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Control to respond: Demand response in districts of buildings by means of adaptive controls

keywords AUTOMATED SYSTEM OPTIMIZATION, BUILDING AUTOMATION, COORDINATED ENERGY MANAGEMENT, DATA ANALYTICS

Reference persons ALFONSO CAPOZZOLI

Research Groups BAEDA lab (www.baeda.polito.it), TEBE

Description Building energy management can enable energy flexibility by enhancing on-site renewable energy exploitation and storage operation, reducing energy costs, and providing services to the grid (i.e., load shifting, peak shaving). However, when the energy management is faced shifting from a single building to a district of buildings, individual demand-side management may have negative effects on the grid reliability, like the peak “rebound” issue. Moreover, uncoordinated management could cause undesirable new peaks or lead to suboptimal solutions to the grid.
To overcome these limitations, coordinated energy management takes advantage of the mutual collaboration between single buildings to provide services to the grid (Demand Response). In this context, adaptive and predictive control strategy may provide great benefits with respect to a more common control strategy.
In this perspective, the thesis project aims at exploring the opportunity to integrate demand response programs to the coordinated energy management paradigm, demonstrating the feasibility of data-driven control strategies
• HVAC modelling in EnergyPlus.
• Knowledge of Python language.
• Advanced control strategies of HVAC systems (e.g. Fuzzy-PID, Model Predictive Control, Reinforcement Learning).


Deadline 24/02/2023      PROPONI LA TUA CANDIDATURA




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