Optimal wind-turbine micro-siting of offshore wind farms: A grid-like layout approach

González, Javier Serrano; García, Ángel Luis Trigo; Payán, Manuel Burgos; Santos, J.R.; Rodríguez, Ángel Gaspar González

doi:10.1016/j.apenergy.2017.05.071

Cited by 53 publications

(17 citation statements)

References 38 publications

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“…In this way, the convergence of the algorithm is improved by preserving solutions that initially do not meet this constrain but that in the next generations have the potential to derive in feasible solutions. For more details, the authors would like to refer to our previous work [51].…”

Section: Individual/potential Solution Codificationmentioning

confidence: 99%

Optimal Micro-Siting of Weathervaning Floating Wind Turbines

et al. 2021

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This paper presents a novel tool for optimizing floating offshore wind farms based on weathervaning turbines. This solution is grounded on the ability of the assembly (wind turbine plus floater) to self-orientate into the wind direction, as this concept is allowed to freely pivot on a single point. This is a passive yaw potential solution for floating wind farms currently in the demonstration phase. A genetic algorithm is proposed for optimizing the levelised cost of energy by determining the geographical coordinates of the pivot points (i.e., the position over which the assembly can rotate to self-orient to the incoming wind direction). A tailored evaluation module is proposed to take into account the weathervaning motion around the pivot point depending on the incoming wind direction. The results obtained show the suitability of the proposed method to solve the addressed problem under realistic conditions. Additionally, the influence of the feasible region defined by the plot and the maximum area occupied on floating offshore wind farm design are also analysed in the proposed test cases. These deployable area constraints are of great importance for the viability of this technology, as it requires more space than classical solutions anchored to a fixed point.

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Section: Individual/potential Solution Codificationmentioning

confidence: 99%

Optimal Micro-Siting of Weathervaning Floating Wind Turbines

et al. 2021

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“…The number and location of substations have a significant impact on the connection topology, and several studies aimed to obtain the optimal match between these two factors [16][17][18][19][20][21][22][23][24]. In these studies, the shortest cable length [17,18], minimal investment [15], highest gain [19], and a minimal energy loss [20] were the optimization objectives, and the substation's spatial coordinates were used as variables.…”

Section: Introductionmentioning

confidence: 99%

“…These were combined with the optimization algorithm to obtain the best substation location and the corresponding matching topology structure. Very few studies have been done to improve the economics of the entire wind farm by jointly optimizing the wind farm fleet location and the connection topology for a given annual wind speed distribution [21][22][23]. In the selection of the optimized algorithm, the optimization of the power collection system involves complex connections between the wind turbines and the substations, and a large number of constraints on power parameters exist, like being revealed as a non-convex, discontinuous, and NP-complete complex optimization [3].…”

Section: Introductionmentioning

confidence: 99%

An Integration Optimization Method for Power Collection Systems of Offshore Wind Farms

Wang

Tang

et al. 2019

Energies

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The traditional power collection system design separately optimizes the connection topology and the cable cross sections, which may result in the inherent shortcoming of lacking the most economical solutions. In this pursuit, the present work envisages the development of an integrated design method for general wind farm power collection systems, which integrated the coupling random fork tree coding, union-find set loop identification, current and voltage drop calculation models, and a high performance optimization algorithm. The proposed coupling random fork tree coding, for the first time, realized the coupling code of the substation location, connection topology, and cable cross sections, providing the basis for the integration design of the power collection system. The optimization results for discrete and regular wind farms indicated that the proposed integration method achieved the best match of topology, substation location, and the cable cross sections, thus presenting the most economical scheme of the power collection system. Compared to the traditional two-step methods, the integration method used more branches while acquiring them, to maintain the lower number of wind turbines in each branch. Furthermore, it also employed large cross-section cables to reduce the energy loss caused by the impedance in the topology, thereby resulting in a slight increased cable cost; however, the total cost was minimized. The proposed method is very versatile and suitable for the optimization of power collection systems containing any number of wind turbines and substations, and can be combined with any evolutionary algorithm.

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“…Subsequently, the optimal substation locations and matching topology were obtained by using MST to generate connections and combine the optimization algorithms. A joint optimization of the wind turbine locations and the connection topology at a given annual wind speed distribution has also been carried out in some studies to improve the economics of the entire wind farm [25][26][27]. In the literature [10,24,28], when the energy loss of the topology was included in the cost model, the optimization results showed that the energy loss was effectively reduced by improving the topology structures.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Energy Loss by Coupling Optimization of Connection Topology and Cable Cross-Section in Offshore Wind Farm

Wang

Han

et al. 2019

Applied Sciences

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The cable cross section of an offshore wind farm power system is conventionally determined on the basis of the maximum current carrying capacity. However, this criterion cannot be matched and optimized with connection topology, which may lead to a large overall resistance level in the topology, thereby causing severe energy loss. In this pursuit, the present work envisages the establishment of a coupling optimization method of connection topology and cable cross-section planning for the first time. Based on this method, the power system of a small discrete wind farm is optimized and its results are compared with the results of the traditional design methods. The results indicate that an optimal matching of connection topology and cable cross section can be achieved using the proposed method. Besides, the optimal topology obtained uses more branches, and the large cross-section cables are reasonably used on the large-current cable segments, thus dramatically reducing the energy loss and minimizing the total cost of the power system. The proposed method is very versatile and suitable for the optimization of power systems containing any number of wind turbines and substations. Moreover, it can be combined with any evolutionary algorithm.

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Optimal wind-turbine micro-siting of offshore wind farms: A grid-like layout approach

Cited by 53 publications

References 38 publications

Optimal Micro-Siting of Weathervaning Floating Wind Turbines

Optimal Micro-Siting of Weathervaning Floating Wind Turbines

An Integration Optimization Method for Power Collection Systems of Offshore Wind Farms

Minimizing Energy Loss by Coupling Optimization of Connection Topology and Cable Cross-Section in Offshore Wind Farm

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