Optimum sizing and simulation of hybrid renewable energy system for remote area

Animesh Masih and Verma Hk

Energy & Environment, 2022, vol. 33, issue 5, 933-951

Abstract: In current scenario, people tend to move towards outskirts and like to settle in places that are close to nature. But, due to urban lifestyle and to fulfill the basic needs, demand of electricity remains the same as in urban areas. This demand of electricity can be only fulfilled by using hybrid renewable energy resources, which is easily available in outskirts. Renewable energy resources are unreliable and more expensive. Researchers are working to make, it more reliable and economic in terms of utilization. This article proposes a metaheuristic grasshopper optimization algorithm (GOA) for the optimal sizing of hybrid PV/wind/battery energy system located in remote areas. The proposed algorithm finds the optimal sizing and configuration of remote village load demand that includes house electricity and agriculture. The optimization problem is solved by minimization of total system cost at a desirable level of loss of power supply’s reliability index (LPSRI). The results of GOA are compared with particle swarm optimization (PSO), genetic algorithm (GA) and hybrid optimization of multiple energy resources (HOMER) software. In addition, results are also validated by modeling and simulation of the hybrid energy system and its configurations at different weather conditions-based results. Hybrid PV/wind/battery is found as an optimal system at remote areas and sizing are  N pv = 60 ,  N wind = 10 ,  N bat = 40 , N inv = 20.25  and  N con = 24.13 with cost of energy (COE) (0.3473$/kWh) and loss of power supplies reliability index (LPSRI) (0%). It is clear from the results that GOA based methods are more efficient for selection of optimal energy system configuration as compared to others algorithms.

Keywords: Battery bank; grasshopper optimization algorithm; genetic algorithms; particle swarm optimization; reliability index (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:engenv:v:33:y:2022:i:5:p:933-951

DOI: 10.1177/0958305X211030112

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