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Storage sizing for grid connected hybrid wind and storage power plants taking into account forecast errors autocorrelation
- 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.
- Subjects :
- Engineering
020209 energy
Storage
02 engineering and technology
Wind
7. Clean energy
Wind speed
Automotive engineering
[SPI]Engineering Sciences [physics]
Intermittent energy source
[ SPI ] Engineering Sciences [physics]
0202 electrical engineering, electronic engineering, information engineering
Electronic engineering
Sizing
Wind power
Renewable Energy, Sustainability and the Environment
business.industry
Correlation Analysis
Autocorrelation
forecast error
Grid
Renewable energy
Power (physics)
13. Climate action
Forecast
business
Subjects
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⟩