Algorithm Development and Software Tool Application of Power Market Risk Assessment

A hybrid algorithm called Risk Dynamic Perception Deep Neural Network (RDP-DNN) has been proposed, which combines the advantages of Convolutional Neural Networks (CNN) and Long Short Term Memory Networks (LSTM) in deep learning, as well as feature selection techniques in machine learning, to improve the accuracy and efficiency of risk assessment in the electricity market.RDP-DNN algorithm can effectively evaluate the main risk types such as market price fluctuation, credit risk and liquidity risk by automatically learning the complex characteristics of power market and capturing the risk signals in time series data. In addition, the PowerRiskAnalyzer software tool is selected in this study, which integrates the functions of data analysis, model construction, risk assessment and report generation, and fully supports the identification, quantification and control of power market risks. Through the application case analysis of a national power grid company in Europe, RDP-DNN algorithm and PowerRiskAnalyzer software tools successfully identify key risk factors such as price fluctuation, demand change, supply uncertainty, extreme weather events and policy changes, and provide targeted risk management suggestions for the company, thus providing solid theoretical and technical support for the healthy development of the power market and the safe and stable operation of the power system.