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AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids

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Seshu TirupathiAuthors Info & Claims

e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems

Pages 381 - 382

https://doi.org/10.1145/3307772.3330158

Published: 15 June 2019 Publication History

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Abstract

We demonstrate progress on the deployment of two sets of technologies to support distribution grid operators integrating high shares of renewable energy sources, based on a market for trading local energy flexibilities. An artificial-intelligence (AI) grid modelling tool, based on probabilistic graphs, predicts congestions and estimates the amount and location of energy flexibility required to avoid such events. A scalable time-series forecasting system delivers large numbers of short-term predictions of distributed energy demand and generation. We discuss the deployment of the technologies at three trial demonstration sites across Europe, in the context of a research project carried out in a consortium with energy utilities, technology providers and research institutions.

References

[1]

Bei Chen, Bradley Eck, Francesco Fusco, Robert Gormally, Mark Purcell, Mathieu Sinn, and Seshu Tirupathi. 2018. Castor: Contextual IoT Time Series Data and Model Management at Scale. Proc. of the 18th ICDM 2018, pp 1487--1492 (2018).

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[2]

Bradley Eck, Francesco Fusco, Robert Gormaly, Mark Purcell, and Seshu Tirupathi. 2019. Scalable Deployment of AI Time-series Models for IoT. In Accepted for presentation at the Workshop AI for Internet of Things (AI4IoT) at the 28th International Joint Conference on Artificial Intelligence (IJCAI).

Google Scholar

[3]

Francesco Fusco. 2018. Probabilistic Graphs for Sensor Data-Driven Modelling of Power Systems at Scale. In Woon W., Aung Z., Catalina Feliu A., Madnick S. (eds) Data Analytics for Renewable Energy Integration. Technologies, Systems and Society.

Google Scholar

[4]

Francesco Fusco, Seshu Thirupathi, and Robert Gormally. 2017. Power systems data fusion based on belief propagation. Proceedings of the IEEE PES Innovative Smart Grid Technologies (ISGT) Conference Europe (2017).

Crossref

Google Scholar

[5]

GOFLEX. Accessed: 2019-04-17. Generalized Operational FLEXibility for Integrating Renewables in the Distribution Grid. https://goflex-project.eu/.

Google Scholar

[6]

Bijay Neupane, Laurynas Siksnys, and Torben Bach Pedersen. 2017. Generation and Evaluation of Flex-Offers from Flexible Electrical Devices. In Proc. of the Eighth International Conference on Future Energy Systems, e-Energy.

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Knap PGerding E(2024)Energy Storage in the Smart Grid: A Multi-agent Deep Reinforcement Learning ApproachTrends in Clean Energy Research10.1007/978-3-031-67987-2_21(221-235)Online publication date: 18-Jul-2024
https://doi.org/10.1007/978-3-031-67987-2_21
Kumar RSaravanan SPandiyan PTiwari R(2023)Impact of artificial intelligence techniques in distributed smart grid monitoring systemSmart Energy and Electric Power Systems10.1016/B978-0-323-91664-6.00005-X(79-103)Online publication date: 2023
https://doi.org/10.1016/B978-0-323-91664-6.00005-X
Neupane BSiksnys LPedersen THagensby RAftab MEck BFusco FGormally RPurcell MTirupathi SCerne GBrus SPapageorgiou IMeindl GRoduit PLehnhoff SIrwin DWang D(2022)GOFLEXProceedings of the Thirteenth ACM International Conference on Future Energy Systems10.1145/3538637.3538865(361-373)Online publication date: 28-Jun-2022
https://doi.org/10.1145/3538637.3538865
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AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids

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e-Energy '19: Proceedings of the Tenth ACM International Conference on Future Energy Systems

June 2019

589 pages

ISBN:9781450366717

DOI:10.1145/3307772

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 15 June 2019

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e-Energy '19: The Tenth ACM International Conference on Future Energy Systems

June 25 - 28, 2019

AZ, Phoenix, USA

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Knap PGerding E(2024)Energy Storage in the Smart Grid: A Multi-agent Deep Reinforcement Learning ApproachTrends in Clean Energy Research10.1007/978-3-031-67987-2_21(221-235)Online publication date: 18-Jul-2024
https://doi.org/10.1007/978-3-031-67987-2_21
Kumar RSaravanan SPandiyan PTiwari R(2023)Impact of artificial intelligence techniques in distributed smart grid monitoring systemSmart Energy and Electric Power Systems10.1016/B978-0-323-91664-6.00005-X(79-103)Online publication date: 2023
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Neupane BSiksnys LPedersen THagensby RAftab MEck BFusco FGormally RPurcell MTirupathi SCerne GBrus SPapageorgiou IMeindl GRoduit PLehnhoff SIrwin DWang D(2022)GOFLEXProceedings of the Thirteenth ACM International Conference on Future Energy Systems10.1145/3538637.3538865(361-373)Online publication date: 28-Jun-2022
https://doi.org/10.1145/3538637.3538865
O'Donncha FAkhriev AEck BBurke MFilgueira RGrant J(2021)Deployment and Management of Time Series Forecasts in Ocean Industry2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671877(4091-4096)Online publication date: 15-Dec-2021
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Ali SChoi B(2020)State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A ReviewElectronics10.3390/electronics90610309:6(1030)Online publication date: 22-Jun-2020
https://doi.org/10.3390/electronics9061030
Fusco FEck BGormally RPurcell MTirupathi S(2020)Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9377845(1301-1308)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9377845
Xu YAhokangas PLouis JPongrácz E(2019)Electricity Market Empowered by Artificial Intelligence: A Platform ApproachEnergies10.3390/en1221412812:21(4128)Online publication date: 30-Oct-2019
https://doi.org/10.3390/en12214128

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A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia

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Combining seasonal ARIMA models with computational intelligence techniques for time series forecasting

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