Journal
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
Volume 56, Issue 4, Pages 3375-3384Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2020.2984410
Keywords
Clustering algorithms; Optimization; Linear programming; Power systems; Pattern clustering; Indexes; Gravitational search algorithm (GSA); incentive-based demand response; intercluster separation; intracluster compactness; load pattern clustering (LPC); typical load pattern
Funding
- National Key R&D Program of China [2018YFE0122200]
- Major Science and Technology Achievements Conversion Project of Hebei Province [19012112Z]
- Science and Technology Project of State Grid Corporation of China [SGHE0000KXJS1800163, kjgw2018-014]
- Key R&D Program of Hebei Province [19214310D]
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This article 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 a good balance between the compactness and separation of the formed clusters. However, few clustering algorithms integrate both of these two aspects into the objective function of clustering for consideration. In the first stage, an adaptive density-based spatial clustering of applications with noise (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.
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