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Postprint
DOI: 10.5445/IR/1000078130
Veröffentlicht am 09.12.2018
Originalveröffentlichung
DOI: 10.1007/s00291-017-0500-4
Scopus
Zitationen: 3
Web of Science
Zitationen: 1

Two-stage stochastic, large-scale optimization of a decentralized energy system : a case study focusing on solar PV, heat pumps and storage in a residential quarter

Schwarz, Hannes; Bertsch, Valentin; Fichtner, Wolf

Abstract:
The expansion of fluctuating renewable energy sources leads to an increasing impact of weather-related uncertainties on future decentralized energy systems. Stochastic modeling techniques enable an adequate consideration of the uncertainties and provide support for both investment and operating decisions in such systems. In this paper, we consider a residential quarter using photovoltaic systems in combination with multistage air-water heat pumps and heat storage units for space heating and domestic hot water. We model the investment and operating problem of the quarter’s energy system as two-stage stochastic mixed-integer linear program and optimize the thermal storage units. In order to keep the resulting stochastic, large-scale program computationally feasible, the problem is decomposed in combination with a derivative-free optimization. The subproblems are solved in parallel on high-performance computing systems. Our approach is integrated in that it comprises three subsystems: generation of consistent ensembles of the required input data by a Markov process, transformation into sets of energy demand and supply profiles and the ac ... mehr


Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Zeitschriftenaufsatz
Jahr 2017
Sprache Englisch
Identifikator ISSN: 0171-6468, 1436-6304
URN: urn:nbn:de:swb:90-781300
KITopen ID: 1000078130
HGF-Programm 37.06.01; LK 01
Erschienen in OR spectrum
Band 40
Heft 1
Seiten 265-310
Vorab online veröffentlicht am 07.12.2017
Schlagworte Large-scale energy system optimization, Stochastic programming, Uncertainty modeling, Markov process
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