Hybrid Solar Power System Optimization based on Multi-Objective PSO Algorithm

摘要

Nowadays, with the rapid development of related technologies, the solar power become an important component of the whole energy system. At present, there are two main forms of solar power generation: photovoltaics (PV) and concentrated solar power (CSP). Among them, PV is much cheaper, but more susceptible to resource conditions, while CSP is more controllable based on the thermal storage section, but still expensive. To integrate the advantages of these two kinds of technologies, the hybrid solar power system is proposed by many researchers. Some single objective optimization algorithms are used in the process of design such systems. However, it is still a dilemma to deal with the cost and stability in the hybrid system. In this paper, we try to propose an Multi-Objective Particle Swarm Optimization (MO_PSO) algorithm to solve this problem, which can consider the performance and cost of the project at the same time. The experimental result based on the real data shown that this algorithm can provide a feasible solution of the hybrid power system with stable output and acceptable cost. Furthermore, this method based on artificial intelligence can be used in other hybrid systems optimization in the smart grid.