Investigating Techniques to Optimise the Layout of Turbines in a Windfarm using a Quantum Computer

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$\times$4 grid configurations (requiring 16 qubits), providing insights into near-term quantum algorithm applicability for renewable energy optimization.

Updated all files to now be in accordance with the published version

References in corpus (5)

• The Variational Quantum Eigensolver: a review of methods and best practices
• Challenges and Opportunities in Quantum Optimization
• Quantum Image Segmentation Based on Grayscale Morphology
• A comparison between D-wave and a classical approximation algorithm and a heuristic for computing the ground state of an Ising spin glass
• Iteration Complexity of Variational Quantum Algorithms

Cited by in corpus (1)

• Benchmarking Variational Quantum Algorithms for Combinatorial Optimization in Practice