Optimal Micro-siting of Wind Farms by Particle Swarm Optimization

Wan, Chunqiu; Jun, Wang; Yang, Geng; Zhang, Xing

doi:10.1007/978-3-642-13495-1_25

Cited by 84 publications

(60 citation statements)

References 11 publications

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“…Firstly, we achieve virtually the same maximum energy yield as the best-performing single-objective optimisation algorithm listed in [15]. Secondly, we achieve maximum energy yields that are about 10% better than the results achieved by the approach outlined in [18]. We see both as strong indicators that our approach with the problem-specific variation operators enable the fast and efficient multi-objective optimisation of wind farm layouts.…”

Section: Resultsmentioning

confidence: 58%

“…The dashed lines show a yield of 1.43 · 10 6 kW and 1.30 · 10 6 kW. These are the averages achieved by the best-performing single-objective optimisation algorithm listed in [15] and the result achieved by the approach outlined in [18] respectively.…”

Section: Discussionmentioning

confidence: 94%

“…The scenario involving 200 turbines is computationally expensive due to the increasing costs of the wake computations. It allows us to compare our approach with the single-objective algorithms listed in [15] and the multiobjective approach outlined in [18] respectively. Figure 6 displays a 3D point cloud that represents the Pareto frontier over 10 runs of an experiment using the NSGA-II algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…For example, Wan et al [16][17][18] use cell-based approaches and compare different bio-inspired algorithms, each applied to the same set of wind farm models and parameters. An alternative to cell placement was explored in [10]: each turbine's location is a decision variable pair of real-valued, spatial (x,y) coordinates.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Fast and effective multi-objective optimisation of wind turbine placement

Tran

Denison

et al. 2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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The single-objective yield optimisation of wind turbine placements on a given area of land is already a challenging optimization problem. In this article, we tackle the multi-objective variant of this problem: we are taking into account the wake effects that are produced by the different turbines on the wind farm, while optimising the energy yield, the necessary area, and the cable length needed to connect all turbines.One key step contribution in order to make the optimisation computationally feasible is that we employ problem-specific variation operators. Furthermore, we use a recently presented cachingtechnique to speed-up the computation time needed to assess a given wind farm layout. The resulting approach allows the multiobjective optimisation of large real-world scenarios within a single night on a standard computer.

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Section: Resultsmentioning

confidence: 58%

Section: Discussionmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Fast and effective multi-objective optimisation of wind turbine placement

Tran

Denison

et al. 2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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“…Another successful population-based metaheuristic algorithm applied to the WFDO problem is the Particle Swarm Optimization (PSO) algorithm [184,192,193,201,203,212,243,264,269,353], which was developed by Eberhart and Kennedy in 1995 [345]. The PSO algorithm is inspired by the social behavior of fish schooling and bird flocking.…”

Section: Metaheuristic Optimizationmentioning

confidence: 99%

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

et al. 2014

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This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research. OPEN ACCESSEnergies 2014, 7 6931

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Offshore wind farm electrical design: a review

Lumbreras

Ramos

2012

Wind Energy

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Wind energy will be indispensable as Europe advances towards a low carbon energy future. Offshore locations in the North and Baltic seas are expected to host large arrays of wind farms that plan to export formidable amounts of electricity to the continent. The design of such plants is an intricate task where the electrical layout plays a crucial role. This complexity calls for the use of advanced optimization tools to support investment and operation decisions. This paper reviews the main approaches already developed in the literature and discusses their implications. Copyright © 2012 John Wiley & Sons, Ltd.

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Optimal Micro-siting of Wind Farms by Particle Swarm Optimization

Cited by 84 publications

References 11 publications

Fast and effective multi-objective optimisation of wind turbine placement

Fast and effective multi-objective optimisation of wind turbine placement

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

Offshore wind farm electrical design: a review

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