Universität zu Köln

Han, Chengyuan ORCID: 0000-0001-5220-402X (2024). Agent-based Studies of Collective Phenomena in Supply Network Operation. PhD thesis, Universität zu Köln.

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Abstract

In econophysics, the intersection of statistical physics and economics offers a unique viewpoint for deciphering complex economic systems. This thesis uses a complex systems framework to study the intricate dynamics of supply networks and energy systems. The research initially investigates the creation of supply networks, with economies of scale emerging as a critical influencer. The research highlights the process of globalization using an abstract theoretical model, demonstrating the shift from localized to centralized production. When the model accounts for differences in agent preferences, it reveals three unique trade regimes: local, centralized, and diversified production. The results emphasize the significant impact of transportation costs, preference diversity, and economic scale effects on global trade patterns. The following section of the thesis examines the concept of demand response in electric power systems, focusing on the advantages of load shifting at the individual household level through an agent-based model. However, the coordinated operation of these systems based on real-time pricing may result in synchronization, potentially creating grid stability issues. Additionally, the thesis presents an extensive statistical study of electricity price time series in the European electricity exchange market. The research identifies time scales intrinsic to price dynamics by addressing non-stationarities and fitting data to appropriate models. A significant finding is a strong correlation between weather conditions and electricity price dynamics, emphasizing the importance of considering external factors in agent-based models. This thesis aims to objectively understand collective behaviors within econo-physical models of supply networks and energy systems. The agent-based models presented establish a basis for future research, highlighting the potential integration of advanced tools such as machine learning. The research yields significant findings and insights that contribute to academic discourse, with profound implications for policymakers, businesses, and stakeholders in the energy sector.

Item Type:	Thesis (PhD thesis)
Creators:	Creators Email ORCID ORCID Put Code Han, Chengyuan chan1@smail.uni-koeln.de https://orcid.org/0000-0001-5220-402X UNSPECIFIED
URN:	urn:nbn:de:hbz:38-724531
Date:	2024
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Physics > Institute for Theoretical Physics
Subjects:	Physics
Uncontrolled Keywords:	Keywords Language Econophysics English Complex systems English Agent-based modeling English Collective phenomena English Complex Network English
Date of oral exam:	29 February 2024
Referee:	Name Academic Title Witthaut, Dirk Professor Schadschneider, Andreas Professor
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/72453