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OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Department of Energy Technology, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden
Author to whom correspondence should be addressed.
Energies 2019, 12(7), 1382;
Received: 20 February 2019 / Revised: 29 March 2019 / Accepted: 3 April 2019 / Published: 10 April 2019
(This article belongs to the Section Energy and Environment)
PDF [2192 KB, uploaded 12 April 2019]


Recent open-data movements give access to large datasets derived from real-world observations. This data can be utilized to enhance energy systems modeling in terms of heterogeneity, confidence, and transparency. Furthermore, it allows to shift away from the common practice of considering average values towards probability distributions. In turn, heterogeneity and randomness of the real-world can be captured that are usually found in large samples of real-world data. This paper presents a methodological framework for an empirical deterministic–stochastic modeling approach to utilize large real-world datasets in long-term energy systems modeling. A new software system—OSeMOSYS-PuLP—was developed and is available now.It adds the feature of Monte Carlo simulations to the existing open-source energy modeling system (the OSeMOSYS modeling framework). An application example is given, in which the initial application example of OSeMOSYS is used and modified to include real-world operation data from a public bus transport system. View Full-Text
Keywords: driving cycle; energy modeling; OSeMOSYS; Python; real-world; transport driving cycle; energy modeling; OSeMOSYS; Python; real-world; transport

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Dreier, D.; Howells, M. OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling. Energies 2019, 12, 1382.

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