Abstract
A large part of the decision-making problems actors of the power system are facing on a daily basis requires scenarios for day-ahead electricity market prices. These scenarios are most likely to be generated based on marginal predictive densities for such prices, then enhanced with a temporal dependence structure. A semi-parametric methodology for generating such densities is presented: it includes: (i) a time-adaptive quantile regression model for the 5%–95% quantiles; and (ii) a description of the distribution tails with exponential distributions. The forecasting skill of the proposed model is compared to that of four benchmark approaches and the well-known the generalist autoregressive conditional heteroskedasticity (GARCH) model over a three-year evaluation period. While all benchmarks are outperformed in terms of forecasting skill overall, the superiority of the semi-parametric model over the GARCH model lies in the former’s ability to generate reliable quantile estimates.
Highlights
Probabilistic forecasts of the day-ahead wholesale price in the form of predictive densities are required to solve many of the challenges faced by participants in today’s electricity markets
The aim of this paper is to present a methodology for the density forecasting of day-ahead electricity prices that permits the generation of operational scenarios
Time-adaptive quantile regression is used to describe the density between the 5% and the 95% quantiles
Summary
Probabilistic forecasts of the day-ahead wholesale price in the form of predictive densities are required to solve many of the challenges faced by participants in today’s electricity markets. Scenarios for day-ahead prices are used in the pricing of hydropower in [3,4] and for the optimal bidding of other types of generation units in [5] This aside, foreseeable developments in the electricity sector, such as continuing growth of renewable energy technologies and the emergence of flexible demand, are likely to further boost the demand for such forecasts. The aim of this paper is to present a methodology for the density forecasting of day-ahead electricity prices that permits the generation of operational scenarios. First and foremost, such a model should produce reliable density forecasts of the untransformed prices. For the purpose of scenario generation, it is important that the forecasts properly describe the full density of the prices (instead of a set of quantiles only, as is sometimes the case for applications in risk management concerned with a single variable)
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