Comparative Study of Wind Turbine Placement Methods for Flat Wind Farm Layout Optimization with Irregular Boundary

Wang, Longyan

doi:10.3390/app9040639

Cited by 15 publications

(8 citation statements)

References 31 publications

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“…In two independent studies, Charhouni et al [25] and Wang et al [26] adapted GA with power and cost as the optimization objectives. Shin et al [8] proposed a PSO-based approach as well as an evolutionary algorithm while optimizing wake loss and annual energy production.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptation of the simulated evolution algorithm for wind farm layout optimization

Khan¹

2022

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Wind energy is a potential replacement for traditional, fossil-fuel-based power generation sources. One important factor in the process of wind energy generation is to design of the optimal layout of a wind farm to harness maximum energy. This layout optimization is a complex, NP-hard optimization problem. Due to the sheer complexity of this layout design, intelligent algorithms, such as the ones from the domain of natural computing, are required. One such effective algorithm is the simulated evolution (SE) algorithm. This paper presents a simulated evolution algorithm engineered to solve the wind farm layout design (WFLD)optimization problem. In contrast to many non-deterministic algorithms, such as genetic algorithms and particle swarm optimization which operate on a population, the SE algorithm operates on a single solution, decreasing the computational time. Furthermore, the SE algorithm has only one parameter to tune as opposed to many algorithms that require tuning multiple parameters. A preliminary empirical study is done using data collected from a potential location in the northern region of Saudi Arabia. Experiments are carried out on a 10 × 10 grid with 15 and 20 turbines while considering turbines with a rated capacity of 1.5 MW. Results indicate that a simulated evolution algorithm is a viable option for the said problem.

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Section: Literature Reviewmentioning

confidence: 99%

Adaptation of the simulated evolution algorithm for wind farm layout optimization

Khan¹

2022

FME Transactions

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“…The efficiency of the wind power generation is calculated as follows Efficiency = P axtual /P ideal (11) Given the above, the wind farm layout design problem can be considered as the wind turbine placement problem where the objective is to minimize the total cost versus total power generated for N number of turbines. Therefore, the objective of this optimization problem can be stated as given in Eq.…”

Section: Direction Of Windmentioning

confidence: 99%

“…During the past recent years, various NIAs have been engineered to design wind farms in an optimal way [3][4][5][6][7][8][9][10][11]. These algorithms have proven efficient in generating the optimalor near-optimal wind farm layouts.…”

Section: Introductionmentioning

confidence: 99%

“…The genetic algorithm also gets the credit for being the first algorithm that was used in initial research works on wind farm design [12]. Until today, GA is being utilized in wind farm layout design problems [4,8,10,11,13,16,17]. Apart from GA, several other intelligent algorithms, such as simulated annealing [6,14,15], cuckoo search [3,7], imperialist competitive algorithm [9], differential evolution [16,17], and many others,have been employed for efficient layout design of a wind farm.…”

Section: Introductionmentioning

confidence: 99%

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The effect of acceleration coefficients in Particle Swarm Optimization algorithm with application to wind farm layout design

Rehman¹,

Khan²,

Alhems³

2020

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Wind energy has become a strong alternative to traditional sources of energy. One important decision for an efficient wind farm is the optimal layout design. This layout governs the placement of turbines in a wind farm. The inherent complexity involved in this process results in the wind farm layout design problem to be a complex optimization problem. Particle Swarm Optimization (PSO) algorithm has been effectively used in many studies to solve the wind farm layout design problem. However, the impact of an important set of PSO parameters, namely, the acceleration coefficients, has not received due attention. Considering the importance of these parameters, this paper presents a preliminary analysis of PSO acceleration coefficients using the conventional and a modified variant of PSO when applied to wind farm layout design. Empirical results show that the acceleration coefficients do have an impact on the quality of final layout, resulting in better overall energy output.

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“…Co-locating offshore wind farms and aquaculture facilities [38,[42][43][44][45]. Device placement has many other technical requirements [46] Co-location with leisure facilities (e.g., diving, recreational fishing etc. )…”

Section: O3 Co-location With Other Industries Such As Leisure Tourimentioning

confidence: 99%

Evaluation of Co-Existence Options of Marine Renewable Energy Projects in Japan

et al. 2019

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Consensus building among local stakeholders is vital for the success of the proposed initial commercial marine renewable energy (MRE) projects in Japan. Even though the literature on stakeholder acceptance highlights the importance of creating local benefits and co-creation options, very few studies and almost no empirical data have been published on the application of non-monetary benefit creation schemes in the context of MRE. Hence, the purpose of this study was to systematically evaluate the possible co-existence options available for Japan’s MRE projects through data collected from interviews and questionnaire surveys in two development sites in Nagasaki and Kitakyushu in Southern Japan. To overcome the limitations of data unavailability and uncertainty, the Dempster Shafer Analytic Hierarchy Process (DS-AHP) was used for evaluating the best co-existence strategy out of five potential options. The results indicate that local fisheries prefer the oceanographic information sharing option whereas most of the other stakeholders prefer using local resources to construct and operate the power plant, creating business involvement opportunities for the local community. Analysis of stakeholders’ decision behaviors suggests that perceived impacts, knowledge, and values influence the preference decision. In addition to the validation of stakeholder preference of the previously proposed co-existence options with empirical data, this study provides a robust method to further evaluate the potential options with the availability of new data.

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Comparative Study of Wind Turbine Placement Methods for Flat Wind Farm Layout Optimization with Irregular Boundary

Cited by 15 publications

References 31 publications

Adaptation of the simulated evolution algorithm for wind farm layout optimization

Adaptation of the simulated evolution algorithm for wind farm layout optimization

The effect of acceleration coefficients in Particle Swarm Optimization algorithm with application to wind farm layout design

Evaluation of Co-Existence Options of Marine Renewable Energy Projects in Japan

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