Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects

TL;DR

This paper introduces a multivariate probabilistic forecasting model for intraday power markets that captures cross-product dependencies using copulas, improving forecast accuracy and market understanding.

Contribution

It develops a novel high-dimensional dependence modeling approach with time-varying parameters, filling a gap in univariate-focused prior research.

Findings

Dependence modeling enhances forecast accuracy

Market coupling impacts price distribution and trading activity

The approach is applicable to other European markets

Abstract

Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabilistic price forecasts. However, research to date has focused on univariate approaches, while in many European intraday electricity markets all delivery periods are traded in parallel. Thus, the dependency structure between different traded products and the corresponding cross-product effects cannot be ignored. We aim to fill this gap in the literature by using copulas to model the high-dimensional intraday price return vector. We model the marginal distribution as a zero-inflated Johnson's $S_U$ distribution with location, scale and shape parameters that depend on market and fundamental data. The dependence structure is modelled using latent beta regression to account for the particular market structure of the intraday electricity market, such as overlapping but independent trading sessions for different delivery days. We allow the dependence parameter to be time-varying. We validate our approach in a simulation study for the German intraday electricity market and find that modelling the dependence structure improves the forecasting performance. Additionally, we shed light on the impact of the single intraday coupling (SIDC) on the trading activity and price distribution and interpret our results in light of the market efficiency hypothesis. The approach is directly applicable to other European electricity markets.