The growing world-wide demand of power from renewable sources has brought up considerable attention to HRES system as the strong supplementary or alternative systems for fossil-fuel based energy generation. The efficient deployment of HRESs has to tackle a number of difficult optimization problems that involve the trade-offs between cost, reliability and sustainability. Sophisticated optimization techniques are the key in system design and operation. This paper classifies and assesses the solar, wind, biomass and energy storage based HRES optimization methodologies. It defines significant modeling methodologies and measures, such as Loss of Power Supply Probability (LPSP) and Levelized Cost of Energy (LCOE). Evaluations are made between traditional programming approaches, meta- heuristic algorithms (GAs, PSO and DE) based ones along with the new AI paradigms in use for adaptive energy management. The primary issues consist in the varying resources to be allocated and varying size of components, storage limitations and computational costs. With proper design, hybrid systems can be cost effective and produce fewer greenhouse gases than single-source systems. Suggested areas for further investigation are AI-based predictive control, next-level storage integration and digital twins as basis for autonomous operation.
Hussein et al. (2026) studied this question.