Wasserstein-Distance-Based Temporal Clustering for Capacity-Expansion Planning in Power Systems

As variable renewable energy sources are steadily incorporated in European power systems, the need for higher temporal resolution in capacity-expansion models also increases.Naturally, there exists a trade-off between the amount of temporal data used to plan power systems for decades ahead and time resolution needed to represent renewable energy variability accurately. We propose the use of the Wasserstein distance as a measure of cluster discrepancy using it to cluster demand, wind availability, and solar availability data. When compared to the Euclidean distance and the maximal distance, the hierarchical clustering performed using the Wasserstein distance leads to capacity-expansion planning that 1) more accurately estimates system costs and 2) more efficiently adopts storage resources. Numerical results indicate an improvement in cost estimation by up to 5% vis-à-vis the Euclidean distance and a reduction of storage investment that is equivalent to nearly 100% of the installed capacity under the benchmark full time resolution.

I. Introduction

The European power sector is undergoing significant and enduring changes. Decarbonization was posed as one of the five European Union headline strategic 2020 targets for smart, sustainable, and inclusive growth. An outcome of this strategy is the target of having 32% of the 28 EU Member States’ energy supply provided by renewable sources by 2030 [1], which implies a deep decarbonization of the power sector as heat and transportation sectors increasingly electrify. As a consequence, an increase of almost 100% in installation of renewable capacity took place between 2004 and 2018 [1], of which the major part was in the form of wind and solar photovoltaic power.