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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. [ABSTRACT FROM AUTHOR]

An important indicator of supply and demand uncertainty on electricity markets is risk premium (RP), an integral part of electricity price. The level of RP is describing the risk the market actors expect in the future due to the market uncertainties. With the electricity market price behavior rapidly changing, the rising market supply uncertainty is increasing the volatility of RP. To better understand this behavior, power market participants need new models that will efficiently use the information available in the processes driving the electricity price and RP and to explain the influence of RES uncertainties on the future RP and electricity prices. A decade ago, researchers investigating RP focused primarily on the uncertainties arising from consumption forecast and generation outages. RES share in generation mix was small and its influence on uncertainty was negligible, leading to much lower volatilities of RP than today. With the increasing influence of RES, typically exhibiting variability on a sub‐hourly level, traditional models using daily electricity price to calculate RP were becoming inadequate. In this dynamic period of electricity market transformation, this paper highlights the importance of RP signals to market actors. A stochastic method for RP calculation is discussed with the associated RP model, driven by the intraday dynamics. An example of the RP signal is presented on historical price data from the German electricity market, highlighting uncertainty pattern developed over the years. With such an approach, the market actors can adjust their trading strategies thus mitigating their market risk exposure. This article is categorized under:Energy Systems Economics > Economics and PolicyEnergy Efficiency > Economics and PolicyEnergy Systems Analysis > Economics and Policy [ABSTRACT FROM AUTHOR]