Clustering-Estimation Linkage Based Estimation Method for Aggregated Baseline Loads of Demand Response

LIU Chunyang; LI Kangping; JI Ling; MI Zengqiang

doi:10.7500/AEPS20220301012

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Clustering-Estimation Linkage Based Estimation Method for Aggregated Baseline Loads of Demand Response

Basic Research | 更新时间：2025-06-16

- Clustering-Estimation Linkage Based Estimation Method for Aggregated Baseline Loads of Demand Response
- Automation of Electric Power Systems Vol. 47, Issue 2, Pages: 79-87(2023)
- 作者机构：
  
  1.新能源电力系统国家重点实验室(华北电力大学),河北省保定市 071003
  2.上海交通大学智慧能源创新学院,上海市 200240
  3.国电南京自动化股份有限公司,江苏省南京市 211100
- 作者简介：
- 基金信息：
  
  the Key Special Project of National Key R&D Program of China “International Scientific and Technological Innovation Cooperation Between Governments/Scientific and Technological Innovation Cooperation Between Hong Kong, Macao and Taiwan”(2018YFE0122200);National Natural Science Foundation of China(52107103);China Huadian Corporation Ltd. Oriented Project(CHDKJ21-01-107)
- DOI：10.7500/AEPS20220301012
  CLC：
- Received：01 March 2022，
  
  Revised：2022-06-15，
  
  Published Online：28 October 2022，
  
  Published：25 January 2023
- 稿件说明：
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刘春阳,李康平,纪陵等.基于聚类-估计联动的需求响应集群基线负荷估计方法[J].电力系统自动化,2023,47(2):79-87.

LIU Chunyang,LI Kangping,JI Ling,et al.Clustering-Estimation Linkage Based Estimation Method for Aggregated Baseline Loads of Demand Response[J].Automation of Electric Power Systems,2023,47(2):79-87.
刘春阳,李康平,纪陵等.基于聚类-估计联动的需求响应集群基线负荷估计方法[J].电力系统自动化,2023,47(2):79-87. DOI： 10.7500/AEPS20220301012.

LIU Chunyang,LI Kangping,JI Ling,et al.Clustering-Estimation Linkage Based Estimation Method for Aggregated Baseline Loads of Demand Response[J].Automation of Electric Power Systems,2023,47(2):79-87. DOI： 10.7500/AEPS20220301012.

摘要

集群基线负荷（ABL）是指负荷聚合商代理的所有用户基线负荷（CBL）之和，其是系统运营商和负荷聚合商进行激励型需求响应补偿结算的依据。基于用户聚类的估计方法是目前常见的ABL估计方法，然而该类方法将“聚类”和“估计”这两个环节分开执行，两者目标并不统一，估计精度有待进一步提升。为此，提出一种基于聚类-估计联动的ABL估计方法，其基本思路是将估计精度作为调整用户聚类的导向，寻找一种最优的用户聚类方式使得ABL估计的精度最高。首先，对所有用户进行一级聚类，将所得类簇中的所有用户负荷进行累加以作为二级聚类的输入；其次，进行二级聚类，并根据估计结果在验证集上的表现对聚类进行调整，进而得到最优聚类结果。在实际数据集上对所提方法与现有方法进行了对比，结果表明所提方法能够有效提升ABL的估计精度。

Abstract

Aggregated baseline load (ABL) refers to the sum of all customer baseline loads (CBL) managed by the load aggregator

which is the basis for system operators and load aggregators to make compensation and settlement in incentive-based demand response. Customer clustering based estimation methods are common ABL estimation methods at present. However

the two steps of “clustering” and “estimation” are implemented separately in this kind of method

and their goals are not unified

so the estimation accuracy needs to be further improved. Therefore

an ABL estimation method based on clustering-estimation linkage is proposed. The basic idea is to take the estimation accuracy as the guidance to adjust customer clustering

and find an optimal customers clustering to achieve the highest accuracy of ABL estimation. Firstly

the first-level clustering on all customers is performed

and all customer loads in the obtained clusters are accumulated as the input of second-level clustering. Secondly

the second-level clustering is carried out

and the clustering is adjusted according to the performance of the estimation results in the validation set

so as to obtain the optimal clustering results. The proposed method is compared with the existing methods on a real data set

and the results show that the proposed method can effectively improve the estimation accuracy of ABL

关键词

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