Research And Application Of Optimization Algorithm Of Pumped Storage Station And Wind-PV Power System

At present, the problem of fossil energy shortage and waste of energy has become an important topic everybody attention in twenty-first Century. It is very important to design and use of new energy sources, wind and light complementary set, a new multiple can have better economic meaning and significance to sustainable development of complementary development mode. But wind power and photovoltaic power generation has characteristics of not accurately predict, random and unstable, caused by wind power and photovoltaic power can t be used effectively and output fluctuations. The wind power, photovoltaic power generation and pumping together is a very good solution to this problem of energy storage, wind power and photovoltaic power generation combined with pumped storage system not only can smooth the output power of wind power and photovoltaic power generation, but also with the reform of the electricity market, the implement of electricity price of peak and valley, also can achieve the purpose of full use of wind power and photovoltaic power generation.This paper analyzes the related knowledge about wind, photovoltaic power and pumped storage power station, and discusses complementary characteristics of wind and photovoltaic. Based on wind power and photovoltaic power generation complementary, configuration of pumped storage power station, composed of the operation of pumped storage power plant and wind farm model combined power generation and photovoltaic power station. According to the characteristics of the hybrid photovoltaic and wind power generation system, to optimize the allocation of power system, reach the effect of stabilizing the fluctuation of power and reducing the waste. An immune particle swarm optimization algorithm based on dynamically changing learning factors are proposed to the defect that the premature, low precision and slow convergence of PSO algorithm in the late, disposing of optimized dispatching of the maximum economic benefit and stabilizing the power fluctuation in the hybrid photovoltaic and wind power generation system, the objective of power fluctuation minimum is introduced to the maximization of economic benefit model. Learning factors for the Immune particle swarm algorithm are asymmetric linear improved dynamically, the algorithm can make the search behave well in global searching, strengthening local searching to get more precise global optimum. The algorithm can be used well in the multi-objective hybrid photovoltaic and wind power generation system, improving the accuracy of the solution and is optimized to achieve the maximum economic benefit, showing the effectiveness of the model and algorithm.Results show that pumped storage operation with wind power generation, photovoltaic power generation is the effective ways to develop and utilize solar energy, wind energy resources, not only to improve the wind power, photovoltaic power plant efficiency, and realize the smoothing of power output of wind farm and photovoltaic power station, with considerable economic benefit and social benefit.