Cost-effective Closed-loop Bilevel Robust Optimization for Joint Chance-constrained Economic Dispatch

The day-ahead economic dispatch (ED) problems are critical in modern power systems operations.Traditionally, ED problems are solved using stochastic optimization (SO) methods, such as chanceconstrained optimization (CCO), robust optimization (RO), and distributionally robust optimization (DRO).These methods usually follow a two-stage open-loop process: firstly, random variables (including distributions, uncertainty sets, and ambiguity sets) are estimated using historical data; then, the ED problem is solved based on these estimations.However, this approach often leads to suboptimal performance because the objectives in the two stages are misaligned: minimizing estimation errors does not necessarily result in the lowest possible ED costs.To tackle this challenge, we propose a closed-loop optimization framework that unifies and jointly optimizes the two stages through bilevel robust optimization.This method ensures the optimal ED cost while simultaneously satisfying joint chance constraints.To enhance computational efficiency, we transform the bilevel robust optimization problem into a more tractable bilevel linear programming problem.Furthermore, we introduce two parametric fine-tuning strategies to improve the performance of our closed-loop solution.Numerical studies using field data validate the effectiveness of the proposed frameworks. CCS Concepts