RT Conference Proceedings
T1 Solar energy forecasting and optimization system for efficient renewable energy integration
A1 Manjarres, Diana
A1 Alonso, Ricardo
A1 Gil-Lopez, Sergio
A1 Landa-Torres, Itziar
A2 Kramer, Oliver
A2 Madnick, Stuart
A2 Woon, Wei Lee
A2 Aung, Zeyar
AB Solar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.
PB Springer Verlag
SN 9783319716428
SN 0302-9743
YR 2017
FD 2017
LK https://hdl.handle.net/11556/2659
UL https://hdl.handle.net/11556/2659
LA eng
NO Manjarres , D , Alonso , R , Gil-Lopez , S & Landa-Torres , I 2017 , Solar energy forecasting and optimization system for efficient renewable energy integration . in O Kramer , S Madnick , W L Woon & Z Aung (eds) , Data Analytics for Renewable Energy Integration : Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 10691 LNAI , Springer Verlag , pp. 1-12 , 5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017 , Skopje , Macedonia, The Former Yugoslav Republic of , 22/09/17 . https://doi.org/10.1007/978-3-319-71643-5_1
NO conference
NO Publisher Copyright: © Springer International Publishing AG 2017.
NO Acknowledgment. This work has been supported in part by the ELKARTEK program of the Basque Government (BID3ABI project), and EMAITEK funds granted by the same institution.
DS TECNALIA Publications
RD 1 sept 2024