High-Performance Optimization Model Based on Novel Conditional Value At Risk Metric for Power Grids With High Wind Power Penetration

Due to the challenges in achieving accurate probabilities, representing uncertainty as intervals helps mitigate issues arising from the lack of distribution information. However, current interval-based methods for optimization modeling in power grids fail to fully capture the uncertainties of interval variables, leading to higher reserve costs. To overcome the conservatism of existing dispatch models, this work develops a novel uncertain interval variable (UIV) for risk assessment, where the radius of an interval is treated as a random variable. Inspired by the Affine algorithm, we propose a novel uncertain interval-based conditional value-at-risk (CVaR) metric, called UP-CVaR, for multiple random variables. The dispatch results for the New England 39-bus and 118-bus systems show that the proposed method can obtain tighter interval dispatch results compared to existing economic dispatch (ED) models. Moreover, as the stochastic level of wind power (mean and standard deviation) increases, the range of scheduling results expands.