• DocumentCode
    1787448
  • Title

    Prosumers as Aggregators in the DEZENT Context of Regenerative Power Production

  • Author

    Montanari, Ugo ; Siwe, Alain Tcheukam

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Pisa, Pisa, Italy
  • fYear
    2014
  • fDate
    8-12 Sept. 2014
  • Firstpage
    167
  • Lastpage
    174
  • Abstract
    Captive consumers of the current traditional and centralized power management systems will become proactive in the future of the smart grid. Their flexibilities will allow them to become prosumers. A prosumer (producer-consumer) is defined as a user that not only consumes electricity, but can also produce and store electricity. A new concept of aggregator has been introduced in the power market. The aggregator exploits the active participation of prosumers in order to provide commercial service in the power market. In this paper, we focus on power market models in which prosumers interact in a distributed environment during the purchase or sale of electric power. We propose a new aggregator which operates in the DEZENT power market model. The aggregator consists of a collection of prosumers who make use of reinforcement learning and of optimization techniques for the planning phase of their electricity production and consumption. In the paper we discuss the assumptions on which the aggregator design is based and we compare its behaviour with that of the aggregator proposed in the EU ADDRESS projects.
  • Keywords
    learning (artificial intelligence); optimisation; power consumption; power engineering computing; power markets; smart power grids; DEZENT context; EU ADDRESS projects; aggregators; captive consumers; centralized power management systems; distributed environment; electric power; electricity consumption; electricity production; optimization techniques; power market; producer-consumer; prosumers; regenerative power production; reinforcement learning; smart grid; Electricity; Learning (artificial intelligence); Optimization; Power markets; Production; Sociology; Statistics; Distributed and real time systems; Optimization; Power markets; Reinforcement learning; Renewable energy sources; Smart power grid;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Self-Adaptive and Self-Organizing Systems (SASO), 2014 IEEE Eighth International Conference on
  • Conference_Location
    London
  • Type

    conf

  • DOI
    10.1109/SASO.2014.30
  • Filename
    7001013