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A New Platform for Automatic Bottom-Up Electric Load Aggregation

Author

Listed:
  • Alfredo Bartolozzi

    (Direzione Territoriale Lazio Abruzzo Molise (DTR LAM)-e-distribuzione SPA, ENEL Group, via della Bufalotta 255, 00139 Rome, Italy)

  • Salvatore Favuzza

    (Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy)

  • Mariano Giuseppe Ippolito

    (Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy)

  • Diego La Cascia

    (Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy)

  • Eleonora Riva Sanseverino

    (Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy)

  • Gaetano Zizzo

    (Department of Energy, Information Engineering and Mathematical Models (DEIM)-University of Palermo, viale delle Scienze-Edificio 9, 90128 Palermo, Italy)

Abstract

In this paper, a new virtual framework for load aggregation in the context of the liberalized energy market is proposed. Since aggregation is managed automatically through a dedicated platform, the purchase of energy can be carried out without intermediation as it happens in peer-to-peer energy transaction models. Differently from what was done before, in this new framework, individual customers can join a load aggregation program through the proposed aggregation platform. Through the platform, their features are evaluated and they are clustered according to their reliability and to the width of range of regulation allowed. The simulations show the deployment of an effective clustering and the possibility to meet the target power demand at a given hour according to each customer’s availability.

Suggested Citation

  • Alfredo Bartolozzi & Salvatore Favuzza & Mariano Giuseppe Ippolito & Diego La Cascia & Eleonora Riva Sanseverino & Gaetano Zizzo, 2017. "A New Platform for Automatic Bottom-Up Electric Load Aggregation," Energies, MDPI, vol. 10(11), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1682-:d:116289
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    References listed on IDEAS

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    Cited by:

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    2. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2018. "An improved incentive-based demand response program in day-ahead and intra-day electricity markets," Energy, Elsevier, vol. 155(C), pages 205-214.
    3. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

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