Using Agent-Based Models to Generate Transformation Knowledge for the German Energiewende—Potentials and Challenges Derived from Four Case Studies

• Published in: Energies (Basel) Vol. 13; no. 22; p. 6133
• Main Authors: Holtz, Georg, Schnülle, Christian, Yadack, Malcolm, Friege, Jonas, Jensen, Thorben, Thier, Pablo, Viebahn, Peter, Chappin, Émile J. L.
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.11.2020
• Subjects: agent-based model; energiewende; energy; insulation; transformation knowledge; transition
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13226133

The German Energiewende is a deliberate transformation of an established industrial economy towards a nearly CO2-free energy system accompanied by a phase out of nuclear energy. Its governance requires knowledge on how to steer the transition from the existing status quo to the target situation (transformation knowledge). The energy system is, however, a complex socio-technical system whose dynamics are influenced by behavioural and institutional aspects, which are badly represented by the dominant techno-economic scenario studies. In this paper, we therefore investigate and identify characteristics of model studies that make agent-based modelling supportive for the generation of transformation knowledge for the Energiewende. This is done by reflecting on the experiences gained from four different applications of agent-based models. In particular, we analyse whether the studies have improved our understanding of policies’ impacts on the energy system, whether the knowledge derived is useful for practitioners, how valid understanding derived by the studies is, and whether the insights can be used beyond the initial case-studies. We conclude that agent-based modelling has a high potential to generate transformation knowledge, but that the design of projects in which the models are developed and used is of major importance to reap this potential. Well-informed and goal-oriented stakeholder involvement and a strong collaboration between data collection and model development are crucial.