Browsing by Author "Sarfarazi, Seyedfarzad"

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Open Access
Aggregation of distributed energy resources in energy communities : a bottom-up analysis
(2024) Sarfarazi, Seyedfarzad; Bertsch, Valentin (Prof. Dr.)
This dissertation is motivated by the ongoing decentralization of the German energy system and the challenges faced by policymakers, regulators, and emerging market actors in ensuring the efficient integration of decentralized energy systems (DESs). Currently, a significant research gap exists between studies focused on the techno-economic analysis of energy communities (ECs) and those examining the overall system integration of DESs. The former often overlooks the feedback effects of ECs on the broader energy system, while the latter frequently lacks the detailed examination necessary to fully capture the diverse and complex nature of EC business models. In response, this thesis seeks to address this gap by proposing novel methodological developments that bridge these two bodies of literature. Specifically, it introduces a methodology for modeling the operation of DESs within ECs as a Stackelberg energy trading game and develops innovative techniques to find the Stackelberg equilibrium and derive an optimal real-time pricing scheme for ECs. Additionally, the thesis evaluates the systemic impacts of DESs using the agent-based electricity market model AMIRIS, among other methods. The methods developed in this thesis enable a holistic analysis of EC integration and can support critical political decision-making processes.
Open Access
Bridging granularity gaps to decarbonize large‐scale energy systems : the case of power system planning
(2021) Cao, Karl‐Kiên; Haas, Jannik; Sperber, Evelyn; Sasanpour, Shima; Sarfarazi, Seyedfarzad; Pregger, Thomas; Alaya, Oussama; Lens, Hendrik; Drauz, Simon R.; Kneiske, Tanja M.
The comprehensive evaluation of strategies for decarbonizing large‐scale energy systems requires insights from many different perspectives. In energy systems analysis, optimization models are widely used for this purpose. However, they are limited in incorporating all crucial aspects of such a complex system to be sustainably transformed. Hence, they differ in terms of their spatial, temporal, technological, and economic perspective and either have a narrow focus with high resolution or a broad scope with little detail. Against this background, we introduce the so‐called granularity gaps and discuss two possibilities to address them: increasing the resolutions of the established optimization models, and the different kinds of model coupling. After laying out open challenges, we propose a novel framework to design power systems in particular. Our exemplary concept exploits the capabilities of power system optimization, transmission network simulation, distribution grid planning, and agent‐based simulation. This integrated framework can serve to study the energy transition with greater comprehensibility and may be a blueprint for similar multimodel analyses.