Optimal Design of a Hybrid Solar–Wind–Battery System using the Grasshopper Optimization Algorithm for Minimization of the Loss of Power Supply Probability

Subject Areas : Renewable energy

Ronak Jahanshahi Bavandpour ¹ ( Department of Electrical Engineering- Darolfonoon University, Qazvin, Iran )
Hamid Ghadiri ² ( Faculty of Electrical, Biomedical and Mechatronics Engineering- Qazvin Branch, Islamic Azad University, Qazvin, Iran )
Hamed Khodadadi ³ ( Department of Electrical Engineering- Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran )

Received: 2021-08-01 Accepted : 2021-10-28 Published : 2021-10-23

Keywords: particle swarm optimization, Optimization, grasshopper optimization algorithm, loss of power supply probability, solar-wind hybrid system,

Abstract :

Renewable energy has been developed in recent years due to the limited sources of fossil fuels, their possibility of depletion, and the related environmental issues. The main challenges of these type of systems is reaching to the optimum size in order to have an affordable system based on storing the solar and wind energy. In this paper, optimization of a solar-wind hybrid system is presented with a saving battery system for supplying a specific hourly load annually to minimize annual system expenses and the probability of Loss of Power Supply Probability (LPSP). Annual expenses of the system include initial investment, maintenance, and replacement costs. The purpose of optimization is to determine the numbers of solar panels, wind turbines, batteries, the height of the wind tower, and the angle of the solar panel toward solar radiation. For this issue, a new method named Grasshopper Optimization Algorithm (GOA) is employed. Also, the effects of changes in inverter efficiency, load demand, and of maximum probability of LPSP on system designing are evaluated. Simulation results show that the efficiency reduction, load increase, and increasing the load and maximum reliability in the system in the form of reducing of LPSP lead to an increase in annual energy costs of systems. Furthermore, the results indicate the superiority of the GOA method toward particle swarm optimization (PSO) in reaching better target function and less cost.

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