Paper: arXiv 2611.10731

Authors: Lion Hirth

Abstract

It is widely held that wind and solar power increase the volatility of electricity prices. This paper argues that the literature conflates two distinct types of price variation: across delivery time and across trading time. Determined simultaneously in the same day-ahead auction, prices for power to be delivered in distinct quarter-hours differ. This price dispersion, which I call “variability”, is rooted in non-storability. After that auction, trading continues in intraday markets until imbalance settlement. The price for the same delivery period changes as new information arises, e.g., weather forecasts. Such price “volatility” is ultimately caused by uncertainty. This paper develops a simple algebraic model of day-ahead and imbalance markets that provides a formal definition of variability and volatility, allows for a decomposition and derives the fundamental drivers, uncertainty and non-storability. It then provides empirical data on price variability and volatility for the past 25 years in Germany, during which the share of wind and solar energy increased from zero to nearly 50 % of generation. Volatility is measured with imbalance prices, intraday price indices and intraday transaction data. Variability followed a U-shaped trajectory, falling from the 2000s to the 2010s but increasing since then. Volatility, in contrast, declined over time. Across 24 European bidding zones, variability has risen since the late 2010s in almost all zones and increases with the share of solar power.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel conceptual distinction between price variability and volatility, supported by a simple algebraic model. It then rigorously tests these concepts using extensive empirical data from the German electricity market over 25 years, demonstrating strong statistical methodology and data validation.

Research Flowchart

  flowchart TD
    A[Research Goal: Have wind and solar increased electricity price volatility? And what is price volatility?] --> B{Key Distinction: Price Variability vs. Volatility};
    B --> C[Methodology: Algebraic model of day-ahead & imbalance markets];
    C --> D[Data: 25 years German electricity prices (day-ahead, imbalance, intraday), European bidding zone data];
    D --> E[Analysis: Decomposition of variability & volatility; Empirical trends over time and across zones];
    E --> F[Finding 1: Variability (across delivery times) followed U-shaped trajectory];
    E --> G[Finding 2: Volatility (across trading times for same delivery) declined over time];
    E --> H[Finding 3: Variability risen since late 2010s in EU zones, increases with solar share];