Research On Reliability Evaluation Method Of Power System Containing Wind Storage And Grid Connection

Power system reliability research is an significant components of power system grid connection planning and ensuring the safety and constancy of system.At the same time,the research of renewable energy integrated power system is also a research hotspot at this stage.However,since wind energy and other renewable energy sources are usually variable and intermittent,they will affect grid safety.If we can study the impact of the uncertain characteristics on the safety of the system,integrate renewable energy into the system to enable dispatchers to better plan for the power grid.Therefore,this article mainly studies and discusses the impact of wind power and energy storage integration on the reliability of the power system.This article introduces the current development status of renewable energy and the current status of the industry combining renewable energy with power systems,and points out the practical engineering value of studying power system reliability.Then,combined with the uncertainty of wind power,the value,method and pressing demand for pickup of the current domestic and foreign wind power and energy storage grid-connected reliability research are introduced in detail.First of all,We established a hidden Markov chain(HMM)wind power interval prediction method based on the gray wolf optimization algorithm(GWO)to optimize the weights for the uncertainty of wind power.First,the training data is processed,a HMM is build,and the processed data is brought into the model for training.The use of hidden Markov chain models can fuse model information,integrate model information,probabilistic information reasoning knowledge and prior knowledge of samples to make wind power calculating more exact.Then,the gray wolf optimization algorithm is used to optimize the output value,and the predicted power interval is obtained under a given confidence level and the predicted evaluation index is obtained.Finally,the simulation comparison with the particle swarm optimization method shows that this method can obtain a higher interval coverage,and the wind power interval prediction quality is higher.Furthermore,we considered the uncertainty of renewable energy such as wind power,that is,based on the GWO-HMM wind power forecast model,we proposed a power system reliability assessment method.Firstly,the uncertainty of wind power power is studied using the power prediction method based on the gray wolf method to optimize the HMM.Then we built the reliability test system,and used the Latin hypercube sampling theory to evaluate the reliability of the power system considering the uncertainty of wind power.Compared with the convention reliability evaluation technique,Latin hypercube sampling method considers the theory of stratified sampling,so that the sample distribution is more uniform,the sampling coverage is higher,and the adaptability is stronger.The comparison with the simulation verification of different methods shows the accuracy and superiority of the method in this paper.Finally,on the basis of considering the uncertainty of wind power,we also considered the uncertainty of energy storage,that is to say,considering the different kinds of power loss errors that may be caused by the failure of energy storage during operation,a power system reliability assessment method based on the uncertainty of wind power and energy storage failure is proposed.On the basis of the wind power GWO-HMM interval forecast model,neural network theory is used to analyze and classify energy storage failures,and consider the impact of energy storage failures of different degrees on the coordinated grid-connected power of wind power and energy storage.Then,the Latin hypercube sampling theory is used to evaluate the reliability of the power system considering the uncertainty of wind power and energy storage.By comparing the reliability index with the original system and the wind farm grid-connected system,it shows the superiority and engineering value of the method in this chapter.