Battery size determination for photovoltaic capacity firming using deep learning irradiance forecasts
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Altres documents de l'autoria: Beltran, Hector; Cardo Miota, Javier; Segarra-Tamarit, Jorge; Pérez, Emilio
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Títol
Battery size determination for photovoltaic capacity firming using deep learning irradiance forecastsData de publicació
2021-01Editor
ElsevierISSN
2352-152XCita bibliogràfica
BELTRAN, Hector, et al. Battery size determination for photovoltaic capacity firming using deep learning irradiance forecasts. Journal of Energy Storage, 2021, vol. 33, p. 102036.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://www.sciencedirect.com/science/article/pii/S2352152X20318594#!Versió
info:eu-repo/semantics/acceptedVersionParaules clau / Matèries
Resum
Large photovoltaic (PV) power plants benefit from the introduction of batteries to increase their dispatchability. Among other services, batteries enable PV plants to firm their hourly energy production and avoid in ... [+]
Large photovoltaic (PV) power plants benefit from the introduction of batteries to increase their dispatchability. Among other services, batteries enable PV plants to firm their hourly energy production and avoid in this way the economic penalties associated with deviations between the contracted commitment made by the renewable generator to the grid and the final energy delivered. Due to the increase in the cost of the plant derived from the storage integration, the size of these batteries must be minimized. This work analyses the minimum battery capacity required for such a service when using a new deep-learning irradiance forecasting methodology. The low prediction error of the developed forecasting tool supports the optimized operation of large PV plants under different European intraday electricity markets with no deviations and reduced battery sizes. Results obtained for a whole year analysis using actual data at three different locations with varying irradiance patterns prove that 1-hour capacity batteries grant PV capacity firming in most intraday continuous market structures regardless of their lead times. [-]
Publicat a
Journal of Energy Storage Volume 33, January 2021, 102036Entitat finançadora
Universitat Jaume I | Generalitat Valenciana | European Social Fund (ESF)
Codi del projecte o subvenció
UJI-B2017-26 | ACIF/ 2019/106
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© 2020 Elsevier Ltd. All rights reserved
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info:eu-repo/semantics/openAccess
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info:eu-repo/semantics/openAccess
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