Meta-Heuristic Optimization Based Two-stage Residential Load Pattern Clustering Approach Considering Intra-cluster Compactness and Inter-cluster Separation

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.