A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants

Recent technological advances in the power generation and information technologies areas are helping to change the modern electricity supply system in order to comply with higher energy efficiency and sustainability standards. Smart grids are an emerging trend that introduce intelligence in the powe...

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Bibliographic Details
Published in:IEEE communications magazine 2013-01, Vol.51 (1), p.106-113
Main Authors: Hernandez, Luis, Baladron, Carlos, Aguiar, Javier M., Carro, Belen, Sanchez-Esguevillas, Antonio, Lloret, Jaime, Chinarro, David, Gomez-Sanz, Jorge J., Cook, Diane
Format: Magazinearticle
Language:English
Subjects:
Agents (artificial intelligence)
Demand
Demand forecasting
Electric power generation
Electric power plants
Electric utilities
Energy management
Intelligence
Mathematical models
Multiagent systems
Neural networks
Power generation
Predictive models
Smart grids
Smart homes
Studies
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Summary:Recent technological advances in the power generation and information technologies areas are helping to change the modern electricity supply system in order to comply with higher energy efficiency and sustainability standards. Smart grids are an emerging trend that introduce intelligence in the power grid to optimize resource usage. In order for this intelligence to be effective, it is necessary to retrieve enough information about the grid operation together with other context data such as environmental variables, and intelligently modify the behavior of the network elements accordingly. This article presents a multi-agent system model for virtual power plants, a new power plant concept in which generation no longer occurs in big installations, but is the result of the cooperation of smaller and more intelligent elements. The proposed model is not only focused on the management of the different elements, but includes a set of agents embedded with artificial neural networks for collaborative forecasting of disaggregated energy demand of domestic end users, the results of which are also shown in this article.
ISSN:0163-6804
1558-1896
DOI:10.1109/MCOM.2013.6400446