A Hybrid Decomposition Approach for Stochastic Unit Commitment with Combined-Cycle Generators

TL;DR

This paper introduces a hybrid Benders' and Dantzig-Wolfe decomposition algorithm for stochastic unit commitment with combined-cycle generators, significantly improving computational efficiency and scalability.

Contribution

It proposes a novel hybrid decomposition method that effectively handles the complex constraints of stochastic UC with CCs, outperforming traditional methods.

Findings

Significant speed-up over traditional Benders' decomposition.

Better convergence rates than Gurobi's branch-and-bound for large scenarios.

Scalable approach demonstrated on a 935-generator test dataset.

Abstract

The U.S. power grid is undergoing a major paradigm shift with the increased development of renewable generators, electric vehicles, and data centers. In response to this growing need, the U.S. has ramped up the construction of combined-cycle generators (CCs). CCs are fast-ramping generators that utilize variable configurations of combustion turbines (CTs) and steam turbines (STs) to achieve much higher efficiency than traditional CTs alone. For schedule optimization, this requires the addition of a large number of binary constraints and variables in Unit Commitment (UC) problem formulations. This paper presents a novel hybrid Benders' (BD) and Dantzig-Wolfe (DW) decomposition algorithm for stochastic UC problems with CCs. The algorithm exploits the separability of the linear constraints in UC through BD and the integer CC constraints through DW. Results are presented for the 935-generator FERC test data set, modified to include mode data for CCs. The algorithm demonstrates a significant speed-up over traditional BD across all cases. It also demonstrates better convergence rates on cases with 25 or more scenarios than both BD and Gurobi's branch-and-bound solver. These cases show that the proposed algorithm is a scalable approach for solving stochastic UC.