Summary
Unit commitment determines which generators should operate during each scheduling period and how much electricity they should produce. The problem becomes computationally demanding when start-up costs, ramp limits, minimum operating times, reserves, and renewable generation uncertainty are considered.
This assignment investigates how different quantum computing approaches can support unit commitment, rather than prescribing a single quantum formulation in advance. The student will explore and compare alternative formulations and algorithms, which may include QUBO-based optimization, gate-based variational algorithms such as QAOA, and hybrid quantum-classical decompositions. Classical optimization will provide a reference and, where appropriate, support feasibility recovery or continuous power dispatch.
Problem definition
Unit commitment is commonly formulated as a mixed-integer optimization problem. Its binary on/off decisions create a rapidly growing search space, while continuous generation variables and intertemporal constraints further increase its complexity.
The binary structure makes unit commitment a relevant application for quantum optimization. However, it is not yet clear which quantum formulation is most suitable. QUBO is one possible representation, but constraint penalties, continuous variables, and qubit requirements may limit its effectiveness. Gate-based formulations may offer different trade-offs in encoding, circuit depth, feasibility, and scalability. The research should therefore investigate which problem decomposition and quantum approach are most appropriate, rather than assuming one formulation from the outset. .
Research objective
Explore, develop, compare and evaluate quantum and hybrid quantum-classical approaches for solving unit commitment under uncertainty, and determine which formulations are most promising for different problem characteristics.
Expected deliverables
- Classical and a selection of quantum-based unit-commitment implementations.
- A reproducible benchmark dataset and codebase.
- Comparative evaluation of quantum formulations and classical methods.
- Identification of the problem characteristics for which quantum approaches is most promising.
- A critical assessment of present quantum-computing hardware limitations and promising follow-up directions, without claiming unsubstantiated quantum advantage.
Required background
The student should have experience with mathematical optimization, power system operation, and Python. Prior knowledge of quantum computing is useful but can be developed during the assignment.
The assignment is co-supervised by Marco Gerards (EEMCS) and Eli Shirazi (ET).
