  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
spatio-temporal detail required to properly analyze the key role of variable
renewable electricity (VRE) for decarbonizing the power sector and end-use
electrification. In contrast, power sector models (PSMs) incorporate high
spatio-temporal resolutions, but tend to have narrower scopes and shorter time
horizons. To overcome these limitations, we present a novel methodology: an
iterative and fully automated soft-coupling framework that combines the
strengths of a IAM and a PSM. This framework uses the market values of power
generation as well as the capture prices of demand 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 and the PSM
DIETER, 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), 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. Since our approach
is based on fundamental economic principles, it is applicable also to other
IAM-PSM pairs.

General Economics;Economics