Successful trading in electricity markets relies on the market actor's ability to accurately forecast the electricity price. The fundamental electricity price models use market information, provided by various price drivers, including the residual that contains a risk premium. In the past, researchers investigating risk premium focused primarily on daily spot price levels, ignoring the intraday information hindering the accurate risk premium determination. This paper presents a new KGB Method for modelling of risk premium, based on “ex-ante” approach focused on a yearly product. The method involves a novel KGB Model and its linearized formulation, the KGB Linear Model, which enables capturing the influence of renewable energy sources on risk premium. The four key drivers of the KGB Linear Model were used providing an insight into the influence of RES generation on risk premium evolution. The method was tested on historical data from the German electricity market. The results for the 2010–2014 period reveal overall influence of PV production share on risk premium is greater than that of wind production share, both increasing the risk premium due to their variability and uncertainty. Using the KGB Method, market actors can forecast risk premium using information readily available to them.

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风险溢价函数的定义

电力市场的成功交易依赖于市场参与者准确预测电价的能力。基本电价模型使用由各种价格驱动因素提供的市场信息，包括包含风险溢价的残差。过去，研究人员在调查风险溢价时主要关注每日现货价格水平，而忽略了阻碍准确确定风险溢价的盘中信息。本文提出了一种新的 KGB 风险溢价建模方法，该方法基于专注于年度产品的“事前”方法。该方法涉及一种新颖的克格勃模型及其线性化公式，即克格勃线性模型，它能够捕捉可再生能源对风险溢价的影响。克格勃线性模型的四个关键驱动因素被用来深入了解 RES 生成对风险溢价演变的影响。该方法在德国电力市场的历史数据上进行了测试。2010-2014 年期间的结果显示，光伏发电份额对风险溢价的总体影响大于风电生产份额的影响，由于其可变性和不确定性，两者都增加了风险溢价。使用克格勃方法，市场参与者可以使用他们随时可用的信息来预测风险溢价。由于其可变性和不确定性，两者都增加了风险溢价。使用克格勃方法，市场参与者可以使用他们随时可用的信息来预测风险溢价。由于其可变性和不确定性，两者都增加了风险溢价。使用克格勃方法，市场参与者可以使用他们随时可用的信息来预测风险溢价。