Trading Electrons: Predicting DART Spread Spikes in ISO Electricity Markets

ArXiv ID: 2601.05085 “View on arXiv”

Authors: Emma Hubert, Dimitrios Lolas, Ronnie Sircar

Abstract

We study the problem of forecasting and optimally trading day-ahead versus real-time (DART) price spreads in U.S. wholesale electricity markets. Building on the framework of Galarneau-Vincent et al., we extend spike prediction from a single zone to a multi-zone setting and treat both positive and negative DART spikes within a unified statistical model. To translate directional signals into economically meaningful positions, we develop a structural and market-consistent price impact model based on day-ahead bid stacks. This yields closed-form expressions for the optimal vector of zonal INC/DEC quantities, capturing asymmetric buy/sell impacts and cross-zone congestion effects. When applied to NYISO, the resulting impact-aware strategy significantly improves the risk-return profile relative to unit-size trading and highlights substantial heterogeneity across markets and seasons.

Keywords: Electricity Markets, Price Spikes, Price Impact Modeling, Bid Stacks, Market Clearing

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail
  • Why: The paper employs advanced statistical modeling (multi-zone spike forecasting, structural price impact models) and derives closed-form optimal position solutions, indicating high math complexity. It is heavily data-driven with out-of-sample validation across multiple ISO markets (NYISO, ISO-NE, ERCOT) over several years, demonstrating high empirical rigor and backtest readiness.
  flowchart TD
    A["Research Goal: Forecast & trade DART price spreads<br>Optimize zonal positions in multi-zone markets"] --> B["Data Inputs: NYISO day-ahead & real-time prices<br>Market bid stacks, zonal correlations"]
    B --> C["Methodology: Unified statistical model<br>Positive/Negative spike prediction across zones"]
    C --> D["Computational Process: Structural price impact model<br>Closed-form optimal INC/DEC quantities"]
    D --> E["Execution: Impact-aware trading strategy<br>Day-ahead commitment vs Real-time adjustment"]
    E --> F["Key Findings: Significantly improved risk-return profile<br>vs unit-size trading; Market/seasonal heterogeneity"]