An analysis of electricity congestion price patterns in North America

Highlights: • Electricity congestion price data allows detecting transmission congestion patterns. • Principal component analysis (PCA) is the main statistical tool applied. • Outputs of the PCA convey information that is easily interpretable. • PCA scores exhibit seasonality, spikes and auto-correlation. • A time series model for scores based on their stylized facts is proposed. The present paper illustrates the use of principal component analysis (PCA) on the congestion component of local (i.e. zonal) electricity price data to detect the most salient congestion patterns in electricity transmission grids managed by either a Regional Transmission Organization (RTO) or an Independent System Operator (ISO). Outputs from the PCA along with some data visualization tools are shown to make the identification of such patterns seamless and straightforward. An empirical analysis is conducted for three North American power systems, namely NYISO, ISO New England and PJM. Finally, a simple time series model representing the evolution of PCA scores is proposed.