| 引用本文: | 杨景旭,李钦豪,张勇军,姚蓝霓.考虑电网需求匹配度的多EV聚合商需求响应削峰优化建模[J].电力自动化设备,2021,41(8): |
| YANG Jingxu,LI Qinhao,ZHANG Yongjun,YAO Lanni.Peak shaving optimization modeling for demand response of multiple EV aggregators considering matching degree of power grid demand[J].Electric Power Automation Equipment,2021,41(8): |
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| 摘要: |
| 针对电动汽车(EV)参与需求响应(DR)以解决配电网峰荷加剧问题时出现的电动汽车聚合商(EVA)响应量与电网需求量不匹配的问题，提出了考虑电网需求匹配度的DR机制，以实现较为灵活和精准的DR。提出考虑电网需求的DR机制流程，对电网响应用户需求量和EVA响应能力进行评估，并提出满足比和用户参与率用于评估多EVA的响应程度，基于此建立欠响应和过响应约束模型；提出考虑补偿电价的用户响应概率模型；提出申报匹配度用于反映EVA申报量与电网需求量的匹配程度，并基于此建立多EVA响应量申报机制和激励电价报价机制；提出响应匹配度以反映EVA完成申报任务的程度，从而提出考虑各EVA响应情况和总体削峰效果的激励电价调整机制，将其作为EVA没有完成响应任务时的惩罚手段；建立各方参与DR的净收益模型，提出DR综合目标，并基于粒子群优化算法对电网公司和EVA决策进行优化。通过算例仿真验证了所提决策优化方法和多EVA参与的DR模型的有效性。 |
| 关键词: 需求响应 电动汽车 申报机制 多聚合商 匹配度 削峰 |
| DOI：10.16081/j.epae.202107006 |
| 分类号:TM732;U469.72 |
| 基金项目:国家自然科学基金资助项目(51777077)；广州市科技计划项目(202102021208) |
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| Peak shaving optimization modeling for demand response of multiple EV aggregators considering matching degree of power grid demand |
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YANG Jingxu, LI Qinhao, ZHANG Yongjun, YAO Lanni
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Research Center of Smart Energy Technology, School of Electric Power, South China University of Technology, Guangzhou 510640, China
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| Abstract: |
| In view of the mismatch problem between response capacity of EVAs(Electric Vehicle Aggregators) and power grid demand when EVs(Electric Vehicles) participate in DR(Demand Response) to solve the problem of increased peak load in distribution network, a DR mechanism considering matching degree of power grid demand is proposed to achieve more flexible and accurate DR. The DR mechanism process considering power grid demand is proposed to evaluate the responsive user demand of power grid and response ability of EVAs, and the satisfaction ratio and user participation rate are proposed to evaluate the response degree of multiple EVAs. Based on this, the constraint models of under-response and over-response are established. A user response probability model considering compensation price is proposed. The declaration matching degree is put forward to reflect the matching degree of EVAs’ declaration capacity and power grid demand, and based on this, the declaration mechanism of multiple EVAs’ response capacity and quotation mechanism of incentive electricity price are established. The response matching degree is proposed to reflect the degree to which the EVAs complete the declaration task, and an incentive electricity price adjustment mechanism considering each EVA’s response and overall peak shaving effect is proposed, which is taken as the punishment method when EVAs fail to complete the response task. The net profit model of all parties participating in DR is established, the comprehensive objective of DR is proposed, and the decision-making of power grid company and EVAs is optimized based on particle swarm optimization algorithm. The effectiveness of the proposed decision-making optimization method and the DR model with participation of multiple EVAs are verified by the simulation example. |
| Key words: demand response electric vehicles declaration mechanism multiple aggregators matching degree peak shaving |
