| KEYWORD |
Development of AI-based models for the management of energy communities
keywords DATA ANALYTICS, ENERGY COMMUNITIES, ENERGY MANAGEMENT, INTELLIGENT BUILDINGS
Reference persons ALFONSO CAPOZZOLI
Research Groups BAEDA lab (www.baeda.polito.it)
Thesis type RESEARCH
Description The overcoming of the traditional way of producing and consuming energy towards a more sustainable energy management has shifted the need of flexibility from the generation side to the demand side. In this context, the Energy Community is the new paradigm where prosumers can acquire a more active role while interacting with the grid by aggregating their loads and generation profiles. Energy Communities can then be seen as a means for optimizing the energy management in smart grids, with positive effects for the members, who can decrease their energy cost, and for the grid, which can benefit from the provided flexibility. Recent studies have proved how coordinated control architecture for energy management in cluster of buildings is effective at achieving such objective. Nonetheless, the development of control strategies and of digital twins at the district level for testing them is particularly demanding due to high complexity of the control problem and its high computational cost.
The obhective of the thesis is the development of generalizable simulation environments for Energy Communities as virtual testbeds for control strategies. The environments are used for the evaluation of advanced control strategies in terms of achievable energy flexibility and energy cost saving for data-driven energy communities, de facto bridging the gap that is currently characterizing the research.
Deadline 02/03/2024
PROPONI LA TUA CANDIDATURA