Unit Commitment (UC) requires the optimization of the operation of generation units with varying loads, at every hour, under different technical and environmental constraints. Many solution techniques were developed for the UC problem, and the researchers are still working on improving the efficiency of these techniques. Particle swarm optimization (PSO) is an effective and efficient technique used for solving the UC problems, and it has gotten a considerable amount of attention in recent years. This study provides a state-of-the-art literature review on UC studies utilizing PSO or PSO-variant algorithms, by focusing on research articles published in the last decade. In this study, these algorithms/methods, objectives, constraints are reviewed, with focus on the UC problems that include at least one of the wind and solar technologies, along with thermal unit(s). Although, conventional PSO is one of the most effective techniques used in solving UC problem, other methods were also develo... [more]

This paper discusses the optimization of hybrid power systems, which consist of solar cells, wind turbines, fuel cells, hydrogen electrolysis, chemical hydrogen generation, and batteries. Because hybrid power systems have multiple energy sources and utilize different types of storage, we first developed a general hybrid power model using the Matlab/SimPowerSystemTM, and then tuned model parameters based on the experimental results. This model was subsequently applied to predict the responses of four different hybrid power systems for three typical loads, without conducting individual experiments. Furthermore, cost and reliability indexes were defined to evaluate system performance and to derive optimal system layouts. Finally, the impacts of hydrogen costs on system optimization was discussed. In the future, the developed method could be applied to design customized hybrid power systems.