Investigating Techniques to Optimise the Layout of Turbines in a Windfarm using a Quantum Computer
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866908494879260672 |
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| author | Hancock, James Craven, Matthew J. McNeile, Craig Vadacchino, Davide |
| author_facet | Hancock, James Craven, Matthew J. McNeile, Craig Vadacchino, Davide |
| contents | 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$4 grid configurations (requiring 16 qubits), providing insights into near-term quantum algorithm applicability for renewable energy optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_13123 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Investigating Techniques to Optimise the Layout of Turbines in a Windfarm using a Quantum Computer Hancock, James Craven, Matthew J. McNeile, Craig Vadacchino, Davide Quantum Physics 68Q12, 90C27 F.1.2; G.1.6 This paper investigates Windfarm Layout Optimization (WFLO), where we formulate turbine placement considering wake effects as a Quadratic Unconstrained Binary Optimization (QUBO) problem. Wind energy plays a critical role in the transition toward sustainable power systems, but the optimal placement of turbines remains a challenging combinatorial problem due to complex wake interactions. With recent advances in quantum computing, there is growing interest in exploring whether hybrid quantum-classical methods can provide advantages for such computationally intensive tasks. We investigate solving the resulting QUBO problem using the Variational Quantum Eigensolver (VQE) implemented on Qiskit's quantum computer simulator, employing a quantum noise-free, gate-based circuit model. Three classical optimizers are discussed, with a detailed analysis of the two most effective approaches: Constrained Optimization BY Linear Approximation (COBYLA) and Bayesian Optimization (BO). We compare these simulated quantum results with two established classical optimization methods: Simulated Annealing (SA) and the Gurobi solver. The study focuses on 4$\times$4 grid configurations (requiring 16 qubits), providing insights into near-term quantum algorithm applicability for renewable energy optimization. |
| title | Investigating Techniques to Optimise the Layout of Turbines in a Windfarm using a Quantum Computer |
| topic | Quantum Physics 68Q12, 90C27 F.1.2; G.1.6 |
| url | https://arxiv.org/abs/2312.13123 |