Analysis of North Sea Offshore Wind Power Variability

Aymeric Buatois, Madeleine Gibescu, Barry G. Rawn and Mart A.M.M. Van der Meijden
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Aymeric Buatois: Department of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, Delft 2628 CD, The Netherlands
Madeleine Gibescu: Department of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, Delft 2628 CD, The Netherlands
Barry G. Rawn: Departement Elektrotechniek, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven 3001, Belgium
Mart A.M.M. Van der Meijden: Department of Electrical Sustainable Energy, Delft University of Technology, Mekelweg 4, Delft 2628 CD, The Netherlands

Resources, 2014, vol. 3, issue 2, 1-17

Abstract: This paper evaluates, for a 2030 scenario, the impact on onshore power systems in terms of the variability of the power generated by 81 GW of offshore wind farms installed in the North Sea. Meso-scale reanalysis data are used as input for computing the hourly power production for offshore wind farms, and this total production is analyzed to identify the largest aggregated hourly power variations. Based on publicly available information, a simplified representation of the coastal power grid is built for the countries bordering the North Sea. Wind farms less than 60 km from shore are connected radially to the mainland, while the rest are connected to a hypothetical offshore HVDC (High-Voltage Direct Current) power grid, designed such that wind curtailment does not exceed 1% of production. Loads and conventional power plants by technology and associated cost curves are computed for the various national power systems, based on 2030 projections. Using the MATLAB-based MATPOWER toolbox, the hourly optimal power flow for this regional hybrid AC/DC grid is computed for high, low and medium years from the meso-scale database. The largest net load variations are evaluated per market area and related to the extra load-following reserves that may be needed from conventional generators.

Keywords: offshore wind; meso-scale models; hybrid AC-DC optimal power flow; wind power variability; DC offshore grid (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2014
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