Synergistic Computing for Sustainable Energy Systems: A Review of Genetic Algorithm-Enhanced Approaches in Hydrogen, Wind, Solar, and Bioenergy Applications

The imperative for sustainable energy solutions has spurred extensive research into renewable resources such as hydrogen, wind, solar, and bioenergy. This paper presents a comprehensive review of recent advancements (2015–2024) in the application of Genetic Algorithms and associated computational technologies for the optimisation and forecasting of these energy systems. This study synthesizes findings across diverse areas including hydrogen storage design, wind farm layout optimization, solar irradiance prediction, and bioenergy production and utilization. The review categorizes the literature based on renewable energy sources and their specific areas of application, such as system optimization, energy management, and forecasting. Furthermore, it examines the role of sensitivity analysis and decision-making frameworks enhanced by Genetic Algorithm-based approaches across these domains. By highlighting the synergistic potential of computational intelligence in addressing the complexities of renewable energy deployment, this review provides valuable insights for researchers and practitioners seeking to accelerate the transition towards a more sustainable energy future.