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

TL;DR

This paper analyzes large cryptocurrency mining firms' electricity consumption in Texas, revealing key factors influencing their usage and proposing a model to aid grid management and pricing strategies.

Contribution

It introduces a novel econometric model for mining electricity consumption that accounts for weather, prices, and operational behaviors, aiding grid reliability and policy development.

Findings

Consumption is mainly influenced by temperature and electricity prices.

Mining firms respond to avoid T&D charges during summer.

The model can generate synthetic datasets for grid impact analysis.

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.