Bridging granularity gaps to decarbonize large‐scale energy systems—The case of power system planning

Energy Science & Engineering

Hendrik Lens¹  Oussama Alaya¹  Jannik Haas²  Seyedfarzad Sarfarazi²  Thomas Pregger²  Evelyn Sperber²  Karl‐Kiên Cao²  Shima Sasanpour²  Simon R. Drauz³  Tanja M. Kneiske³ 

[1] Department Power Generation and Automatic Control Institute of Combustion and Power Plant Technology (IFK) University of Stuttgart Stuttgart Germany;Department of Energy Systems Analysis Institute of Networked Energy Systems German Aerospace Center (DLR) Stuttgart Germany;Grid Planning and Grid Operation Division Fraunhofer Institute for Energy Economics and Energy System Technology Kassel Germany;

关键词: decarbonization;    decentral flexibility;    energy system modeling;    granularity gaps;    model coupling;    security of supply;   

DOI  :  10.1002/ese3.891

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown