ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Wind Energy
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1689106
A grid integration Planning Approach for Partitioned Super Large offshore wind farm considering Onshore Grid Accommodation Capacity
Provisionally accepted
- 1East China Grid Company Limited, Shanghai, China
- 2Department of Electric Power and Automation Engineering, Shanghai University of Electric Power, Shanghai, China
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The large-scale offshore wind development is crucial for the energy transition. Current research has not considered onshore grid accommodation capacity for the integration system planning of offshore wind farm. The question now severely constrains the deployment of giga-scale projects, i.e., Super Large Offshore Wind Farm (SLOWF). To achieve the rational accommodation of wind power, a planning method of transmission system for partitioned SLOWF is proposed, considering the accommodation capacity of the onshore power grid. Firstly, based on the constraints of the onshore partition grid's accommodation capacity, the Density Peak Clustering Algorithm (DPCA) is applied to partition the SLOWF. Secondly, a double-layer planning model for the transmission system of the partitioned SLOWF is established. The upper-layer model focuses on optimizing the layout of offshore converter stations based on the partitioned SLOWF. The lower-layer model addresses the planning of DC submarine cables and onshore converter stations. The overall economic optimum of the system is achieved by iterative solving of the double-layer model. Finally, simulation validation is conducted based on the link of a SLOWF integrated an actual coastal grid. The results demonstrate that the proposed model and solution method significantly reduce planning costs and improve convergence efficiency.
Keywords: super large offshore wind farm (SLOWF)1, transmission system planning2, converter station location and type3, collaborative planning4, density peak clustering algorithm (DPCA)5, partition6
Received: 20 Aug 2025; Accepted: 22 Oct 2025.
Copyright: © 2025 Xu, Zhang, Zhang, Zhao, Ling and Bian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaoyan Bian, bianxy@shiep.edu.cn
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