A Multi-Agent System Architecture for Smart Grid Management and Forecasting of Energy Demand in Virtual Power Plants

[EN] 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.We would like to express our thanks to the coordinators of the project OptimaGrid for the information provided on MAS-based micro-grids, and the creators of a MAS INGENIAS methodology. This article has been partially funded by the project SociAAL (Social Ambient Assisted Living), supported by Spanish Ministry for Economy and Competitiveness, with grant TIN2011-28335-C02-01, by the Programa de Creacion y Consolidacion de Grupos de Investigacion UCM-Banco Santander for the group number 921354 (GRASIA group).Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J.; Chinarro, D.... (2013). A Multi-Agent System Architecture for Smart Grid Management and Forecasting of Energy Demand in Virtual Power Plants. IEEE Communications Magazine. 51(1):106-113. https://doi.org/10.1109/MCOM.2013.6400446S10611351

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