A closed-loop data-driven optimization framework for the unit commitment problem: A Q-learning approach under real-time operation. (15th January 2023) - Jiménez, Diego; Angulo, Alejandro; Street, Alexandre; Mancilla-David, Fernando
Abstract: Real-time operation of electric power systems under high penetration of renewable energy generation has to incorporate strategies for managing the uncertainty associated with this type of power sources. In the case of the unit commitment problem, models based on robust optimization have been widely used for the day-ahead computation of power dispatch and reserves schedule. Typical approaches use uncertainty sets with fixed levels of conservativeness, without considering the real-time performance of the solutions. This paper proposes a solution scheme for the adaptive robust unit commitment problem using online adjusted data-driven uncertainty sets. Conservativeness parameters of the uncertainty sets are dynamically calculated as a function of previous operating results and incoming data via reinforcement learning, resulting in an online learning-optimization framework. The paper also develops an experimental framework to simulate real-time operation under the proposed methodology. Out-of-sample experiments illustrate the effectiveness of the proposed scheme against well-known benchmarks with fixed robustness levels. Results show improvements in power generation costs, voltage violations, and use of reserves. Two systems of different sizes are analyzed, illustrating the scalability of the proposed approach. Highlights: Q-learning algorithms for data-driven robust uncertainty sets adaptation. Uncertainty sets dynamical adaptation based on real-time system indicators. Novel framework for real-time power systems operation simulation. Results comparison against two-stage robust and stochastic optimization approaches. Statistically significant lower generation costs and use of system reserves.