@article{PeereBlanke2022,
  author    = {Peere, Wouter and Blanke, Tobias},
  title     = {GHEtool: An open-source tool for borefield sizing in Python},
  series = {Journal of Open Source Software},
  volume    = {7},
  journal   = {Journal of Open Source Software},
  number    = {76},
  editor    = {Vernon, Chris},
  issn      = {2475-9066},
  doi       = {10.21105/joss.04406},
  pages     = {1 -- 4, 4406},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@inproceedings{BlankeSchmidtGoettscheetal.2022,
  author    = {Blanke, Tobias and Schmidt, Katharina S. and G{\"o}ttsche, Joachim and D{\"o}ring, Bernd and Frisch, J{\´e}r{\^o}me and van Treeck, Christoph},
  title     = {Time series aggregation for energy system design: review and extension of modelling seasonal storages},
  series = {Energy Informatics},
  volume    = {5},
  booktitle = {Energy Informatics},
  number    = {1, Article number: 17},
  editor    = {Weidlich, Anke and Neumann, Dirk and Gust, Gunther and Staudt, Philipp and Sch{\"a}fer, Mirko},
  publisher = {Springer Nature},
  issn      = {2520-8942},
  doi       = {10.1186/s42162-022-00208-5},
  pages     = {1 -- 14},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}