Cabling Optimization in Wind Power Plants, Enhancing the Cable Type-Based Formulation

Ramon Abritta (), Alexey Pavlov, Damiano Varagnolo, Børre T. Børresen and Ivo Chaves da Silva Junior
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Ramon Abritta: Department of Geosciences, Norwegian University of Science and Technology, S.P. Andersens veg 15, 7031 Trondheim, Norway
Alexey Pavlov: Department of Geosciences, Norwegian University of Science and Technology, S.P. Andersens veg 15, 7031 Trondheim, Norway
Damiano Varagnolo: Department of Engineering Cybernetics, Norwegian University of Science and Technology, O.S. Bragstads Plass 2D, 7034 Trondheim, Norway
Børre T. Børresen: Equinor ASA, Arkitekt Ebbells veg 10, 7053 Trondheim, Norway
Ivo Chaves da Silva Junior: Department of Electrical Energy, Federal University of Juiz de Fora, St. José Lourenço Kelmer, Juiz de Fora 36036900, Brazil

Energies, 2025, vol. 18, issue 16, 1-19

Abstract: The collection grid represents a relevant share of the total initial investments into a wind power plant. Planning/optimizing collection grids is a task that grows severely in complexity according to the size of the analyzed plant, i.e., its number of wind turbines. This paper enhances a well-known mixed-integer linear programming formulation based on the types of cables available for installation and meant for radial grids. More specifically, this work proposes valid constraints that tighten the search space and enable faster convergence. Results indicate that small wind power plants do not benefit from the novel constraints, whereas the time to solve medium and large plants can significantly decrease. However, comparisons against an alternative algorithm based on the flowing power reveal that the proposed enhancement to the cable type-based formulation does not make it the most computationally efficient. In studies regarding Thanet, a wind power plant with 100 wind turbines, the mean convergence time has decreased from 18% up to 85% for different cases when applying the proposed constraints to the cable type-based formulation. Nonetheless, such durations are 2 to 15 times more extensive than what is required by the power-based algorithm. Thus, this paper seeks to raise awareness regarding the assessed algorithms and aid in more efficient inter-array cabling optimization studies.

Keywords: cabling optimization; mixed-integer linear programming; wind power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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