Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

TLDR

Unit commitment, a critical task in electric power system operations, faces new challenges as supply and demand uncertainty rises with the integration of variable generation such as wind power and price‑responsive demand. To address these challenges, we propose a two‑stage adaptive robust unit commitment model for the security‑constrained problem under nodal net injection uncertainty. The authors develop a practical solution methodology that combines Benders decomposition with outer approximation and evaluate it on a large‑scale ISO New England power system through extensive numerical studies. The model outperforms conventional stochastic programming by requiring only a deterministic uncertainty set, yielding solutions robust to all uncertainty realizations and delivering economic and operational advantages over traditional reserve adjustment approaches.

Abstract

Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.