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About 75% of the world's energy consumption takes place in cities. Although their large energy consumption attracts a large number of research projects, only a small fraction of them deal with approaches to model energy systems of city districts. These are particularly complex due to the existence of multiple energy sectors (multi-energy systems, MES), different consumption sectors (mixed-use), and different stakeholders who have many different interests.
This contribution is a review of the characteristics of energy system models and existing modeling tools. It evaluates current studies and identifies typical characteristics of models designed to optimize MES in mixed-use districts. These models operate at a temporal resolution of at least 1 h, follow either bottom-up or hybrid analytical approaches and make use of mixed-integer programming, linear or dynamic.
These characteristics were then used to analyze minimum requirements for existing modeling tools. Thirteen of 145 tools included in the study turned out to be suitable for optimizing MES in mixed-use districts. Other tools where either created for other fields of application (12), do not include any methodology of optimization (39), are not suitable to cover city districts as a geographical domain (44), do not include enough energy or demand sectors (20), or operate at a too coarse temporal resolution (17). If additional requirements are imposed, e.g. the applicability of non-financial assessment criteria and open source availability, only two tools remain.
Overall it can be stated that there are very few modeling tools suitable for the optimization of MES in mixed-use districts.
Im Rahmen des Forschungsprojekts „Ressourcenplan im Quartier – R2Q“ startete im Frühjahr 2019 ein großer Forschungsverbund aus Hochschulen, wissenschaftlichen Instituten, Praxispartnern und einer Kommune, um die Verwendung der Ressourcen Wasser, Fläche, Baustoffe und Energie in Quartieren zu bilanzieren und zu bewerten, damit ihre effiziente Verwendung im Quartier mit Hilfe neuer rechtlicher Festsetzungen zukünftig gewährleistet werden kann.
Rund 75 % des weltweiten Energieverbrauchs findet innerhalb urbaner Energiesysteme statt. Solche Systeme beinhalten mehrere Energiesektoren (Elektrizität, Wärme, Kälte, …), Verbrauchssektoren (Wohnen, Gewerbe, Industrie, Landwirtschaft, Mobilität, …) und Interessensgruppen und sind deshalb besonders komplex. Durch den Einsatz von Methoden der Energiesystemmodellierung können diese komplexen Systeme simuliert, analysiert und optimiert werden. Mit Simulationsmodellen können Kosten, Emissionen und verschiedene andere Systemparameter prognostiziert werden. Mithilfe von Optimierungsalgorithmen können Technologien miteinander verglichen, Anlagen dimensioniert und Betriebsweisen optimiert werden. Die Erkenntnisse aus Energiesystemmodellen können zur Einhaltung verschiedener politischer und sozialer Ziele, wie beispielsweise die Reduktion von Treibhausgasemissionen, der Bedarf nach kostengünstiger Energieversorgung oder auch die Stärkung der regionalen Wirtschaft, beitragen.
Im Projekt R2Q werden Ansätze der Energiesystemmodellierung für den Einsatz in der Planung urbaner Energiesysteme aufgearbeitet, angepasst und für städteplanerische Prozesse verfügbar gemacht. In ersten Modelldurchläufen für ein Testgebiet in Herne konnte durch die Kombination verschiedener Technologien eine rechnerische Minimierung der monetären Kosten um 19 % bei gleichzeitiger Reduktion der CO2-Emissionen um 36 % ermittelt werden. Durch ein emissionsoptimiertes Szenario können die CO2-Emissionen um 47 % reduziert werden, was jedoch mit einer Steigerung der Kosten um 29 % einhergeht.
(1) The use of renewable energy for power and heat supply is one of the strategies to reduce greenhouse gas emissions. As only 14% of German households are supplied with renewable energy, a shift is necessary. This shift should be realized with the lowest possible environmental impact. This paper assesses the environmental impacts of changes in energy generation and distribution, by integrating the life cycle assessment (LCA) method into energy system models (ESM). (2) The integrated LCA is applied to a case study of the German neighborhood of Herne, (i) to optimize the energy supply, considering different technologies, and (ii) to determine the environmental impacts of the base case (status quo), a cost-optimized scenario, and a CO2-optimized scenario. (3) The use of gas boilers in the base case is substituted with CHPs, surface water heat pumps and PV-systems in the CO2-optimized scenario, and five ground-coupled heat pumps and PV-systems for the cost-optimized scenario. This technology shift led to a reduction in greenhouse gas emissions of almost 40% in the cost-optimized, and more than 50% in the CO2-optimized, scenario. However, technology shifts, e.g., due to oversized battery storage, risk higher impacts in other categories, such as terrestrial eco toxicity, by around 22%. Thus, it can be recommended to use smaller battery storage systems. (4) By combining ESM and LCA, additional environmental impacts beyond GHG emissions can be quantified, and therefore trade-offs between environmental impacts can be identified. Furthermore, only applying ESM leads to an underestimation of greenhouse gas emissions of around 10%. However, combining ESM and LCA required significant effort and is not yet possible using an integrated software.
Heating networks are highly relevant for the achievement of climate protection goals of urban energy systems. This is due to their high renewable energy potential combined with high plant efficiency and utilization rates. For the optimal integration and sector coupling of heating networks in holistic urban energy systems, open source energy system modeling tools are highly recommended. In this contribution, two open source approaches (the "Spreadsheet Energy System Model Generator"-integrated DHNx-Python module (DHNx/SESMG) and Thermos) are theoretically compared, and practically applied to a real-world energy system. Deviations within the results can be explained by incorrectly pre-defined parameters within Thermos and cannot be adjusted by the modeler. The simultaneity is underestimated in the case study by Thermos by more than 20%. This results in undersized heating plant capacities and a 50% higher number of buildings connected to the network. However, Thermos offers a higher end-user usability and over 100 times faster solving. DHNx/SESMG, in contrast, offers the possibility to adjust more model parameters individually and consider multiple energy sectors. This enables a holistic modeling of urban energy systems and the model-based optimization of multi-sectoral synergies.
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.
Der Spreadsheet Energy System Model Generator (SESMG) ist ein Werkzeug zur Modellierung und Optimierung von (urbanen) Energiesystemen. Der SESMG hat eine browserbasierte grafische Benutzeroberfläche, eine tabellenbasierte Dateneingabe und eine ausführliche Dokumentation, was einen einfachen Einstieg ermöglicht. Zudem erfordern die Installation und Anwendung keine Programmierkenntnisse. Im SESMG sind verschiedene Modellierungsmethoden implementiert, wie z. B. die Anwendung des Multi-Energie-System-Ansatzes, die multikriteriale Optimierung, modellbasierte Methoden zur Reduktion des Rechenaufwands sowie die automatisierte Erstellung von räumlich hoch aufgelösten Energiesystemmodellen. Somit können urbane Energiesysteme mithilfe des SESMGs mit vergleichsweise geringem Aufwand, aber unter Berücksichtigung einer Vielzahl von Parametern und Randbedingungen, modelliert und optimiert werden.