From 0731c5504006b485e06937bab3c7135f07def320 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Mon, 20 Jan 2025 17:32:35 +0000 Subject: [PATCH] Creating 10.21105.joss.06274.jats --- .../paper.jats/10.21105.joss.06274.jats | 949 ++++++++++++++++++ 1 file changed, 949 insertions(+) create mode 100644 joss.06274/paper.jats/10.21105.joss.06274.jats diff --git a/joss.06274/paper.jats/10.21105.joss.06274.jats b/joss.06274/paper.jats/10.21105.joss.06274.jats new file mode 100644 index 000000000..80cb486d1 --- /dev/null +++ b/joss.06274/paper.jats/10.21105.joss.06274.jats @@ -0,0 +1,949 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6274 +10.21105/joss.06274 + +ETHOS.FINE: A Framework for Integrated Energy System +Assessment + + + +https://orcid.org/0000-0003-1792-5598 + +Klütz +Theresa + + + + +https://orcid.org/0000-0002-8825-5934 + +Knosala +Kevin + + + + + +https://orcid.org/0009-0003-2396-0282 + +Behrens +Johannes + + + + + +https://orcid.org/0000-0003-0664-3886 + +Maier +Rachel + + + + + +https://orcid.org/0000-0003-1232-8110 + +Hoffmann +Maximilian + + + + +https://orcid.org/0000-0002-1982-8794 + +Pflugradt +Noah + + + + +https://orcid.org/0000-0002-1671-3262 + +Stolten +Detlef + + + + + + +Forschungszentrum Jülich GmbH, Institute of Climate and +Energy Systems – Jülich Systems Analysis, 52425 Jülich, +Germany + + + + +RWTH Aachen University, Chair for Fuel Cells, Faculty of +Mechanical Engineering, 52062 Aachen, Germany + + + + +13 +1 +2025 + +10 +105 +6274 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2025 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +energy system modeling +optimization +energy + + + + + + Summary +

The decarbonization of energy systems worldwide requires a + transformation in their design and operation across all sectors, + including the residential and commercial, industrial, and + transportation sectors. Energy system models are often used to assess + these changes. These models provide scenarios for potential future + system designs and show how new technologies and infrastructure will + meet future energy demand. Thus, they support investment decisions and + policy making. The Python-based Framework for Integrated Energy System + Assessment (ETHOS.FINE) is a software package + that provides a toolbox for modeling, analyzing and evaluating such + energy systems using mathematical optimization.

+

ETHOS.FINE is not limited to a single + instance of energy systems. Instead, it can be freely adapted to + consider multiple commodities, regions, time steps and investment + periods. The optimization objective is to minimize the net present + value of the system and is subject to technical and environmental + constraints. If only one investment period is considered, the net + present value equals the total annual costs of the system. The generic + object-oriented implementation allows for arbitrary spatial scales and + number of regions – from the local level, e.g., individual buildings, + to the regional one, e.g., districts or industrial sites, to the + national and international levels.

+

To reduce model size and complexity, the spatial technological + resolution can be aggregated using built-in aggregation methods that + are described in Patil et al. + (2022). + These methods include the aggregation of modeled regions and their + given input parameters, and the grouping of technologies within the + regions, e.g. to aggregate multiple time series for variable renewable + energy sources. This also applies to the temporal resolution of the + model. Apart from using the full temporal resolution defined by the + input data, integrated time series aggregation methods using the + built-in Python package tsam1 + allow to reduce the complexity of the model and its computation time + (M. + Hoffmann et al., 2022), while still allowing the flexibility of + seasonal storage technologies, despite the reduced model complexity + (Kotzur + et al., 2018). ETHOS.FINE supports the + aggregation of time steps to typical periods, the segmentation of the + time series and the combination of both. The aggregation methods, + spatial and temporal aggregation, can be used directly in + ETHOS.FINE by calling the corresponding + functions. In addition, ETHOS.FINE allows the + investigation of transformation paths by considering multiple + investment periods in a perfect foresight approach, as well as the + stochastic optimization for a single year optimization with multiple + sets of input parameters, e.g., changing energy demand forecasts or + weather conditions, to find more robust energy system designs.

