Extreme scenario extraction of a grid with large scale wind power integration by combined entropy-weighted clustering method

Abstract

Large-scale integration of wind power into a power system introduces uncertainties to its operation and planning, making the power system operation scenario highly diversified and variable. In conventional power system planning, some key operation modes and most critical scenarios are typically analyzed to identify the weak and high-risk points in grid operation. While these scenarios may not follow traditional empirical patterns due to the introduction of large-scale wind power. In this paper, we propose a weighted clustering method to quickly identify a system’s extreme operation scenarios by considering the temporal variations and correlations between wind power and load to evaluate the stability and security for system planning. Specifically, based on an annual time-series data of wind power and load, a combined weighted clustering method is used to pick the typical scenarios of power grid operation, and the edge operation points far from the clustering center are extracted as the extreme scenarios. The contribution of fluctuations and capacities of different wind farms and loads to extreme scenarios are considered in the clustering process, to further improve the efficiency and rationality of the extreme-scenario extraction. A set of case studies was used to verify the performance of the method, providing an intuitive understanding of the extreme scenario variety under wind power integration.