• DocumentCode
    3726663
  • Title

    Demand Response Shifting Management Applied to Distributed Generation and Pumping

  • Author

    Diogo Boldt;Pedro Faria;Zita Vale

  • Author_Institution
    Res. Group on Intell. Eng. &
  • fYear
    2015
  • Firstpage
    1537
  • Lastpage
    1544
  • Abstract
    Recent energy policies in countries around the world, including in Europe, point to the need to integrate growing amounts of distributed generation in electric power systems. This situation led to several changes in the operation and planning of power systems. This paper presents a methodology focusing on demand response programs, distributed generation and pumping, which is aimed to be used by a Virtual Power Player, who is able to manage the available resources minimizing the operation costs. The influence of demand response shifting management, in which was possible to shift load from a critical period to other more benefic, was also taken into account. In this paper it was used Artificial Intelligence, Artificial Neural Networks (ANN), to predict the power the VPP would have to pump to reservoirs to fulfill the reservoir operator demands along the day. The case study includes 2223 consumers and 47 distributed generators units. The implemented scenario corresponds to a real day in Portuguese power system, 9th March 2014.
  • Keywords
    "Artificial neural networks","Reservoirs","Wind power generation","Distributed power generation","Optimization","Load management"
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence, 2015 IEEE Symposium Series on
  • Print_ISBN
    978-1-4799-7560-0
  • Type

    conf

  • DOI
    10.1109/SSCI.2015.217
  • Filename
    7376793