Techno-Economic Optimization of Grid-Integrated Hybrid Storage System using GA

Abstract

Microgrids (MG) are innovative in lowering GHG emissions from electricity production by using renewable energy sources. The technical and economic feasibility of MG operation with hybrid energy storage systems is challenged by the intermittent nature of renewable generation. The hybrid energy storage system has been investigated for decades. We provide a hybrid energy storage system with a Grid-connected MG integration model to assess its technological, economic, and environmental impacts. The MG model included photovoltaic panels, wind turbines, lead-acid batteries, electrolyzer modules, fuel cells, and H2 cylinder tanks. The mathematical function for each component used in the system is developed individually to estimate the annual hourly energy generation and consumption. Annual hourly data sets of load consumption are used as load models. The number of components needed for the MG operation to run economically feasible is achieved using the Genetic algorithm (GA) optimization technique thereafter reducing the Levelized cost of energy (LCOE) of the system. An energy dispatching technique is employed to efficiently distribute energy across the hybrid storage and load models. We examined different MG energy penetration levels of 25%, 50%, 75%, and 100% in terms of peak power distribution capacities to load demand relative to the existing grid. MG with 100% integration strives to maintain full load demand without buying energy from the grid. The LCOE and GHG emissions for each Grid-MG integration scenario are calculated. At 100% of the penetration scenario, the LCOE was found 0.0611 ($/kWh) which was best among all the other penetration scenarios.