| 引用本文: | 周星月,黄向敏,张勇军,唐渊,姚蓝霓,杨景旭.基于需求响应潜力模糊评估的电动汽车实时调控优化模型[J].电力自动化设备,2022,42(10): |
| ZHOU Xingyue,HUANG Xiangmin,ZHANG Yongjun,TANG Yuan,YAO Lanni,YANG Jingxu.Real-time scheduling and optimization model of electric vehicles based on fuzzy evaluation of demand response potential[J].Electric Power Automation Equipment,2022,42(10): |
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| 摘要: |
| 针对快充场所电动汽车(EV)大规模接入造成的配电网过载问题，提出了EV需求响应潜力模糊评估方法与实时调控优化模型。首先，基于EV电池安全电量、EV充电需求、充电桩额定功率的限制建立用户客观响应能力约束模型，以及考虑激励水平的用户主观响应意愿评估模型。其次，结合客观响应能力和主观响应意愿建立用户响应潜力评估模型，采用模糊推理确定充电电价、当前电量需求和剩余驻留时间等因素对用户响应意愿的影响。然后，提出激励型实时需求响应的双层优化模型及其求解方法，上层优化模型以EV聚合商激励成本最小化为目标对EV聚合商激励电价进行优化，下层优化以用户平均充电满意度最高为目标对EV充放电功率进行优化，从而充分挖掘用户的响应潜力，兼顾电网公司、EV聚合商、用户各方的利益。最后，通过多组仿真验证了所提模型和方法的有效性。 |
| 关键词: 电动汽车 需求响应潜力 模糊推理 激励机制 实时优化 |
| DOI：10.16081/j.epae.202207030 |
| 分类号:TM732;U469.72 |
| 基金项目:国家自然科学基金资助项目(52177085)；中国博士后科学基金资助项目(2020M682704) |
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| Real-time scheduling and optimization model of electric vehicles based on fuzzy evaluation of demand response potential |
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ZHOU Xingyue1, HUANG Xiangmin1, ZHANG Yongjun1, TANG Yuan1, YAO Lanni1, YANG Jingxu2
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1.School of Electric Power, South China University of Technology, Guangzhou 510640, China;2.Digital Grid Research Institute of China Southern Power Grid Co.,Ltd.,Guangzhou 510670, China
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| Abstract: |
| Aiming at the overload problem of distribution network caused by large-scale access of EVs(Electric Vehicles) in fast charging places, a fuzzy evaluation method of demand response potential of EVs and a real-time scheduling optimization model are proposed. Firstly, based on the constraints of safety capacity of EV battery, EV charging demand, rated power of charging piles, a constraint model of user objective response capacity is proposed, and an evaluation model of user subjective response willingness considering incentive level is proposed. Secondly, combining objective response capacity and subjective response willingness, an evaluation model of EV user response potential is proposed, and the influences of charging price, current capacity demand and remaining residence time on user response willingness are determined by fuzzy reasoning. Then, a two-level optimization model of incentive type real-time demand response and its solution method are proposed. The upper optimization model optimizes the incentive price of EV aggregator with the goal of minimizing the incentive cost of EV aggregator, and the lower optimization model optimizes the charging and discharging power of EVs with the goal of the highest average charging satisfaction of users, thus fully taping the response potential of users and taking into account the interests of grid company, EV aggregator and users. Finally, the effectiveness of the proposed model and method is verified by several groups of simulations. |
| Key words: electric vehicles demand response potential fuzzy reasoning incentive mechanism real-time optimization |
