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

被引:6
作者
Eck, Bradley [1 ]
Fusco, Francesco [1 ]
Gormally, Robert [1 ]
Purcell, Mark [1 ]
Tirupathi, Seshu [1 ]
机构
[1] IBM Res, Dublin, Ireland
来源
E-ENERGY'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS | 2019年
基金
欧盟地平线“2020”; 欧洲研究理事会;
关键词
D O I
10.1145/3307772.3330158
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页码:381 / 382
页数:2
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