Optimization Techniques and Multi-Objective Analysis in Hybrid Solar-Wind Power Systems for Grid-Connected Supply

Renewable energy has become a vital source for generating electricity, mitigating greenhouse gas emissions, and eliminating the reliance on non-renewable forms of energy. This work highlights on the urgent need for clean, efficient, and resilient energy sources through the development of advanced optimization methods. The hybrid solar-wind systems are meant to maximize power generation. Complexity arises when determining the optimum design, size, functionality, materials, distribution, and mechanism of each system as they are combined to ensure system reliability and optimal outcomes. The development of the new optimization methods (such as genetic algorithms, heuristic approaches, and particle swarm techniques) was generated to overcome the encountered barriers in traditional or classical approaches; such as in iterative techniques and linear programming methods. Additionally, the multi-objective analysis has been involved in various studies where vectors, criteria, and alternatives are applied with respect to the main objectives. However, further research efforts are encouraged to innovatively optimize hybrid solar-wind systems; including the optimization of hybrid solar-wind sizing (HSWS), design, capacity, battery storage, energy distribution systems and techno-economical considerations.