Research Operation Health Assessment Model Of Power Market And Risk Early Warning Analysis

With the continuous promotion of the new round of power system reform in China,the complex power system status and trading environment put forward higher requirements for the efficient and stable operation of the power market.Regular evaluation of the operation of the power market is helpful to grasp its overall operation and trend,which is of great significance to the sustainable development of the future power market.Based on the analysis of power market and risk early warning research at home and abroad,in order to effectively evaluate the current operation of the power market,this paper creatively puts forward the concept of "power market operation health",which is used to measure whether the operation of the current power market is safe,efficient and sustainable.Then,the evaluation index suitable for the operation health of China’s power market is determined,and the corresponding evaluation system is established.In this paper,from the five aspects of "supply side","demand side","market coordinated operation","market security" and "sustainable development",the evaluation index system of power market operation health is established.In order to effectively avoid information distortion and loss in the evaluation process,this paper combines cloud model,matter-element extension theory,ideal point method and cloud entropy optimization algorithm,and constructs matter-element extension cloud model based on cloud entropy optimization algorithm and ideal point method.Where,the matter-element extension cloud model is used to clearly represent the characteristics of the object to be evaluated;the improved ideal point method is used to determine the weight of the index,which is different from the general subjective and objective weight for linear combination.The ideal point method can realize the adaptive adjustment of the optimal weight according to the actual situation of the index,and the cloud entropy optimization algorithm can further give a higher reliability evaluation result on the basis of considering the "3En" rule and the "50%association degree" rule.Finally,combining the health degree and risk of power market operation to set the risk early warning threshold,a risk early warning method based on multi-layer perceptron(MLP)is proposed,which provides reference for power market operation evaluation and early warning.In the empirical analysis,the electricity market operation of the simulated y Province in October 2019 is evaluated.The results show that the comprehensive health level is medium health,and the importance and health level of each index are given,and the reasons for this result are analyzed.In addition,in order to verify the validity and stability of the model,the paper introduces the reliability factor to measure the discrete degree of the evaluation results,and uses two other cloud entropy solution methods to evaluate and compare the evaluation objects,which proves the credibility of the evaluation results of the model framework proposed in this paper.Finally,through SPSS software,the MLP neural network model is implemented to predict and analyze the health risk of simulated power market operation in Y Province,which proves the feasibility of the model and provides reference for the early warning analysis of health risk of power market operation.