+ + + Methodology +

ETHOS.FINE comprises seven main classes: The + EnergySystemModel class can be seen as the container of the model, + collecting all relevant input data for its setup. All technologies to + be considered are added to this container. The Component class + contains the parameters, variables, and constraints common to all + system components, such as capacity limits and restrictions on the + operation of technologies. The five classes - Source, Sink, + Conversion, Transmission, and Storage - provide the functionality to + model energy generation and consumption, conversion processes, energy + storage for later use, and energy transfer between regions. Each class + introduces a specific set of constraints that is added to the + optimization program. The specific set of constraints is described in + the additional model classes. Supplemental subclasses provide + additional component features, e.g., the ability to model partial load + behavior and ramping constraints for power plants. The described + structure is shown in + [fig:finestructure]. + Objects of the Source, Sink, Conversion and Storage classes are + assigned to the modeled locations, which are represented as nodes in + the model. Transmission class objects are assigned to the connections + between the nodes.

+ +

a) Structure of the main classes in + ETHOS.FINE. Additional model classes contain + the definition of the specific variables, sets and constraints for + each class to build the optimization model. b) Simplified + representation of the model structure in + ETHOS.FINE with the corresponding component + classes. Each node represents a region that can exchange goods and + energy carriers via transmission components (based on Groß + (2023)). +

+ + +

The energy system model can be set up as a linear program (LP), a + quadratic program (QP), or a mixed integer linear program (MILP), + depending on the chosen representation of the added components. The + optimization program is written as a + Pyomo2 instance to allow a + flexible choice of solvers, i.e. ETHOS.FINE + optimizes energy systems using both, commercial and open source + solvers. In a future version, other Python libraries, e.g. linopy + (Hofmann, + 2023), may be integrated to improve the setup of the + optimization program. Depending on the spatial and temporal resolution + of the modeled system, the input parameters are primarily given as + Pandas.DataFrames3 with regions + and time steps serving as indices and columns. The model output + provides detailed information on the investment required in each + region for the installation and operation of the selected components, + as well as the temporally-resolved operation of each component. This + also includes charging and discharging of storage components and + commodity flows between regions via transmission components. In + addition, the framework provides plotting options for spatially and + temporally resolved results. Model input and output can be saved to + netCDF files to support reproducibility.

+ + + Statement of need +

ETHOS.FINE offers a unique generic model + setup with a high degree of freedom for model developers. Beyond + energy system models, its generic implementation allows the modeling + of all kinds of optimization problems, such as material flows and + resource consumption or conversion as part of life cycle analysis. + ETHOS.FINE was developed to provide a flexible + techno-economic analysis tool to analyze the energy transition on all + levels of interest especially with regards to sector-coupled systems. + The software exhibits many of the features described by Groissböck + (2019) + and is under constant development. Its code is openly accessible on + GitHub which allows for contributions and feedback from a wider + modeling community. The use cases described in the next section + demonstrate the broad range of analyses that can be conducted with the + tool.

+

There are several other open-source available energy system + modeling frameworks that are also implemented in Python, e.g. Calliope + (Pfenninger + & Pickering, 2018), PyPSA + (Brown + et al., 2018), oemof + (Hilpert + et al., 2018) and CLOVER + (Sandwell + et al., 2023). The tools are used for similar use cases, + providing different sets of functionalities and possible analysis + tools. They differ mainly in the setup process of the models. + ETHOS.FINE offers a highly flexible alternative + to these tools.

+

ETHOS.FINE is designed to be used by + researchers, students, and for teaching purposes in the field of + energy system modeling. In particular, its exceptional capabilities + with respect to complexity reduction + (Kotzur + et al., 2021) using spatial + (Patil + et al., 2022) and temporal aggregation + (M. + Hoffmann et al., 2020, + 2021, + 2022; + M. + A. C. Hoffmann, 2023), as well as heuristics for dealing with + MILPs + (Kannengießer + et al., 2019; + Singh + et al., 2022) open a wide field of applications from small to + global scale energy system models. For newcomers who are not familiar + with programming, it also has the flexibility to set up models by + using Excel files, the usability of which is described in one of the + example Jupyter notebooks published in the GitHub repository.

