Wagner, Lukas

The energy transition towards a fully decarbonized energy system is one of society’s greatest challenges. An increasing share of variable renewable energy generation requires flexible generation and flexible consumption to counteract the associated volatility. Furthermore, providing energy flexibility holds significant financial saving potential for operators of flexible energy resources. However, the implementation of energy flexibility measures is hindered by limited personnel capacities and a lack of expertise required for developing suitable optimization models. Thus, this thesis presents a methodology for the automatic generation of optimization models for the operational planning of systems of flexible energy resources. To establish a solid methodological foundation for these models, a systematic review of existing modeling approaches for flexible energy resources is conducted. Based on this review, a suitable modeling approach has been elaborated and further refined through an analysis of the required level of detail for optimization models used in the control of flexible energy resources. The results indicate that mixed-integer linear programming optimization models with a low level of detail and temporal resolutions aligned with market intervals are sufficient to represent resource behavior and generate optimized schedules for resource operation. The methodology for automatic model generation builds on a modular generic model structure that represents the flexibility characteristics of resources. Models of individual resources are aggregated to jointly optimize the operation of a system of resources. The generic model is complemented by algorithms for deriving parameter sets from time series data. Additionally, connections between resources are extracted from an information model representing the system structure. Integrating the model structure and parameter derivation into an automated model generation methodology reduces manual effort and streamlines the creation of optimization model instances. Each component of the methodology has been independently evaluated to ensure its accuracy and applicability. Furthermore, the methodology in its entirety was applied in two case studies focusing on a combined heat and power system and a refrigeration system. These case studies demonstrate the applicability of the methodology by showcasing its ability to generate accurate optimization models without manual intervention. The automatically generated optimization models can then be used to plan resource operation within systems. For instance, during periods of high renewable energy generation, resources can be scheduled to maximize energy utilization. By enabling flexible resource operation, this approach contributes to balancing energy supply and demand, thereby supporting a successful energy transition.

The integration of increasing shares of intermittent renewable energy necessitates flexibility in both energy generation and consumption. Typically, the operation of flexible energy resources is orchestrated through optimization models. However, the manual creation of these models is a complex and error-prone task, often requiring the expertise of domain specialists. This work introduces a methodology for the automatic generation of optimization models for systems of flexible energy resources to simplify the modeling process and increase the use of energy flexibility. This methodology utilizes a modular, generic model structure designed to depict systems of flexible energy resources. It incorporates algorithms for model parameter derivation from operational data and an information model that represents the system’s structure and dependencies of resources. The efficacy of this methodology is demonstrated in two case studies, highlighting its relevance and ability to significantly streamline the optimization modeling process by minimizing the need for manual intervention.

Die rasche Expansion erneuerbarer Energiequellen führt zu erheblicher Volatilität und Unvorhersehbarkeit in der Energieversorgungskette, was die Stabilität und Zuverlässigkeit des Stromnetzes herausfordert. Diese Arbeit präsentiert daher eine Methode, die bestehende statische Optimierungsmodelle flexibler Energiequellen echtzeitfähig macht und damit ermöglicht, dass diese sowohl für die mittelfristige Planung als auch für Echtzeitanwendungen eingesetzt werden können. Durch die Anpassung statischer Modelle für die Echtzeitanwendung ermöglicht eine in diesem Beitrag vorgeschlagene zweistufige Optimierungsstrategie flexible Anpassungen von Betriebsplänen, wodurch erneuerbare Energien auch trotz unvorhergesehener Schwankungen bestmöglich genutzt werden können. Dieser Ansatz gewährleistet nicht nur die Zuverlässigkeit des Netzes, sondern verbessert auch die wirtschaftliche Effizienz durch die Optimierung der Ressourcennutzung. Die Wirksamkeit dieser Methode wird durch eine Fallstudie mit einem System von Elektrolyseuren demonstriert, die deutliche Vorteile gegenüber traditionellen statischen Optimierungsmethoden bei der Ausrichtung des Energieverbrauchs auf die Erzeugung erneuerbarer Energie aufzeigt.

Due to increasing amounts of energy being supplied by intermittent renewable energy sources, future energy systems need to be able to react flexibly to sudden changes. To address this challenge, a large body of research is devoted to improving the utilization of energy flexibility of various types of energy resources on the supply and demand side. This is commonly done by applying mathematical models. The objective of this paper is to review such models and identify common modeling approaches used to quantify and optimize the use of flexible energy resources. The literature review is conducted systematically, encompassing 215 publications that feature 694 models of different energy resources. The main distinction of this work, in comparison to other reviews on the topic of energy flexibility lies in its focus on the mathematical formulation of models of energy resources and the quantification of the models’ level of detail. The aim is to identify common modeling approaches and the corresponding research problems they address. The review shows that the majority of research efforts are directed towards the management and optimization of distributed energy resources. The most commonly employed modeling approach involves abstract black-box models with limited levels of detail. Linear programming and meta-heuristics are frequently used to optimize energy flexibility. A critical reflection of the current common practice is conducted and recommendations for future research are formulated