Effective investment planning decisions are crucial to ensure cyber-physical infrastructures satisfy performance requirements over an extended time horizon. Computing these decisions often requires solving Capacity Expansion Problems (CEPs). In the context of regional-scale energy systems, these problems are prohibitively expensive to solve due to large network sizes, heterogeneous node characteristics, and a large number of operational periods. To maintain tractability, traditional approaches aggregate network nodes and/or select a set of representative time periods. Often, these reductions do not capture supply-demand variations that crucially impact CEP costs and constraints, leading to suboptimal decisions. Here, we propose a novel graph convolutional autoencoder approach for spatio-temporal aggregation of a generic CEP with heterogeneous nodes (CEPHN). Our architecture leverages graph pooling to identify nodes with similar characteristics and minimizes a multi-objective loss function. This loss function is tailored to induce desirable spatial and temporal aggregations with regard to tractability and optimality. In particular, the output of the graph pooling provides a spatial aggregation while clustering the low-dimensional encoded representations yields a temporal aggregation. We apply our approach to generation expansion planning of a coupled 88-node power and natural gas system in New England. The resulting aggregation leads to a simpler CEPHN with 6 nodes and a small set of representative days selected from one year. We evaluate aggregation outcomes over a range of hyperparameters governing the loss function and compare resulting upper bounds on the original problem with those obtained using benchmark methods. We show that our approach provides upper bounds that are 33% (resp. 10%) lower those than obtained from benchmark spatial (resp. temporal) aggregation approaches.

翻译：有效的投资规划决策对于确保网络-物理基础设施在长期时间范围内满足性能要求至关重要。计算这些决策通常需要求解容量扩展问题（CEP）。在区域级能源系统背景下，由于网络规模庞大、节点特征异构以及运营周期数量众多，这类问题的求解成本过高。为保持可解性，传统方法采用网络节点聚合和/或选取代表性时段集。但这些简化往往无法捕捉对CEP成本与约束具有关键影响的供需变化，从而导致次优决策。本文针对含异构节点的通用CEP（CEPHN），提出了一种新颖的图卷积自编码器时空聚合方法。该方法利用图池化技术识别特征相似的节点，并最小化一个多目标损失函数。该损失函数被专门设计为：在可解性与最优性之间诱导出理想的时空聚合。具体而言，图池化输出提供空间聚合，而对低维编码表示进行聚类则生成时间聚合。我们将该方法应用于新英格兰地区一个耦合88节点电力与天然气系统的发电容量扩展规划。所得到的聚合结果构建了一个仅含6个节点的简化CEPHN，并从一年时段中选取了少量代表性日期。我们针对控制损失函数的超参数范围评估聚合效果，并将原问题的上界结果与基准方法进行对比。结果表明，与基准空间（及时间）聚合方法相比，本方法可获得低33%（及10%）的上界。