TY - JOUR A1 - Scheller, Fabian A1 - Doser, Isabel A1 - Sloot, Daniel A1 - McKenna, Russell A1 - Bruckner, Thomas T1 - Exploring the Role of Stakeholder Dynamics in Residential Photovoltaic Adoption Decisions: A Synthesis of the Literature JF - Energies N2 - Despite the intensive research on residential photovoltaic adoption, there is a lack of understanding regarding the social dynamics that drive adoption decisions. Innovation diffusion is a social process, whereby communication structures and the relations between sender and receiver influence what information is perceived and how it is interpreted. This paper addresses this research gap by investigating stakeholder influences in household decision-making from a procedural perspective, so-called stakeholder dynamics. A literature review derives major influence dynamics which are then synthesized based on egocentric network maps for distinct process stages. The findings show a multitude of stakeholders that can be relevant in influencing photovoltaic adoption decisions of owner-occupied households. Household decision-makers are mainly influenced by stakeholders of their social network like family, neighbors, and friends as well as PV-related services like providers and civil society groups. The perceived closeness and likeability of a stakeholder indicate a higher level of influence because of greater trust involved. Furthermore, the findings indicate that social influence shifts gradually from many different stakeholders to a few core stakeholders later on in the decision-making process. These insights suggest that photovoltaic (PV) adoption may be more reliably predicted if a process perspective is taken into account that not only distinguishes between different stakeholders but considers their dynamic importance along the process stages. In addition, especially time- and location-bound factors affect the influence strength. This clearly shows the importance of local and targeted interventions to accelerate the uptake. Y1 - 2020 U6 - https://doi.org/10.3390/en13236283 VL - 13 IS - 23 SP - 6283 EP - 6283 ER - TY - JOUR A1 - Scheller, Fabian A1 - Doser, Isabel A1 - Schulte, Emily A1 - Johanning, Simon A1 - McKenna, Russell A1 - Bruckner, Thomas T1 - Stakeholder dynamics in residential solar energy adoption: findings from focus group discussions in Germany JF - Energy Research & Social Science N2 - Although there is a clear indication that stages of residential decision making are characterized by their own stakeholders, activities, and outcomes, many studies on residential low-carbon technology adoption only implicitly address stage-specific dynamics. This paper explores stakeholder influences on residential photovoltaic adoption from a procedural perspective, so-called stakeholder dynamics. The major objective is the understanding of underlying mechanisms to better exploit the potential for residential photovoltaic uptake. Four focus groups have been conducted in close collaboration with the independent institute for social science research SINUS Markt- und Sozialforschung in East Germany. By applying a qualitative content analysis, major influence dynamics within three decision stages are synthesized with the help of egocentric network maps from the perspective of residential decision-makers. Results indicate that actors closest in terms of emotional and spatial proximity such as members of the social network represent the major influence on residential PV decision-making throughout the stages. Furthermore, decision-makers with a higher level of knowledge are more likely to move on to the subsequent stage. A shift from passive exposure to proactive search takes place through the process, but this shift is less pronounced among risk-averse decision-makers who continuously request proactive influences. The discussions revealed largely unexploited potential regarding the stakeholders local utilities and local governments who are perceived as independent, trustworthy and credible stakeholders. Public stakeholders must fulfill their responsibility in achieving climate goals by advising, assisting, and financing services for low-carbon technology adoption at the local level. Supporting community initiatives through political frameworks appears to be another promising step. Y1 - 2021 U6 - https://doi.org/10.1016/j.erss.2021.102065 SN - 2214-6326 VL - 76 SP - 102065 EP - 102065 ER - TY - JOUR A1 - Scheller, Fabian A1 - Burgenmeister, Balthasar A1 - Kondziella, Hendrik A1 - Kühne, Stefan A1 - Reichelt, David G. A1 - Bruckner, Thomas T1 - Towards integrated multi-modal municipal energy systems: An actor-oriented optimization approach JF - Applied Energy N2 - Against the backdrop of a changing political, economic and ecological environment, energy utilities are facing several challenges in many countries. Due to an increasing decentralization of energy systems, the conventional business could be undermined. Yet the reliable integration of