| 引用本文: | 侯 慧,何梓姻,侯婷婷,等.大规模车网互动需求响应策略及潜力评估综述[J].电力系统保护与控制,2024,52(14):177-187.[点击复制] |
| HOU Hui,HE Ziyin,HOU Tingting,et al.A review of demand response strategies and potential evaluation for large-scale vehicle to grid[J].Power System Protection and Control,2024,52(14):177-187[点击复制] |
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| 大规模车网互动需求响应策略及潜力评估综述 |
| 侯慧1,2,何梓姻1,2,侯婷婷3,方仍存3,杨天蒙4,唐金锐1,2,石英1,2 |
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| (1.武汉理工大学自动化学院，湖北 武汉 430070;2.武汉理工大学深圳研究院，广东 深圳 518000;3.国网湖北省
电力有限公司经济技术研究院，湖北 武汉 430077;4.国家电网有限公司东北分部，辽宁 沈阳 110180) |
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| 摘要: |
| 大量电动汽车的接入为电网带来了新的挑战与机遇，电动汽车作为具有负荷与储能双重属性的特殊灵活性资源，车网互动过程中需要实施调度方案及需求响应策略对其进行引导。针对现有问题，全面综述了相关研究。首先，为综述电动汽车有序充放电模型，基于用户出行习惯探讨了电动汽车聚类方法，并得出了考虑特征变量聚类可有效提高聚类模型精确度的结论。其次，梳理了现有的需求响应策略，通过权衡协调各类需求响应策略，能有效激发用户调度潜力。然后，基于策略研究，从数据与机理两类评估角度总结了如何提高需求响应评估精度。最后对未来研究做出展望：未来电网侧可聚焦峰谷时段细化研究、聚合商侧可针对不同用户聚类建立更适宜的调度策略、未来仍需探寻多市场主体有效商业模式等。 |
| 关键词: 电动汽车 车网互动 用户出行习惯 需求响应策略 潜力评估 |
| DOI：10.19783/j.cnki.pspc.246003 |
| 投稿时间：2023-08-19修订日期：2024-03-22 |
| 基金项目:国家自然科学基金项目资助(52177110)；深圳市科技计划项目资助(JCYJ20210324131409026)；国网湖北省电网公司项目资助(521538220005) |
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| A review of demand response strategies and potential evaluation for large-scale vehicle to grid |
| HOU Hui1,2,HE Ziyin1,2,HOU Tingting3,FANG Rengcun3,YANG Tianmeng4,TANG Jinrui1,2,SHI Ying1,2 |
| (1. School of Automation, Wuhan University of Technology, Wuhan 430070, China; 2. Shenzhen Research Institute, Wuhan
University of Technology, Shenzhen 518000, China; 3. Economics and Technology Research Institute, State Grid Hubei Electric
Power Company, Wuhan 430077, China; 4. Northeast Branch of State Grid Corporation of China, Shenyang 110180, China) |
| Abstract: |
| Electric vehicles (EV) have brought new challenges and opportunities to the power grid. EV is a special flexible resource with both load and energy storage properties. It is imperative to implement scheduling schemes and demand response (DR) scheduling strategies in the interaction process of vehicle to grid. Given the existing problems, relevant research is reviewed comprehensively. First, to review the orderly charging and discharging model of EV, the clustering methods are discussed based on the user’s travel habits, drawing the conclusion that it can effectively improve the accuracy of the model considering the characteristic variables clustering. Second, the existing DR strategies are reviewed. By balancing and coordinating various DR strategies, the scheduling potential of users can be effectively stimulated. Third, based on DR strategies, it summarizes how to improve the accuracy of DR evaluation from the perspectives of data and mechanism. Finally, future research is prospected: the power grid can focus on research on peak-valley period refinement, the aggregators should set up more appropriate strategies for different clusters, and the market players need effective business models in future. |
| Key words: electric vehicle vehicle to grid users’ travel habits demand response strategy potential evaluation |
