论文标题
改变电力市场：量化绿色能量矩阵的价格影响
Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix
论文作者
论文摘要
我们使用机器学习分析了2012年至2022年初之间欧洲电力交易所（EPEX）批发电价的驱动因素。与标准的线性最小平方模型相比，我们使用的不可知论随机森林方法能够将样本内均方根误差（RMS）降低约50％。这表明非线性和相互作用效应是批发电力市场的关键。使用机器学习的样本外预测错误（略低于），即使使用最小平方模型的样本中最小成方误差也低。限制功耗和绿色绿色能源矩阵对批发电价的努力的影响是一阶。 CO2允许价格以及来源能源商品的价格也会强烈影响电价。和碳许可价格影响显然增加了2021年后（尤其是基本负荷价格）。在能源中，天然气对电价的影响最大。重要的是，随着时间的流逝，风能进料的作用逐渐增长，现在其影响与煤炭的作用大致相当。
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.