small-scale renewable technologies and associated system transformations could represent an opportunity as well. Municipal energy utilities might play a decisive role regarding successful transition. For better decision-making, they need to investigate under which conditions certain novel business cases can become a sustainable part of their future strategy. The development of the strategy is a challenging task which needs to consider different conditions as business portfolio, the customer base, the regulatory framework as well as the market environment. Integrated Multi-Modal Energy System (IMMES) models are able to capture necessary interactions. This research introduces a model-driven decision support system called Integrated Resource Planning and Optimization (IRPopt). Major aim is to provide managerial guidance by simulating the impact of business models considering various market actors. The mixed-integer linear programming approach exhibits a novel formal interface between supply and demand side which merges technical and commercial aspects. This is achieved by explicit modeling of municipal market actors on one layer and state-of-the-art technology components on another layer as well as resource flow relations and service agreements mechanism among and between the different layers. While this optimization framework provides a dynamic and flexible policy-oriented, technology-based and actor-related assessment of multi-sectoral business cases, the encapsulation in a generic software system supports the facilitation. Based on the actor-oriented dispatch strategy, flexibility potential of community energy storage systems is provided to demonstrate a real application. Y1 - 2018 U6 - https://doi.org/10.1016/j.apenergy.2018.07.027 SN - 1872-9118 VL - 228 SP - 2009 EP - 2023 ER - TY - JOUR A1 - Reichelt, David Georg A1 - Kühne, Stefan A1 - Scheller, Fabian A1 - Abitz, Daniel A1 - Johanning, Simon ED - Reussner, R. H. ED - Koziolek, A. ED - Heinrich, R. T1 - Towards an Infrastructure for Energy Model Computation and Linkage JF - INFORMATIK 2020 N2 - Decision makers strive for optimal ways of production and usage of energy. To adjust their behavior to the future situation of markets and technology, the execution of different models predicting e.g. energy consumption, energy production, prices and consumer behavior is necessary. This execution is itself time-consuming and requires input data management. Furthermore, since different models cover different aspects of the energy domain, they need to be linked. To speed up the linkage and reduce manual errors, these linkage needs to be automated. We present IRPsim, an infrastructure for computation of different models and their linkage. The IRPsim-infrastructure enables management of model data in a structured database, parallelized model execution and automatic model linkage. Thereby, IRPsim allows researchers and practitioners to use energy system models for strategic business model analysis. Y1 - 2020 U6 - https://doi.org/10.18420/inf2020_21 SP - 225 EP - 235 ER - TY - JOUR A1 - Biemann, Marco A1 - Gunkel, Philipp Andreas A1 - Scheller, Fabian A1 - Huang, Lizhen A1 - Liu, Xiufeng T1 - Data Center HVAC Control Harnessing Flexibility Potential via Real-Time Pricing Cost Optimization Using Reinforcement Learning JF - IEEE Internet of Things Journal N2 - With increasing electricity prices, cost savings through load shifting are becoming increasingly important for energy end users. While dynamic pricing encourages customers to shift demand to low price periods, the nonstationary and highly volatile nature of electricity prices poses a significant challenge to energy management systems. In this article, we investigate the flexibility potential of data centers by optimizing heating, ventilation, and air conditioning systems with a general model-free reinforcement learning (RL) approach. Since the soft actor-critic algorithm with feedforward networks did not work satisfactorily in this scenario, we propose instead a parameterization with a recurrent neural network architecture to successfully handle spot-market price data. The past is encoded into a hidden state, which provides a way to learn the temporal dependencies in the observations and highly volatile rewards. The proposed method is then evaluated in experiments on a simulated data center. Considering real temperature and price signals over multiple years, the results show a cost reduction compared to a proportional, integral and derivative controller while maintaining the temperature of the data center within the desired operating ranges. In this context, this work demonstrates an innovative and applicable RL approach that incorporates complex economic objectives into agent decision-making. The proposed control method can be integrated into various Internet of Things-based smart building solutions for energy management. Y1 - 2023 U6 - https://doi.org/10.1109/jiot.2023.3263261 VL - 10 IS - 15 SP - 