A data-driven merit order: Learning a fundamental electricity price model

TL;DR

This paper introduces a novel data-driven merit order model for electricity prices that combines fundamental market insights with data learning, improving forecast accuracy while maintaining interpretability.

Contribution

The paper presents a new integrated model that estimates key parameters from data, extending classical merit order models with additional market features.

Findings

Outperforms traditional fundamental and machine learning models in accuracy.

Retains interpretability of market mechanisms.

Enhances forecasting with extensions like hydro power and cross-border flows.

Abstract

Electricity price forecasting approaches generally fall into two categories: data-driven models, which learn from historical patterns, or fundamental models, which simulate market mechanisms. We propose a novel and highly efficient data-driven merit order model that integrates both paradigms. The model embeds the classical expert-based merit order as a nested special case, allowing all key parameters, such as plant efficiencies, bidding behavior, and available capacities, to be estimated directly from historical data, rather than assumed. We further enhance the model with critical embedded extensions such as hydro power, cross-border flows and corrections for underreported capacities, which considerably improve forecasting accuracy. Applied to the German day-ahead market, our model outperforms both classic fundamental and state-of-the-art machine learning models. It retains the interpretability of fundamental models, offering insights into marginal technologies, fuel switches, and dispatch patterns, elements which are typically inaccessible to black-box machine learning approaches. This transparency and high computational efficiency make it a promising new direction for electricity price modeling.