Towards Optimization of Hybrid Renewable Energy Systems with Genetic Algorithms that Address Several Objectives

Abstract

Clean, and sustainable energy in demand, hybrid renewable energy systems (HRES), which is solar and wind power combined, are becoming a rapidly growing interest. However, finding the optimally HRES configuration is deXsigning since it is dependent on the combination of technical, economical and environmental factors. In this paper we investigate how recently multi objective genetic algorithms are being applied to improve the design and operation of hybrid renewable energy systems under stringent objectives, for instance, cost and reliability, as well as environmental impact. Given that the world is reducing reliance on fossil fuels, a hybrid renewable energy system seems like the obvious solution to obtain reliable, sustainable power generation. With the combination of multiple renewables such as solar panels, wind turbines and energy storage, HRES is able to address problems with the intermittency of individual renewables as well as reduce reliance on traditional backup power. However, the selection of optimal counterbalance mix and sizing of HRES component has been a difficult task. Furthermore, it leads to a reduction in the total system costs. Maximizing power output and reliability (reducing environmental impacts and emission), Increasing energy storage (Capacity for Energy Storage), Systems with Multi Objective Genetic Algorithms.