Title: Capacity configuration optimisation of hybrid renewable energy system using improved grey wolf optimiser

Authors: Huili Wei; Shan Chen; Tianhong Pan; Jun Tao; Mingxing Zhu

Addresses: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, China ' School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, China ' School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China ' School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China ' School of Electrical Engineering and Automation, Anhui University, Hefei, Anhui, China

Abstract: An appropriate capacity configuration of the Hybrid Renewable Energy System (HRES) contributes to reduce the equipment cost of the system configuration, and improve the operational reliability of the system. Aiming at minimising the Annualised Cost of System (ACS) and the Loss of Power Supply Probability (LPSP), a capacity configuration optimisation model of a PV-wind HRES is set up in this work. An improved Grey Wolf Optimiser (iGWO) is proposed to optimise the system's configuration. First, the Tent chaotic strategy is used to initialise the population. Then, the convergence factor is improved to balance the local and global search ability of GWO. Finally, the meteorological data of the wind speed and solar radiation in a typical year in Zhenjiang, China, are taken as a case to verify the economy and feasibility of the optimal configuration. The results show that the proposed method not only ensures the operation reliability, but also improves the economic performance of HRES.

Keywords: hybrid renewable energy system; optimisation; capacity configuration; improved grey wolf optimiser.

DOI: 10.1504/IJCAT.2022.123234

International Journal of Computer Applications in Technology, 2022 Vol.68 No.1, pp.1 - 11

Received: 02 Feb 2021
Accepted: 23 Apr 2021
Published online: 06 Jun 2022 *