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Item type:Publication, Access status: with files , Bridging economics and physics in energy systems analysis: Effects of flexibility representation on model outcomes(Elsevier, 2026-07-15); ; ; ; Efficiently decarbonizing fossil-based energy systems requires extensive electrification of end-users’ heating and mobility sectors, alongside the provision of demand-side flexibility. Aggregators can harness this flexibility to enable cost-efficient supply in response to market incentives. While numerous studies address flexibility in energy systems, the underlying models conceptualize it in markedly different ways. This study, therefore, develops a meta-analytical modeling framework, derived from a systematic literature review, that couples market optimization with physics-based dynamic simulation to examine how varying the modeling depth and aggregation level of flexibility components, specifically heat pumps and battery electric vehicles, affects optimization results. Findings show that greater modeling depth yields more realistic optimization outcomes but at a higher computational cost. Endogenous heat pump coefficient-of-performance and storage modeling dampens price-arbitrage incentives and apparent flexibility, underscoring the need for detailed physical representation. Taking into account partial load behavior of electric cars avoids energy underprovisioning due to reduced efficiency. Aggregation level has only a minor influence when appropriate dispatch models are used. Transparent reporting of model depth, aggregation choices, and validation practices is thus crucial to ensure robust insights for utilities and policymakers.Publicationtype: Journal Article46 15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Incentive design for demand-side flexibility in transmission system operation: a bi-level market equilibrium approachIncreasing renewable electricity generation and the electrification of industry, mobility, and heating through sector coupling pose significant challenges to grid operators in maintaining secure and reliable system operations. Demand-side sector coupling applications increase electricity demands and stress electricity grids, but they also offer transmission system operators increased flexibility for congestion management. Due to the complexity of directly controlling decentralized demand-side technologies, incentive mechanisms present a promising solution for harnessing demand-side flexibility. This study investigates various incentive schemes that encourage grid-supportive demand-side behavior, focusing on heat pumps by developing a bi-level programming framework. The framework models the decision-making processes of key stakeholders, including a TSO, an aggregator, and a market clearing agent, considering model-endogenous wholesale market equilibrium formation and congestion management optimization. The economic efficiency of different design options for grid congestion management is evaluated using an extended IEEE test system adjusted to an exemplary German market configuration. The findings highlight the critical importance of time-dynamic premium design concepts due to the variability of renewable generation. While incentive-based market interventions increase electricity market costs and thereby shift consumer rents to producers, the reduced transmission system operation cost leads to overall gains in total system welfare.Publicationtype: Journal Article16 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Integrierte Produktions- und Absatzplanung - Integration von Substitutionsentscheidungen in ein Produktionsprogrammplanungsmodell für konfigurierbare Produkte(2026); Manufacturers of configurable products must efficiently align demand volumes and capacities despite high product variety. This thesis introduces an optimization model for targeted product mix control under capacity constraints. Economic objectives and sales risks are considered simultaneously. A context-sensitive distance measure (“cpDist”) quantifies the market attractiveness of alternative configurations. This enables the anticipation of customer substitution behaviour within production planning.Publicationtype: Doctoral Thesis39 47 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Graph convolutional neural network assisted Monte Carlo Tree Search for the Capacitated Vehicle Routing Problem with Time WindowsThe Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a well-known combinatorial optimization problem that extends the classical Vehicle Routing Problem to account for additional real-world constraints such as truck capacity and customer time windows. Recent studies have explored the use of deep graph convolutional networks (GCNs) to predict the arcs that are part of the optimal tour for the Travelling Salesman Problem and related routing problems. We propose a novel context complemented graph convolutional network (CCGCN) which is integrated into a Monte Carlo Tree Search (MCTS) to solve the CVRPTW sequentially. The deep convolutional part of the proposed neural network builds efficient CVRPTW graph representations, whereas the context part processes information of partially built tours to output probabilities on which vertex to add next to the tour, which are used during the expansion of the search tree. For simulating final solution values within the Monte Carlo search tree, we conducted experiments using two different heuristics: LKH3 and a beam search. By leveraging the probabilities provided by our CCGCN and employing a variation of Upper Confidence Bound applied to trees, we effectively manage the tradeoff between exploration and exploitation, thus reducing the optimality gap compared to solutions generated solely by LKH3. Evaluations of the proposed heuristic are performed on benchmark instances of the CVRPTW with up to 100 customers; these show promising results.Publicationtype: Book Part68 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , The energy aggregator problem – a holistic mixed-integer linear programming approachIn this paper, a new mixed integer linear programming (MILP) model for the day-ahead operation of energy aggregators (EA) is developed. Synergies between the different types of flexibility and energy trading options enable EAs in decentralized and renewable energy systems to provide economic benefits to participating households but require a detailed consideration of technological properties and constraints of the respective types of resources. Therefore, the