| KEYWORD |
Comparison of optimization techniques in Demand Management applications within renewable energy communities
keywords DEMAND MANAGEMENT, ENERGY COMMUNITIES, RENEWABLE ENERGY
Reference persons PAOLO LAZZERONI
Description Decarbonization of energy consumption is one of the pillars of the future energy transition with lower carbon footprint. In this context, Renewable Energy Communities (RECs) play a key role in the development of a society based on the self-consumption of local energy production from renewable sources. However, the exploitation of intermittent Renewable Energy Sources can be improved through the use of energy storage systems as well as the adoption of load management (Demand Management) strategies: in both cases, the goal is to reduce the mismatch between the demand and the energy production.
Specifically, this thesis seeks to evaluate, in the context of CERs, a comparison of different Demand Management strategies based on heuristic (e.g., genetic algorithms) and deterministic (e.g., Mixed Integer Linear Programming) optimization techniques.
Familiarity with Python (or other programming languages) is strongly recommended to develop analysis and simulations.
Deadline 19/03/2025
PROPONI LA TUA CANDIDATURA