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Demand-side energy management by cooperative combination of plans: A multi-objective method applicable to isolated communities

Author

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  • de Christo, Tiago Malavazi
  • Perron, Sylvain
  • Fardin, Jussara Farias
  • Simonetti, Domingos Sávio Lyrio
  • de Alvarez, Cristina Engel

Abstract

Nowadays a diversity of demand-side energy management methods have been investigated and experimented, however, the low acceptance and participation of the users and the extra costs for the monitoring and control devices installation are still listed by the literature as the main barriers to be overcome. In many cases, activities can be performed in several ways, but once planned, the replanning or cancellation can become impracticable. In Antarctic Research Stations and isolated communities, the planning of activities is even more critical due climatic time windows and facility availability. Considering these aspects, this work proposes and analyses a demand-side management method based on the cooperative combination of activity plans. The method does not depend on the installation of load-control devices neither knowledge of the user about electricity or tariffs. Based on options of plans informed by the users, the proposed multi-objective optimization algorithm search for the set of plans that both minimizes the cost of energy production and the discomfort of the whole community. Simulations performed for a wind-solar-diesel microgrid with 100 users in scenarios of lack and excess of renewable resource indicate that the proposed method can contribute to the adjustment of the aggregate demand profile of users served by isolated microgrids. In the simulations, the problem of overgeneration by the renewable sources was solved and 8.6% of fuel savings was achieved by the intervention in only 51% of the users. Improvements in the load factor of the generators and in their total operation time were also observed. As consequence, a reduction in the maintenance costs of the generators is also expected.

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  • de Christo, Tiago Malavazi & Perron, Sylvain & Fardin, Jussara Farias & Simonetti, Domingos Sávio Lyrio & de Alvarez, Cristina Engel, 2019. "Demand-side energy management by cooperative combination of plans: A multi-objective method applicable to isolated communities," Applied Energy, Elsevier, vol. 240(C), pages 453-472.
    • Handle: RePEc:eee:appene:v:240:y:2019:i:c:p:453-472
    DOI: 10.1016/j.apenergy.2019.02.011
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    7. Jinghong Zhou & Ke Chen & Weidong Wang, 2023. "A Power Evolution Game Model and Its Application Contained in Virtual Power Plants," Energies, MDPI, vol. 16(11), pages 1-22, May.
    8. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    9. Máximo A. Domínguez-Garabitos & Víctor S. Ocaña-Guevara & Félix Santos-García & Adriana Arango-Manrique & Miguel Aybar-Mejía, 2022. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market," Energies, MDPI, vol. 15(4), pages 1-28, February.
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