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Distributed Optimization with Application to Power Systems and Control

Engelmann, Alexander

In many engineering domains, systems are composed of partially independent subsystems—power systems are composed of distribution and transmission systems, teams of robots are composed of individual robots, and chemical process systems are composed of vessels, heat exchangers and reactors. Often, these subsystems should reach a common goal such as satisfying a power demand with minimum cost, flying in a formation, or reaching an optimal set-point. At the same time, limited information exchange is desirable—for confidentiality reasons but also due to communication constraints. Moreover, a fast and reliable decision process is key as applications might be safety-critical.
Mathematical optimization techniques are among the most successful tools for controlling systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization control the subsystems in a distributed or decentralized fashion, reducing or avoiding central coordination. These methods have a long and successful history. Classical distributed optimization algorithms, however, are typically designed for convex problems. ... mehr

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Volltext §
DOI: 10.5445/IR/1000127849
Veröffentlicht am 11.01.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Hochschulschrift
Publikationsdatum 11.01.2021
Sprache Englisch
Identifikator KITopen-ID: 1000127849
Verlag Karlsruher Institut für Technologie (KIT)
Umfang XV, 202 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Automation und angewandte Informatik (IAI)
Prüfungsdatum 21.10.2020
Referent/Betreuer Prof. T. Faulwasser
Schlagwörter distributed optimization, decentralized optimization, non-convex, distributed optimal power flow, distributed MPC
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