Application of the Hybrid Big Bang-Big Crunch algorithm for optimal sizing of a stand-alone hybrid PV/wind/battery system

In this paper an efficient method based on Hybrid Big Bang-Big Crunch (HBB-BC) algorithm is presented for optimal sizing of a stand-alone hybrid power system including photovoltaic panel, wind turbine and battery bank. The optimization is carried out to continuously satisfy the load demand with minimizing the total present cost (TPC) of the system. TPC includes all the costs throughout the useful life of the system, which are translated to the initial moment of the investment. In the optimization problem, the reliability index of energy not supplied (ENS) is also considered to have a reliable system. The HBB-BC algorithm is an effective and powerful method that has high accuracy and fast convergence as well as its implementation is easy. This algorithm using the Particle Swarm Optimization (PSO) capacities improves the capability of the Big Bang-Big Crunch (BB-BC) algorithm for better exploration. In addition, the HBB-BC uses a mutation operator after position updating to avoid local optimum and to explore new search areas. This study is applied to a village in Qazvin, Iran that still lacks access to grid electricity due to economic and geography issues. The performance of the proposed algorithm is compared with PSO and Discrete Harmony Search (DHS) algorithms. Simulation results confirm that HBB-BC algorithm with high accuracy can find the optimal solution and it has the best performance in comparison with two mentioned algorithms. (C) 2016 Elsevier Ltd. All rights reserved.