A Joint Planning Method For Power System Source Storage Network Based On Typical Wind Power Scenarios

In recent years,due to the continuous intensification of environmental pollution and the increasing scarcity of fossil fuels,renewable energy generation technologies such as wind energy have developed rapidly,and their high penetration will become an important trend in the coming decades.However,due to the uncertainty and volatility of wind power generation,the joint planning problem of power system source storage network has become an uncertainty problem containing random variables.Therefore,studying how to describe the uncertainty of wind power generation and based on this,conducting comprehensive planning for power sources,power grids,and energy storage is of great strategic significance for the transformation of low-carbon energy.This article focuses on the negative impact of wind power output uncertainty on power system planning,and adopts a conditional deep convolution generation adversarial network method to generate wind power scenarios.By using an improved K-means algorithm,a large number of generated wind power scenarios are reduced to obtain typical wind power scenarios,which describe the uncertainty of wind power output.This method uses real historical data validation to verify its effectiveness,and compares with the scene generation method based on Markov and Coupla theory.The research results indicate that the scenarios generated using this method are more accurate in depicting the uncertainty of wind power output,providing a basis for subsequent power system planning.In order to fully utilize wind power generation in the power system and reduce wind abandonment,this paper establishes a source storage network joint planning method that takes energy storage as the main flexible resource and considers the balance of flexible supply and demand.Firstly,this article proposes to apply the wind power scenario generated by adversarial networks based on conditional deep convolution to the joint planning method of source network storage,in order to describe the uncertainty of wind power generation.The grey wolf optimization algorithm based on tracking search mode is used to solve the planning problem.Comparison with traditional grey wolf algorithms and particle swarm optimization algorithms shows that this method can better solve the planning problem.Secondly,in order to improve the flexibility of the power system,by comparing different energy storage layouts and conventional planning methods without considering flexibility,research has shown that using the joint planning method of source storage and grid can effectively solve the problem of insufficient flexibility brought about by future wind power development,considering the balance of flexibility supply and demand.This method can achieve coordinated planning of power supply,energy storage,and grid,thus enabling them to collaborate with each other,Achieving the goal of optimal overall economic performance.