arXiv subjects

Rosemary Barrass

Publications and source records attributed to Rosemary Barrass.

2 recordsLinked to original sources

arXiv2026

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

The U.S. power grid is undergoing a paradigm shift as energy demand grows in scale and volatility. In response to this growing need, the U.S. has increased adoption of combined-cycle generators (CCs). CCs are fast-ramping generators that utilize variable configurations of combustion turbines (CTs) and steam turbines (STs) to achieve higher efficiency than traditional CTs. For schedule optimization, modeling these CCs requires the addition of a large number of binary constraints and variables to Unit Commitment (UC) problem formulations. This paper presents a novel hybrid Benders' (BD) and Dantzig-Wolfe (DW) decomposition algorithm, called CRG, for stochastic UC problems with CCs. CRG exploits the separability of the linear constraints in UC through BD and the integer CC constraints through DW. A novel set of valid inequalities are proposed for significantly tightening the lower bound produced by CRG. CRG is tested on the 935-generator FERC test data set, modified to include CC mode data. Results demonstrate better primal solutions than BD on cases with at least 20 load scenarios. CRG scales computationally better than Gurobi's branch-and-bound solver, which exceeds 64GB RAM allocations at 45 scenarios. Results show that the proposed algorithm is a scalable approach for solving large-scale stochastic UC with CCs.

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arXiv2025

Leveraging Quantum Computing for Accelerated Classical Algorithms in Power Systems Optimization

The recent advent of commercially available quantum annealing hardware (QAH) has expanded opportunities for research into quantum annealing-based algorithms. In the domain of power systems, this advancement has driven increased interest in applying such algorithms to mixed-integer problems (MIP) like Unit Commitment (UC). UC focuses on minimizing power generator operating costs while adhering to physical system constraints. Grid operators solve UC instances daily to meet power demand and ensure safe grid operations. This work presents a novel hybrid algorithm that leverages quantum and classical computing to solve UC more efficiently. We introduce a novel Benders-cut generation technique for UC, thereby enhancing cut quality, reducing expensive quantum-classical hardware interactions, and lowering qubit requirements. Additionally, we incorporate a $k$-local neighborhood search technique as a recovery step to ensure a higher quality solution than current QAH alone can achieve. The proposed algorithm, QC4UC, is evaluated on a modified instance of the IEEE RTS-96 test system. Results from both a simulated annealer and real QAH are compared, demonstrating the effectiveness of this algorithm in reducing qubit requirements and producing near-optimal solutions on noisy QAH.

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