Distributed Genetic Algorithm for Optimization of Wind Farm Annual Profits

Huang, Heng‐Tsung Danny

doi:10.1109/isap.2007.4441654

Cited by 67 publications

(58 citation statements)

References 7 publications

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“…In the first category, the approaches divide the site into a number of cells and use a discrete optimization algorithm that decides whether or not to place a turbine in the cell. A genetic algorithm optimizes the corresponding boolean matrix [17,11,12,26,10,19,27].…”

Section: Introductionmentioning

confidence: 99%

On learning to generate wind farm layouts

Wilson¹,

Awa

Cussat‐Blanc

et al. 2013

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

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Optimizing a wind farm layout is a very complex problem that involves many local and global constraints such as interturbine wind interference or terrain peculiarities. Existing methods are either inefficient or, when efficient, take days or weeks to execute. Solutions are contextually sensitive to the specific values of the problem variables; when one value is modified, the algorithm has to be re-run from scratch. This paper proposes the use of a developmental model to generate farm layouts. Controlled by a gene regulatory network, virtual cells have to populate a simulated environment that represents the wind farm. When the cells' behavior is learned, this approach has the advantage that it is re-usable in different contexts; the same initial cell is responsive to a variety of environments and the layout generation takes few minutes instead of days.

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Section: Introductionmentioning

confidence: 99%

On learning to generate wind farm layouts

Wilson¹,

Awa

Cussat‐Blanc

et al. 2013

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

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show abstract

“…Others [10]- [12] proposed a topology optimization under energy cost constraint. Other studies used software such NEPLAN to assess the reliability/performance of wind farms under certain topologies [13]- [15], but only few research articles deal with wind farm topology optimization under both cost and performance constraints [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of Wind Turbines Considering Constraints and Legislation

Tazi¹,

Châtelet²,

Bouzidi³

2017

JOCET

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Abstract-Onshore wind turbine are subject to more and more regulations and constraints. To reach the optimal installed capacity with maximum reliability and profits, numbers of algorithms have been developed for wind farm power plants. However, according to literature, no exact methods have yet been tested on wind farms using real data and wind based conditions. In this article we develop the recent work that has been done until far on wind turbine power and reliability analysis, and apply an algorithm to find the best topology. The proposed resolution technique is based on finding the best topology of the system under the constraints of Performance (availability), Costs and the Global Warming Potential of the system. A case study is done using data and constraints similar to those collected from wind farm constructors, managers and maintainers. Multi-State Systems (MSS), Universal generating function (UGF), wind and Load charge functions are applied.

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“…After optimization with the objective of minimizing cost per unit power output, both the best number of wind turbines and the optimized layout were obtained. Later, some researchers improved the optimized results of GA through adopting better coding methods [2e4], introducing more realistic models [5e10], using various shapes of grid cells [11,12] and exchanging some individuals among multiple small sub-populations [13]. Greedy algorithm is another popular optimization method for wind turbine layout optimization.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm

et al. 2016

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a b s t r a c tWind turbine layout optimization in wind farm is one of the most important technologies to increase the wind power utilization. This paper studies the wind turbine layout optimization with multiple hub heights wind turbines using greedy algorithm. The linear wake model and the particle wake model are used for wake flow calculation over flat terrain and complex terrain, respectively. Three-dimensional greedy algorithm is developed to optimize wind turbine layout with multiple hub heights for minimizing cost per unit power output. The numerical cases over flat terrain and complex terrain are used to validate the effectiveness of the proposed greedy algorithm for the optimization problem. The results reveal that it incurs lower computational costs to obtain better optimized results using the proposed greedy algorithm than the one using genetic algorithm. Compared to the layout with identical hub height wind turbines, the one with multiple hub height wind turbines can increase the total power output and decrease the cost per unit power output remarkably, especially for the wind farm over complex terrain. It is suggested that three-dimensional greedy algorithm is an effective method for more benefits of using wind turbines with multiple hub heights in wind farm design.

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Distributed Genetic Algorithm for Optimization of Wind Farm Annual Profits

Cited by 67 publications

References 7 publications

On learning to generate wind farm layouts

On learning to generate wind farm layouts

Reliability Analysis of Wind Turbines Considering Constraints and Legislation

Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm

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