The increasing exposure to renewable energy has amplified the need for risk management in electricity markets. Electricity price risk poses a major challenge to market participants. We propose an approach to model and fore- cast electricity prices taking into account information on renewable energy production. While most literature focuses on point forecasting, our method- ology forecasts the whole distribution of electricity prices and incorporates spike risk, which is of great value for risk management. It is based on func- tional principal component analysis and time-adaptive nonparametric density estimation techniques. The methodology is applied to electricity market data from Germany. We find that renewable infeed effect both, the location and the shape of spot price densities. A comparison with benchmark methods and an application to risk management are provided.
electricity prices residual load probabilistic forecasting value at risk expected shortfall functional data analysis