Klemm, Christian
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The Educational Journal of Renewable Energy Short Reviews (EduJRESR, formally published as ‘EGU Journal of Renewable Energy Short Reviews’) is a teaching project rather than a regular scientific journal.
To publish in this journal, it is a premise to take part in the master course wind power, hydro power and biomass usage at the department of Energy, Building Services and Environmental Engineering of the Münster University of Applied Sciences.
Students receive an equivalent of 2.5 credit points (European Credit Transfer and Accumulation System – ECTS) for their engagement in the course and for publishing a short review article of at most 3 000 words in this periodical. The publication process closely mimics the typical publication procedure of a regular journal.
The peer-review process, however, is conducted within the group of course-participants.
Although being just an exercise, we think that publishing the outcome of this course in a citable manner is not only promoting the motivation of our students, but may also be a helpful source of introductory information for researchers and practitioners in the field of renewable energies. We encourage students to write their articles in English, but this is not mandatory. The reader will thus find a few articles in German language.
To further encourage students practicing English writing, perfect grammar is not part of the assessment.
We especially thank our students for working with LATEX on Overleaf, although LATEX is new to some of them. In this way, the editorial workload was reduced to a minimum. We also thank our students for sharing their work under the creative commons attribution licence (CC-BY). We appreciate their contribution to scientific information, being available to every person of the world, almost without barriers. We also thank the corresponding authors and publishers of the cited work, for granting permission.
This study identifies supply options for sustainable urban energy systems, which are robust to external system changes. A multi-criteria optimization model is used to minimize greenhouse gas (GHG) emissions and financial costs of a reference system. Sensitivity analyses examine the impact of changing boundary conditions related to GHG emissions, energy prices, energy demands, and population density. Options that align with both financial and emission reduction and are robust to system changes are called “no-regret” options. Options sensitive to system changes are labeled as “potential-risk” options.
There is a conflict between minimizing GHG emissions and financial costs. In the reference case, the emission-optimized scenario enables a reduction of GHG emissions (-93%), but involves higher costs (+160%) compared to the financially-optimized scenario.
No-regret options include photovoltaic systems, decentralized heat pumps, thermal storages, electricity exchange between sub-systems and with higher-level systems, and reducing energy demands through building insulation, behavioral changes, or the decrease of living space per inhabitant. Potential-risk options include solar thermal systems, natural gas technologies, high-capacity battery storages, and hydrogen for building energy supply.
When energy prices rise, financially-optimized systems approach the least-emission system design. The maximum profitability of natural gas technologies was already reached before the 2022 European energy crisis.
The Educational Journal of Renewable Energy Short Reviews (EduJRESR, formally published as `EGU Journal of Renewable Energy Short Reviews') is a teaching project rather than a regular scientific journal. To publish in this journal, it is a premise to take part in the master course wind power, hydro power and biomass usage at the department of Energy, Building Services and Environmental Engineering of the Münster University of Applied Sciences.
Students receive an equivalent of 2.5 credit points (European Credit Transfer and Accumulation System - ECTS) for their engagement in the course and for publishing a short review article of at most 3000 words in this periodical. The publication process closely mimics the typical publication procedure of a regular journal. The peer-review process, however, is conducted within the group of course-participants.
Although being just an exercise, we think that publishing the outcome of this course in a citable manner is not only promoting the motivation of our students, but may also be a helpful source of introductory information for researchers and practitioners in the field of renewable energies. We encourage students to write their articles in English, but this is not mandatory. The reader will thus find a few articles in German language. To further encourage students practicing English writing, perfect grammar is not part of the assessment.
We especially thank our students for working with LaTeX on Overleaf, although LaTeX is new to some of them. In this way, the editorial workload was reduced to a minimum. We also thank our students for sharing their work under the creative commons attribution licence (CC-BY). We appreciate their contribution to scientific information, being available to every person of the world, almost without barriers. We also thank the corresponding authors and publishers of the cited work, for granting permission to reuse graphics free of charge. All other figures had to be replaced or removed prior to publication.
The Spreadsheet Energy System Model Generator (SESMG) is a tool for modeling and optimizing energy systems with a focus on urban systems. The SESMG is easily accessible as it comes with a browser-based graphical user interface, spreadsheets to provide data entry, and detailed documentation on how to use it. Programming skills are not required for the installation or application of the tool. The SESMG includes advanced modeling features such as the application of the multi-energy system (MES) approach, multi-objective optimization, model-based methods for reducing computational requirements, and automated conceptualization and result processing of urban energy systems with high spatial resolution. Due to its accessibility and the applied modeling methods, urban energy systems can be modeled and optimized with comparatively low effort.
Traditionelle, lineare Energiesysteme werden zunehmend zu vernetzten, regenerativen Energiesystemen transformiert. Mit dem auf dem „Open Energy Modelling Framework” (oemof) basierenden „Spreadsheet Energy System Model Generator” (SESMG) wurde ein Tool entwickelt, welches die Komplexität und Wechselwirkungen moderner Energiesysteme auf urbaner Ebene automatisiert abbildet. Zur Erstellung individueller Energiesystemmodelle sind ausschließlich quartiersspezifische Parameter notwendig, technische und wirtschaftliche Parameter sind standardmäßig hinterlegt. Mit Hilfe von Algorithmen werden Energieversorgungsszenarien identifiziert, welche individuell definierte Zielgrößen (z. B. monetäre Kosten oder Treibhausgasemissionen) minimieren. Durch die implementierten Methoden zur Modellvereinfachungen können auch mit begrenzten Rechenressourcen (insb. Rechenzeit und Arbeitsspeicherbedarf) große Systeme modelliert und optimiert werden. Die Zielszenarien werden als Diagramme und für die Weiterverarbeitung mit Geoinformationssystemen aufbereitet, sodass die Ergebnisse analysiert, plausibilisiert und präsentiert werden können.
Local and regional energy systems are becoming increasingly entangled. Therefore, models for optimizing these energy systems are becoming more and more complex and the required computing resources (run-time and random access memory usage) are increasing rapidly. The computational requirements can basically be reduced solver-based (mathematical optimization of the solving process) or model-based (simplification of the real-world problem in the model). This paper deals with identifying how the required computational requirements for solving optimization models of multi-energy systems with high spatial resolution change with increasing model complexity and which model-based approaches enable to reduce the requirements with the lowest possible model deviations. A total of 12 temporal model reductions (reduction of the number of modeled time steps), nine techno-spatial model reductions (reduction of possible solutions), and five combined reduction schemes were theoretically analyzed and practically applied to a test case. The improvement in reducing the usage of computational resources and the impact on the quality of the results were quantified by comparing the results with a non-simplified reference case. The results show, that the run-time to solve a model increases quadratically and memory usage increases linearly with increasing model complexity. The application of various model adaption methods have enabled a reduction of the run-time by over 99% and the memory usage by up to 88%. At the same time, however, some of the methods led to significant deviations of the model results. Other methods require a profound prior knowledge and understanding of the investigated energy systems to be applied. In order to reduce the run-time and memory requirements for investment optimization, while maintaining good quality results, we recommend the application of (1) a pre-model that is used to (1a) perform technological pre-selection and (1b) define reasonable technological boundaries, (2) spatial sub-modeling along network nodes, and 3) temporal simplification by only modeling every nth day (temporal slicing), where at least 20% of the original time steps are modeled. Further simplifications such as spatial clustering or larger temporal simplification can further reduce the computational effort, but also result in significant model deviations.
