A review of current challenges and trends in energy systems modeling. (November 2018)

• Record Type: Journal Article
• Authors: Lopion, Peter; Markewitz, Peter; Robinius, Martin; Stolten, Detlef
• Is Part Of: Renewable & sustainable energy reviews. Volume 96(2018)
• Page Start: 156
• Page End: 166
• Publication Date: 2018-11
• Subjects: Energy systems modeling -- Energy models -- Energy policy -- Greenhouse gas reduction; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2018.07.045
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: The requirements made on energy system models have changed during the last few decades. New challenges have arisen with the implementation of high shares of Renewable Energies. Along with the climate goals of the Paris Agreement, the national greenhouse gas strategies of industrialized countries involve the total restructuring of their energy systems. In order to archive these climate goals, fitted and customized models are required. For that reason, this paper focuses on national energy system models that incorporate all energy sectors and can support governmental decision making processes. The reviewed models are evaluated in terms of their characteristics, like their underlying methodology, analytical approach, time horizon and transformation path analysis, spatial and temporal resolution, licensing and modeling language. These attributes are set in the context of the region and time in which they were developed in order to identify trends in modeling. Furthermore, the revealed trends are set in the context of current challenges in energy systems modeling. Combining specified research questions and specific greenhouse gas reduction strategies, this paper will help researchers and decision makers find appropriate energy system models. Highlights: Historical trends in the development of energy system models were analyzed. Newly-developed models are more flexible in terms of spatial or temporal resolution. Python has become the most popular programming language in energy systems modeling.