论文标题
逆方法：当在高空间分辨率下分类时，在空间低分辨率的容量扩展模型中的可行性如何？
Inverse methods: How feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution?
论文作者
论文摘要
在空间上高度分辨的容量扩展模型通常被简化为较低的空间分辨率，因为它们是计算密集型的。简化将站点具有不同的可再生功能，同时忽略可能导致拥塞的传输线。结果，当能力以更高的空间细节喂养时，结果可能代表一个不可行的系统。到目前为止，还没有关于如何分解结果以及空间高度分解的分解模型是否可行的详细研究。这是具有挑战性的，因为没有独特的方法可以颠倒聚类。 本文分为两个部分，以应对这些挑战。首先，提出了分解空间低分辨率结果的方法：（a）区域结果在其原始高度分辨的区域中的区域结果均匀分布，（b）分别对每个区域的重新估算，（c）一种将“多余电力”最小化的方法。其次，通过运行运营调度，研究了由此产生的高度分辨模型的可行性。虽然重新优化会产生最佳结果，但第三个逆方法为减少计算工作提供了可比的结果。在可行性方面，研究设计加强了通过单个区域对国家进行建模的建模是不够的。最先进的模型减少了欧洲100-200个区域的模型，但取决于模型的分辨率和逆方法，仍会产生3％-7％的负载分离模型。
Spatially highly-resolved capacity expansion models are often simplified to a lower spatial resolution because they are computationally intensive. The simplification mixes sites with different renewable features while ignoring transmission lines that can cause congestion. As a consequence, the results may represent an infeasible system when the capacities are fed back at higher spatial detail. Thus far there has been no detailed investigation of how to disaggregate results and whether the spatially highly-resolved disaggregated model is feasible. This is challenging since there is no unique way to invert the clustering. This article is split into two parts to tackle these challenges. First, methods to disaggregate spatially low-resolved results are presented: (a) an uniform distribution of regional results across its original highly-resolved regions, (b) a re-optimisation for each region separately, (c) an approach that minimises the "excess electricity". Second, the resulting highly-resolved models' feasibility is investigated by running an operational dispatch. While re-optimising yields the best results, the third inverse method provides comparable results for less computational effort. Feasibility-wise, the study design strengthens that modelling countries by single regions is insufficient. State-of-the-art reduced models with 100-200 regions for Europe still yield 3%-7% of load-shedding, depending on model resolution and inverse method.
