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OSeMOSYS-PuLP: A Stochastic Modeling Framework for Long-Term Energy Systems Modeling
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.ORCID iD: 0000-0002-0437-2093
KTH, School of Industrial Engineering and Management (ITM), Energy Technology.ORCID iD: 0000-0001-6419-4957
2019 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 7, article id 1382Article in journal (Refereed) Published
Abstract [en]

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 systemOSeMOSYS-PuLPwas 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.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 12, no 7, article id 1382
Keywords [en]
driving cycle, energy modeling, OSeMOSYS, Python, real-world, transport
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-252647DOI: 10.3390/en12071382ISI: 000465561400199Scopus ID: 2-s2.0-85065497585OAI: oai:DiVA.org:kth-252647DiVA, id: diva2:1321864
Note

QC 20190610

Available from: 2019-06-10 Created: 2019-06-10 Last updated: 2019-06-10Bibliographically approved

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Dreier, DennisHowells, Mark I.

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