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An Econometric Analysis of Large Flexible Cryptocurrency-mining Consumers in Electricity Markets

Subir Majumder, Ignacio Aravena, Le Xie

TL;DR

This paper tackles how large flexible cryptocurrency mining loads interact with Texas electricity markets by building an econometric AR-X model with SARIMA components. It uses a data-transformation workflow to address skewness and heteroskedasticity, and develops separate non-summer and summer models that incorporate temperature, price lags, peak-price signals, and 4CP-related factors. Key findings show that short-term mining consumption is not driven by cryptocurrency prices, but primarily by ambient temperature, electricity prices, and demand-response hedging, with strong model fit especially in summer ($R^2$ approaching 0.99). The resulting framework supports generating synthetic grid data for planning and policy, and offers a pathway to pricing mechanisms that leverage mining-load flexibility to enhance grid reliability in ERCOT and potentially other high-penetration regions.

Abstract

In recent years, power grids have seen a surge in large cryptocurrency mining firms, with individual consumption levels reaching 700MW. This study examines the behavior of these firms in Texas, focusing on how their consumption is influenced by cryptocurrency conversion rates, electricity prices, local weather, and other factors. We transform the skewed electricity consumption data of these firms, perform correlation analysis, and apply a seasonal autoregressive moving average model for analysis. Our findings reveal that, surprisingly, short-term mining electricity consumption is not directly correlated with cryptocurrency conversion rates. Instead, the primary influencers are the temperature and electricity prices. These firms also respond to avoid transmission and distribution network (T&D) charges - commonly referred to as four Coincident peak (4CP) charges - during the summer months. As the scale of these firms is likely to surge in future years, the developed electricity consumption model can be used to generate public, synthetic datasets to understand the overall impact on the power grid. The developed model could also lead to better pricing mechanisms to effectively use the flexibility of these resources towards improving power grid reliability.

An Econometric Analysis of Large Flexible Cryptocurrency-mining Consumers in Electricity Markets

TL;DR

This paper tackles how large flexible cryptocurrency mining loads interact with Texas electricity markets by building an econometric AR-X model with SARIMA components. It uses a data-transformation workflow to address skewness and heteroskedasticity, and develops separate non-summer and summer models that incorporate temperature, price lags, peak-price signals, and 4CP-related factors. Key findings show that short-term mining consumption is not driven by cryptocurrency prices, but primarily by ambient temperature, electricity prices, and demand-response hedging, with strong model fit especially in summer ( approaching 0.99). The resulting framework supports generating synthetic grid data for planning and policy, and offers a pathway to pricing mechanisms that leverage mining-load flexibility to enhance grid reliability in ERCOT and potentially other high-penetration regions.

Abstract

In recent years, power grids have seen a surge in large cryptocurrency mining firms, with individual consumption levels reaching 700MW. This study examines the behavior of these firms in Texas, focusing on how their consumption is influenced by cryptocurrency conversion rates, electricity prices, local weather, and other factors. We transform the skewed electricity consumption data of these firms, perform correlation analysis, and apply a seasonal autoregressive moving average model for analysis. Our findings reveal that, surprisingly, short-term mining electricity consumption is not directly correlated with cryptocurrency conversion rates. Instead, the primary influencers are the temperature and electricity prices. These firms also respond to avoid transmission and distribution network (T&D) charges - commonly referred to as four Coincident peak (4CP) charges - during the summer months. As the scale of these firms is likely to surge in future years, the developed electricity consumption model can be used to generate public, synthetic datasets to understand the overall impact on the power grid. The developed model could also lead to better pricing mechanisms to effectively use the flexibility of these resources towards improving power grid reliability.
Paper Structure (25 sections, 7 equations, 10 figures, 2 tables)

This paper contains 25 sections, 7 equations, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Exploratory Data Analysis
  3. Data Transformation
  4. Correlation Analysis
  5. Value of cryptocurrencies
  6. The cooling energy requirements
  7. Price responses
  8. The predictors contributing to 4CP responses
  9. Auto-regressive Model
  10. Empirical Observation of Cryptocurrency Mining Firms' Behavior
  11. Demand response model for the non-summer months
  12. Temperature effect
  13. Price effects
  14. Peak price effect
  15. Autoregressive component
  16. ...and 10 more sections

Figures (10)

  • Figure 1: Trend of large cryptocurrency mining loads in a typical ERCOT load zone.
  • Figure 2: Histogram of the various hourly datasets for Apr.-Oct. 2022.
  • Figure 3: Q-Q plots for transformed datasets.
  • Figure 4: Comparing RSI of Bitcoin and daily energy consumption of crypto-mining firms.
  • Figure 5: Correlation identifying how much of crypto-miners electricity consumption is responsible for cooling.
  • ...and 5 more figures