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arXiv:2312.13123 · Journal of quantum computing

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

James Hancock, Matthew J. Craven, Craig Mcneile A, Davide Vadacchino

Published Jan 1, 2025• 1 citation
arXivHTMLPDF

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 ... | Find, read and cite all the research you need on Tech Science Press
DOI
10.32604/jqc.2025.068127

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Abstract Layout optimization problems involve finding the optimal arrangement of elements in order to maximize efficiency. For instance, the wind farm layout optimization (WFLO) problem consists of the best turbine placement to maximize energy production while minimizing wake losses. As its nonlinear and combinatorial nature makes it challenging for traditional optimization methods, alternative approaches such as quantum annealing and quantum‐classical hybrid methods offer a promising alternative for tackling such complex problems. Here, WFLO is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem using the Jensen wake model. A quantum annealer is compared, the Gurobi solver, and the Quantum Approximate Optimization Algorithm (QAOA). The quantum annealer provides solutions one order of magnitude faster than Gurobi with at most 3% lower power output, making it suitable for rapid suboptimal approximations. These findings highlight the trade‐off between the quality of the solution and the computational time and demonstrate how quantum methods, especially when combined with classical solvers, can contribute to efficient renewable energy optimization.
This study investigates quantum computing approaches for solving the windfarm layout optimization (WFLO) problems formulated as a quadratic unconstrained binary optimization (QUBO) problem. We investigate two encoding methods that require fewer than one qubit per grid point: the previously developed Pauli correlation encoding (PCE) and a novel single-qubit operator encoding (SQOE). These methods are tested on three windfarm configurations - two from prior WFLO scaling studies and a new real-world model based on an existing windfarm in Wales. The improved encoding methods allow us to solve WFLO problems on 9×99\times 99×9 grids using up to 20 qubits on a quantum computer simulator. The results show that both encoding methods perform competitively and demonstrate favorable scaling characteristics across the tested systems.
Abstract As a possible application of quantum computing in the field of ocean engineering, an attempt is made to identify the optimum layout of an offshore wind farm with the quasi-quantum annealing method while assuming a wake model that conforms to the standard Jensen–Katic wake model but can be implemented in the Ising model. The optimum layouts obtained in the present study are compared with those identified with other conventional methods such as the genetic algorithm. It is confirmed that the quasi-quantum annealing method works as well as other methods in identifying the optimum layouts, while the optimum layout of an offshore wind farm composed of even more than 100 wind turbines can be identified with reasonable CPU time.
Abstract: This study investigates a quantum-inspired optimisation framework for energy-aware virtual machine (VM) placement in cloud data centres based on a Quadratic Unconstrained Binary Optimisation (QUBO) formulation. The proposed approach encodes the VM–host assignment problem into a QUBO Hamiltonian that jointly minimises energy consumption and penalises service level agreement (SLA) violations and capacity constraint violations. Quantum-inspired solvers implemented using TensorFlow Quantum and Qiskit are employed to approximate low-energy configurations, which translate to near-optimal placement decisions. The framework is evaluated on realistic workload traces calibrated to empirical characteristics of production cloud environments and executed on a heterogeneous testbed of physical servers. Experimental results demonstrate that the quantum-inspired QUBO optimisation achieves up to 17.8% lower energy consumption compared with a power-aware best-fit decreasing heuristic, while simultaneously reducing SLA violations by 6.6%. The results further show that the QUBO-based approach provides more stable server utilisation profiles and a higher degree of VM consolidation without incurring excessive migration overhead. These findings indicate that quantum-inspired optimisation, even when executed on classical hardware through TensorFlow Quantum and Qiskit simulators, offers a promising direction for improving the sustainability and efficiency of large-scale cloud data centres. Keywords Quantum-inspired optimisation; QUBO; energy-aware VM placement; TensorFlow Quantum; Qiskit; cloud data centres.
To address the problem, we need to deploy two edge servers in a specific area to maximize the computational demand of the coverage. The problem can be transformed into a maximum coverage problem where the goal is to select a set of locations to deploy edge servers such that the total computational demand being covered is maximized. To solve this problem, we use a greedy algorithm. For quantum computing, the problem can be modeled as a quadratic unconstrained binary optimization (QUBO) problem. The solution process involves calculating the coverage area and computational requirements of possible server locations, constructing a QUBO model, solving the model using a solver and simulator, and parsing the results to obtain the optimal server deployment locations. The final result is the coordinate locations of the edge servers with optimized coverage of computational demands and the corresponding amount of coverage computational demands.
We present an implementation of τ-EO for quadratic unconstrained binary optimization (QUBO) problems. To this end, we transform modify QUBO from its conventional Boolean presentation into a spin glass with a random external field on each site. These fields tend to be rather large compared to the typical coupling, presenting EO with a challenging two-scale problem, exploring smaller differences in couplings effectively while sufficiently aligning with those strong external fields. However, we also find a simple solution to that problem that indicates that those external fields apparently tilt the energy landscape to a such a degree such that global minima become more easy to find than those of spin glasses without (or very small) fields. We explore the impact of the weight distribution of the QUBO formulation in the operations research literature and analyze their meaning in a spin-glass language. This is significant because QUBO problems are considered among the main contenders for NP-hard problems that could be solved efficiently on a quantum computer such as D-Wave.