main contribution of this work is the development of a new EA model (EAM), which combines a holistic perspective with a high level of technical detail to better address the complexity of the EA decision. Most importantly, power-to-heat systems are integrated with their inherent thermal relations between heat pumps, heater rods and heat storages. In combination with other energy resources such as photovoltaic systems, electric vehicles, household battery storages and time-shiftable loads, households are modeled as systems with interdependent electrical power and heat flows. Moreover, three different trading levels (wholesale, local markets and internal trading) are taken into account. The model application to a case study with up to 111 individually modeled prosumer households in a summer and a winter scenario reveals high synergetic potential of EAs resulting from the flexibility of multiple trading options in combination with the flexibility of various energy resources. The results validate the efficacy of the model, as significant economic benefits for households are realized in comparison to a base case of non-aggregated households, showing that the three trading levels significantly contribute to these benefits. Further analyses give insights into the interdependent synergetic relations between different flexible resources, underlining the importance of a holistic optimization approach that explicitly takes these relations into account. For future research, the EAM is proposed as a base model to depict the behavior of EAs.Publicationtype: Journal Article40 37 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Real-Time Flexibility Allocation among Distributed Energy Resources: A Digital Twin-Driven Dynamic Optimization ApproachThe transition towards decentralized energy systems, driven by the integration of distributed energy resources (DERs), presents significant challenges for grid flexibility and operational planning. In this paper, a novel digital twin-driven optimization methodology is introduced for real-time flexibility provision in decentralized energy cells. By using dynamic simulation models, the proposed approach disaggregates centralized load requests into component-specific signals tailored to Electric Heat Pumps (EHPs) and Battery Electric Storage Systems (BESSs). A two-stage framework is proposed: a preparation phase, where dynamic system behaviors are analyzed and linearized to create simplified gray-box models for optimization, and a provision phase, where optimized control signals are generated to meet realtime flexibility demands. A case study demonstrates the effectiveness of the methodology, highlighting the capability to manage diverse DERs under varying conditions. The results underscore the potential of digital twin-driven frameworks to enhance grid stability and support the evolving needs of decentralized energy systems.Publicationtype: Conference Paper38 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Anticipating customer substitution: a data-driven, distance-based approach for out-of-stock product configurationsPurpose: When facing capacity bottlenecks, manufacturers of configurable, multi-variant products may adjust the product mix to uphold the scheduled output. However, maintaining market attractiveness by choosing the right product configurations as substitutes is a non-trivial task as it involves anticipating the substitution behaviour of customers. Substitution behaviour models currently used in quantitative production planning models for configurable products are either based on domain knowledge of experts, which makes them bias-prone, or they require extensive market research. The purpose of this study is to present a data-driven approach. Design/methodology/approach: Based on data science concepts, distance measures are applied to derive distances between different product configurations from historical order data. Different design options for such a distance measure are discussed regarding configurable products and tested with automotive industry data. Furthermore, the study shows ways to validate the distance results. Findings: The experiments show that the presented distance measure represents the expected customer substitution behaviour quite well. A context-sensitive distance measure including rank information of ordinal product features is most suitable for the automotive data sets. Originality/value: This study presents a new approach to model the substitution behaviour of customers. The attractiveness of a potential substitute is represented by a distance from the customer’s first-choice configuration. The presented distance measure provides an inexpensive tool using existing data instead of expensive market research. Thus, it supports the integration of substitution into quantitative production planning models that deal with a large variety of configurable products.Publicationtype: Journal Article29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Optimierung des Energiemanagements durch Aggregatoren(2025); In this work, a mixed-integer linear optimization model is developed to depict the decision-making problem of energy aggregators (EAs), which includes the deployment planning of various types of flexible energy resources and the trading of energy on different markets for a set of households assigned to the EA. The central research contribution compared to previous works in the development of the EA model (EAM) is that, on the one hand, it represents a holistic approach, as different types of energy resources and energy markets are integrated into the aggregated energy management of a community of households, and on the other hand, it features a high level of detail in the modeling of the technical restrictions of energy resources, in particular power-to-heat systems with heat pumps. In a case study with two seasonal scenarios, each consisting of 62 days, the EAM is verified and validated for day-ahead planning for up to 111 households. The results show that the participating households can achieve clear economic added value through the EA and that this added value is based on complex synergies between the system components, which can only be exploited through holistic and differentiated modeling. Further analyses consider an additional improvement in the efficiency of EA energy management through the foresighted use of energy storage systems. Standardized strategies are identified that contribute to an increase in household trading surpluses for different constellations of