With the rapid development of renewable energy technique, Wind/PV hybrid power system is more economical and reliable than a single PV or wind turbine for their complementary both in time and geography. Therefore, Wind/PV hybrid power system is widely used in many area in recent years. However, with the increase of installation capacity of the Wind/PV hybrid power system, traditional capacity design by experience can not meet the accuracy and the optimization in design and operation. To deal with the problem, a comprehensive objective function model is presented which not only include the investment cost, but also the reliability and optimal operation of the system. The objective function consists of the investment of wind turbine, PV solar, storage cell and the cost of loss of power energy in the system which can be calculated by reliability. The reliability can be evaluated by the system model which made up of wind turbine model, PV solar model and storage cell model built in this paper. In these energy sources model, it is not only including the photovoltaic cells and the number of batteries, but also adding the type and number of Wind turbine as well as the inclination of photovoltaic cells, making the results of a more accurate. By transforming the investment cost and reliability into comprehensive cost, the presented multi - optimization problem transformed a single optimization problem. The solution for hybrid power system capacity optimal configuration is a classical nonlinear hybrid integer optimization problem. An improved particle swarm optimization algorithm is presented to deal with this problem. On the basis of analyzing the standard PSO algorithm, two improved strategy are applied. Firstly, a convergence factor is adopted to enhance its search efficiency; secondly, a migration operation is used to improve the algorithm's global optimal searching ability. Furthermore the improved particle swarm algorithm also integrates the standard particle swarm opt--imization and genetic algorithm with the advantages of higher capacity and faster global convergence of the search efficiency. The proposed algorithm is tested on one island power system and the results analysis show its feasibility and effectiveness.
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