Performing price scenario analysis and stress testing using quantile regression: A case study of the Californian electricity market

This paper uses quantile regression to demonstrate how electricity price distributions are linked fundamental supply and demand variables. It investigates the California market (zone SP15) for selected trading hours using data from January 8, 2013 September 24, 2016. The approach quantifies a non-linear relationship between fundamentals prices, just as predicted by merit order curve. Natural gas, greenhouse gas allowance prices load all have positive effect on with increasing quantiles. In contrast, solar production wind both negative prices. of increases quantiles, whereas decreases also includes stress testing case study in which producer faces risk high production, lower tail distribution. Overall, results proposed can be helpful management tool participants market.