Floating Offshore Wind Farm Inter-Array Cabling Topology Optimisation with Metaheuristic Particle Swarm Optimisation

Sergi Vilajuana Llorente, José Ignacio Rapha, Magnus Daniel Kallinger and José Luis Domínguez-García ()
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Sergi Vilajuana Llorente: Power Systems Group, Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 2a, 08930 Sant Adrià de Besòs, Spain
José Ignacio Rapha: Power Systems Group, Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 2a, 08930 Sant Adrià de Besòs, Spain
Magnus Daniel Kallinger: Power Systems Group, Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 2a, 08930 Sant Adrià de Besòs, Spain
José Luis Domínguez-García: Power Systems Group, Catalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 2a, 08930 Sant Adrià de Besòs, Spain

Clean Technol., 2025, vol. 7, issue 4, 1-21

Abstract: Floating offshore wind is now receiving much attention as an expansion to bottom-fixed, especially in deep waters with large wind resources. In this regard, improving the performance and efficiency of floating offshore wind farms (FOWFs) is currently a highly addressed topic. The inter-array (IA) cable connection is a key aspect to be optimised. Due to floating offshore wind (FOW) particularities such as dynamic cable designs, higher power capacities, and challenging installation, IA cabling is expected to be a primary cost driver for commercial-scale FOWFs. Therefore, IA cabling optimisation can lead to large cost reductions. In this work, an optimisation with an adaptive particle swarm optimisation (PSO) algorithm for such wind farms is proposed, considering the floating substructures’ horizontal translations and its impact on the dynamic cable length. The method provides an optimised IA connection, reducing acquisition costs and power losses by using a clustered minimum spanning tree (MST) as an initial solution and improving it with the PSO algorithm. The PSO achieves a reduction in the levelised cost of energy (LCOE) between 0.018% (0.022 EUR/MWh) and 0.10% (0.12 EUR/MWh) and a reduction in cable acquisition costs between 0.18% (0.3 M EUR) and 1.34% (3.8 M EUR) compared to the initial solution, showing great potential for future commercial-sized FOWFs.

Keywords: floating wind; cabling optimisation; levelised cost of energy; dynamic cables; power losses; clustering; particle swarm optimisation (search for similar items in EconPapers)
JEL-codes: Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2025
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