13876 EP - 13894 ER - TY - JOUR A1 - Biemann, Marco A1 - Scheller, Fabian A1 - Liu, Xiufeng A1 - Huang, Lizhen T1 - Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control JF - Applied Energy N2 - Controlling heating, ventilation and air-conditioning (HVAC) systems is crucial to improving demand-side energy efficiency. At the same time, the thermodynamics of buildings and uncertainties regarding human activities make effective management challenging. While the concept of model-free reinforcement learning demonstrates various advantages over existing strategies, the literature relies heavily on value-based methods that can hardly handle complex HVAC systems. This paper conducts experiments to evaluate four actor-critic algorithms in a simulated data centre. The performance evaluation is based on their ability to maintain thermal stability while increasing energy efficiency and on their adaptability to weather dynamics. Because of the enormous significance of practical use, special attention is paid to data efficiency. Compared to the model-based controller implemented into EnergyPlus, all applied algorithms can reduce energy consumption by at least 10% by simultaneously keeping the hourly average temperature in the desired range. Robustness tests in terms of different reward functions and weather conditions verify these results. With increasing training, we also see a smaller trade-off between thermal stability and energy reduction. Thus, the Soft Actor Critic algorithm achieves a stable performance with ten times less data than on-policy methods. In this regard, we recommend using this algorithm in future experiments, due to both its interesting theoretical properties and its practical results. Y1 - 2021 U6 - https://doi.org/10.1016/j.apenergy.2021.117164 SN - 03062619 VL - 298 SP - 117164 EP - 117164 ER - TY - JOUR A1 - Braeuer, Fritz A1 - Kleinebrahm, Max A1 - Naber, Elias A1 - Scheller, Fabian A1 - McKenna, Russell T1 - Optimal system design for energy communities in multi-family buildings: the case of the German Tenant Electricity Law JF - Applied Energy Y1 - 2022 U6 - https://doi.org/10.1016/j.apenergy.2021.117884 SN - 1872-9118 VL - 305 SP - 117884 EP - 117884 ER - TY - JOUR A1 - Dominković, D. F. A1 - Weinand, J. M. A1 - Scheller, Fabian A1 - D’Andrea, M. A1 - McKenna, R. T1 - Reviewing two decades of energy system analysis with bibliometrics JF - Renewable and Sustainable Energy Reviews N2 - The field of Energy System Analysis (ESA) has experienced exponential growth in the number of publications in the last two decades. This paper presents a comprehensive bibliometric analysis on ESA by employing different statistical techniques to investigate the underlying science's structure, characteristics, and patterns. The focus of results is on quantitative indicators relating to the number and type of publication outputs, collaboration links between institutions, authors and countries, and dynamic trends within the field. The five and twelve most productive countries have 50% and 80% of ESA publications, respectively. The dominant institutions are even more concentrated within a small number of countries. A significant concentration of published papers within countries and institutions was also confirmed by analysing collaboration networks. These show dominant collaboration within the same university or at least the same country. There is also a strong link among the most successful journals, authors and institutions. Within the field, the Energy journal has had the most publications, its editor-in-chief is the author with both the highest overall number of publications and the most highly cited publications. In terms of the dynamics within the field in the past decade, recent years have seen a higher impact of topics related to flexibility and hybrid/integrated energy systems alongside a decline in individual technologies. This paper provides a holistic overview of two decades' research output and enables interested readers to obtain a comprehensive overview of the key trends in this active field. Y1 - 2022 U6 - https://doi.org/10.1016/j.rser.2021.111749 SN - 1364-0321 VL - 153 SP - 111749 EP - 111749 ER - TY - CHAP A1 - Johanning, Simon A1 - Abitz, Daniel A1 - Schulte, Emily A1 - Scheller, Fabian A1 - Bruckner, Thomas T1 - PVactVal: A Validation Approach for Agent-based Modeling of Residential Photovoltaic Adoption T2 - 18th International Conference on the European Energy Market (EEM) N2 - Agent-based simulation models are an important tool to study the effectiveness of policy interventions on the uptake of residential photovoltaic systems by households, a cornerstone of sustainable energy system transition. In order for these models to be trustworthy, they require rigorous validation.However, the canonical approach of validating emulation models through calibration with parameters that minimize the difference of model results and reference data fails when the model is subject to