Uncertainty Risk Assessment of Virtual Power Plants Using Bayesian Network Models

During the operation of a virtual power plant, many uncertain factors such as fluctuations in renewable energy supply, market prices, and equipment performance increase its operational risks. In response to the above issues, this paper takes the operational risk of virtual power plants as the research object, based on Bayesian networks and combined with various uncertainty factors, establishes a Bayesian network model to effectively analyze and assist in the operation risk of virtual power plants. By conducting statistical analysis on historical meteorological data, transaction data, and equipment operation status during the operation of virtual power plants, a Bayesian network is constructed and trained. This paper adopts a fault diagnosis method based on expert knowledge. This paper analyzes the impact of multiple uncertain factors on the operation of virtual power plants in multiple operating scenarios, including the impact of new energy consumption on the stable operation of the power grid, and the impact of electricity price fluctuations on the economic benefits of the power grid. Virtual power plants can quickly respond to high-risk weather such as typhoons (15 minutes) and tornadoes (20 minutes), reducing losses caused by disasters. Through this study, new theories and methods will be provided for risk assessment of virtual power plants, and reference and guidance will be provided for risk management in other industries.