Exploring the Impact of Bitcoin Mining on Power Grids

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Texas is home to the largest cryptocurrency mining facility in North America, and all the mining facilities in the state combined account for around 15% of mining worldwide. To mine cryptocurrency, computers must run computation algorithms to verify transactions. Those who solve the algorithm get a reward in the form of cryptocurrency such as Bitcoin, and the more calculations the computer can solve, the higher the chance of getting the reward.

This energy-intensive process has led to the scale of computing demands rivaling the consumption of a city’s worth of electricity. The Texas Comptroller estimates that by 2023, the cryptocurrency mining facilities in the state could demand as much power as Houston, the fourth-largest city in the US. Already, the mining facilities in the state are consuming roughly as much energy as Austin.

Despite the amount of energy these operations require, Texas political leaders have promoted the state as a destination for mining companies, with some rural areas welcoming them and reaping economic benefits. Professor Le Xie from the Department of Electrical and Computer Engineering at Texas A&M University has been studying the impacts these mining facilities have on the Texas grid. His research has centered on examining the impact on grid reliability, carbon dioxide emissions, and wholesale energy market prices.

Xie’s findings were published in the March issue of the Institute of Electrical and Electronics Engineers Transactions on Energy Markets, Policy and Regulation and the June issue of Advances in Applied Energy. He said that if the facilities are modeled as a constant demand, they will have a substantial impact on grid reliability, but if they are flexible and can be turned off in times of grid precarity, they can provide more energy to the Texas grid.

Cryptocurrency and crypto mining is still a relatively new industry, with Bitcoin only around since 2009. It is important to carefully study and analyze any demand that goes beyond certain thresholds, so that decision-makers can have better decision support.

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