Regime-switching modelling of the fluctuations of offshore wind generation

Regime-switching modelling of the fluctuations of offshore wind generation

The magnitude of power fluctuations at large offshore wind farms has a significant impact on the control and management strategies of their power output. If focusing on the minute scale, it looks like different regimes yield different behaviours of the wind power output. The use of statistical regim...

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Bibliographic Details
Published in:Journal of wind engineering and industrial aerodynamics Vol. 96; no. 12; pp. 2327 - 2347
Main Authors: Pinson, P., Christensen, L.E.A., Madsen, H., Sørensen, P.E., Donovan, M.H., Jensen, L.E.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Ltd 01-12-2008
Elsevier Science
Subjects:
Applied sciences
Buildings. Public works
Climatology and bioclimatics for buildings
Control
Energy
Exact sciences and technology
Fluctuation
Fluctuations
Focusing
Forecasting
Horns
Hydraulic constructions
Modelling
Natural energy
Offshore
Offshore engineering
Offshore structure (platforms, tanks, etc.)
Offshore structures
Reduction
Regime-switching
Wind energy
Wind power
Denmark
Control
Fluctuations
Offshore
Regime-switching
Modelling
Wind power
Forecasting
Windfarm
Aerodynamics
Forecast model
Modeling
Linear model
Markov chain
Case study
Wind energy
regime switching model
Offshore structure
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Summary:The magnitude of power fluctuations at large offshore wind farms has a significant impact on the control and management strategies of their power output. If focusing on the minute scale, it looks like different regimes yield different behaviours of the wind power output. The use of statistical regime-switching models is thus investigated. Regime-switching approaches relying on observable (i.e. based on recent wind power production) or non-observable (i.e. a hidden Markov chain) regime sequences are considered. The former approach is based on either self-exciting threshold autoregressive (SETAR) or smooth transition autoregressive (STAR) models, while Markov-switching autoregressive (MSAR) models comprise the kernel of the latter one. The particularities of these models are presented, as well as methods for the estimation of their parameters. The competing approaches are evaluated on a one-step ahead forecasting exercise with time-series of power production averaged at a 1, 5, and 10-min rate, at the Horns Rev and Nysted offshore wind farms in Denmark. For the former wind farm, the one-step ahead root mean square error (RMSE) is contained between 0.8% and 5% of installed capacity, while it goes from 0.6% to 3.9% of installed capacity for the case of Nysted. It is shown that the regime-switching approach based on MSAR models significantly outperforms those based on observable regime sequences. The reduction in one-step ahead RMSE ranges from 19% to 32% depending on the wind farm and time resolution considered. The presented results clearly demonstrate that the magnitude of fluctuations of offshore wind power cannot be considered as simply influenced by the generation level only.
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content type line 23
ISSN:0167-6105
1872-8197
DOI:10.1016/j.jweia.2008.03.010