| 引用本文: | 吴洲洋,艾欣,胡俊杰,吴界辰.基于充电行为预测的电动汽车参与系统调频备用：容量挖掘与风险评估[J].电力自动化设备,2022,42(4): |
| WU Zhouyang,AI Xin,HU Junjie,WU Jiechen.EVs’ participation in system frequency regulation reserve based on charging behavior prediction: capacity mining and risk evaluation[J].Electric Power Automation Equipment,2022,42(4): |
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| 摘要: |
| 以电动汽车(EV)聚合商在调频辅助服务市场提供备用容量为研究对象，考虑EV用户充电行为的不确定性和用能需求，建立了EV聚合商充电功率及备用上报的优化决策模型。首先，对EV充电记录及调频信号的历史数据进行分析，提出了充电EV数量的预测方法以及调频信号的时间序列特征分析方法；然后，考虑EV聚合商响应调频信号的准确度降低或EV用户的充电需求无法得到满足的风险，提出了基于条件风险价值的风险成本评估方法，用于权衡EV聚合商提供备用的收益及风险，并建立了以收益最大化为目标的优化模型；最后，基于仿真算例对比分析了不同应用场景下EV聚合商提供辅助服务备用的特性及优势，验证了所提模型的有效性。 |
| 关键词: 电动汽车 灵活性资源 辅助服务 概率预测 风险评估 调频备用 条件风险价值 |
| DOI：10.16081/j.epae.202112009 |
| 分类号:U469.72 |
| 基金项目:国家自然科学基金资助项目(51877078)；北京市自然科学基金资助项目(3182037)；北京市科技新星计划项目(Z201100006820106) |
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| EVs’ participation in system frequency regulation reserve based on charging behavior prediction: capacity mining and risk evaluation |
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WU Zhouyang, AI Xin, HU Junjie, WU Jiechen
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State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
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| Abstract: |
| Taking EV(Electric Vehicle) aggregators providing reserve capacity in the frequency regulation auxiliary service market as the research object, the optimization decision model of EV aggregators’ charging power and reserve reporting is established considering the charging behavior uncertainties and energy demand of EV users. Firstly, the EV charging records and the historical data of frequency regulation signal are analyzed, and the prediction method of charging EV quantity and the time series characteristic analysis method of frequency regulation signal are proposed. Then, considering the risks that the accuracy of EV aggregators’ response to frequency regulation signal decreases or the charging demands of EV users cannot be met, a risk cost evaluation method based on conditional value at risk is proposed to weigh the benefits and risks of EV aggregators’ provision of reserve, and an optimization model is established to maximize the benefits. Finally, the characteristics and advantages of auxiliary service reserve provided by EV aggregators in different application scenarios are compared and analyzed based on simulation examples, and the validity of the proposed model is verified. |
| Key words: electric vehicles flexible resources ancillary services probability prediction risk evaluation frequency regulation reserve conditional value at risk |
