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

Abstract

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.

Techno-economic modelling and optimisation of excess heat and cold recovery for industries: A review

“…OSe-MOSYS was linked to two other tools, a bottom-up model built from scratch to project household demand and the software LoadProGen, a stochastic load profile generator. Dreier et al developed OSeMOSYS-PULP, a stochastic modelling framework for long-term energy systems modelling by adding the feature of Monte Carlo simulations to OSeMOSYS [68]. The enhancements and the improvements made to OSeMOSYS indicate the modularity of the modelling tool.…”

Supporting Decarbonization Strategies of Local Energy Systems by De-Risking Investments in Renewables: A Case Study on Pantelleria Island

“…A further need in energy models is related to the inclusion of future uncertainties in the main model inputs [33] to highlight the reliability of the obtained outcomes and identify the actions to be implemented independently of the evolution of high-level dynamics. In this regard, Leibowicz [34] proposed the use of stochastic programming to support decision making on carbon taxes; Dreier et al [35] successfully developed an empirical deterministic-stochastic modeling approach, enabling the use of large real-world datasets for prescribing future energy scenarios; Guevara et al [36] introduced a machine learning framework to overcome uncertainties in strategic investment for national energy systems. Nevertheless, although future uncertainties should also be included in local energy plans, large datasets are not always available on a local scale.…”

Afyonkarahisar için elektrik üretimi eniyilemesi

“…OSeMOSYS kullanılarak; Tunus [27], Bangladeş [28], Avustralya [29], Suudi Arabistan [30] ve Portekiz [31] modellemeleri ulusal bazda tamamlanırken, Drina Nehri [32] ve Güney Amerika (SAMBA) [33] gibi bölgeler karşılıklı etkileşimler de hesaba katılarak çok bölgeli modellemeye tabi tutulmuştur. Bölgesel çalışmalar kimi zaman Brezilya Curitiba Şehri [34], Hindistan Katgoon Köyü [35] veya Kolombiya'daki Choco bölge modellemesi [36] gibi küçük ölçekteki yerleşim birimlerine kadar daraltılabilirken; kimi zaman da bir nehir ya da kıtayı toplu olarak kapsayabilmektedir.…”