many stochastic influences. The residential photovoltaic diffusion model PVact features numerous stochastic influences that prevent straightforward optimization-driven calibration.From the analysis of the results of a case-study on the cities Dresden and Leipzig (Germany) based on three error metrics (mean average error, root mean square error and cumulative average error), this research identifies a parameter range where stochastic fluctuations exceed differences between results of different parameterization and a minimization-based calibration approach fails.Based on this observation, an approach is developed that aggregates model behavior across multiple simulation runs and parameter combinations to compare results between scenarios representing different future developments or policy interventions of interest. Y1 - 2022 SN - 978-1-6654-0896-7 U6 - https://doi.org/10.1109/eem54602.2022.9921039 SP - 1 EP - 6 ER - TY - JOUR A1 - Johanning, Simon A1 - Scheller, Fabian A1 - Abitz, Daniel A1 - Wehner, Claudius A1 - Bruckner, Thomas T1 - A modular multi-agent framework for innovation diffusion in changing business environments: conceptualization, formalization and implementation JF - Complex Adaptive Systems Modeling N2 - Understanding how innovations are accepted in a dynamic and complex market environment is a crucial factor for competitive advantage. To understand the relevant factors for this diffusion and to predict success, empirically grounded agent-based models have become increasingly popular in recent years. Despite the popularity of these innovation diffusion models, no common framework that integrates their diversity exists. This article presents a flexible, modular and extensible common description and implementation framework that allows to depict the large variety of model components found in existing models. The framework aims to provide a theoretically grounded description and implementation framework for empirically grounded agent-based models of innovation diffusion. It identifies 30 component requirements to conceptualize an integrated formal framework description. Based on this formal description, a java-based implementation allowing for flexible configuration of existing and future models of innovation diffusion is developed. As a variable decision support tool in decision-making processes on the adoption of innovations the framework is valuable for the investigation of a range of research questions on innovation diffusion, business model evaluation and infrastructure transformation. Y1 - 2020 U6 - https://doi.org/10.1186/s40294-020-00074-6 VL - 8 IS - 1 ER - TY - CHAP A1 - Johanning, Simon A1 - Schulte, Emily A1 - Abitz, Daniel A1 - Scheller, Fabian A1 - Bruckner, Thomas ED - Johanning, Simon ED - Scheller, Fabian ED - Kühne, Stefan ED - Bruckner, Thomas T1 - PVactVal: Ein Ansatz für die operationale Validierung von Aufdach-PV Diffusionsmodellen T2 - Agentenbasierte Modellierung urbaner Transformationsprozesse T3 - Studien zu Infrastruktur und Ressourcenmanagement - Band 12 Y1 - 2022 SN - 9783832554132 U6 - https://doi.org/10.30819/5413.11 SP - 139 EP - 152 ER - TY - JOUR A1 - Kachirayil, Febin A1 - Weinand, Jann Michael A1 - Scheller, Fabian A1 - McKenna, Russell T1 - Reviewing local and integrated energy system models: insights into flexibility and robustness challenges JF - Applied Energy N2 - The electrification of heating, cooling, and transportation to reach decarbonization targets calls for a rapid expansion of renewable technologies. Due to their decentral and intermittent nature, these technologies require robust planning that considers non-technical constraints and flexibility options to be integrated effectively. Energy system models (ESMs) are frequently used to support decision-makers in this planning process. In this study, 116 case studies of local, integrated ESMs are systematically reviewed to identify best-practice approaches to model flexibility and address non-technical constraints. Within the sample, storage systems and sector coupling are the most common types of flexibility. Sector coupling with the transportation sector is rarely considered, specifically with electric vehicles even though they could be used for smart charging or vehicle-to-grid operation. Social aspects are generally either completely neglected or modeled exogenously. Lacking actor heterogeneity, which can lead to unstable results in optimization models, can be addressed through building-level information. A strong emphasis on cost is found and while emissions are also frequently reported, additional metrics such as imports or the share of renewable generation are nearly entirely absent. To guide future modeling, the paper concludes with a roadmap highlighting flexibility and robustness options that either represent low-hanging fruit or have a large impact on results. Y1 - 2022 U6 - https://doi.org/10.1016/j.apenergy.2022.119666 SN - 1872-9118 VL - 324 SP - 119666 EP - 119666 ER -