days. In addition, a fairness analysis is carried out, which shows that the internal trading between households depicted in the EAM is problematic in terms of fairness, as the flexibility provided to the community is not sufficiently rewarded. An ex-post redistribution of the realized community surpluses is proposed as an approach to improve fairness between households. Furthermore, the EAM is considered in the context of local energy markets. For this purpose, a model of a local market is developed that is integrated into a higher-level day-ahead process together with the EAM, thereby iteratively depicting local energy trading between EAs. Application in a case study shows that the composition of EAs' households has a strong influence on the resulting trading relationships. For example, specialization of EAs in specific types of households, resulting in heterogeneity among EAs, is advantageous in terms of local market efficiency compared to a set of unspecialized and thus homogeneous EAs, which, on the other hand, results in more internal energy exchange between the households of the respective EAs. In the case of a heterogeneous composition of EAs, a further study analyzes the possibility of targeted market interventions to reduce line congestion. A comparison of four different approaches, each with nine variants, leads to the conclusion that such a market-based approach can contribute positively to reducing line congestion, but that the potential is limited and the approaches presented should therefore be considered as supporting other congestion management tools. In conclusion, it is clear that EAs can play an important role in future energy systems by providing economically efficient energy management for households. The EAM developed in this thesis represents a basic model for depicting the decision-making problems of such energy management. There are many configuration options and influencing factors that must be taken into account for the successful application of the concept and the realization of the potential of EAs.Publicationtype: Doctoral Thesis71 38 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Optimierung von Containertransporten im Hafenhinterland per LKW unter Berücksichtigung von Kooperationsmöglichkeiten(2024); The growth in container transportation worldwide in the past is a major challenge for players in the maritime supply chain regarding empty container logistics. The highest proportion of costs in the maritime supply chain arises from transportation in the port hinterland. Truck carriers are exposed to intense competition. Lack of economies of scale, low margins and service interchangeability require new solutions to strengthen their economic position. Here, in the scientific community the concept of horizontal collaboration is discussed, in which competitors exchange customer orders in a profit-maximizing manner. The aim of horizontal collaboration is not only to increase efficiency and profit for all parties involved, but also to establish it in a fair and stable manner in the long run. An interview study among truck carriers in Hamburg shows that the perception of the market is decisive for the willingness to collaborate. Those carriers who are proactive towards collaboration, prefer to share their customer orders only with selected trustworthy competitors. Furthermore, the academic literature supports the need for monetary and non-monetary benefits of collaboration to be fairly distributed among participants. Based on a literature review and an interview study, a mathematical model is developed and evaluated using two real-world case studies from the Hamburg port hinterland. This model makes a significant contribution to research, as it is the first to consider the aspects of trust groups, compensation payments and fairness mechanisms simultaneously. The application of the model shows that the profit rises with the size of the trust group. This is already the case for two collaborating carriers that form a trust group. It must be considered that own customer orders should be made available for sharing with the trust group. An evaluation regarding fuel consumption shows that avoiding empty container trips and empty trips also reduces CO2 emissions. The results also show that week-based fairness mechanisms achieve a higher profit increase compared to day-based fairness mechanisms. As a fundamental result it should be emphasized that a simultaneous application of profit- and customer order-based fairness mechanisms meets the aim of achieving a fair and long-term stable collaboration.Publicationtype: Doctoral Thesis73 178 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A typology of the collaboration willingness of carriers in hinterland logisticsPurpose: A large part of maritime container supply chain costs is generated by carriers in port hinterland logistics. Carriers which operate in the hinterland are under pressure to reduce costs and increase profitability, and they face challenges of fierce price competition. This study aims to explore how collaboration is perceived and implemented by carriers in truck container logistics in the port hinterland as a way to tackle these issues. Design/methodology/approach: This study adopts a qualitative multiple case study approach. Qualitative interviews with carriers in the port hinterland of Hamburg, Germany, were conducted and analyzed using grounded theory. Findings: The study reveals two collaboration types in the hinterland, based on the different carriers' interpretation of market conditions as changeable or as given, driving their collaboration mindsets and strategic actions: The developer, who has a proactive collaboration mindset and practices strategic maneuvers toward changing poor market conditions through collaboration, and the adapter, who has a defensive collaboration mindset and perceives market conditions as given and constraining collaboration. Research limitations/implications: The qualitative results will help researchers better understand how collaboration practices depend on the carriers' subjective interpretations and perceptions of the market. Practical implications: Based on the findings, managers of carriers gain an understanding of the different types of actors in their market and the relevance of acknowledging these types. Consequently, they can design appropriate strategic measures toward collaboration. Originality/value: The findings for the first time provide exploratory insights of carriers' mindsets.Publicationtype: Journal Article30