Principal spatiotemporal mismatch and electricity price patterns in a highly decarbonized networked European power system

As the European power system decarbonizes, the variability of the mismatch between renewable generation and demand, as well as that of electricity prices, are expected to increase substantially. Because mismatch and prices show complex temporal and spatial interaction, we propose the use of principal component analysis (PCA) to investigate them. We unveil their main spatiotemporal patterns, examine their cross-correlation, and their dependence on the transmission capacity expansion and [Formula: see text] emissions reduction in a highly renewable cost-optimal electricity model. We find that the majority of variance in both the mismatch and price time series is explained by just three principal components (PCs). Hence, a convenient switch of basis vectors allows expressing the time series as combinations of few components which are shown to have intuitively interpretable structures. Moreover, we find that the temporal coherence between the first three PCs of mismatch and prices are substantially reinforced as the system decarbonizes.