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An Empirically Grounded Model of Green Electricity Adoption in Germany: Calibration, Validation and Insights into Patterns of Diffusion

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Abstract

Spatially explicit agent-based models (ABM) of innovation diffusion have experienced growing attention over the last few years. The ABM presented in this paper investigates the adoption of green electricity tariffs by German households. The model represents empirically characterised household types as agent types which differ in their decision preferences regarding green electricity and other psychological properties. Agent populations are initialised based on spatially explicit socio demographic data describing the sociological lifestyles found in Germany. For model calibration and validation we use historical data on the German green electricity market including a rich dataset of spatially explicit customer data of one of the major providers of green electricity. In order to assess the similarity of the simulation results to historical observations we introduce two validation measures which capture different aspects of the green electricity diffusion. One measure is based on the residuals of spatially-aggregated time series of model indicators and the other measure considers a temporally aggregated but spatially disaggregated indicator of spatial spread. Finally, we demonstrate the descriptive richness of the model by investigating simulation outputs of the calibrated model in more detail. In particular, the results provide insights into the dynamics of the spatial and lifestyle heterogeneity “underneath†the diffusion curve of green electricity in Germany.

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  • Friedrich Krebs, 2017. "An Empirically Grounded Model of Green Electricity Adoption in Germany: Calibration, Validation and Insights into Patterns of Diffusion," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(2), pages 1-10.
    • Handle: RePEc:jas:jasssj:2016-55-2
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    2. Mittal, Anuj & Krejci, Caroline C. & Dorneich, Michael C., 2019. "An agent-based approach to designing residential renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 1008-1020.
    3. Georg Holtz & Christian Schnülle & Malcolm Yadack & Jonas Friege & Thorben Jensen & Pablo Thier & Peter Viebahn & Émile J. L. Chappin, 2020. "Using Agent-Based Models to Generate Transformation Knowledge for the German Energiewende—Potentials and Challenges Derived from Four Case Studies," Energies, MDPI, vol. 13(22), pages 1-26, November.

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