Raising awareness in model-based energy scenario studies—a transparency checklist

Cao, Karl‐Kiên; Cebulla, Felix; Vilchez, Jonatan J. Gómez; Mousavi, Babak; Prehofer, Sigrid

doi:10.1186/s13705-016-0090-z

Cited by 59 publications

(40 citation statements)

References 44 publications

(42 reference statements)

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“…[12,13]). Another qualitative approach is presented in [14]. In order to increase transparency of energy scenario based studies a transparency checklist is proposed.…”

Section: Methodsmentioning

confidence: 99%

A Qualitative Evaluation Approach for Energy System Modelling Software—Case Study Results for the Open Energy Modelling Framework (Oemof)

Hilpert¹,

Kaldemeyer²,

Wiese³

et al. 2017

Preprint

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Abstract:The research field of energy system analysis is dealing with increasingly complex energy systems and their respective challenges. Moreover, the requirement for open science has become a focal point of public interest. Both drivers have triggered the development of a broad range of (open) energy models and frameworks in recent years. However, there are hardly any approaches on how to evaluate these tools in terms of their capabilities to tackle energy system modelling challenges. This paper describes a first step towards a flexible evaluation of software to model energy systems. We propose a qualitative approach as an useful supplementary to existing model fact sheets and transparency checklists. We demonstrate the applicability by evaluating the newly developed "Open Energy Modelling Framework" with respect to existing challenges in energy system modelling. The case study results highlight that challenges related to complexity and scientific standards can be tackled to a large extent while the challenges of model utilization and interdisciplinary modelling are only tackled partially. However, the challenge of uncertainty remains for the most part unaddressed at present. Advantages of the evaluation approach lie in its simplicity, flexibility and transferability to other tools. Disadvantages mostly stem from its qualitative nature. Our analysis reveals that some challenges in the field of energy system modelling cannot be addressed by a software as they are on meta level like model result communication and interdisciplinary modelling.

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“…[12,13]). Another qualitative approach is presented in [14]. In order to increase transparency of energy scenario based studies a transparency checklist is proposed.…”

Section: Methodsmentioning

confidence: 99%

A Qualitative Evaluation Approach for Energy System Modelling Software—Case Study Results for the Open Energy Modelling Framework (Oemof)

Hilpert¹,

Kaldemeyer²,

Wiese³

et al. 2017

Preprint

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“…The openness of code and data are identified as key requirements for energy system models [19] to comply with scientific standards like improved reproducibility and greater scrutiny [20]. By allowing reuse and collaborative development, open models and data can increase productivity and -through greater transparency [21] -also increase credibility in the policy discourse [22]. Further, they allow for public participation [17].…”

Section: Introductionmentioning

confidence: 99%

Open Power System Data – Frictionless data for electricity system modelling

et al. 2019

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The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of input data for modelling has the character of a public good: it reduces overall societal costs for quantitative energy research as redundant work is avoided, and it improves transparency and reproducibility in electricity system modelling. This paper describes the Open Power System Data platform that aims at realising the efficiency and quality gains of centralised data provision by collecting, checking, processing, aggregating, documenting and publishing data required by most modellers. We conclude that the platform can provide substantial benefits to energy system analysis by raising efficiency of data pre-processing, providing a method for making data pre-processing for energy system modelling traceable, flexible and reproducible and improving the quality of original data published by data providers.

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“…In this sense, energy system modellers face the challenge to provide at least all relevant assumptions in a traceable and transparent way (cf. [5]). In this context we want to point out the difference between transparency and openness.…”

Section: Introductionmentioning

confidence: 99%

Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases

Gotzens

Heinrichs

Hörsch

et al. 2019

Energy Strategy Reviews

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In energy modelling, open data and open source code can help enhance traceability and reproducibility of model exercises which contribute to facilitate controversial debates and improve policy advice. While the availability of open power plant databases increased in recent years, they often differ considerably from each other and their data quality has not been systematically compared to proprietary sources yet. Here, we introduce the python-based 'powerplantmatching' (PPM), an open source toolset for cleaning, standardizing and combining multiple power plant databases. We apply it once only with open databases and once with an additional proprietary database in order to discuss and elaborate the issue of data quality, by analysing capacities, countries, fuel types, geographic coordinates and commissioning years for conventional power plants. We find that a derived dataset purely based on open data is not yet on a par with one in which a proprietary database has been added to the matching, even though the statistical values for capacity matched to a large degree with both datasets. When commissioning years are needed for modelling purposes in the final dataset, the proprietary database helps crucially to increase the quality of the derived dataset.

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Raising awareness in model-based energy scenario studies—a transparency checklist

Cited by 59 publications

References 44 publications

A Qualitative Evaluation Approach for Energy System Modelling Software—Case Study Results for the Open Energy Modelling Framework (Oemof)

A Qualitative Evaluation Approach for Energy System Modelling Software—Case Study Results for the Open Energy Modelling Framework (Oemof)

Open Power System Data – Frictionless data for electricity system modelling

Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases

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Raising awareness in model-based energy scenario studies—a transparency checklist

Cited by 59 publications

References 44 publications

A Qualitative Evaluation Approach for Energy System Modelling Software&mdash;Case Study Results for the Open Energy Modelling Framework (Oemof)

A Qualitative Evaluation Approach for Energy System Modelling Software&mdash;Case Study Results for the Open Energy Modelling Framework (Oemof)

Open Power System Data – Frictionless data for electricity system modelling

Performing energy modelling exercises in a transparent way - The issue of data quality in power plant databases

Contact Info

Product

Resources

About

A Qualitative Evaluation Approach for Energy System Modelling Software—Case Study Results for the Open Energy Modelling Framework (Oemof)

A Qualitative Evaluation Approach for Energy System Modelling Software—Case Study Results for the Open Energy Modelling Framework (Oemof)