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Two-stage stochastic, large-scale optimization of a decentralized energy system - a residential quarter as case study

Schwarz, Hannes; Bertsch, Valentin; Fichtner, Wolf ORCID iD icon


The trend towards decentralized energy systems with an emphasis on renewable energy sources (RES) causes increased fluctuations and non-negligible weather-related uncertainties on the future supply side. Stochastic modeling techniques enable an adequate consideration of uncertainties in the investment and operation planning process of decentralized energy systems. The challenge is that modeling of real energy systems ends up in large-scale problems, already as deterministic program. In order to keep the stochastic problem feasible, we present a module-based, parallel computing approach using decomposing techniques and a hill-climbing algorithm in combination with high-performance computing (HPC) for a two-stage stochastic optimization problem. Consistent ensembles of the required input data are simulated by a Markov process and transformed into sets of energy demand and supply profiles. The approach is demonstrated for a residential quarter using photovoltaic (PV) systems in combination with heat pumps and storages. Depending on the installed technologies, the quarter is modeled either as stochastic linear program (SLP) or stochastic mixed-integer linear program (SMILP). ... mehr

Volltext §
DOI: 10.5445/IR/1000049862
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industriebetriebslehre und Industrielle Produktion (IIP)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2015
Sprache Englisch
Identifikator ISSN: 2196-7296
KITopen-ID: 1000049862
Verlag Karlsruher Institut für Technologie (KIT)
Umfang 31 S.
Serie Working Paper Series in Production and Energy ; 10
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