Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Applied Sciences

Yun-Hao Li²  Jian-Xue Wang¹ 

[1] Shaanxi key Laboratory of Smart Grid, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

关键词: transmission network expansion planning;    clustering analysis;    uncertainty;    multi-objective optimization;   

DOI  :  10.3390/app6010003

来源: mdpi

【 摘 要 】

This paper presents a flexible transmission network expansion planning (TNEP) approach considering uncertainty. A novel hybrid clustering technique, which integrates the graph partitioning method and rough fuzzy clustering, is proposed to cope with uncertain renewable generation and load demand. The proposed clustering method is capable of recognizing the actual cluster distribution of complex datasets and providing high-quality clustering results. By clustering the hourly data for renewable generation and load demand, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and heavy investment in transmission, the traditional TNEP, which caters to rated renewable power output, is usually uneconomic. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the curtailment of renewable generation is considered as one of the optimization objectives. The solution framework applies a modified NSGA-II algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment costs and renewable generation curtailments. Numerical results on the IEEE RTS-24 system demonstrated the robustness and effectiveness of the proposed approach.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.