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Intelligent Local Energy Communities: A Multiagent System Approach

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Agents and Artificial Intelligence (ICAART 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12613))

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Abstract

The electric power grid undergoes a transformation, with many consumers becoming both producers and consumers of electricity. This transformation poses challenges to the existing grid as it was not designed to have reverse power flows. Local energy communities are effective in addressing those issues and engaging grid users to play an active role in the energy transition. Such communities encourage the consumption of the excess of renewable energy locally, which reduces the stress on the grid and the costs for the users. In this paper, we present a multiagent system developed to implement an intelligent local energy community. The multiagent system models the energy grid as a network of computational agents that solve energy flow problems in a coordinated way and use the solutions for controlling flexible loads. The model effectively distributes the tasks among the agents considering the flows of electricity and heat. The Alternative Direction Method of Multipliers determines the agent interaction protocol. The obtained results demonstrate the ability of the multiagent system to automate an intelligent operation of the community while reducing the energy costs and ensuring the grid stability.

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Notes

  1. 1.

    For better readability, we use \(y(\tau )=x(\tau )+u(\tau )\) for the net agents and \(y(\tau )=z(\tau )-u(\tau )\) for the device agents, \(\tau = 1,\ldots ,H\).

  2. 2.

    The B, SH and HWS agents solve their subproblems using Dykstra’s projection method [2, 17] with a starting point \(x(\tau ) = y(\tau )\) for \(\tau = 1,\ldots ,H\).

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Acknowledgements

This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 774431 (DRIvE).

Author information

Authors and Affiliations

  1. CEA, LIST, 91191, Gif-sur-Yvette, France

    Roman Denysiuk & Meritxell Vinyals

  2. University of Cagliari, Via Ospedale 72, Cagliari, Italy

    Fabio Lilliu & Diego Reforgiato Recupero

  3. R2M Solution s.r.l., Via Fratelli Cuzio 42, Pavia, Italy

    Diego Reforgiato Recupero

Authors
  1. Roman Denysiuk
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  2. Fabio Lilliu
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  3. Meritxell Vinyals
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  4. Diego Reforgiato Recupero
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Corresponding author

Correspondence to Roman Denysiuk .

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Editors and Affiliations

  1. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  2. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

  3. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

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Denysiuk, R., Lilliu, F., Vinyals, M., Reforgiato Recupero, D. (2021). Intelligent Local Energy Communities: A Multiagent System Approach. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2020. Lecture Notes in Computer Science(), vol 12613. Springer, Cham. https://doi.org/10.1007/978-3-030-71158-0_2

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  • DOI: https://doi.org/10.1007/978-3-030-71158-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71157-3

  • Online ISBN: 978-3-030-71158-0

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