Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix

TL;DR

This study uses machine learning to analyze European electricity prices from 2012 to 2022, revealing the significant influence of renewable energy, fuel prices, and policy measures on market dynamics.

Contribution

It demonstrates the effectiveness of non-linear machine learning models in capturing complex interactions affecting electricity prices and quantifies the evolving impact of renewable energy and policy changes.

Findings

Non-linear models reduce RMSE by 50% compared to linear models.

CO2 permit prices and fuel costs significantly influence prices.

Wind energy's impact on prices has increased to match coal.

Abstract

We analyse the drivers of European Power Exchange (EPEX) wholesale electricity prices between 2012 and early 2022 using machine learning. The agnostic random forest approach that we use is able to reduce in-sample root mean square errors (RMSEs) by around 50% when compared to a standard linear least square model. This indicates that non-linearities and interaction effects are key in wholesale electricity markets. Out-of-sample prediction errors using machine learning are (slightly) lower than even in-sample least square errors using a least square model. The effects of efforts to limit power consumption and green the energy matrix on wholesale electricity prices are first order. CO2 permit prices strongly impact electricity prices, as do the prices of source energy commodities. And carbon permit prices impact has clearly increased post-2021 (particularly for baseload prices). Among energy sources, natural gas has the largest effect on electricity prices. Importantly, the role of wind energy feed-in has slowly risen over time, and its impact is now roughly on par with that of coal.