Cable routing optimization for offshore wind power plants via wind scenarios considering power loss cost model

Rongsen Jin, Peng Hou, Guangya Yang, Yuanhang Qi, Cong Chen and Zhe Chen

Applied Energy, 2019, vol. 254, issue C

Abstract: Offshore wind power plants have been considered as one of the fastest-growing types of renewable energy technologies that is superior to the onshore wind farms with low impacts on habitat, better wind condition, higher energy efficiency, etc. The cost of submarine cables takes a significant proportion of the overall capital cost for a large-scale offshore wind farm, rendering the task of optimization of electrical infrastructure a critical role in modern wind farm design. With the increasing capacity and offshore distance, the impact of power losses in the cables on the economic performance of the wind farm becomes significant. Therefore, both the investment on the cables and the cost from the associated energy loss need to be considered in the optimization model. In this work, a detailed power loss cost model accounting for the wake effect’s impact on the wind turbine output is proposed. The cable cost and the associated power losses cost are considered in the objective function. The offshore substation location, cable connection layout, and cable sectional area are optimized simultaneously while ensuring an uncrossed cable connection layout via a line segment intersection detection algorithm. Due to the non-convexity of the optimization model, an adaptive particle swarm optimization algorithm is adopted. The proposed method was validated through a case of a real offshore wind farm, where the simulation results show that the cable connection layout formulation and sectional area selection varies significantly when different power loss model is applied. A 3.14% total cost reduction can be achieved by using the proposed method compared with the case without the power loss model.

Keywords: Offshore wind farm; Cable connection layout; Power loss; Wake effect; Nonconvex; Evolutionary algorithm (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (14)

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DOI: 10.1016/j.apenergy.2019.113719

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