Optimization models are a widely used tool in academia. In order to build these models, various parameters need to be specified, and often, simplifications are necessary to ensure the tractability of the models; both of which introduce uncertainty about the model results. However, a widely accepted way to quantify how these uncertainties propagate does not exist. Using the example of power system expansion modeling, we show that uncertainty propagation in optimization models can systematically be described by quantifying the sensitivity to different model parameters and model designs. We quantify the sensitivity based on a misallocation measure with clearly defined mathematical properties for two prominent examples: the cost of capital and different model resolutions. When used to disclose sensitivity information in power system studies our approach can contribute to openness and transparency in power system research. It is found that power system models are particularly sensitive to the temporal resolution of the underlying time series.

优化模型是学术界广泛使用的工具。为了构建这些模型，需要指定各种参数，并且通常需要进行简化以确保模型的可处理性；两者都引入了模型结果的不确定性。然而，不存在一种广泛接受的量化这些不确定性如何传播的方法。使用电力系统扩展建模的例子，我们表明优化模型中的不确定性传播可以通过量化对不同模型参数和模型设计的敏感性来系统地描述。我们基于具有明确定义的数学属性的错误分配度量来量化敏感度，用于两个突出的例子：资本成本和不同的模型分辨率。当用于披露电力系统研究中的敏感性信息时，我们的方法有助于提高电力系统研究的开放性和透明度。发现电力系统模型对基础时间序列的时间分辨率特别敏感。