Application of hybrid multi-objective moth flame optimization technique for optimal performance of hybrid micro-grid system

The research work carried out here deals with the application of a Hybrid Micro-Grid System (HMGS), which includes solar/wind/battery storage/diesel generator applied to three different parts of India. For a better analysis of all the three cases, an efficient and recent metaheuristic optimization method named hybrid multi-objective moth flame optimization (HMOMFO) technique has been used in MATLAB. The aim is to find better candidate solutions for which particle swarm optimization (PSO) technique and levy flight method are integrated, with the moth flame optimization (MFO) algorithm Moreover a new enhanced differential evolution algorithm (EDE) with self-adjustable parameters has been integrated with the second phase of the hybrid algorithm to enhance the searching and exploitation capabilities of the proposed algorithm. Here, for an initial load of 15 households, simulation and statistical results show that HMOMFO proves to be successful in terms of minimizing the price of electrical energy (PEE). Results further assure that minimum values of loss of power supply probability (LPSP) for Durgapur, Hospet, and Tirunelveli, are obtained using HMOMFO, with fewer iterations. The results also contain optimum photovoltaic (PV) power, battery bank performance in terms of autonomy days (AD), the optimum number of wind turbine generators (WT), and diesel generators (DG). The results demonstrate the mastery and effectiveness of the proposed HMOMFO against three area hybrid micro-grid systems. (C) 2020 Elsevier B.V. All rights reserved.