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Abstract

This paper investigates Windfarm Layout Optimization (WFLO), where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Wind energy plays a critical role in the transition toward sustainable power systems, but the optimal placement of turbines remains a challenging combinatorial problem due to complex wake interactions. With recent advances in quantum computing, there is growing interest in exploring whether hybrid quantum-classical methods can provide advantages for such computationally intensive tasks. We investigate solving the resulting QUBO problem using the Variational Quantum Eigensolver (VQE) implemented on Qiskit’s quantum computer simulator, employing a quantum noise-free, gate-based circuit model. Three classical optimizers are discussed, with a detailed analysis of the two most effective approaches: Constrained Optimization BY Linear Approximation (COBYLA) and Bayesian Optimization (BO). We compare these simulated quantum results with two established classical optimization methods: Simulated Annealing (SA) and the Gurobi solver. The study focuses on 4 × 4 grid configurations (requiring 16 qubits), providing insights into near-term quantum algorithm applicability for renewable energy optimization.

Publication Date

2025-08-11

Publication Title

Journal of Quantum Computing

Volume

7

Issue

1

ISSN

2579-0145

Acceptance Date

2025-07-21

Deposit Date

2025-08-12

Keywords

Quantum computing, QUBO, Windfarm layout optimization, VQE

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

First Page

55

Last Page

79

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