CVAR-BASED WASSERSTEIN DISTRIBUTIONALLY ROBUST SELF-SCHEDULING UNDER PRICE UNCERTAINTY

Under electricity market price uncertainty, power generators need to provide appropriate generation scheduling strategies to maximize their profits. This study proposes a CVaR-based Wasserstein distributionally robust optimization model to address the self-scheduling problem under price uncertainty. Using optimization duality theory, the model is reformulated into a second-order cone programming problem and solved with a commercial solver (Mosek). Furthermore, a region-partitioning-based approximate model is proposed, which utilizes the alternating direction method of multipliers (ADMM) for distributed computation to improve computational performance. Simulation experiments on three test systems are conducted to validate the effectiveness of the proposed model. The simulation results demonstrate that the model effectively balances risk control and profit maximization and is suitable for solving large-scale self-scheduling problems.