Long-term energy system modelling: the impact of different time-series clustering algorithms
keywords CLUSTERING, ENERGY SYSTEMS, MODELLING, RENEWABLE ENERGY SOURCES
Reference persons GIULIANA MATTIAZZO
External reference persons Riccardo Novo (firstname.lastname@example.org)
Paolo Marocco (email@example.com)
Description The reliability of energy system models strongly depends on the temporal detail used in their implementation. Cutting-edge methods for the inclusion of clustered time-series in long-term, optimisation-based energy models have proved to be desirable when planning the energy transition process. However, additional work has to be developed to identify the most suitable clustering algorithms for such applications.
The thesis work aims at creating a framework for the development, comparison and testing of different clustering algorithms in long-term energy system models, and their implementation to one or several case studies. The influence of extreme days implementation will be also investigated. The work will be developed through the Python programming language, and will also make use of the OSeMOSYS energy system modelling framework.
See also https://www.sciencedirect.com/science/article/pii/S2590174522000976?via%3Dihub
Required skills Energy system modelling; Programming skills
Deadline 07/10/2023 PROPONI LA TUA CANDIDATURA