TY  - JOUR
A1  - Peere, Wouter
A1  - Blanke, Tobias
ED  - Vernon, Chris
T1  - GHEtool: An open-source tool for borefield sizing in Python
JF  - Journal of Open Source Software
N2  - GHEtool is a Python package that contains all the functionalities needed to deal with borefield design. It is developed for both researchers and practitioners. The core of this package is the automated sizing of borefield under different conditions. The sizing of a borefield is typically slow due to the high complexity of the mathematical background. Because this tool has a lot of precalculated data, GHEtool can size a borefield in the order of tenths of milliseconds. This sizing typically takes the order of minutes. Therefore, this tool is suited for being implemented in typical workflows where iterations are required. 

GHEtool also comes with a graphical user interface (GUI). This GUI is prebuilt as an exe-file because this provides access to all the functionalities without coding. A setup to install the GUI at the user-defined place is also implemented and available at: https://www.mech.kuleuven.be/en/tme/research/thermal_systems/tools/ghetool.
KW  - geothermal
KW  - energy
KW  - borefields
KW  - sizing
Y1  - 2022
U6  - http://dx.doi.org/10.21105/joss.04406
SN  - 2475-9066
VL  - 7
IS  - 76
SP  - 1
EP  - 4, 4406
ER  - 
TY  - CHAP
A1  - Blanke, Tobias
A1  - Schmidt, Katharina S.
A1  - Göttsche, Joachim
A1  - Döring, Bernd
A1  - Frisch, Jérôme
A1  - van Treeck, Christoph
ED  - Weidlich, Anke
ED  - Neumann, Dirk
ED  - Gust, Gunther
ED  - Staudt, Philipp
ED  - Schäfer, Mirko
T1  - Time series aggregation for energy system design: review and extension of modelling seasonal storages
T2  - Energy Informatics
N2  - Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, “Time series aggregation for energy system design: Modeling seasonal storage”, has developed a seasonal storage model to address this issue. Simultaneously, the paper “Optimal design of multi-energy systems with seasonal storage” has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.
KW  - Energy system
KW  - Renewable energy
KW  - Mixed integer linear programming (MILP)
KW  - Typical periods
KW  - Time-series aggregation
Y1  - 2022
U6  - http://dx.doi.org/10.1186/s42162-022-00208-5
SN  - 2520-8942
N1  - Proceedings of the 11th DACH+ Conference on Energy Informatics, 15-16 September 2022, Freiburg, Germany.
VL  - 5
IS  - 1, Article number: 17
SP  - 1
EP  - 14
PB  - Springer Nature
ER  -