How to assess the quality and transparency of energy scenarios: Results of a case study

Abstract The exploration and evaluation of strategies for decarbonizing the energy system is the subject of a series of national and international studies conducted by governmental, industrial and independent stakeholders. These studies play an important role in the energy policy debate on understanding and assessing different transformation paths of the energy system, technology options and their implications. They support strategic decisions on the type and scale of investments in the energy system under uncertain future conditions. However, in recent years the increasing complexity of these studies lead to a decreasing transparency even though their transparency and traceability is important for society, politics, research, and industry. In this article, three energy scenarios at different regional scales are reviewed according to their compliance with our pre-defined criteria of transparency. They are analysed in detail with regard to their objectives, methods, data used, results obtained and traceability. Our comparison shows that the results are often presented sufficiently in order to inform decision makers. However, the underlying model-based methods lack information on data exchange between the models, the transparent description of model couplings and a discussion on the rationality of method selection and the strengths and weaknesses of the applied approaches. Based on our findings, we present some general advice for energy scenario developers on how to ensure transparency and traceability in future energy scenario studies.

[1]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[2]  M. Thring World Energy Outlook , 1977 .

[3]  P. Jochem,et al.  Perceived price complexity of dynamic energy tariffs: An investigation of antecedents and consequences , 2017 .

[4]  M. Farrell Transparency , 2016, LabOratory.

[5]  Iain Staffell,et al.  The importance of open data and software: Is energy research lagging behind? , 2017 .

[6]  James Gaede,et al.  A Question of Authenticity: Status Quo Bias and the International Energy Agency's World Energy Outlook , 2016 .

[7]  Kashem M. Muttaqi,et al.  Sustainable energy system design with distributed renewable resources considering economic, environmental and uncertainty aspects , 2015 .

[8]  Sergey Paltsev,et al.  Energy scenarios: the value and limits of scenario analysis , 2017 .

[9]  Qie Sun,et al.  The future potential for Carbon Capture and Storage in climate change mitigation – an overview from perspectives of technology, economy and risk , 2015 .

[10]  Yongjun Sun,et al.  Sensitivity analysis of macro-parameters in the system design of net zero energy building , 2015 .

[11]  Stefan Pfenninger,et al.  Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability , 2017 .

[12]  Valentin Bertsch,et al.  Combining local preferences with multi-criteria decision analysis and linear optimization to develop feasible energy concepts in small communities , 2018, Eur. J. Oper. Res..

[13]  Felix Cebulla,et al.  Raising awareness in model-based energy scenario studies—a transparency checklist , 2016 .

[14]  Michael Siegrist,et al.  Acceptance of nuclear power: The Fukushima effect , 2013 .

[15]  Linda Steg,et al.  The influence of values on evaluations of energy alternatives , 2015 .

[16]  R. Siezen,et al.  others , 1999, Microbial Biotechnology.

[17]  Nicholas Frank Pidgeon,et al.  Public values for energy futures: Framing, indeterminacy and policy making , 2015 .

[18]  Henri Moll,et al.  A review of the bandwidth and environmental discourses of future energy scenarios: Shades of green and gray , 2017 .

[19]  Patrik Söderholm,et al.  Challenges in top-down and bottom-up soft-linking: Lessons from linking a Swedish energy system model with a CGE model , 2017 .

[20]  Nate Blair,et al.  2015 Standard Scenarios Annual Report: U.S. Electric Sector Scenario Exploration , 2015 .

[21]  Patrícia Fortes,et al.  Long-term energy scenarios: : Bridging the gap between socio-economic storylines and energy modeling , 2015 .

[22]  Anjali Nursimulu Assessment of Future Energy Demand: A Methodological Review Providing Guidance to Developers and Users of Energy Models and Scenarios , 2015 .

[23]  Joseph H. A. Guillaume,et al.  An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together? , 2016, Environ. Model. Softw..

[24]  Simon Hilpert,et al.  A qualitative evaluation approach for energy system modelling frameworks , 2018 .

[25]  Pantelis Capros,et al.  GEM-E3 Model Documentation , 2013 .

[26]  W. Winiwarter,et al.  EU Reference Scenario 2016 - Energy, transport and GHG emissions Trends to 2050. , 2016 .

[27]  G. Finnveden,et al.  Scenario types and techniques: Towards a user's guide , 2006 .

[28]  Hannah Kosow,et al.  Context scenarios and their usage for the construction of socio-technical energy scenarios , 2016 .

[29]  Dominik Möst,et al.  Energiesystemanalyse : Tagungsband des Workshops "Energiesystemanalyse" vom 27. November 2008 am KIT Zentrum Energie, Karlsruhe , 2009 .

[30]  Aie,et al.  Energy Technology Perspectives 2012 , 2006 .

[31]  Martin Glauer,et al.  Transparency, reproducibility, and quality of energy system analyses – A process to improve scientific work , 2018 .

[32]  Yue-Jun Zhang,et al.  The dynamic volatility spillover between European carbon trading market and fossil energy market , 2016 .

[33]  Manfred Fischedick,et al.  Zur Interpretation von Energieszenarien , 2015 .

[34]  J. Alcamo Environmental futures : the practice of environmental scenario analysis , 2008 .

[35]  Emanuele Borgonovo,et al.  Sensitivity to energy technology costs: a multi-model comparison analysis. , 2015 .

[36]  Tobias Naegler,et al.  Moving towards socio-technical scenarios of the German energy transition—lessons learned from integrated energy scenario building , 2019, Climatic Change.

[37]  Iain Staffell,et al.  Opening the black box of energy modelling: strategies and lessons learned , 2017, ArXiv.

[38]  Fabian Gotzens,et al.  Performing energy modelling exercises in a transparent way the issue of data quality in power plant databases , 2018, Energy Strategy Reviews.

[39]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[40]  Robbie Morrison,et al.  Energy system modeling: Public transparency, scientific reproducibility, and open development , 2018 .