Multi-objective optimal sizing of hybrid micro-grid system using an integrated intelligent technique

Abstract

An optimal sizing model for a hybrid micro-grid system (HMGS) consisting of solar photovoltaic, wind turbine, diesel generator and battery is developed. A tri-objective formulation, considering techno-socio-economic factors, is proposed for finding the optimal size and configuration. Dynamic domain search is employed using a hybrid intelligent computational technique that integrated the improved particle swarm optimization (PSO) algorithm with an enhanced differential evolution (DE) model for improving the search efficiency. The idea behind the hybridization of these two popular paradigms is to deploy the higher exploration capability of the PSO during the initial search and then employ the fast convergence property of the DE to achieve an optimal solution through exploitation. A fuzzy attainment module (FAM) is appended to identify the best solution based on the highest possible attainment level for all objectives. Two power management strategies are modelled and their effect on emission curtailment is studied. Optimal sizing is analysed considering the effect of selected optimization objective(s) and multiple choices are offered to the designer based on preferences. For the selected remote island, the system with solar PV capacity 78.44 kW, wind turbine of 95 kW with a battery of 2 kW is found to be the best option. The levelized cost of energy (LCOE) is 0.0799 $/kWh, human development index (HDI) is 0.6454, unmet power is zero and the satisfaction level of all the three objectives is 72.82%.