TY - JOUR
T1 - An expert survey to assess the current status and future challenges of energy system analysis
AU - Scheller, Fabian
AU - Wiese, Frauke
AU - Weinand, Jann Michael
AU - Dominković, Dominik Franjo
AU - McKenna, Russell
N1 - Funding Information:
We would like to thank all of the survey participants for their time and expertise in completing the survey. We also thank the reviewers for their thoughtful comments and efforts towards improving our manuscript. Fabian Scheller kindly acknowledges the financial support of the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 713683 (COFUNDfellowsDTU).
PY - 2021/11
Y1 - 2021/11
N2 - Decision support systems like computer-aided energy system analysis (ESA) are considered one of the main pillars for developing sustainable and reliable energy strategies. Although today's diverse tools can already support decision-makers in a variety of research questions, further developments are still necessary. Intending to identify opportunities and challenges in the field, we classify modelling topics into modelling capabilities (32), methodologies (15), implementation issues (15) and management issues (7) from an extensive literature review. Based on a quantitative expert survey of energy system modellers (N = 61) mainly working with simulation and optimisation models, the Status of Development and the Complexity of Realisation of those modelling topics are assessed. While the rated items are considered to be more complex than actually represented, no significant outliers are determinable, showing that there is no consensus about particular aspects of ESA that are lacking development. Nevertheless, a classification of the items in terms of a specially defined modelling strategy matrix identifies capabilities like land-use planning patterns, equity and distributional effects and endogenous technological learning as “low hanging fruits” for enhancement, as well as a large number of complex topics that are already well implemented. The remaining “tough nuts” regarding modelling capabilities include non-energy sector and social behaviour interaction effects. In general, the optimisation and simulation models differ in their respective strengths, justifying the existence of both. While methods were generally rated as quite well developed, combinatorial optimisation approaches, as well as machine learning, are identified as important research methods to be developed further for ESA.
AB - Decision support systems like computer-aided energy system analysis (ESA) are considered one of the main pillars for developing sustainable and reliable energy strategies. Although today's diverse tools can already support decision-makers in a variety of research questions, further developments are still necessary. Intending to identify opportunities and challenges in the field, we classify modelling topics into modelling capabilities (32), methodologies (15), implementation issues (15) and management issues (7) from an extensive literature review. Based on a quantitative expert survey of energy system modellers (N = 61) mainly working with simulation and optimisation models, the Status of Development and the Complexity of Realisation of those modelling topics are assessed. While the rated items are considered to be more complex than actually represented, no significant outliers are determinable, showing that there is no consensus about particular aspects of ESA that are lacking development. Nevertheless, a classification of the items in terms of a specially defined modelling strategy matrix identifies capabilities like land-use planning patterns, equity and distributional effects and endogenous technological learning as “low hanging fruits” for enhancement, as well as a large number of complex topics that are already well implemented. The remaining “tough nuts” regarding modelling capabilities include non-energy sector and social behaviour interaction effects. In general, the optimisation and simulation models differ in their respective strengths, justifying the existence of both. While methods were generally rated as quite well developed, combinatorial optimisation approaches, as well as machine learning, are identified as important research methods to be developed further for ESA.
KW - Energy models
KW - Expert survey
KW - Mathematical optimisation models
KW - Modelling approaches
KW - Modelling challenges
KW - Simulation models
UR - http://www.scopus.com/inward/record.url?scp=85124049408&partnerID=8YFLogxK
U2 - 10.1016/j.segy.2021.100057
DO - 10.1016/j.segy.2021.100057
M3 - Article
AN - SCOPUS:85124049408
VL - 4
JO - Smart Energy
JF - Smart Energy
SN - 2666-9552
M1 - 100057
ER -