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

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[Formula: see text] 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.

Design and Optimization of a Grid-Connected Solar Energy System: Study in Iraq

“…It was observed that the proposed approach is a viable option to be applied for the optimal design of a hybrid generation system. Investigating the optimal design of a HES that consists of PV, wind, and battery using a metaheuristic grasshopper optimization algorithm to provide electricity for remote areas was proposed in [15]. The results show that the proposed algorithm is efficient in finding the optimal design of the suggested HES.…”

Analyzing the Prospect of Hybrid Energy in the Cement Industry of Pakistan Using Homer Pro

“…It was determined that the suggested strategy is an effective option to use for the optimum design of a half-and-half age framework. To provide energy to remote areas, it was suggested to investigate the best configuration of a HES made up of solar, wind, and batteries using a metaheuristic grasshopper improvement computation [14]. In [15], An energy channel calculation was used to determine the best measurement technique for a PV, wind turbine, and battery crossover age architecture.…”

Investigating the Optimal DOD and Battery Technology for Hybrid Energy Generation Models in Cement Industry Using HOMER Pro

“…For the best design of a hybrid framework, it was concluded that the proposed approach is an efficient option. Using a metaheuristic grasshopper improvement computation, it was suggested that the best configuration of a HES composed of solar, wind, and batteries be investigated in order to supply energy to remote areas [23].The best method for measuring a PV, wind turbine, and battery edge design was determined using an energy channel calculation in [24].The designers discovered that, furthermore to meeting the essential limitations, utilizing the energy network estimation in conjunction with the recommended method is helpful for establishing the ideal economic plan for the HES.…”