Energy End-Use Flexibility of the Next Generation of Decision-Makers in a Smart Grid Setting: An Exploratory Study

  • Inês F. G. ReisEmail author
  • Marta A. R. Lopes
  • Paula F. V. Ferreira
  • Carlos Henggeler Antunes
  • Madalena Araújo
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 269)


Demand Response (DR) mechanisms have been developed to reshape consumption patterns in face of price signals, enabling to deal with the increasing penetration of intermittent renewable resources and balance electricity demand and supply. Although DR mechanisms have been in place for some time, it is still unclear to what extent end-users are ready, or willing, to embrace DR programs that can be complex and imply adjustments of daily routines. This work aims to understand how the next generation of Portuguese decision makers, namely young adults in higher education, are prepared to deal with energy decisions in the context of the challenges brought by the smart grids. Results demonstrate that cost savings and the contribution to environmental protection are found to be important motivating factors to enroll into DR programs, which should be further exploited in future actions for the promotion of end-user engagement. Moreover, DR solutions are well-accepted by higher education students, although with limited flexibility levels. In addition, there is room to exploit the willingness to adopt time-differentiated tariffs, yet savings should be clearer and more attractive to end-users. Also, the framing effect should be considered when promoting this type of time-differentiated tariffs.


Energy decision-makers Smart grids End-use flexibility Smart technologies 



This work was partially supported by project grants UID/MULTI/00308/2019 and UID/CEC/00319/2019 and by the European Regional Development Fund through the COMPETE 2020 Programme, FCT—Portuguese Foundation for Science and Technology within projects ESGRIDS (POCI-01-0145-FEDER-016434), Learn2Behave (02/SAICT/2016-023651), MAnAGER (POCI-01-0145-FEDER-028040), and POCI-01-0145-FEDER-007043, as well as by the Energy for Sustainability Initiative of the University of Coimbra.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.INESC Coimbra - Institute for Systems Engineering and Computers, DEECCoimbraPortugal
  2. 2.Department of Environment - ESACPolytechnic Institute of CoimbraCoimbraPortugal
  3. 3.ALGORITMI Research CentreUniversity of MinhoGuimarãesPortugal
  4. 4.Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbraPortugal

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