Research On Grid-connected Optimization Dispatch Of Wind-PV-Storage Complementary System

Forty years since the reform and opening up,China's national economy has developed across the ages.In order to meet China's rapid development,coal-based power generation has brought about energy shortages and environmental pollution problems.Therefore,research on clean renewable energy generation and Development has received more and more attention.Wind power generation and photovoltaic power generation are the two most important renewable energy power generation methods.However,due to their intermittent and output instability,the grid will have an impact on the grid,making it difficult to be completely consumed,resulting in a large number of Abandoning the wind and abandoning electricity violates the original intention of new energy power generation.Therefore,in order to effectively eliminate the intermittent and volatility of wind power generation,this paper is based on the natural complementary features of wind power output,and is equipped with a pumped storage power station with large storage capacity and rapid adjustability,which constitutes a combined power generation system with wind and solar storage.Wind power generation,photovoltaic power generation and pumped storage capacity constraints were established,and an optimization model with minimum output power volatility and tracking load curve for complementary systems was developed.Based on the above two wind and solar storage complementary system scheduling strategies,it is connected to the thermal power plant,and the goal is to minimize the cost of power generation and minimize the penalty cost of pollutant discharge,including the combination of thermal power units and grids.The system's day-to-day joint scheduling model was tested with a system consisting of seven thermal power plants,a wind power station and a pumped storage power station.Based on the particle swarm optimization algorithm,because of its simple,easy to understand,less parameters to be adjusted and artificial immune algorithm can ensure the diversity of antibodies in the immune system and accelerate the convergence speed,the two are combined to propose an improved hybrid.The algorithm is an immune particle swarm algorithm that can self-learn and adjust the weighting factor.The algorithm is used to optimize scheduling research.Using MATLAB software to solve the model,the wind and solar storage complementary system and the thermal power unit's daily scheduling results under two scheduling strategies are obtained.It can be seen from the simulation that under the minimum optimization model of complementary system output power volatility,the wind-solar storage complementary system can effectively eliminate the intermittent and volatility of wind and light output,reduce the impact on the grid during grid connection;and optimize the model with tracking load curve as the target.Under the grid connection,the peak-to-valley difference of the grid load can be reduced to achieve the effect of smoothing the output of the thermal power unit.