Energy management for DSM in residential buildings: a comparative analysis of different solutions
keywords DEMAND RESPONSE, DEMAND SIDE MANAGEMENT, ENERGY CONSUMPTION, ENERGY MANAGEMENT, ICT, SMAR CITY, MACHINE LEARNING, MULTI AGENT SYSTEM, POWER FLEXIBILITY, SMART CITITES, SMART GRID, SMART GRIDS, SMART HOME
External reference persons Pietro Rando Mazzarino (email@example.com)
Thesis type EXPERIMENTAL
Description One of the main objectives of the Energy Center LAB initiative consists on designing a Multi-Energy-System (MES) Co-Simulation platform able to qualitatively and quantitatively solve energetic transition scenarios. The Co-simulation platform encompasses many different aspects in order to asses the complexity of Smart cities and in particular smart grid. One of the aspects addressed is the residential Demand Side Management (DSM). Many literature solutions on intelligent energy management systems at the building level enable DSM strategies by quantifying power flexibility. Some example are control methods such as Receding Horizon Control (RHC), Model predictive control (MPC),
Rule based Control (RBC) or strategies based upon Deep reinforcement learning (DRL).
This thesis focuses on understanding and implementing some of the most valid strategies in order to carry out a comprehensive analysis on benefits and drawbacks of different solutions. Pre-existing platform will be used as a test-bed
and Modular programming, Machine learning and Operational research methods will be used to implement the individual strategies.
Required skills Good programing skills in python
Deadline 13/12/2022 PROPONI LA TUA CANDIDATURA