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Integrated assessment models (IAMs) are a central tool for the quantitativeanalysis of climate change mitigation strategies. However, due to their global,cross-sectoral and centennial scope, IAMs cannot explicitly represent thespatio-temporal detail required to properly analyze the key role of variablerenewable electricity (VRE) for decarbonizing the power sector and end-useelectrification. In contrast, power sector models (PSMs) incorporate highspatio-temporal resolutions, but tend to have narrower scopes and shorter timehorizons. To overcome these limitations, we present a novel methodology: aniterative and fully automated soft-coupling framework that combines thestrengths of a IAM and a PSM. This framework uses the market values of powergeneration as well as the capture prices of demand in the PSM as price signalsthat change the capacity and power mix of the IAM. Hence, both models makeendogenous investment decisions, leading to a joint solution. We apply themethod to Germany in a proof-of-concept study using the IAM REMIND and the PSMDIETER, and confirm the theoretical prediction of almost-full convergence bothin terms of decision variables and (shadow) prices. At the end of the iterativeprocess, the absolute model difference between the generation shares of anygenerator type for any year is <5% for a simple configuration (no storage, noflexible demand), and 6-7% for a more realistic and detailed configuration(with storage and flexible demand). For the simple configuration, wemathematically show that this coupling scheme corresponds uniquely to aniterative mapping of the Lagrangians of two power sector optimization problemsof different time resolutions, which can lead to a comprehensive modelconvergence of both decision variables and (shadow) prices. Since our approachis based on fundamental economic principles, it is applicable also to otherIAM-PSM pairs.

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