Optimal Dispatch Of Power Systems With Large-scale Wind Power

Energy is the foundation of a country's economic and social development.With the progress of society and the rapid development of the national economy,China's energy demand is steadily increasing.However,fossil energy is facing a crisis of exhaustion year by year.Sustained development is included in the economic development goals,while pursuing rapid economic development and attaching importance to environmental protection.Therefore,vigorously developing renewable energy and improving the utilization rate of renewable energy have become the key to economic development.Wind energy has been widely used as a renewable energy with more reserves,wider distribution and lower cost in China.China not only ranks first in the proportion of global wind power installed capacity,but also the country with the fastest growth in global wind power installed capacity.However,with the gradual increase of the scale of wind power grid connection,wind power brings advantages and also brings new challenges to the operation of the power system.As an uncontrollable natural resource,wind energy has volatility,randomness,peak inversion,etc.Due to its inherent characteristics,the dispatching operation of the power system may make a decision to abandon the wind,resulting in a waste of clean energy.Therefore,the establishment of a new and more accurate economic dispatch model with large-scale wind power systems is of great significance in maintaining the safe and stable operation of power systems,promoting the absorption of renewable energy,and improving the economics of power system operations.Wind power forecasting is the basis for dispatching large-scale wind power systems.First,after analyzing the factors affecting wind power forecasting,the input data for wind power forecasting is determined and preprocessed,and the BP neural network optimized by the gravity search algorithm is used The network makes point predictions on wind power.Considering the uncertainty of wind power output,in actual operation,only the predicted value of the wind power point is often unable to meet the demand.Based on the prediction of the wind power point obtained by the BP neural network optimized by the gravitational algorithm,the forecast error is carried out.Non-parametric estimation to obtain wind power interval prediction.Further establish a dynamic economic dispatch model of the power system with large-scale wind power.The economic dispatch model is the minimum sum of the cost of thermal power and the cost of wind power.The cost of thermal power includes the basic operating cost of the thermal power unit considering the valve point effect and theenvironmental pollution treatment cost caused by the thermal power unit The wind power cost is the wind power operation cost and the wind power penalty cost based on the wind power interval prediction.Considering that the particle swarm algorithm is prone to fall into the local optimal solution,its weight coefficient is dynamically updated,and the simulated annealing algorithm is used to optimize the particle swarm algorithm after the weight coefficient is updated,and the optimized particle swarm algorithm is used to establish the The power system dynamic economic dispatch model is used for solving.In this thesis,the IEEE39 node system with wind farms is built,and the superiority of the model and the feasibility of the algorithm are verified by MATLAB simulation.The impact of various factors on the economic dispatch of the power system is analyzed and compared.It is a large-scale wind power grid-connected power system Economic dispatch provides theoretical basis and practical reference significance.