Optimal design and sizing of energy storage solution-based hybrid energy system considering economic and technical objective functions: A case study

The declining availability of fossil fuels requires a shift to renewable energy in electricity generation. This study focuses on designing and sizing hybrid energy systems at the University of Ajman using photovoltaic systems and energy storage. The optimization aims to minimize economic and technical objectives such as total net present cost (TNPC) and loss of power supply probability (LPSP). The MATLAB software and metaheuristic algorithms, including improved grey wolf optimization, the water cycle algorithm, and teaching-learning-based optimization are used for modeling and optimization. The proposed hybrid energy system is designed in different configuration sizing like photovoltaic-battery and photovoltaic-fuel cell. The different configuration sizing is implemented considering a proposed energy management based on energy balance strategy. Finally, three case studies are proposed by metaheuristic optimization techniques to numerically simulate optimal sizing and design of hybrid energy resources. The analysis examines three case studies featuring different configurations: Case A with a photovoltaic-fuel cell, Case B with photovoltaic-battery, and Case C combining both photovoltaic-battery and fuel cell systems. The findings from the case studies with algorithms indicate that the optimization by teaching-learning-based optimization algorithm in Case B has superior configuration and optimal sizing of resources compared to the other cases. The configuration in Case B by teaching-learning-based optimization algorithm has minimum amounts of the TNPC, levelized cost of electricity and LPSP with values 307,979.8$, 0.151133 kWh and 0 than other algorithms and configurations, respectively.