Hybrid energy system optimization with battery storage for remote area application considering loss of energy probability and economic analysis

Abstract

In this paper, an optimized stand-alone hybrid energy system consists of photovoltaic (PV) arrays, wind turbines (WT), and battery (BA) storage (HPV/WT/BA) presented with the objective of total net present cost (TNPC) minimization subjected to loss of energy probability (LOEP) considering the effect of interest rate (IR) changes. The hybrid system optimization is done based on real annual data of irradiance, wind speed, temperature and demand of a remote site. The main goal of the optimization is optimal sizing of the hybrid system components as the number of PVs, WTs, and batteries and transferred power to load by the inverter with minimizing the TNPC and satisfying the LOEP. A new method named improved grasshopper optimization algorithm (IGOA) based on a nonlinearly decreasing inertia weight strategy is applied for the determination of optimal sizing of the system with the lowest TNPC and best LOEP. Energy management is adapted with the operation of the hybrid system based on load following strategy (LFS). The results showed that the proposed methodology based IGOA finds the HPV/BA system as an optimal combination to supply the site demand with the lowest TNPC and better reliability. The superiority of the IGOA is confirmed in comparison with conventional GOA and well-known particle swarm optimization (PSO) methods in achieving an optimal solution with lower cost and higher reliability. The results of considering IR changes effect are cleared that the LOEP and cost of energy (COE) are increased by 0.56% and 5.37%, respectively and TNPC is decreased by 1.66% due to 1% increasing the IR in optimization of the HPV/BA system. So, the results of interest rate changes indicate a significant effect on the system cost and reliability. Moreover, with increasing the storage capital cost, the TNPC and COE are increased and the reliability level is dropped.