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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. [ABSTRACT FROM AUTHOR]

As part of its climate strategy, the EU aims at increasing the share of electricity from renewable energy sources (RES-E) in overall electricity generation. Attaining this target poses a considerable challenge as the electricity sector is "locked" into a carbon-intensive system, which hampers the adoption of RES-E technologies. Electricity generation, transmission and distribution grids as well as storage and demand response are subject to important path dependences, which put existing, non-renewable energy sources at an advantage. This paper examines how an EU framework for RES-E support policies should be designed to facilitate a carbon lock-out. For this purpose, we specify the major technological, economic and institutional barriers to RES-E. For each of the barriers, a policy review is carried out which assesses the performance of existing policy instruments and identifies needs for reform. The review reveals several shortcomings: while policies targeting generation are widely in place, measures to address barriers associated with electricity grids, storage and demand are still in their infancy and have to be extended. Moreover, the implementation of policies has been fragmented across EU Member States. In this respect, national policies should be embedded into an integrated EU-wide planning of the RES-E system with overarching energy scenarios and partially harmonized policy rules. [ABSTRACT FROM AUTHOR]