Xiaoyu Zhou, Xiaofeng Liu, Huai Liu, Zhenya Ji, Feng Li
{"title":"Optimal dispatching strategy for residential demand response considering load participation","authors":"Xiaoyu Zhou, Xiaofeng Liu, Huai Liu, Zhenya Ji, Feng Li","doi":"10.1016/j.gloei.2024.01.004","DOIUrl":null,"url":null,"abstract":"<div><p>To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance.</p></div>","PeriodicalId":36174,"journal":{"name":"Global Energy Interconnection","volume":"7 1","pages":"Pages 38-47"},"PeriodicalIF":1.9000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2096511724000045/pdf?md5=981fff84d609004807e0ec4801ee7ae9&pid=1-s2.0-S2096511724000045-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Energy Interconnection","FirstCategoryId":"1087","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2096511724000045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
To facilitate the coordinated and large-scale participation of residential flexible loads in demand response (DR), a load aggregator (LA) can integrate these loads for scheduling. In this study, a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR. First, based on the operational characteristics of flexible loads such as electric vehicles, air conditioners, and dishwashers, their DR participation, the base to calculate the compensation price to users, was determined by considering these loads as virtual energy storage. It was quantified based on the state of virtual energy storage during each time slot. Second, flexible loads were clustered using the K-means algorithm, considering the typical operational and behavioral characteristics as the cluster centroid. Finally, the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load. The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys, thereby improving the load management performance.
为了促进住宅柔性负载协调、大规模地参与需求响应(DR),负载聚合器(LA)可以整合这些负载进行调度。在本研究中,考虑到柔性负载参与需求响应,制定了一种住宅需求响应优化调度策略。首先,根据电动汽车、空调和洗碗机等柔性负载的运行特性,将这些负载视为虚拟储能,从而确定了它们的 DR 参与度,即计算用户补偿价格的基础。它根据每个时段的虚拟储能状态进行量化。其次,使用 K-means 算法对灵活负荷进行聚类,将典型的运行和行为特征作为聚类中心点。最后,通过引入基于直向负载的 DR 机制,实施了 LA 调度策略。仿真结果表明,所提出的 DR 方法能有效降低峰值负荷并填补谷值,从而提高负荷管理性能。