Energy and locational workload management in data centers

Mitigating Curtailment and Carbon Emissions through Load Migration between Data Centers

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

As part of the Net Zero Carbon Water Cycle Program (NZCWCP) for Victoria state in Australia, we have sought to understand the potential to reduce household energy consumption and related Greenhouse Gas (GHG) emissions by influencing water use. Digital metering data disaggregated into 57 million discrete water usage events across 105 households at a resolution of 10 millilitres at 10 second intervals from June 2017 to March 2020, from a previous Yarra Valley Water (Melbourne, Australia) study, was analysed, together with the dynamic relationship between the multiple energy sources (natural gas, grid electricity, solar) used to heat water for showers in each hour of the day. Water-related energy (WRE) use, including water desalination and treatment, pumping, heating, wastewater collection and treatment, comprised 12.6% of Australia’s primary energy use in 2019. Water heating (by natural gas and electricity) comprised the largest component of WRE use for across residential, commercial, and industrial sectors. Furthermore, 69% of Victoria’s total water usage was by residential customers in 2020-2021. WRE GHG emissions were around 3.8% of Victoria’s total GHG emissions in 2018. Showers (~50% of residential WRE), system losses (~27% of residential WRE), and clothes washers (~9% of residential WRE) are the three largest components of WRE consumption. The main objective of this work is the creation of industry-accessible tools to improve knowledge and management options from the understanding of reductions in cost and GHG emissions from household showering WRE use. Potential options considered, to reduce water and energy use, as well as associated GHG emissions and customer utility bills, include (a) behaviour management such as water and energy pricing to change time of use behaviours, and (b) the adoption of efficient shower head improvements. Shower WRE and GHG emissions were found able to be strongly impacted by small changes in daily routines. GHG emissions reduction from showering could be reduced up to 20 (in summer) - 22% (in winter) by shifting demand time of showering or replacing residential showerheads. Extrapolated to state and Australian scales, reductions in water usage could be up to 14 GL (Victoria) and 144 GL (Australia), and reductions in GHG emissions 1,600 ktCO2eq (Victoria) and 17,300 ktCO2eq (Australia). It provides fundamental new information which could inform a suite of new management options to impact water-related energy from showers, and related GHG emissions and customer water and energy cost.

In the current decade of rapid expansion of ubiquitous data storage and cloud computing services, the demand for data center services has seen an enormous increase which is resulting in a continuously rising pressure on the environment in terms of energy consumption and greenhouse gas (GHG) emissions. The recently started project, All4Green, explores potential ICT solutions for collaboration amongst data centers, energy providers, and end-users in order to enable energy providers to save CO2 emissions at the very source of energy conversion. This paper presents an overview of objectives and concepts of the research, discussing the so-called data centers' eco-system, the technical approach to collaboration and GreenSLAs as economic incentives.

Greenhouse gas (GHG) emissions reduction in the electricity sector: Implications of increasing renewable energy penetration in Ghana's electricity generation mix

5.16 Energy Management in Data Centers

As the volume of data grows rapidly, storing big data in a single data center is no longer feasible. Hence, companies have developed two scenarios to store their big data in multiple data centers. In the first scenario, the company's big data are distributed in multiple data centers without data replication. In the second scenario, data are also stored in multiple data centers but important data are replicated in these data centers to increase data safety and availability. However, in these scenarios, analyzing big data distributed in multiple data centers becomes a challenging task. In this paper, we propose two data distribution strategies to support big data analysis across geo-distributed data centers. In these strategies, we use the recent Random Sample Partition data model to convert big data into sets of random sample data blocks and distribute these data blocks into multiple data centers either without replication or with replication. In analyzing big data in multiple data centers without replication, we randomly select samples of data blocks from multiple data centers and download the sample data blocks to one data center for analysis. In the second strategy with replication of data blocks, we can analyze big data on any data center by randomly selecting a sample of data blocks replicated from other data centers. This strategy avoids data transformation between data centers. We demonstrate the performance of the two strategies in big data analysis by using simulation results produced on one local data center and four AWS data centers in North America, Asia, and Australia.

