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Edge Computing Parallel Approach for Efficient Energy Sharing in a Prosumer Community

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  • Luigi Scarcello

    (ICAR-CNR, Institute for High Performance Computing and Networking Via P. Bucci 8/9C, 87036 Rende, Italy
    These authors contributed equally to this work.)

  • Andrea Giordano

    (ICAR-CNR, Institute for High Performance Computing and Networking Via P. Bucci 8/9C, 87036 Rende, Italy
    These authors contributed equally to this work.)

  • Carlo Mastroianni

    (ICAR-CNR, Institute for High Performance Computing and Networking Via P. Bucci 8/9C, 87036 Rende, Italy
    These authors contributed equally to this work.)

Abstract

The transition towards more sustainable energy management can be supported by the diffusion of energy communities, i.e., coalitions of prosumers that are willing to exchange the energy produced locally. The optimization of energy management requires the solution of a prosumer problem that can become impractical when the number of users increases. This paper presents a parallel approach, based on an edge computing architecture, which is suitable for large communities. The users are partitioned into groups whose proportions, in terms of producers and consumers, mirror the composition of the whole community. The prosumer problems for the different groups are first solved separately and in parallel by local edge nodes. Then, the solutions are combined by a central entity to redistribute the energy among the groups and minimize the exchange of energy with the external grid. A set of experiments show that the parallel approach, when compared with an approach that solves the optimization problem in a single stage, leads to a notable reduction of computing resources, and becomes feasible in large communities for which the single-stage approach is impossible. Moreover, the achieved solution is close to the optimal solution in terms of energy costs.

Suggested Citation

  • Luigi Scarcello & Andrea Giordano & Carlo Mastroianni, 2022. "Edge Computing Parallel Approach for Efficient Energy Sharing in a Prosumer Community," Energies, MDPI, vol. 15(13), pages 1-24, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4543-:d:844455
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    References listed on IDEAS

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    1. Soheil Younesi & Bahman Ahmadi & Oguzhan Ceylan & Aydogan Ozdemir, 2022. "Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems," Energies, MDPI, vol. 15(24), pages 1-18, December.

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