标题： REMIND v3.0.0 长期综合评估模型与 DIETER v1.0.2 小时电力部门模型的双向耦合 显示英文标题

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

摘要： 综合评估模型（IAMs）是定量分析气候变化缓解策略的核心工具。 然而，由于其全球性、跨部门性和百年范围，IAMs无法明确表示用于正确分析可变可再生能源（VRE）在电力部门脱碳和终端用电电气化中的关键作用所需的时空细节。 相比之下，电力部门模型（PSMs）具有较高的时空分辨率，但往往范围较窄且时间跨度较短。 为克服这些限制，我们提出了一种新方法：一种迭代且完全自动化的软耦合框架，结合了IAM和PSM的优势。 该框架使用PSM中的电力发电市场价值以及需求捕获价格作为价格信号，改变IAM中的容量和电力组合。 因此，两个模型都做出内生的投资决策，从而得到一个联合解。 我们在概念验证研究中将该方法应用于德国，使用IAM REMIND和PSM DIETER，并确认了在决策变量和（影子）价格方面的几乎完全收敛的理论预测。 在迭代过程结束时，对于任何年份的任何发电机组类型，生成份额的绝对模型差异在简单配置（无储能、无灵活需求）下小于5%，在更现实和详细的配置（有储能和灵活需求）下为6-7%。 对于简单配置，我们数学上证明了这种耦合方案唯一对应于两个不同时间分辨率的电力部门优化问题的拉格朗日函数的迭代映射，这可能导致决策变量和（影子）价格的全面模型收敛。 由于我们的方法基于基本经济原理，因此也适用于其他IAM-PSM对。 显示英文摘要

摘要： 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.