Comparison of Crow Algorithm and Genetic Algorithm in Controlling Autonomous Renewable Hybrid Energy Systems

Abstract

Renewable Hybrid Autonomous Power Systems are very important for remote and isolated zones. These systems are characterized by the diversity of energy systems that can solve power problems in isolated and remote zones that cannot be connected to electric grids. The energy from sun exists during the day and the wind is intermittent. Thus, for solving such problem, different renewable energy technologies represented in hybrid renewable energy systems are needed. The hybrid system includes PV plus Wind, PV plus Diesel, PV plus Wind and Diesel, etc.  "Autonomous Hybrid Renewable Energy System" (AHRES) consists of two or more energy sources; one of them at least is renewable and integrated with power control equipment and an optional storage system. This paper aims at comparing between the utilization of new optimization technique, namely (Crow Algorithm), and the Genetic  algorithm in controlling the autonomous system where this technique is used for controlling suggested autonomous system in the Egyptian Island located in the Red Sea namely "Al-Fanadir Island".  The wind speed and solar irradiance parameters are obtained for Al-Fanadir Island.  The Load Profile of Al-fanadir Island is obtained from Ministry of electricity and renewable energy in Egypt. The selection between the system's Reliability and Cost is a basic compromise for designing hybrid systems.  In this technique, optimization of "Autonomous hybrid renewable energy system" is investigated. The study showed that both the Crow Algorithm and GA are effective t ools in controlling the autonomous hybrid renewable energy system.