摘要
作为需求响应的重要形式,激励型需求响应(incentive based demand response,IBDR)对提升电力系统运行的灵活性具有重要作用。用户基线负荷(customer baseline load,CBL)是计算IBDR参与者经济补偿的依据,其估计准确性会直接影响项目参与者和提供者的利益。在现有CBL估计方法中,对照组法通过不参与需求响应(demand response,DR)项目的用户的实际负荷来估计DR参与者的CBL,相比于其他方法通常估计精度更高。然而当对照组用户数量不足够多时,该方法的估计精度将会急剧下降、甚至完全失效。为解决这一问题,该文提出一种基于拉丁超立方抽样和场景消减的居民用户CBL估计方法。首先基于拉丁超立方抽样和场景消减,生成每个时段的代表性负荷场景,再通过迭代消减融合将单时段场景连接形成日负荷曲线场景,以此增加对照组样本多样性。以伦敦居民用户实测负荷数据为例,验证了该文所提方法具有可行性。
As an important form of demand response,incentive-based demand response(IBDR) plays a significant role in improving the flexibility of power system.As the economic compensation calculation basis,the accuracy of customer baseline load(CBL) estimation directly affects the interests of the provider and participants of the IBDR program.In current CBL estimation methods,the CONTROL group method uses the actual load data of customers who don’t participate in the demand response(DR) program to estimate the CBL of DR participants,which usually exhibits higher accuracy than other methods.However,when there are not enough amounts of customers in the CONTROL group,the accuracy of this method may decrease sharply or even become completely invalid.A residential CBL estimation method based on the latin hypercube sampling and scenario subtraction is proposed to solve this problem.The representative load scenarios in each time period are generated by the latin hypercube sampling and scenario reduction.Then the daily load scenarios are generated by the iterative reduction and fusion to increase the sample diversity in the CONTROL group.The effectiveness of the proposed method is verified by the actual load data of residential customers in London.
作者
付文杰
王喻玺
申洪涛
陶鹏
王少林
李康平
葛鑫鑫
王飞
FU Wenjie;WANG Yuxi;SHEN Hongtao;TAO Peng;WANG Shaolin;LI Kangping;GE Xinxin;WANG Fei(State Grid Hebei Electric Power Co.,Ltd.,Shijiazhuang 050022,Hebei Province,China;Department of Electrical Engineering,North China Electric Power University,Baoding 071003,Hebei Province,China;State Grid Xinjiang Electric Power Co.,Ltd.,Urumqi 830018,Xinjiang Uygur Autonomous Region,China;Department of Electrical Engineering,Tsinghua University,Haidian District,Beijing 100084,China)
出处
《电网技术》
EI
CSCD
北大核心
2022年第6期2298-2307,共10页
Power System Technology
基金
国家重点研发计划项目(2018YFE0122200)
国家电网有限公司科技项目(SGHEYX00SCJS2000037)。
关键词
激励型需求响应
用户基线负荷
对照组
拉丁超立方抽样
incentive-based demand response
customer baseline load
CONTROL group
latin hypercube sampling