+ + + Examples for previous usage +

ETHOS.FINE has been used in various studies + for energy system analyses at different scales, taking advantage of + its ability to dynamically adapt to computational complexity. First + applications can be found in Welder et al. + (2018) + and Welder et al. + (2019): + The authors analyzed hydrogen-to-electricity reconversion pathways in + a multi-regional energy system model implemented in + ETHOS.FINE for the northern part of Germany. + Later, Welder + (2022) + and Groß + (2023) + used the framework to model the future energy system of Germany with a + high spatial resolution and thereby to investigate the need for new + infrastructure. D. G. Caglayan et al. + (2019) + built an ETHOS.FINE model of the European + energy system, and analyzed the influence of varying weather years on + the cost-optimal system design based on 100% use of renewable energy + source. Their findings are also used to determine a robust system + design based on variable renewable energy sources, ensuring security + of supply for a wide range of weather years + (D. + Caglayan et al., 2021). Knosala et al. + (2021) + evaluated hydrogen technologies in residential buildings in a + multi-commodity, single-building model. The building model from this + work was also used for a sensitivity analysis of energy carrier costs + for the application of hydrogen in residential buildings + (Knosala + et al., 2022). Spiller et al. + (2022) + analyzed the carbon emission reduction potentials of hotels on energy + self-sufficient islands. More recently, Weinand et al. + (2023) + used the framework to assess the Rhine Rift Valley for its potential + for lithium extraction from deep geothermal wells. Meanwhile, Jacob et + al. + (2023) + investigated the potential of Carnot batteries in the German + electricity system. Busch et al. + (2023) + analyzed the role of liquid hydrogen, also on a national scale, while + Franzmann et al. + (2023) + examined the cost potential of green hydrogen for global trade. These + examples illustrate the variety of applications that can be addressed + by ETHOS.FINE.

+

ETHOS.FINE is part of the Energy + Transformation paTHway Optimization Suite + (ETHOS)4, + a collection of modeling tools developed by the Institute of Energy + and Climate Research - Jülich Systems Analysis at Forschungszentrum + Jülich. ETHOS offers a holistic view of energy + systems at arbitrary scales, providing tools for geospatial analysis + of renewable energy potential, time series simulation tools for + residential and industrial sector, discrete choice models for the + transportation sector, modeling of global energy supply routes, and + local infrastructure assessments, among others. An example of the use + of this model suite can be found in Stolten et al. + (2022). + The model framework ETHOS.FINE serves as a + basis for several model implementations within + ETHOS, e.g. for the optimization programs to + analyse of the transformation of single buildings, the transport + sector, and the local, German, European and global energy system, or + to determine the cost potential of global hydrogen production.

+ + + Acknowledgements +

We acknowledge contributions from Lara Welder, Robin Beer, Julian + Belina, Toni Busch, Arne Burdack, Henrik Büsing, Dilara Caglayan, + Philipp Dunkel, David Franzmann, Patrick Freitag, Maike Gnirß, Thomas + Grube, Lars Hadidi, Heidi Heinrichs, Jason Hu, Shitab Ishmam, Timo + Kannengießer, Sebastian Kebrich, Leander Kotzur, Stefan Kraus, Felix + Kullmann, Dane Lacey, Jochen Linssen, Nils Ludwig, Lilly Madeisky, + Drin Marmullaku, Gian Müller, Lars Nolting, Kenneth Okosun, Olalekan + Omoyele, Shruthi Patil, Jan Priesmann, Oliver Rehberg, Stanley Risch, + Martin Robinius, Thomas Schöb, Julian Schönau, Kai Schulze, Bismark + Singh, Andreas Smolenko, Lana Söltzer, Maximilian Stargardt, Peter + Stenzel, Chloi Syranidou, Johannes Thürauf, Henrik Wenzel, Lovindu + Wijesinghe, Christoph Winkler, Bernhard Wortmann and Michael Zier + during the development of this software package.

+

This work was initially supported by the Helmholtz Association + under the Joint Initiative “Energy System 2050 - A Contribution of the + Research Field Energy.” The authors also gratefully acknowledge + financial support by the Federal Ministry for Economic Affairs and + Energy of Germany as part of the project METIS (project number + 03ET4064, 2018-2022).

+

This work was supported by the Helmholtz Association under the + program Energy System Design.

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tsam - Time Series Aggregation Module, + https://github.com/FZJ-IEK3-VSA/tsam,

+ + +

Pyomo, Pyoton Optimization Modeling Language, + https://pyomo.org/,

+ + +

Pandas, Python Data Analysis Library, + https://pandas.pydata.org/,

+ + +

ETHOS - Energy Transformation paTHway + Optimization Suite, + https://www.fz-juelich.de/en/iek/iek-3/expertise/model-services

+ + + +