Optimization of the internal grid of an offshore wind farm using Genetic algorithm

Dahmani, Ouahid; Bourguet, Salvy; Guérin, Patrick M.; Machmoum, Mohamed; Rhein, Pauline; Josse, Lionel

doi:10.1109/ptc.2013.6652324

Cited by 10 publications

(10 citation statements)

References 15 publications

(17 reference statements)

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“…Thus, a second simplification is introduced during the generation of the connections among the WTs (submatrix AM WT ) and the connections among the OSSs (matrix AM OSS ). The idea is that the connection possibilities among the WT nodes and among the OSS nodes depend on their locations in the network [23], [24]. This constraint is used in order to reduce the number of elements N V in the two submatrices AM WT and AM OSS needed for the representation of the different network configurations.…”

Section: B Reduction Of Search Spacementioning

confidence: 99%

Optimization of the Connection Topology of an Offshore Wind Farm Network

Dahmani¹,

Bourguet²,

Machmoum³

et al. 2015

IEEE Systems Journal

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In this paper, an approach based on a genetic algorithm is presented in order to optimize the connection topology of an offshore wind farm network. The main objective is to introduce a technique of coding a network topology to a binary string. The first advantage is that the optimal connections of both middleand high-voltage alternating-current grids are considered, i.e., the radial clustering of wind turbines, the number and locations of the offshore electrical substations, and the number of high-voltage cables. The second improvement consists of removing infeasible network configurations as designs with crossing cables and thereby reduces the search space of solutions.Index Terms-Genetic algorithm (GA), network planning, offshore wind farm (OWF), optimization, Prim's algorithm.

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Section: B Reduction Of Search Spacementioning

confidence: 99%

Optimization of the Connection Topology of an Offshore Wind Farm Network

Dahmani¹,

Bourguet²,

Machmoum³

et al. 2015

IEEE Systems Journal

Self Cite

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“…Matlab has been reported to be the most popular tool for the study of wind farm optimization in the literatures [26,27]. However, it has the disadvantages of limited license of optimization toolbox and is inefficient in dealing with loops compared with other low level programming language.…”

Section: Ga Optimizationmentioning

confidence: 99%

A new constraint handling method for wind farm layout optimization with lands owned by different owners

Wang

Tan

et al. 2015

Renewable Energy

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a b s t r a c tFor wind farm optimizations with lands belonging to different owners, the traditional penalty method is highly dependent on the type of wind farm land division. The application of the traditional method can be cumbersome if the divisions are complex. To overcome this disadvantage, a new method is proposed in this paper for the first time. Unlike the penalty method which requires the addition of penalizing term when evaluating the fitness function, it is achieved through repairing the infeasible solutions before fitness evaluation. To assess the effectiveness of the proposed method on the optimization of wind farm, the optimizing results of different methods are compared for three different types of wind farm division. Different wind scenarios are also incorporated during optimization which includes (i) constant wind speed and wind direction; (ii) various wind speed and wind direction; and (iii) the more realistic Weibull distribution. Results show that the performance of the new method varies for different land plots in the tested cases. Nevertheless, it is found that optimum or at least close to optimum results can be obtained with sequential land plot study using the new method for all cases. It is concluded that satisfactory results can be achieved using the proposed method. In addition, it has the advantage of flexibility in managing the wind farm design, which not only frees users to define the penalty parameter but without limitations on the wind farm division.

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“…The main disadvantage of the latest works is that the problem is tackled from a MST perspective, meaning that parallel cables can be placed, being this a not-so-common practice by OWF developers. GAs has been used in works such as [4], [13], and [14]. In [4] is not very clear, among other things, the penalization strategy and how cable crossings can be avoided, therefore their results can not be reproduced.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic-based Design and optimization of Offshore Wind Farms Collection Systems

Minguijon

Pérez-Rúa

Das

et al. 2019

2019 IEEE Milan PowerTech

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An optimization framework for automated design of offshore wind farms collection systems is proposed in this paper. The core of the framework consists of a metaheuristic algorithm, namely a Genetic Algorithm (GA). The GA is designed for searching high-quality feasible solutions in terms of the capital expenditure (CAPEXcs); a subsequent step runs a power flow in order to calculate electrical power losses for estimating the collection systems share on the Levelized Cost of Energy (LCOEcs). Finally, after several executions of the full framework, the feasible solution bringing the cheapest LCOEcs is selected. The main inputs are the coordinate's location of the wind turbines and the offshore substation (OSS), wind power production time series, and the set of considered cables for the collection system design. The proposed approach offers a full search space exploration for feasible solutions, while taking into account cables capacities and disallowing for cable crossings. The results show that this framework can find feasible solutions improving benchmark methods by 8%.

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Optimization of the internal grid of an offshore wind farm using Genetic algorithm

Cited by 10 publications

References 15 publications

Optimization of the Connection Topology of an Offshore Wind Farm Network

Optimization of the Connection Topology of an Offshore Wind Farm Network

A new constraint handling method for wind farm layout optimization with lands owned by different owners

Metaheuristic-based Design and optimization of Offshore Wind Farms Collection Systems

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