• Corpus ID: 253801707

Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution

@inproceedings{Hirsch2022SimulationbasedFF,
  title={Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution},
  author={Simon Hirsch and Florian Ziel},
  year={2022}
}

• Simon Hirsch, F. Ziel
• Published 23 November 2022
• Economics

: During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However, compared to day-ahead markets, the drivers for the intraday price process are still sparsely researched. In this paper, we propose a modelling strategy for the location, shape and scale parameters of the return distribution in intraday markets, based on fundamental variables. We consider wind and solar forecasts and their intraday updates, outages, price information and a novel measure for the shape of the merit-order, derived from spot auction curves as explanatory variables. We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market. The approach yields significant improvements in the forecasting performance, especially in the tails of the distribution. At the same time, we are able to derive the contribution of the driving variables. We find that, apart from the first lag of the price changes, none of our fundamental variables have explanatory power for the expected value of the intraday returns. This implies weak-form market efficiency as renewable forecast changes and outage information seems to be priced in by the market. We find that the volatility is driven by the merit-order regime, the time to delivery and the closure of cross-border order books. The tail of the distribution is mainly influenced by past price differences and trading activity. Our approach is directly transferable to other continuous intraday markets in Europe. This builds on the work of Narajewski and Ziel (2020a) to develop a simulation-based probabilistic forecasting model for the path of the five-minute-volume-weighted price P between 185 and 30 minutes before delivery. Instead of directly modelling the price P as it is common in day-ahead forecasting and is done in other forecasting studies on the intraday market (Janke and Steinke, 2019, Uniejewski et al., 2019), the first differences ∆ P will be modelled. The path of P is thus the cumulative sum of the initial price P 0 and all price differences in the forecasting period. Following the suggestions of Narajewski and we assume the first differences ∆ P follow a mixture distribution of the Dirac distribution δ 0 with an atom at 0 and a continuous distribution F . In Narajewski and Ziel (2020a), the latter is assumed to be t -distributed without any detailed justification, except the observation that ∆ P tends to be heavy tailed. This extends the approach in four dimensions: