Research On Dispatching Of Multi-source Regional Grid Based On Wind Farm Integrated System

The environmental pollution problems caused by fossil energy have become a major obstacle to the sustainable development strategy of national sources.Using non-pollution and renewable energy to replace fossil energy sources,is one of the future development trends of electric power.Wind energy,as one of the new energy power sources,has the advantages of cleaning and large storage capacity,and has been widely developed and used.Due to the random uncertainty of wind power,when large-scale wind power is integrated into the power system,which brings some challenges to the power system stable operation.Therefore,the research on the dynamic characteristics of wind farm integrated system,wind power prediction and optimal dispatching of multi-source regional power network is of great significance to improve the development and utilization of wind power.Focusing on dispatching or wind farm integrated system based on power forecasting following researches have been carried out in this paper.(1)The multi-source hybrid electric power system model containing wind turbines,hydropower units and thermal power units is built up,based on these mathematical model.The multi-source hybrid electric power system model is simulated and analyzed in the condition of wind speed fluctuation.Simulation results show that when the power output of wind turbines changes,the multi-source hybrid power system model can accurately describe the dynamic characteristics of power system.(2)A wind power forecasting method based on particle swarm optimization and back-propagation neural network(PSO-BP)algorithm is studied in this paper.This method uses the global search ability of particle swarm optimization(PSO)algorithm to obtain the initial weights and biases of BP neural network,which can effectively solve the problems of slow convergence speed and easy to fall into local optimal value of original BP algorithm.The forecasting results of PSO-BP neural network and BP neural network are compared and analyzed,and the comparison results show that the MAE and RMSE of wind power forecasting based on PSO-BP are less 7.02%,and 9.37% respectively than that based on BP neural network,which proves that the PSO-BP neural network is better than BP neural network in wind power forecasting.(3)Based on the multi-source hybrid electric power system model and wind power prediction model,the method of dynamic economic dispatch of power system integrated wind based on Multi-agent and particle swarm optimization(MA-PSO)algorithm is researched.The MA-PSO algorithm combines the global characteristics of PSO algorithm and the intelligent characteristics of multi-agent system(MAS),which can effectively solve the multi-constraint,large-scale,non-linear programming problem.The optimization results of MA-PSO algorithm and PSO algorithm are compared and analyzed,and the cost of MA-PSO algorithm is less than 3.8230×10~3 $ than that of PSO algorithm every day,and saving rate is up to 9.14%.The comparison results show that the MA-PSO algorithm has good performance and high convergence precision;at the same time,MA-PSO algorithm is applied to the economic dispatch problem,which can obtain better economic benefits and environmental benefits.