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

Emanuel Kohlscheen and Richhild Moessner

No 9807, CESifo Working Paper Series from CESifo

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 − indicating 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.

Keywords: carbon permit; CO2 emissions; commodities; electricity market; energy; EPEX; machine learning; natural gas; oil; wind energy (search for similar items in EconPapers)
JEL-codes: C54 D40 L70 Q02 Q20 Q40 (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-env
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Working Paper: Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_9807

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