Open AccessBridging granularity gaps to decarbonize large‐scale energy systems—The case of power system planning
Author(s) -
Cao KarlKiên,
Haas Jannik,
Sperber Evelyn,
Sasanpour Shima,
Sarfarazi Seyedfarzad,
Pregger Thomas,
Alaya Oussama,
Lens Hendrik,
Drauz Simon R.,
Kneiske Tanja M.
Publication year - 2021
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.891
Subject(s) - blueprint , computer science , bridging (networking) , exploit , granularity , scalability , distributed computing , electric power system , scope (computer science) , systems engineering , data science , industrial engineering , risk analysis (engineering) , power (physics) , engineering , mechanical engineering , computer network , physics , computer security , quantum mechanics , database , programming language , operating system , medicine
The comprehensive evaluation of strategies for decarbonizing large‐scale energy systems requires insights from many different perspectives. In energy systems analysis, optimization models are widely used for this purpose. However, they are limited in incorporating all crucial aspects of such a complex system to be sustainably transformed. Hence, they differ in terms of their spatial, temporal, technological, and economic perspective and either have a narrow focus with high resolution or a broad scope with little detail. Against this background, we introduce the so‐called granularity gaps and discuss two possibilities to address them: increasing the resolutions of the established optimization models, and the different kinds of model coupling. After laying out open challenges, we propose a novel framework to design power systems in particular. Our exemplary concept exploits the capabilities of power system optimization, transmission network simulation, distribution grid planning, and agent‐based simulation. This integrated framework can serve to study the energy transition with greater comprehensibility and may be a blueprint for similar multimodel analyses.