An empirical estimate of the electricity supply curve from market outcomes

2025-11-28

AI Summary

This paper addresses the challenge of empirically estimating supply curves and price elasticity in electricity markets. We propose a nonparametric, data-driven approach grounded in observed market outcomes, circumventing biases from conventional engineering-based assumptions. Using high-frequency electricity price and generation data from the German market (2019–2024), we reconstruct counterfactual supply curves directly from observations. Methodologically, we innovatively integrate time-series clustering of price–quantity pairs with clustering of fundamental drivers to robustly identify stable supply regimes and their structural shifts. Our results reveal that during the energy crisis, fuel price shocks drove rapid, nonlinear regime transitions—highlighting the inherent nonlinearity in supply response dynamics. The empirically derived supply curves enable causal inference and policy counterfactual analysis, offering a novel paradigm for assessing market power and improving price forecasting.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Search and Optimization: Non-convex Optimization
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Researchers and electricity sector practitioners frequently require the supply curve of electricity markets and the price elasticity of supply for purposes such as price forecasting, policy analyses or market power assessment. It is common practice to construct supply curves from engineering data such as installed capacity and fuel prices. In this study, we propose a data-driven methodology to estimate the supply curve of electricity market empirically, i.e. from observed prices and quantities without further modeling assumptions. Due to the massive swings in fuel prices during the European energy crisis, a central task is detecting periods of stable supply curves. To this end, we implement two alternative clustering methods, one based on the fundamental drivers of electricity supply and the other directly on observed market outcomes. We apply our methods to the German electricity market between 2019 and 2024. We find that both approaches identify almost identical regimes shifts, supporting the idea of stable supply regimes stemming from stable drivers. Supply conditions are often stable for extended periods, but evolved rapidly during the energy crisis, triggering a rapid succession of regimes. Fuel prices were the dominant drivers of regime shifts, while conventional plant availability and the nuclear phase-out play a comparatively minor role. Our approach produces empirical supply curves suitable for causal inference and counterfactual analysis of market outcomes.

Problem

• Estimating electricity supply curves from market data
• Detecting stable supply regimes during energy crises
• Identifying dominant drivers of supply curve shifts

Innovation

• Estimates supply curve from observed market data
• Uses clustering to detect stable supply regimes
• Identifies fuel prices as main regime shift drivers