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延长时间内满足性能要求至关重要。计算这些决策通常需要解决容量扩展问题 (CEP)。在区域规模能源系统的背景下,由于网络规模巨大、节点特征异构、和大量的操作周期,这些问题通常难以解决。为了保持可处理性,传统方法对网络节点进行聚合和/或选择一组典型的时间段。通常,这些约简并不捕捉对 CEP 成本和约束关键的供需变化,导致次优决策。在这里,我们提出了一种新颖的图卷积自编码器方法,用于处理具有异构节点的通用 CEP (CEPHN) 的时空聚合。我们的架构利用图池化来识别具有相似特征的节点,并最小化多目标损失函数。此损失函数旨在在可处理性和最优性方面诱导理想的空间和时间聚合。特别是,图池化的输出提供了一个空间聚合,而聚类低维编码表示可产生一个时间聚合。我们将我们的方法应用于新英格兰的一个耦合的 88 节点电力和天然气系统的发电扩展规划。由此产生的聚合导致了一个较简单的 CEPHN,其仅包含 6 个节点,并从一年中选择了少量典型的天数。我们评估超参数对损失函数的影响并比较其结果与基准方法所得到的原问题上限。我们表明,我们的方法提供了比基准空间 (resp. 时间) 聚合方法得到的上限低 33% (resp. 10%)。