Preprints
https://doi.org/10.5194/egusphere-2022-885
https://doi.org/10.5194/egusphere-2022-885
 
07 Oct 2022
07 Oct 2022
Status: this preprint is open for discussion.

Bidirectional coupling of a long-term integrated assessment model REMIND v3.0.0 with an hourly power sector model DIETER v1.0.2

Chen Chris Gong1, Falko Ueckerdt1, Robert Pietzcker1, Adrian Odenweller1,3, Wolf-Peter Schill2, Martin Kittel2, and Gunnar Luderer1,3 Chen Chris Gong et al.
  • 1Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
  • 2German Institute for Economic Research (DIW Berlin), Berlin, Germany
  • 3Global Energy Systems Analysis, Technische Universität Berlin, Berlin, Germany

Abstract. Integrated assessment models (IAMs) are a central tool for the quantitative analysis of climate change mitigation strategies. However, due to their global, cross-sectoral and centennial scope, IAMs cannot explicitly represent the temporal and spatial details required to properly analyze the key role of variable renewable electricity (VRE) for decarbonizing the power sector and enabling emission reductions through end-use electrification. In contrast, power sector models (PSMs) can incorporate high spatio-temporal resolutions, but tend to have narrower sectoral and geographic scopes and shorter time horizons. To overcome these limitations, here we present a novel methodology: an iterative and fully automated soft-coupling framework that combines the strengths of a long-term IAM and a detailed PSM. The key innovation is that the framework uses the market values of power generations as well as the capture prices of demand flexibilities in the PSM as price signals that change the capacity and power mix of the IAM. Hence, both models make endogenous investment decisions, leading to a joint solution. We apply the method to Germany in a proof-of-concept study using the IAM REMIND v3.0.0 and the PSM DIETER v1.0.2, and confirm the theoretical prediction of almost-full convergence both in terms of decision variables and (shadow) prices. At the end of the iterative process, the absolute model difference between the generation shares of any generator type for any year is <5 % for a simple configuration (no storage, no flexible demand) under a “proof-of-concept” baseline scenario, and 6–7 % for a more realistic and detailed configuration (with storage and flexible demand). For the simple configuration, we mathematically show that this coupling scheme corresponds uniquely to an iterative mapping of the Lagrangians of two power sector optimization problems of different time resolutions, which can lead to a comprehensive model convergence of both decision variables and (shadow) prices. The remaining differences in the two models can be explained by a slight mismatch between the standing capacities in the real-world and optimal modeling solutions purely based on cost competition. Since our approach is based on fundamental economic principles, it is applicable also to other IAM-PSM pairs.

Chen Chris Gong et al.

Status: open (until 02 Dec 2022)

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  • RC1: 'Comment on egusphere-2022-885', Anonymous Referee #1, 21 Nov 2022 reply

Chen Chris Gong et al.

Model code and software

Coupling software code Luderer et al. https://doi.org/10.5281/zenodo.7053246

Chen Chris Gong et al.

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Short summary
To mitigate climate change, the global economy must drastically reduced its greenhouse gas emissions, for which the power sector plays a key role. Until now, long-term models which simulate this transformation cannot always accurately depict the power sector due to a lack of resolution. Our work bridges this gap by linking a long-term model to an hourly model. The result is an almost full harmonization of the models in generating a power sector mix until 2100 with hourly resolution.