Requirements of Risk Warning and Abductive for Power System Operation Based on Renewable Energy Accommodation Enhancement

With the acceleration of the construction of new power systems and the continuous expansion of the scale of the power grid, the access of flexible resources such as distributed renewable energy, virtual power plants and electric vehicles has made a large number of new adjustment objects appear on each side of the power grid load and storage, and the difficulty of cognitive analysis and adjustment of the power grid operation state has increased significantly. The traditional analysis model based on mechanism modeling is difficult to deal with the cognitive problem of multi-agent complex system. In view of the safe and stable operation of power grids in some regions after access to massive distributed flexible resources such as reverse transmission, overvoltage, and difficulty in peak regulation, it is urgent to study the refined cognition of power grid operation situation and the abductive technology of risk assessment based on probability statistical analysis and deep learning, so as to improve the cognition level and control ability of power grid risk. The power grid operation risk early warning and abduction technologies absorbed by renewable energy are further studied. The characteristics of abnormal situations are restored through the augmented matrix and abduction analysis is carried out, and the weak links of system operation and points causing risks are located to provide multi-dimensional cognitive information of power grid operation for subsequent optimization and dispatch.