Abstract
Flexibility for the energy grid is expected to become a crucial aspect as the variety of both energy sources and appliances increases in diversity. Moreover, since automation becomes even more present in people’s daily activities and interaction, more focus will be required from humans when dealing with the climate changes. Fortunately, energy communities have the potential to build upon the idea of cooperation between humans and energy systems. In the framework of an energy community where people are guided on a daily basis by an intelligent recommendation system, the paper proposes a recommendation strategy implemented in a multi-agent model of a community that focuses on sending straightforward signals, requiring people to modify their consumption in order to achieve their collective objective. The strategy includes a comfort threshold parameter, defined specifically to allow the implementation of several demanding/relaxing variations. Comparing to our previous work presented in [14], we focus on developing a simplistic recommendation strategy, with hourly goals that are easy to understand by the community members. Moreover, we take a step further in investigating the issue of comfort by proposing a no-alert threshold that could be adapted, depending on the community’s aim for increased performance or increased effort. A case-study with several multi-agent simulation scenarios is included, emphasising the impact of the previously mentioned parameter on the collective performances, quantified through the net energy exchanged with the grid (NEEG).
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Acknowledgement
The results presented in this article has been funded by the Ministry of Investments and European Projects through the Human Capital Sectorial Operational Program 2014–2020, Contract no. 62461/03.06.2022, SMIS code 153735.
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Simoiu, M.Ş., Făgărăşan, I., Ploix, S., Calofir, V., Iliescu, S.S. (2023). A Recommendation Strategy Proposal for an Energy Community Modeled as a Multi-agent System. In: Borangiu, T., Trentesaux, D., Leitão, P. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2022. Studies in Computational Intelligence, vol 1083. Springer, Cham. https://doi.org/10.1007/978-3-031-24291-5_4
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