan1, WEI Zhaobin1, ZHAO Zikai1, WANG Yeqiao1, ZHAO Mengyu2
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1.Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China;2.Xuchang Power Supply Company of State Grid Henan Electric Power Co.,Ltd.,Xuchang 461000, China
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| Abstract: |
| Aiming at the spatial-temporal access randomness of EVs(Electric Vehicles),a decentralized optimization scheduling method of ordered charging and discharging for CSS(Charging-Storage Station) based on ILDRM(Improved Lagrange Dual Relaxation Method) is proposed. Firstly, according to the trip chain and Markov decision theory, the spatial-temporal access model of EVs considering the randomness of the travel paths and the energy consumption model per unit mileage of EVs under different temperatures and traffic conditions are established. Secondly, considering the charging and discharging constraints of EVs and the operation constraints of CSS and distribution, the optimization mathematical model of CSS side is established with the objective function of maximizing the revenue of CSS. Then, based on ILDRM, a decentralized optimization solving method is proposed. Finally, taking a typical urban road topology as an example, the revenue of each CSS, load curve and computational efficiency under different travel paths, temperatures, traffic conditions and scheduling strategies are compared and analyzed. The results show that, considering various environmental factors comprehensively, the proposed method makes the scheduling results of CSS more comprehensive and practical, and the computational efficiency is greatly improved. |
| Key words: electric vehicles trip chain charging-storage station Markov decision theory decentralized optimization ordered charging and discharging |
