Modelling, solution and application of optimization techniques in HRES: From conventional to artificial intelligence

The escalating costs of electricity, coupled with the urgent environmental challenges posed by fossil fuel consumption, underscore the necessity of transitioning to renewable energy sources (RES). Hybrid renewable energy systems (HRES), which integrate resources such as solar, wind, and hydropower, offer a promising pathway for sustainable energy solutions that reduce greenhouse gas (GHG) emissions, mitigate environmental degradation, and alleviate financial burdens on consumers. Despite their potential, the deployment of HRES remains complex due to factors such as resource variability, multidimensional system architectures, and substantial net present costs. This study provides a comprehensive review of recent advancements in HRES optimization, evaluating classical methods, artificial intelligence (AI)-based approaches, hybrid algorithms, and software-driven tools. The findings highlight the advantages of AI-based techniques, which demonstrate superior global optimization capabilities and reduced computational times, albeit with certain limitations. Hybrid algorithms, combining multiple optimization techniques, emerge as particularly effective for enhancing system efficiency and reliability. Optimization platforms like HOMER are also noted for their practical utility in facilitating HRES design through user-friendly interfaces. This analysis underscores the potential of optimized HRES configurations to contribute significantly to load management, reduce GHG emissions, and achieve cost savings, ultimately guiding readers in selecting the most suitable optimization strategies for various HRES applications.