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Meta-Heuristic Optimization Based Two-stage Residential load pattern clustering Approach Considering Intra-cluster Compactness and Inter-cluster Separation

Meta-Heuristic Optimization Based Two-stage Residential Load...

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54th Annual Meeting of the IEEE-Industry-Applications-Society (IEEE IAS)

作者： Li, Kangping Ge, Xinxin Lu, Xiaoxing Wang, Fei Mi, Zengqiang North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China North China Elect Power Univ Hebei Key Lab Distributed Energy Storage & Microg Baoding 071003 Peoples R China

ISBN: (纸本)9781538645390

This paper proposes a meta-heuristic optimization based two-stage residential load pattern clustering (LPC) approach to address two main issues that exist in the most current LPC methods: 1) unreasonable typical load pattern (TLP) extraction, 2) a good clustering should achieve reasonable balance between the intra-cluster compactness and inter-cluster separation of the formed clusters. However, most of the current clustering algorithms usually only take one of the aspects into consideration. In the first stage, an adaptive DBSCAN is proposed to automatically detect the uncommon load curves and obtain the TLP of each individual customer. In the second stage, LPC is formulated as an optimization problem in which clustering validity index (CVI) considering both compactness and separation is used as the objective function. Gravitational search algorithm (GSA) is adopted to solve this optimization problem. Four different CVIs are investigated to find the most appropriate one for LPC. A comparative case study using the real load data from 208 households from the U.K. verified the effectiveness of the proposed approach.

关键词： load pattern clustering Typical load pattern Gravitational search algorithm Intra-cluster compactness Inter-cluster separation

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A Meta-Heuristic Optimization Based Residential load pattern clustering Approach Using Improved Gravitational Search Algorithm

A Meta-Heuristic Optimization Based Residential Load Pattern...

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IEEE-Power-and-Energy-Society Innovative Smart Grid Technologies Conference (ISGT)

作者： Li, Kangping Che, Jianfeng Wang, Bo Zhang, Jingjing Wang, Fei Mi, Zengqiang North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing 100192 Peoples R China

ISBN: (纸本)9781538624531

More in-depth understanding about the load pattern clustering (LPC) can enhance the knowledge on end-users' electricity consumption behavior characteristics to improve the design of demand-side response schemes and service level for utility companies. However, traditional clustering methods such as K-means only take the compactness of the formed clusters into account without considering the separation, which makes the clustering results unreasonable. Moreover, K-means is sensitive to the initial centroids and easy to trap into local optimum. To address these issues, a meta -heuristic optimization based residential LPC approach is proposed in this paper. First, the density based spatial clustering of applications with noises (DBSCAN) is adopted to remove the outlier load profiles and obtain typical load patterns. Second, LPC is formulated as an optimization problem which considers both compactness and separation of the formed clusters. Then an improved Gravitational Search Algorithm (IGSA) is proposed to solve it. To improve the global search capabilities of the standard GSA, memory management strategies from PSO and multi-mutation/crossover/selection mechanisms from difference evolution are modified and adopted in this research. Finally, a case study is carried out to verify the feasibility and effectiveness of the proposed method, in which IGSA is compared with three other well-known clustering methods including K-means, PSO and GSA. The simulation results indicate that IGSA shows better performance in terms of the clustering quality.

关键词： load pattern clustering Improved gravitational search algorithm Multi-mutation mechanisms Difference evolution

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A Baseline load Estimation Approach for Residential Customer based on load pattern clustering 9th

A Baseline Load Estimation Approach for Residential Customer...

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9th International Conference on Applied Energy (ICAE)

作者： Li, Kangping Wang, Bo Wang, Zheng Wang, Fei Mi, Zengqiang Zhen, Zhao North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding 071003 Peoples R China China Elect Power Res Inst State Key Lab Operat & Control Renewable Energy & Beijing 100192 Peoples R China Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) estimation is an important issue in the implementation of DR programs for assessing the performance of DR programs and designing economic compensation mechanisms. The accurate estimation of CBL is critical to the success of DR programs because it involves the interests of multi-stakeholders including utilities and customers. Motivated by the inaccuracy of existing CBL methods, this paper proposes a residential CBL estimation approach based on load pattern (LP) clustering to improve the accuracy of CBL estimation. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to extract typical load patterns (TLPs) of each individual customer in order to avoid the adverse effects from aggregating many dissimilar LPs together as the real TLP. Second, K-means clustering is utilized to segment residential customers into several different clusters based on the similarity of LPs. Finally, CBLs for DR participants are estimated based on the actual load of nonparticipants at the same cluster during DR event periods. The proposed methods are compared with some traditional methods on a smart metering dataset from Ireland. The results show that the proposed methods have a better performance on accuracy than averaging and regression methods. (C) 2017 The Authors. Published by Elsevier Ltd.

关键词： Demand response Customer baseline load load pattern clustering

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A Baseline load Estimation Approach for Residential Customer based on load pattern clustering

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Energy Procedia 2017年 142卷 2042-2049页

作者： Kangping Li Bo Wang Zheng Wang Fei Wang Zengqiang Mi Zhao Zhen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) Baoding 071003 China State Key Laboratory of Operation and Control of Renewable Energy & Storage System (China Electric Power Research Institute) Haidian District Beijing 100192 China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA

Demand response (DR) is a key technology enabling reliable and flexible power system operation more economically and environment-friendly than conventional manners from supply side. Customer baseline load (CBL) estimation is an important issue in the implementation of DR programs for assessing the performance of DR programs and designing economic compensation mechanisms. The accurate estimation of CBL is critical to the success of DR programs because it involves the interests of multi-stakeholders including utilities and customers. Motivated by the inaccuracy of existing CBL methods, this paper proposes a residential CBL estimation approach based on load pattern (LP) clustering to improve the accuracy of CBL estimation. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to extract typical load patterns (TLPs) of each individual customer in order to avoid the adverse effects from aggregating many dissimilar LPs together as the real TLP. Second, K-means clustering is utilized to segment residential customers into several different clusters based on the similarity of LPs. Finally, CBLs for DR participants are estimated based on the actual load of non-participants at the same cluster during DR event periods. The proposed methods are compared with some traditional methods on a smart metering dataset from Ireland. The results show that the proposed methods have a better performance on accuracy than averaging and regression methods.

关键词： Demand response Customer baseline load load pattern clustering

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