Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy Sources

Fridgen, Gilbert; Körner, Marc-Fabian; Walters, Steffen; Weibelzahl, Martin

doi:10.1007/s12599-021-00686-z

Cited by 17 publications

(6 citation statements)

References 68 publications

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“…An analysis of the integrated energy system combining a renewable source power plant and a computational data center has demonstrated that such an approach offers higher net present value than the standalone power plant solution. 51 However, the main difference between a conventional data center and bitcoin is the feasible interruptibility of the latter. Similar conclusions were reached for specific analyses of wind power in Brazil 52 and both wind and solar power in Texas.…”

Section: Renewable Energymentioning

confidence: 99%

“…Bitcoin mining and operations of the likes of Amazon Web Services, Google, or Netflix fit the bill. An analysis of the integrated energy system combining a renewable source power plant and a computational data center has demonstrated that such an approach offers higher net present value than the standalone power plant solution . However, the main difference between a conventional data center and bitcoin is the feasible interruptibility of the latter.…”

Section: Bitcoin In Greenhouse Gas Emissions and Renewable Energymentioning

confidence: 99%

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Renewable Energy Transition Facilitated by Bitcoin

Velický

2023

ACS Sustainable Chem. Eng.

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Reduction of greenhouse gas emissions has been a top priority for activists, scientists, and policy makers across the globe, and it is one of the main drivers for the transition to renewable energy generation. Bitcoin is a decentralized global transaction network of an eponymous digital currency. It has been praised for its openness, decentralization, and censorship resistance, as well as criticized for its inefficiency, criminal use, and enormous electricity consumption. We discuss the challenges in the renewable energy transition, properties of the bitcoin network, and the role of bitcoin mining operations in the global energy production and consumption. Although the adoption path for bitcoin is likely to be volatile with an uncertain outcome, the opportunities offered by bitcoin mining in reduction of the greenhouse gas emissions and renewable energy transition are greater than generally assumed.

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Section: Renewable Energymentioning

confidence: 99%

Section: Bitcoin In Greenhouse Gas Emissions and Renewable Energymentioning

confidence: 99%

Renewable Energy Transition Facilitated by Bitcoin

Velický

2023

ACS Sustainable Chem. Eng.

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“…Research stream on energy informatics and CO 2 emission tracing applications is widespread, ranging, among others, from topics concerning energy efficiency (Watson et al 2010), energy flexibility, e.g., provided by electric vehicles (Holly et al 2020) or its exploitation via artificial intelligence (Fridgen et al 2022;Hanny et al 2022), over data centers that are enabled to act in the sense of the energy transition (Klingert 2018;Fridgen et al 2021), to the potential of IS for future energy data spaces to improve collaboration and energy systems' robustness (Körner et al 2022). In 2014, Goebel et al provided an overview of goals, themes, and use cases of Energy Informatics research that distinguishes relevant topics between "smart energy-saving systems" and "smart grids" (Goebel et al 2014).…”

Section: Background and Related Workmentioning

confidence: 99%

Enabling end-to-end digital carbon emission tracing with shielded NFTs

Babel

Gramlich²,

Körner³

et al. 2022

Energy Inform

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In the energy transition, there is an urgent need for decreasing overall carbon emissions. Against this background, the purposeful and verifiable tracing of emissions in the energy system is a crucial key element for promoting the deep decarbonization towards a net zero emission economy with a market-based approach. Such an effective tracing system requires end-to-end information flows that link carbon sources and sinks while keeping end consumers’ and businesses’ sensitive data confidential. In this paper, we illustrate how non-fungible tokens with fractional ownership can help to enable such a system, and how zero-knowledge proofs can address the related privacy issues associated with the fine-granular recording of stakeholders’ emission data. Thus, we contribute to designing a carbon emission tracing system that satisfies verifiability, distinguishability, fractional ownership, and privacy requirements. We implement a proof-of-concept for our approach and discuss its advantages compared to alternative centralized or decentralized architectures that have been proposed in the past. Based on a technical, data privacy, and economic analysis, we conclude that our approach is a more suitable technical backbone for end-to-end digital carbon emission tracing than previously suggested solutions.

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“…Fridgen et al [29], like many other researchers of the past few years, concentrate on the massive amount of energy consumed by two intensive and widespread computing processes: machine learning and cryptocurrency mining on systems, such as Bitcoin (the most widely traded and mined cryptocurrency, the precursor to the current age of blockchain-based applications [30]). Based on the findings of Fridgen et al, there is some hope though that the existence of these high-cost systems would drive further investment into renewable energy sources.…”

Section: The Environmental Impact Of Computingmentioning

confidence: 99%

A System for Sustainable Usage of Computing Resources Leveraging Deep Learning Predictions

Cioca

Schuszter

2022

Applied Sciences

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In this paper, we present the benefit of using deep learning time-series analysis techniques in order to reduce computing resource usage, with the final goal of having greener and more sustainable data centers. Modern enterprises and agile ways-of-working have led to a complete revolution of the way that software engineers develop and deploy software, with the proliferation of container-based technology, such as Kubernetes and Docker. Modern systems tend to use up a large amount of resources, even when idle, and intelligent scaling is one of the methods that could be used to prevent waste. We have developed a system for predicting and influencing computer resource usage based on historical data of real production software systems at CERN, allowing us to scale down the number of machines or containers running a certain service during periods that have been identified as idle. The system leverages recurring neural network models in order to accurately predict the future usage of a software system given its past activity. Using the data obtained from conducting several experiments with the forecasted data, we present the potential reductions on the carbon footprint of these computing services, from the perspective of CPU usage. The results show significant improvements to the computing power usage of the service (60% to 80%) as opposed to just keeping machines running or using simple heuristics that do not look too far into the past.

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Not All Doom and Gloom: How Energy-Intensive and Temporally Flexible Data Center Applications May Actually Promote Renewable Energy Sources

Cited by 17 publications

References 68 publications

Renewable Energy Transition Facilitated by Bitcoin

Renewable Energy Transition Facilitated by Bitcoin

Enabling end-to-end digital carbon emission tracing with shielded NFTs

A System for Sustainable Usage of Computing Resources Leveraging Deep Learning Predictions

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