Unit Commitment Problem with Energy Storage Under Correlated Renewables Uncertainty

Unit Commitment Problem with Energy Storage Under Correlated Renewables Uncertainty” introduces a novel approach to address the challenges of renewable integration. The study acknowledges the growing variability and correlation in power availability due to renewable generation and proposes a day-ahead unit commitment (UC) problem formulation that incorporates energy storage and considers multistage correlated uncertainty. Using a variant of the stochastic dual dynamic programming (SDDP) method, which can handle temporal correlations effectively, the researchers solve the complex UC problem. Results obtained from the IEEE 118-bus system demonstrate the significant advantages of considering multistage uncertainty and correlations. Applying their approach to the Chilean power system, the researchers present superior UC solutions that adapt generation to changing uncertainty at a lower cost. Additionally, they propose a more efficient deterministic UC solution that outperforms current industry practices. These advancements promise to enhance the integration of renewable energy sources, enabling a more sustainable and environmentally friendly future. The extensive integration of renewable generation in electricity systems is significantly increasing the variability and correlation in power availability and the need for energy storage capacity. This increased uncertainty and storage capacity should be considered in operational decisions such as the short-term unit commitment (UC) problem. In this work, we formulate a day-ahead UC problem with energy storage, considering multistage correlated uncertainty on renewables’ power availability. We solve this multistage stochastic unit commitment (MSUC) problem with integer variables in the first stage using a new variant of SDDP that can explicitly deal with temporal correlations. Our computational results on the IEEE 118-bus system demonstrate the significance of considering multistage uncertainty and correlations, comparing our solution with other multistage solutions, two-stage solutions, and deterministic solutions typically used by industry. We also solve the MSUC problem for a representation of the Chilean power system, finding superior UC solutions for scenarios where adapting generation to the unfolding uncertainty is costly. Finally, we demonstrate that the MSUC approach can be used to define a more efficient deterministic UC solution, outperforming the current industry practice. History: This article is part of the Operations Research Special Issue on Computational Advances in Short-Term Power System Operation. Funding: This work was supported by the Instituto de Sistemas Complejos de Ingeniería [Grants ANID AFB 220003, FONDECYT 1201844, and FONDECYT 1231924].