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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Managing renewable intermittency in smart grid
T2 - IEEE 19th International Symposium on Electrical Apparatus and Technologies (SIELA) 2016
AU - Gelazanskas, Linas
AU - Akurugoda Gamage, Kelum Asanga
N1 - ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2016/8/15
Y1 - 2016/8/15
N2 - This paper discusses a novel wind generation balancing technique to improve renewable energy integration to the system. Novel individual hot water heater controllers were modelled with the ability to forecast and look ahead the required energy, while responding to electricity grid imbalance. Artificial intelligence and machine learning techniques were used to learn and predict energy usage. In this research wind power data was used in most cases to represent the supply side, where focus was on the actual generation deviation from plan. It proved to be possible to balance the generation and increase system efficiency while maintaining user satisfaction. The methods developed in this research are not limited to wind power balancing and can also be used with any other type of renewable generation source.
AB - This paper discusses a novel wind generation balancing technique to improve renewable energy integration to the system. Novel individual hot water heater controllers were modelled with the ability to forecast and look ahead the required energy, while responding to electricity grid imbalance. Artificial intelligence and machine learning techniques were used to learn and predict energy usage. In this research wind power data was used in most cases to represent the supply side, where focus was on the actual generation deviation from plan. It proved to be possible to balance the generation and increase system efficiency while maintaining user satisfaction. The methods developed in this research are not limited to wind power balancing and can also be used with any other type of renewable generation source.
KW - energy management
KW - renewable sources
KW - residential hot water heaters
KW - smart grid
U2 - 10.1109/SIELA.2016.7543001
DO - 10.1109/SIELA.2016.7543001
M3 - Conference contribution/Paper
SN - 9781467395229
BT - Proceedings of the IEEE 19th International Symposium on Electrical Apparatus and Technologies (SIELA) 2016
PB - IEEE
Y2 - 29 May 2016 through 1 June 2016
ER -