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Engineering Local Electricity Markets for Residential Communities

Mengelkamp, Esther Marie

In line with the progressing decentralization of electricity generation, local electricity markets (LEMs) support electricity end customers in becoming active market participants instead of passive price takers. They provide a market platform for trading locally generated (renewable) electricity between residential agents (consumers, prosumers, and producers) within their community.

Based on a structured literature review, a market engineering framework for LEMs is developed. The work focuses on two of the framework's eight components, namely the agent behavior and the (micro) market structure. Residential agent behavior is evaluated in two steps. Firstly, two empirical studies, a structural equation model-based survey with 195 respondents and an adaptive choice-based conjoint study with 656 respondents, are developed, conducted and evaluated. Secondly, a discount price LEM is designed following the surveys' results. Theoretical solutions of the LEM bi-level optimization problem with complete information and heuristic reinforcement learning with incomplete information are investigated in a multi-agent simulation to find the profit-maximizing market allocations. ... mehr

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Volltext §
DOI: 10.5445/IR/1000095229
Veröffentlicht am 28.05.2019
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Hochschulschrift
Publikationsjahr 2019
Sprache Englisch
Identifikator KITopen-ID: 1000095229
HGF-Programm 37.06.01 (POF III, LK 01)
Networks and Storage Integration
Verlag Karlsruhe
Umfang X, 362 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Informationswirtschaft und Marketing (IISM)
Prüfungsdatum 23.05.2019
Referent/Betreuer Prof. C. Weinhardt
Schlagwörter local electricity market, local energy market, reinforcement learning, bi-level optimization, business model, SGAM
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