The impact of variable renewables on the distribution of hourly electricity prices and their variability: A panel approach

Abstract

This paper investigates the impact of intermittent renewable generation on the distribution of electricity prices and their variability in Denmark and Germany. We exploit hourly data from 2015 to 2020 and employ a novel panel quantile approach - the Quantiles via moments (MMQR) method. Previous research has mainly used aggregated-daily data and have applied a time-series setting. We argue that since the electricity price formation and renewable energy generation can show great variations during a day, a panel setting with 24 individuals-hours could offer higher accuracy. Therefore, we apply a panel approach that accounts for both the time and cross-sectional dimension of electricity prices. The panel allows us to control for time-invariant (hourly-specific) characteristics and can reveal hidden market dynamics that exist during a day. The combination of hourly-specific effects and the quantile approach enable us to estimate the renewable sources effect on various price quantiles while controlling for market dynamics. In this way, we investigate extreme market cases accounting for the range and distribution of the electricity prices data. The results suggest that the merit-order effect occurs in both countries, with wind and solar generation having diverse effects on the electricity price distribution. Thus, policy makers should consider this diversifying effect to develop efficient renewable support schemes. We also explore non-linearities by including different demand levels in our model and investigate price variability. The outcomes indicate that wind generation increases (decreases) the occurrence of price fluctuations for low demand (high demand) in both countries. Meanwhile, in Germany, solar power stabilizes price fluctuations for high demand levels, stronger than wind. Market risk information could be useful for organizations in recognizing beneficial investment opportunities or hedging strategies. We finally aggregate the hourly observations into daily and compare the estimation outcomes. The results prompt us to believe that aggregated time-series tend to underestimate the RES impact on prices. In addition, we estimate the same models using hourly data in a time-series approach in order to verify that the diverse effect between aggregated time-series and hourly panel data is driven by the time-invariant characteristics, and not the data resolution. We find that the hourly time-series underestimate the merit-order effect, like in the aggregated time-series case, which supports our claims that the results are steered by the cross-sectional dimension. Thus, a panel approach could provide higher accuracy estimates of the RES influence on electricity prices. In conclusion, hourly-related features seem to affect the merit-order effect and its robustness, and a panel approach should be considered when investigating electricity markets.