An Integrated Probabilistic Forecasting and Robust Optimization Method for AC-OPF Under High-Penetration Renewable Energy

The high penetration of renewable energy presents significant challenges for the secure and economical operation of power systems. To address the uncertainty in power system operations, this paper proposes a novel integrated probabilistic forecasting and robust optimization (PFRO) method to solve a robust optimal power flow (ROPF) problem driven by prediction intervals (PIs). In order to better balance robustness and cost-effectiveness in robust optimization, the proposed PFRO approach defines uncertainty sets based on prediction intervals with explicit probabilistic interpretations. Then the boundaries of uncertainty sets can be adaptively adjusted via optimizing predictor parameters according to the subsequent decision-making of ROPF. Furthermore, the proposed PFRO approach reduces the conservativeness of ROPF and bridges the gap between forecasting and decision-making. To accurately capture the statistical characteristics of renewable generation, a similar dataset is constructed for training the forecasting model. The nonconvex integrated optimization problem is then transformed into a form that can be solved directly using a difference of convex algorithm (DCA). Numerical simulations based on two test systems and renewable energy generation data demonstrates the overall superiority of the proposed approach in terms of forecasting accuracy and operational value.