High Resolution Modeling and Analysis of Cryptocurrency Mining's Impact
on Power Grids: Carbon Footprint, Reliability, and Electricity Price
- URL: http://arxiv.org/abs/2212.14189v2
- Date: Fri, 14 Apr 2023 07:05:54 GMT
- Title: High Resolution Modeling and Analysis of Cryptocurrency Mining's Impact
on Power Grids: Carbon Footprint, Reliability, and Electricity Price
- Authors: Ali Menati, Xiangtian Zheng, Kiyeob Lee, Ranyu Shi, Pengwei Du, Chanan
Singh, Le Xie
- Abstract summary: This paper investigates the tri-factor impact of such large loads on carbon footprint, grid reliability, and electricity market price in the Texas grid.
We show that the flexibility of mining loads can significantly mitigate power shortages and market disruptions.
- Score: 2.285928372124628
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Blockchain technologies are considered one of the most disruptive innovations
of the last decade, enabling secure decentralized trust-building. However, in
recent years, with the rapid increase in the energy consumption of
blockchain-based computations for cryptocurrency mining, there have been
growing concerns about their sustainable operation in electric grids. This
paper investigates the tri-factor impact of such large loads on carbon
footprint, grid reliability, and electricity market price in the Texas grid. We
release open-source high-resolution data to enable high-resolution modeling of
influencing factors such as location and flexibility. We reveal that the
per-megawatt-hour carbon footprint of cryptocurrency mining loads across
locations can vary by as much as 50% of the crude system average estimate. We
show that the flexibility of mining loads can significantly mitigate power
shortages and market disruptions that can result from the deployment of mining
loads. These findings suggest policymakers to facilitate the participation of
large mining facilities in wholesale markets and require them to provide
mandatory demand response.
Related papers
- Distributed Energy Management and Demand Response in Smart Grids: A
Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems.
In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
arXiv Detail & Related papers (2022-11-29T01:18:58Z) - Blockchain based Secure Energy Marketplace Scheme to Motivate Peer to Peer Microgrids [2.1074825621539617]
This paper proposes a scheme as a marketplace where users interact with each other to buy and sell energy at better rates.
Agreement between owner of resources and consumer is recorded on blockchain based smart contracts.
This paper also proposes an extra layer of security to leverage a shielded execution environment so that information of energy generated, utilized, and shared cannot be changed by consumers and third parties even if the system is compromised.
arXiv Detail & Related papers (2022-06-15T02:20:40Z) - Does Crypto Kill? Relationship between Electricity Consumption Carbon
Footprints and Bitcoin Transactions [4.7805617044617446]
We predict that the carbon footprint of cryptocurrency transactions has a higher dependency on carbon-rich fuel sources than green or renewable fuel sources.
We provide a machine learning framework to model such transactions and correlate them with the electricity generation patterns to estimate and analyze their carbon cost.
arXiv Detail & Related papers (2022-05-16T18:03:45Z) - Applications of blockchain and artificial intelligence technologies for
enabling prosumers in smart grids: A review [12.609078866334615]
Governments' net zero emission target aims at increasing the share of renewable energy sources.
This paper addresses how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets.
arXiv Detail & Related papers (2022-02-21T10:27:31Z) - Modelling the transition to a low-carbon energy supply [91.3755431537592]
A transition to a low-carbon electricity supply is crucial to limit the impacts of climate change.
Reducing carbon emissions could help prevent the world from reaching a tipping point, where runaway emissions are likely.
Runaway emissions could lead to extremes in weather conditions around the world.
arXiv Detail & Related papers (2021-09-25T12:37:05Z) - The impact of online machine-learning methods on long-term investment
decisions and generator utilization in electricity markets [69.68068088508505]
We investigate the impact of eleven offline and five online learning algorithms to predict the electricity demand profile over the next 24h.
We show we can reduce the mean absolute error by 30% using an online algorithm when compared to the best offline algorithm.
We also show that large errors in prediction accuracy have a disproportionate error on investments made over a 17-year time frame.
arXiv Detail & Related papers (2021-03-07T11:28:54Z) - Exploring market power using deep reinforcement learning for intelligent
bidding strategies [69.3939291118954]
We find that capacity has an impact on the average electricity price in a single year.
The value of $sim$25% and $sim$11% may vary between market structures and countries.
We observe that the use of a market cap of approximately double the average market price has the effect of significantly decreasing this effect and maintaining a competitive market.
arXiv Detail & Related papers (2020-11-08T21:07:42Z) - A Multi-Agent Deep Reinforcement Learning Approach for a Distributed
Energy Marketplace in Smart Grids [58.666456917115056]
This paper presents a Reinforcement Learning based energy market for a prosumer dominated microgrid.
The proposed market model facilitates a real-time and demanddependent dynamic pricing environment, which reduces grid costs and improves the economic benefits for prosumers.
arXiv Detail & Related papers (2020-09-23T02:17:51Z) - Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A
Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
We study a risk-aware energy scheduling problem for a microgrid-powered MEC network.
We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
arXiv Detail & Related papers (2020-02-21T02:14:38Z) - Toward Low-Cost and Stable Blockchain Networks [10.790006312359795]
We propose a blockchain mining resources allocation algorithm to reduce the mining cost in PoW-based (proof-of-work-based) blockchain networks.
arXiv Detail & Related papers (2020-02-19T06:42:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.