Optimal Substation Location Of A Wind Farm Using Different Metaheuristic Algorithms

Mokhi, Chakib El; Addaim, Adnane

doi:10.1109/icoa49421.2020.9094469

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…The capital cost of electrical infrastructure accounts for 15%-30% of the total initial cost of an OWF [8]. Thus, the application of optimization techniques for electrical infrastructure at the planning stage can bring obvious economic benefits [9]. Particularly, the collector system topology (CST) is the key part of the electrical infrastructure in an OWF.…”

Section: Mstmentioning

confidence: 99%

Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing

Chen,

Zhang,

et al. 2024

Prot. Control Mod. Power Syst.

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During the construction of an offshore wind farm (OWF), the capital cost of the collector cable system accounts for a large proportion of the total cost. Consequently, the optimal design of the collector system topology (CST) is one of the most crucial tasks in OWF planning. However, for a large-scale OWF, the optimal design of CST is a complex integer programming problem with high-dimension variables and various constraints. Therefore, it is difficult to acquire a high-quality optimal design scheme. To address this issue, this paper proposes a new grouping-based optimal design of CST for a large-scale OWF. First, all the wind turbines are divided into multiple groups according to their geographical locations and the maximum allowed connected wind turbines by each cable. This not only reduces the optimization dimension and difficulty, but also effectively satisfies the 'no cross' constraint by putting the geographically closed wind turbines into the same group. Secondly, the electrical topology among different wind turbines in each group is initially generated by an improved dynamic minimum spanning tree (DMST). The division groups of the OWF are then adjusted to further reduce the capital cost by improved simulated annealing. To verify the proposed technique, comparison case studies are carried out with five algorithms on two different OWF. Index Terms-Offshore wind farm, collector system topology, grouping-based optimal design, meta-heuristic algorithm, graph theory. NOMENCLATURE A. Abbreviations OWFoffshore wind farm CST collector system topology DMST dynamic minimum spanning tree WT wind turbine _____________________________________

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Section: Mstmentioning

confidence: 99%

Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing

Chen,

Zhang,

et al. 2024

Prot. Control Mod. Power Syst.

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“…where C 1 , C 2 , C 3 , and C 4 are coefficients defining the weighting of the various contributors determined by tuning the Particle Swarm Optimization to the present optimization problem, p is the best position of the relevant particle, g is the best position of the swarm and σ ∈ [0, 1] is a random number [23,25].…”

Section: Optimization With Particle Swarm Algorithmmentioning

confidence: 99%

“…The collector topology is radial and the substation is located outside the turbine array, therefore it is a suitable example to verify and demonstrate the reliability of the optimization approach. More data about this wind farm is in Table 2 [25]. For this case study, the presumed total cable length for the power collection system is 58.50 km.…”

Section: Real Case Study (Horns Rev 1)mentioning

confidence: 99%

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Optimization of Wind Turbine Interconnections in an Offshore Wind Farm Using Metaheuristic Algorithms

Mokhi

Addaim

2020

Sustainability

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Wind energy is currently one of the fastest-growing renewable energy sources in the world. For this reason, research on methods to render wind farms more energy efficient is reasonable. The optimization of wind turbine positions within wind farms makes the exploitation of wind energy more efficient and the wind farms more competitive with other energy resources. The investment costs alone for substation and electrical infrastructure for offshore wind farms run around 15–30% of the total investment costs of the project, which are considered high. Optimizing the substation location can reduce these costs, which also minimizes the overall cable length within the wind farm. In parallel, optimizing the cable routing can provide an additional benefit by finding the optimal grid network routing. In this article, the authors show the procedure on how to create an optimized wind farm already in the design phase using metaheuristic algorithms. Besides the optimization of wind turbine positions for more energy efficiency, the optimization methods of the substation location and the cable routing for the collector system to avoid cable losses are also presented.

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“…Je-Seok Shin et al introduced a methodology to design an optimal cable layout of the inner grid as well as the location of the offshore substation (Shin and Kim, 2016). Mokhi et al compared the effectiveness of particle swarm optimization and genetic algorithm in optimizing the substation location and found that the particle swarm optimization algorithm yielded faster optimal results but had the issue of uneven loop division in the collection lines (El Mokhi and Addaim, 2020). Yang et al 2020 searched for the location with the minimum cost for the substation along a straight line between the centroid of the wind farm and the point with the minimum offshore distance.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive layout optimization of the transmission system in a deepwater wind farm cluster

Xu,

Jiao,

Han

et al. 2023

Front. Energy Res.

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Compared to nearshore wind farms, deepwater offshore wind farms have better wind resources and broader development space and will be the main trend of wind power development in the future. Due to the long distance from the shore, the investment and operation costs of the transmission system in a deepwater wind farm cluster are high and the electricity generated by the cluster is commonly transmitted by high-voltage direct current transmission technology to decrease power losses. For a given converter station, the comprehensive optimization of the layouts of the substations, AC export cables from the substation to the converter station, and cable arrays within each wind farm are complex and have not been well studied. This paper proposes a multi-level nested optimization strategy to minimize the total cost of the collection cables for a deepwater offshore wind farm cluster. The proposed method applies the Informed-RRT* method to optimize the layout of AC export cables while the cable arrays are arranged by Prim’s algorithm. During the optimization process, the gradient descent algorithm is used to optimize the positions of the substations, obtaining the transmission system scheme with the minimum cable investment. For a certain deepwater wind farm cluster, the total cable cost after optimization is reduced by 3.58%, which indicates the effectiveness of this method.

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Optimal Substation Location Of A Wind Farm Using Different Metaheuristic Algorithms

Cited by 8 publications

References 5 publications

Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing

Grouping-Based Optimal Design of Collector System Topology for a Large-Scale Offshore Wind Farm by Improved Simulated Annealing

Optimization of Wind Turbine Interconnections in an Offshore Wind Farm Using Metaheuristic Algorithms

Comprehensive layout optimization of the transmission system in a deepwater wind farm cluster

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