Fast Sampling Method For Grid Risk Assessment And System Implementation

With the rapid development of modern power grid, the problem of uncertainty in power grid running processes is becoming more and more serious. Using quantitative risk analysis methods to assess the uncertainty in the power grid, as well as developing appropriate strategies for risk control, is of great significance for the security and stability of the power system. There are two basic methods, analytical method and Monte Carlo method, which are used for grid risk assessment. As the sampling frequency of Monte Carlo method has nothing to do with the system scale, it’s suitable for risk assessment of large-scale grid, except for the contradiction of computing time and accuracy, which takes a long time for sampling and analogy to obtain the reliability index with high accuracy. In view of the increasing demands for assess speed in risk assessment system, research in sampling methods and improvement in relevant aspects to increase the computational efficiency of the system have become a research topic which has a certain theoretical significance as well as application value.Firstly, this paper describes the analytical method of risk assessment and the main content of the Monte Carlo method. Basing on this, detailed steps will be discussed about the application of Monte Carlo non-sequential sampling method in the grid risk assessment, and how to create the desired model for assessing the probability of failure and risk indicator system. Since the sampling frequency of the Monte Carlo method is inversely proportional to the accuracy, this paper proposed improved dispersed sampling method combined with the expected fault set, several improvements and optimization will be given about the Monte Carlo sampling and related links, which will get a higher sampling efficiency while maintaining the required accuracy. After that, basing on Visual C#.NET platform, it will give an introduction about risk assessment software for provincial power grid which is, to achieve the improved algorithm of physical systems theory. Finally, an example test with the original data of provincial power grid is given to verify the availability of the algorithm and the improvement of efficiency.