Perspectives on purpose-driven coupling of energy system models. (15th February 2023)

• Record Type: Journal Article
• Authors: Chang, Miguel; Lund, Henrik; Thellufsen, Jakob Zinck; Østergaard, Poul Alberg
• Is Part Of: Energy. Volume 265(2023)
• Publication Date: 2023-02-15
• Subjects: Energy system models -- Model coupling -- Model linking -- Soft-linking -- Multi-model frameworks -- Energy systems analysis; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.126335
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Energy system models (ESMs) are essential for planning the energy transition and understanding its impacts. However, this transition is inherently complex and cannot always be understood by using just one model. Consequently, efforts linking different model classes are common practice to get insights into the energy system and the different dimensions around it. While existing literature has focused on proposing how such multi-model analyses could be structured, presenting applied cases, or looking into how specific aspects of other knowledge domains are included in energy modelling, a high-level overview of the practice of model coupling with ESMs is lacking. This article puts this practice into perspective by providing an outlook on two aspects: coupling ESM paradigms and model coupling with other knowledge dimensions. Coupling ESMs paradigms have often been used to expand modelling resolution, yet further emphasis should be placed on illustrating contrasting near-optimal system designs and expanding the solution space beyond optimality criteria. Model coupling across knowledge domains is desirable when providing meaningful insights about specific themes, yet, increased complexity of data, multi-model frameworks, and coordination across practices would make an all-encompassing model impractical and calls for purpose-driven model coupling to answer specific questions about the energy transition. Highlights: Multi-model analyses are a commonly applied practice in energy planning. Coupling across ESM paradigms can help explore near-optimal system options. Archetypical themes and knowledge domains in model coupling are presented. The practice of model coupling is contextualized under a multi-level perspective. Purpose-driven model coupling must balance scope, complexity, & end-use tradeoffs.