Performance Verification of Bayesian Network–Based Security Risk Management and Control System for Power Trading Institutions

Abstract

Risk in power trading is unavoidable for various reasons. The impact of this risk would vary based on the trading characteristics that mainly depend on the market design and power purchase agreements. So, a security risk management and control system for power trading institutions based on a Bayesian network is designed to reduce the risk of power trading projects. As a part of the network, we first provided the overall architecture of the risk management and control system, which includes a malicious network behaviour detection module, controller selection module, data transmission module, and management and control result visualisation module. Second, the hardware test design was implemented by analysing each module’s working principle and function. Based on the hardware design of the system, the regression analysis method is used to evaluate the risk of power transactions, followed by market fluctuation prediction to obtain the prediction result induced risks. The relationship between security risks and risk-influencing factors is analysed using the Bayesian network. The initial list of risks is established, the uncertain risk factors are reasoned, and the security risk management and control model of power trading institutions is tested to achieve the goal of risk management and control. The experimental results show that this method’s risk management and control efficiency are high. At the same time, this method effectively realised comprehensive risk identification by reducing the loss to power enterprises and has near-practical application value.