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Storage sizing for grid connected hybrid wind and storage power plants taking into account forecast errors autocorrelation

Authors :
Georges Kariniotakis
Jesus Lugaro
Robin Girard
Nils Siebert
Andrea Michiorri
Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques (PERSEE)
MINES ParisTech - École nationale supérieure des mines de Paris
Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)
Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques ( PERSEE )
MINES ParisTech - École nationale supérieure des mines de Paris-PSL Research University ( PSL )
Source :
Renewable Energy, Renewable Energy, Elsevier, 2018, 117, pp.380-392. ⟨10.1016/j.renene.2017.10.070⟩, Renewable Energy, Elsevier, 2018, 117, pp.380-392. 〈10.1016/j.renene.2017.10.070〉
Publication Year :
2018
Publisher :
HAL CCSD, 2018.

Abstract

International audience; This paper describes a research on the influence of wind power prediction error autocorrelation on the sizing of storage coupled with a wind farm. The stochastic nature of renewable energies resources such as wind speed or solar radiation represents a challenge for the grid integration of renewable energy plants. The imbalances between renewable power predictions and realised production are generally penalised by system operators since additional reserves are required to maintain the stability of the grid. The coupling of storage devices with renewable energy plants is one of the solutions studied to reduce those imbalances. In this work, a methodology to manage imbalances and to size storage in order to achieve a determined level of controllability is proposed. It is applied to a specific use case: the integration of a combined wind-storage plant in French Guyana. The influence of the autocorrelations of errors on the battery size is investigated in detail and a methodology for producing wind prediction errors time series is presented.

Details

Language :
English
ISSN :
09601481 and 18790682
Database :
OpenAIRE
Journal :
Renewable Energy, Renewable Energy, Elsevier, 2018, 117, pp.380-392. ⟨10.1016/j.renene.2017.10.070⟩, Renewable Energy, Elsevier, 2018, 117, pp.380-392. 〈10.1016/j.renene.2017.10.070〉
Accession number :
edsair.doi.dedup.....49d1ffb2a2a137d4e12d14302af13d40
Full Text :
https://doi.org/10.1016/j.renene.2017.10.070⟩