Optimization of an off-grid integrated hybrid renewable energy system with various energy storage technologies using different dispatch strategies

Abstract

ABSTRACT A large population of India is living in villages, some of them are living in remote areas isolated from the grid. It is not feasible or economical to extend the grid connection to provide electricity for those villages, but an autonomous integrated hybrid renewable energy system can be a viable option. Hence, this study proposed to provide electricity and freshwater availability for the un-electrified off-grid villages of Odisha state in India with available renewable energy resources. In order to provide a continuous power supply, the study focused on two energy storage technologies, such as hydrogen and battery storage systems along with a diesel generator. In the context of battery technology, Lead Acid (LA), Lithium-Ion (Li-Ion), and Nickel-Iron (Ni-Fe) are considered. To obtain an optimally configured Integrated Hybrid Renewable Energy System (IHRES), a total of four IHRES configurations are modeled such as Photo Voltaic Panels (PV)/Wind Turbines (WT)/Biomass Generator (BMG)/Diesel Generator (DG)/Ni-Fe, PV/WT/BMG/DG/LA, PV/WT/BMG/DG/Li-Ion, and PV/WT/BMG/DG/Hydrogen Storage System (HSS) and conducted a study with two dispatch strategies, such as load following (LF) and cycle charging (CC), using four metaheuristic algorithms in the MATLAB® environment. From the results, it is found that the proposed Salp Swarm Algorithm has proven its robustness and convergence efficiency when compared to other algorithms in finding the global best optimal values. The Ni-Fe battery-based IHRES with CC strategy provided a minimum life cycle cost (LCC) and cost of energies (COE) as $6,33,789 and 0.23793 $/kWh, respectively, and is considered as a base case. The other energy storage technology-based IHRESs with LA, Li-Ion, and HSSs are obtained an LCC and COEs respectively 74%, 161%, and 231% higher than the base case LCC and COEs. In view of the total annual fuel consumption and carbon emissions, it has shown its best performance with CC strategy compared to LF strategy. Finally, the sensitivity analysis has been conducted to the optimal configuration with variable input parameters, such as interest rate and diesel prices.