Supply-Demand Balance Assessment Method for Power Systems Considering Extreme Scenarios

With the continuous increase in the proportion of new energy generation capacity, the stochastic nature and weather-coupled characteristics of its output pose significant challenges to power system supply-demand balance. Traditional methods struggle to quantify risks associated with numerous extreme scenarios and insufficiently characterize low-probability, high-impact scenarios such as long-term low output of new energy and "extreme heat and windless" weather. This paper proposes a supply-demand balance state assessment method that constructs an 8760-hour full-scenario set through stratified sampling and achieves scenario reduction using an improved clustering algorithm. On this basis, extreme scenarios are supplemented to form a specific scenario set. Combining with a time-series production simulation algorithm for sources-grids-loads-storage, a four-level assessment system is established based on power deficit magnitude and duration. Validation using 2030 development planning data of a regional power grid demonstrates the feasibility and effectiveness of the proposed model and method, providing an efficient quantitative assessment tool for supply-demand analysis in high-proportion renewable energy power systems.