Consideration of marginal electricity in real-time minimization of distributed data centre emissions

For all its cachet, you might think that hybrid drivetrain technology is inherently green. But only 13 of 34 hybrid vehicles assessed achieve better than a 25% reduction in greenhouse gas (GHG) emissions, and just 3 exceed a 40% reduction, according to an evaluation by the Union of Concerned Scientists (UCS).1 Moreover, reductions in GHG emissions do not necessarily correlate with reductions in other toxic emissions. Like any engine output–improving technology, hybrid technology can boost both fuel efficiency and power—but the more you boost one, the less you can boost the other. That dichotomy spurred the UCS to develop its “hybrid scorecard,” which rates each hybrid according to how well it lives up to its promise of reducing air pollution.2 All the vehicles were from model year 2011 except for one, the 2012 Infiniti M Hybrid. First the UCS scored each hybrid on how much it reduced its GHG emissions relative to its conventional counterpart, on a scale of zero (least reduction) to 10 (greatest reduction). These scores reflect the percentage in fuel efficiency gain. For example, the Toyota Prius gets 50 mpg3 compared with 28 mpg for the comparable Toyota Matrix. This represents a 44.0% reduction in GHG emissions, earning the Prius a GHG score of 9.4. At the bottom of the scale, the 21-mpg hybrid VW Touareg reduces GHG emissions only 10% over the 19-mpg conventional Toureg, for a score of 0.0. With a 46% improvement, the luxury Lincoln MKZ Hybrid had the greatest reduction over its conventional counterpart. The UCS also scored hybrids for absolute emissions (rather than relative to the conventional model) of air pollutants including particulate matter, carbon monoxide, hydrocarbons, and nitrogen oxides. These scores, on a scale of zero (dirtiest) to 10 (cleanest), are based on California certifications for tailpipe emissions. As the scorecard showed, a vehicle that emits less heat-trapping gases may not necessarily emit less of other air pollutants. For example, the Mercedes Benz S400 Hybrid scored 9 on air pollution reduction, alongside the Prius and the Lincoln MKZ, but only 1.3 on GHG emissions. HYBRID SCORECARD: Top 10 Nonluxury Hybrids by Total Environmental Improvement Score “Hybrid technology doesn’t add additional challenges [to reducing exhaust pollutants] that can’t be addressed through design of the vehicle’s emission controls,” says Don Anair, senior vehicles analyst at the UCS. “Numerous manufacturers of hybrids are meeting the lowest emissions levels. Hybrid manufacturers who aren’t delivering the lowest smog-forming emissions have chosen not to do so.” Each vehicle’s air pollution and GHG scores were averaged into a total “environmental improvement score,” again with the MKZ and the Prius leading the pack, and the Touareg scraping bottom. The UCS also scored “hybrid value” (the cost of reducing GHG emissions in dollars per percent reduction) and “forced features” (options you must buy with the hybrid whether you want them or not). HYBRID SCORECARD: Top 10 Luxury Hybrids by Total Environmental Improvement Score Luke Tonachel, vehicles analyst with the Natural Resources Defense Council, compliments the scorecard for illustrating that hybrid technology is not automatically green. He says, “We should improve the efficiency of all vehicles, and [hybrid technology] is just one technology that can get us there if applied with that goal in mind.” Nonetheless, Jamie Kitman, the New York bureau chief for Automobile Magazine, questions the wisdom of emphasizing percentage improvement in gas mileage rather than absolute miles per gallon. At 21 mpg, the hybrid Cadillac Escalade 4WD represents a 29% improvement over the 15-mpg conventional model, saving nearly 2 gallons per 100 miles. But the hybrid Escalade is still a gas guzzler, and Kitman says he wishes people would see through the marketing that encourages them to buy SUVs and “crossovers” rather than ordinary cars, which are more efficient than either. Says Anair, “The scorecard shows that automakers can pair hybrid technology with advanced emission controls to help tackle climate change while reducing the health impacts from breathing polluted air.” However, he adds, alluding to the stark variation in how much hybrid technology boosted fuel efficiency, “Not all automakers are delivering on the full promise of this technology.”

