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 energy (VRE) in decarbonizing the power
sector and enabling emission reductions through end-use electrification. In
contrast, power sector models (PSMs) can incorporate high spatiotemporal
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
and 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 REgional Model of INvestments and Development (REMIND) v3.0.0 and the PSM Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2 and confirm the theoretical prediction of
almost-full convergence in terms of both 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 based purely on cost competition. Since our
approach is based on fundamental economic principles, it is also applicable
to other IAM–PSM pairs.