To develop AUTENS systems, it is necessary to study the ways in which users, both in individual and aggregate form, can become completely autonomous in the supply of energy. To this aim, the project includes the following activities.

  • A socio-economic survey to (i) obtain profiles of electrical and thermal energy demands in the current context and (ii) understand the social acceptability of the predicted energy self-sufficiency scenarios. This requires investigating the willingness of users to change their energy consumption style in the case of limited energy availability, as well as the opportunity to trade off residential and industrial energy consumptions.
  • Analyzing ways through which the integration of storage systems, renewable sources, electronic platforms, and ICT tools can maximize flexibility and energy efficiency, while minimizing the impact on people’s well-being and production activities.
  • The integration of limited quantities of energy from biomass to be produced locally, according to the climatic characteristics of the location.
  • The development of intelligent techniques, based on monitoring the energy consumption and the conditions of buildings/plants, to provide users with a computer application to support decisions. An interdisciplinary study will be carried out to derive privacy-preserving methods for the collection, analysis, and sharing of data on energy consumption and provide guidelines.
  • The study of legal approaches for regulating the self-production and circulation of energy, within the national and European legal frameworks, to verify their ability to represent a real incentive for AUTENS.

Finally, the project plans to upgrade an existing demonstrator for the hardware in the loop emulation of the systems under study. This new demonstrator will allow to assess the performance of less traditional components, such as hybrid thermal/photovoltaic (PV/T) solar panels, geothermal heat-pump systems (GSHP), phase-change storage systems (PCM). It will also be possible to implement a monitoring and control system based on machine-learning algorithms.