Flexible transmission network expansion planning under uncertainty based on a self-adaptive clustering technique

Abstract

This paper presents a flexible transmission network expansion planning (TNEP) approach with consideration of uncertain renewable generation. A novel hybrid clustering method, which integrates graph partitioning method and rough fuzzy clustering, is proposed to cope with the uncertainties. The proposed clustering method is able to self-adaptively recognize the actual cluster distribution of complex data sets and provide high quality clustering results. Through clustering the hourly data of renewable power output, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and huge investment in transmission, traditional TNEP, which usually caters for rated renewable power output, is always uneconomical. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the renewable generation curtailment is regarded as one of the optimization objectives. The solution framework applies the NSGA-Ċ algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment cost and renewable generation curtailment. The robustness and effectiveness of the proposed approach are validated through a numerical case.