In this paper, we used the life-cycle analysis (LCA) method to evaluate the energy consumption and greenhouse gas (GHG) emissions of natural gas (NG) distributed generation (DG) projects in China. We took the China Resources Snow Breweries (CRSB) NG DG project in Sichuan province of China as a base scenario and compared its life cycle energy consumption and GHG emissions performance against five further scenarios. We found the CRSB DG project (all energy input is NG) can reduce GHG emissions by 22%, but increase energy consumption by 12% relative to the scenario, using coal combined with grid electricity as an energy input. The LCA also indicated that the CRSB project can save 24% of energy and reduce GHG emissions by 48% relative to the all-coal scenario. The studied NG-based DG project presents major GHG emissions reduction advantages over the traditional centralized energy system. Moreover, this reduction of energy consumption and GHG emissions can be expanded if the extra electricity from the DG project can be supplied to the public grid. The action of combining renewable energy into the NG DG system can also strengthen the dual merit of energy conservation and GHG emissions reduction. The marginal CO2 abatement cost of the studied project is about 51 USD/ton CO2 equivalent, which is relatively low. Policymakers are recommended to support NG DG technology development and application in China and globally to boost NG utilization and control GHG emissions.

With the development of cloud computing and 5G communication, the power load of data centers has increased rapidly. While bringing a huge burden on the environment, high electricity bills also put heavy economic pressure on cloud service providers. In order to improve the economic efficiency of enterprises, data centers need energy management. This paper proposes a real-time energy management method based on Model Predictive Control (MPC) for large-scale data centers powered by renewable energy. In this work, the energy consumption model integrates renewable energy, dynamic electricity prices, battery, TES tank, delayed execution of batch jobs, etc. In addition, Monte Carlo simulation is used to deal with the uncertainty of renewable energy output, workload and electricity prices. The proposed MPC algorithm can use predictive data and forecast error distribution to perform real-time energy management on data centers. Finally, case study demonstrates the effectiveness of the proposed MPC algorithm in real-time energy management of data centers.

With the increase of cloud computing and internet services, data centers are emerging to satisfy the requirement, leading to incremental energy consumption demand and emissions of green house gases. Thus, integration of renewable energies with the traditional power grid is preferred to reduce the environmental impact and increase energy efficiency, which lead to a demand of energy management strategies to coordinate the energy demand and generation. In this paper, we review the challenges for the sustainable data centers in smart grids with regards to energy management strategies, integration with renewable energies and cyber-attacks and propose possible solutions. Through the analysis of the data centers from the perspective of both smart grids level and micro-grid level, the research challenges and potential research directions in the energy management of sustainable data centers have been discussed.

In this new era of technology the overall energy cost and service time for hosting and delivering the resources present in various data centers over the internet has abridged all due to Cloud Computing. As the Cloud Computing is giving tremendous support to computations, storages and communication, the energy efficiency in cloud computing has become the premier for the furtherance of information and technology (ICT). The emission of green house gases is too detrimental for all living beings because they are posing threat to our biodiversity. In general, Cloud Computing can be congenitally energy efficient technology for ICT which abates the all- embracing energy consumption in the resources related to data centers. Cloud Computing can be boon as an energy efficient technology for ICT on the condition that energy savings strategy has till now pivoted on various aspects related to system's hardware and networking scenario. A new model is proposed for energy -efficiency of cloud computing that helps to keep track on how much level of degradation of atmosphere has occurred by the emissions of various green house gases by various huge data centers and how our computing can become eco- friendly. This paper even identifies the various key challenges that arise when such energy-saving techniques are extended for use in cloud computing techniques.

Health Care and Climate Change: Challenges and Pathways to Sustainable Health Care.