A comparative study on hybrid GA-PSO performance for stand-alone hybrid energy systems optimization

Abstract

As global energy demands surge and the environmental implications of fossil fuel dependence become more pronounced, there is an urgent need to transition toward more sustainable and eco-friendly energy alternatives. This underscores the dire need for sustainable, secure, and environmentally friendly energy solutions. To this end, efficient energy management strategies combined with the optimal design of hybrid renewable energy systems are paramount for ju-diciously harnessing renewable resources. In such systems, wind turbines, photovoltaic panels, diesel generators, and battery storage must be meticulously sized to ensure cost-efficiency, en-vironmental sensitivity, and resilience against unpredictable load variations. Addressing these design challenges, our study emphasized the significance of strategic efficiency, prudential com-ponent selection, and system dependability. We designed an off-grid hybrid renewable energy system, incorporating photovoltaic panels, wind turbines, battery storage, and diesel generators, to meet the annual energy requirements of a university campus. After recording data for a full year, which included metrics on solar radiation, wind speed, ambient temperature, and campus load, we developed a model founded on comprehensive energy management strategies. This model aims to identify optimal design parameters, reduce annual costs, achieve sustainable en-ergy benchmarks, and ensure a harmonious power exchange between system components. For optimization, we used an array of algorithms, notably the genetic algorithms, particle swarm optimization, gravity search algorithms, and hybrid algorithms, such as the hybrid genetic al-gorithm-particle swarm optimization and the hybrid gravity search algorithm-particle swarm optimization, supplemented by the HOMERPro software. Our findings revealed that the inte-gration of photovoltaic panels with battery storage led to an annual system cost of $671,474.98, a levelized cost of energy of $0.1800, a total net present cost of $10,898,221.74, and a renewable energy fraction of 100%. It became evident that the hybrid genetic algorithm combined with particle swarm optimization, when aligned with astute energy management strategies, was more effective in determining optimal design parameters than other methodologies. Through this re-search, we offer profound insights into the dynamics of hybrid renewable energy systems, serv-ing as a guide for pragmatic design and tangible implementation.

Keywords

• Cost of Energy
• Genetic Algorithm Partical Swarm Optimization
• Energy Management System Optimal Sizing Design
• Stand-Alone Renewable Microgrid System

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