Predicting DRAM reliability in the field with machine learning
Ioana Giurgiu, Jacint Szabo, et al.
Middleware 2017
A variety of energy resources has been identified as being flexible in their electric energy consumption or generation. This energetic flexibility can be used for various purposes such as minimizing energy procurement costs or providing ancillary services to power grids. To fully leverage the flexibility available from distributed small-scale resources, their flexibility must be quantified and aggregated. This paper introduces a generic and scalable approach for flexible energy systems to quantitatively describe and price their flexibility based on zonotopic sets. The description allows aggregators to efficiently aggregate the flexibility of large numbers of systems and to make control and market decisions on the aggregate level. In addition, an algorithm is presented that distributes aggregate-level control decisions among the individual systems of the population in an economically fair and computationally efficient way. The algorithm is applied to the problem of disaggregating reference schedules resulting from day-ahead energy markets.
Ioana Giurgiu, Jacint Szabo, et al.
Middleware 2017
Rahul Nair, Hoang Thanh Lam, et al.
Transp. Res. Part C Emerg. Technol.
Jacint Szabo, Sebastien Blandin, et al.
AAMAS 2017
Olle Sundström, Olivier Corradi, et al.
IEVC 2012