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.

翻译：高空间分辨率的容量扩展模型常因计算密集而简化为较低空间分辨率。这种简化混合了不同可再生能源特性的站点，同时忽略了可能导致拥堵的输电线路。因此，当容量数据以更高空间细节反馈时，结果可能表征一个不可行的系统。目前尚未有研究详细探讨如何分解结果，以及高空间分辨率的分解模型是否可行。这一问题具有挑战性，因为聚类逆过程并不唯一。本文分为两部分应对这些挑战。首先，提出了低空间分辨率结果的分解方法：(a) 将区域结果均匀分布至其原始高分辨率区域，(b) 对各区域分别进行重新优化，(c) 最小化“过剩电力”的方法。其次，通过运行运行调度来评估所得高分辨率模型的可行性。重新优化方法取得最佳结果，而第三种逆方法在计算量更少的情况下提供了可比的结果。在可行性方面，研究设计进一步证实了以单一区域建模国家是不够的。对于欧洲，采用100-200个区域的当前最优简化模型仍会产生3%-7%的负荷削减，具体取决于模型分辨率及逆方法。