Data Centres, AI, and Electrification — Legal and Corporate Approaches to Growing Power Demands in Canada

Energy management and reduction is one of the major challenges in the Cloud industry's data centres. The Uptime Institute's 2012 report, the industry's leading authority, highlighted the crisis in data centre energy management. 30% of data centres will run out of power, space and cooling in 2012. On average there's a 90% energy overhead in data centre operations, yet 55% of servers use less than 10% of their capacity. The 500,000 data centres worldwide have a server electricity consumption of 400 Billion kWh/year (approx $40 Billion ) which is growing at 12% CAGR, yet only 11% of the centres monitor their consumption. The global CO2 emissions of these centres, is equivalent to the airline industry, 2% of global emissions. It is expected that by 2020 20% of Europe's electrical energy will be data centre related. Data centre cooling requirements also have an enormous hydro footprint. A 1 MW data centre in a U.S-type climate has an annual hydro footprint of 99 million litres/annum Yet studies have shown that operational energy savings of 40% and substantial reductions in physical space, cooling requirements and water consumption can be achieved, where comprehensive and effective energy monitoring and management systems are employed. These can deliver a ROI within months, and further savings which extend over the centre's lifetime. The technologies which have the potential to deliver these benefits, DCIM (Data Centre Infrastructure Management) systems have only emerged in the last 3 to 4 years and are still in their infancy. Not surprisingly, a new global industry providing innovative, green, sustainable data centre technologies will be worth €45 Billion by 2016. The main barriers to effective monitoring/management is instrumentation and training. Vast volumes of data are gathered and must be competently analysed. Most monitoring systems, involve some form of physical metering and cabling and downtime, a major challenge when 10's thousands of servers are involved. Furthermore, all approaches physical and hybrid approaches limit the level of monitoring visibility to the server, individual processes cannot be monitored. A spin-out company from University College Dublin Ireland, Stratergia, has developed a unique data centre energy management system PAPILLON. It is totally software-based, measuring every server's energy consumption in real-time. It is intuitive to use, operates on any platform and can be installed in hours without any downtime or retrofitting. It uses a client-server type architecture. Software agents on servers communicate periodically, to a master server which has power models for each server type. Using the power models, the master computes in real-time and saves in a data base, all relevant energy information of the centre. In operation, Papillon identifies and quantifies energy saving actions that can be implemented, and coupled with electricity network data, more sustainable energy sources can be introduced into the power input of the centre. Papillon has been trialled at Swedish Compare Testlab and is one of the core tools being installed at state-of-the-art data centres at Kajaani, Finland and Cenit, Spain for a new international EU-funded Masters programme in green, sustainable data centre management.

The data center in this study had a total floor area of 8,580 square feet (ft{sup 2}) with one-foot raised-floors. It was a rack lab with 440 racks, and was located in a 208,240 ft{sup 2} multi-story office building in San Jose, California. Since the data center was used only for testing equipment, it was not configured as a critical facility in terms of electrical and cooling supply. It did not have a dedicated chiller system but served by the main building chiller plant and make-up air system. Additionally, it was served by a single electrical supply with no provision for backup power. The data center operated on a 24 hour per day, year-round cycle, and users had all hour full access to the data center facility. The study found that data center computer load accounted for 23% of the overall building electrical load, while the total power consumption attributable to the data center including allocated cooling load and lighting was 30% of the total facility load. The density of installed computer loads (rack load) in the data center was 63 W/ft{sup 2}. Power consumption density for all data center allocated load (including cooling and lighting) was 84 W/ft{sup 2}, approximately 12 times the average overall power density in rest of the building (non-data center portion). For the data center, 75% of the overall electric power was the rack critical loads, 11% of the power was consumed by chillers, 9% by CRAH units, 1% by lighting system, and about 4% of the power was consumed by pumps. The ratio of HVAC to IT power demand in the data center in this study was approximately 0.32. General recommendations for improving overall data center energy efficiency include improving the lighting control, airflow optimization, and control of mechanical systems serving the data center in actual operation. This includes chilled water system, airflow management and control in data centers. Additional specific recommendations or considerations to improve energy efficiency are provided in this report.

SummaryThe environmental impacts of data centers that provide information and communication technologies (ICTs) services are strongly related to electricity generation. With the increasing use of ICT, many data centers are expected to be built, causing more absolute impacts on the environment. Given that electricity distribution networks are very complex and dynamic systems, an environmental evaluation of future data centers is uncertain. This study proposes a new approach to investigate the consequences of future data center deployment in Canada and optimize this deployment based on the Energy 2020 technoeconomic model in combination with life cycle assessment methodology. The method determines specific electricity sources that will power the future Canadian data centers and computes related environmental impacts based on several indicators. In case‐study scenarios, the largest deployment of data centers leads to the smallest impact per megawatt of data centers for all of the environmental indicators. It is found that an increase in power demand by data centers would lead to a reduction in electricity exports to the United States, driving the United States to generate more electricity to meet its energy demand. Given that electricity generation in the United States is more polluting than in Canada, the deployment of data centers in Canada is indirectly linked to an increase in overall environmental impacts. However, though an optimal solution should be found to mitigate global greenhouse gas emissions, it is not clear whether the environmental burden related to U.S. electricity generation should be attributed to the Canadian data centers.

Distribution grid electrical performance and emission analysis of combined cooling, heating and power (CCHP)-photovoltaic (PV)-based data center and residential customers

As a global community our use of data is increasing exponentially with emerging technologies such as artificial intelligence (AI), leading to a vast increase in the energy demand for data centres worldwide. Delivering this increased energy demand is a global challenge, which the rapid growth of renewable generation deployment could solve. For many data centre giants such as Google, Amazon, and Microsoft this has been the solution to date via power purchase agreements (PPAs). However, insufficient investment in grid infrastructure globally has both renewable generation developers and data centre developers facing challenges to connect to the grid. This paper considers the costs and carbon emissions associated with stand-alone hybrid renewable and gas generation microgrids that could be deployed either before a grid connection is available, or to allow the data centre to operate entirely off-grid. WindPRO 4.0 software is used to find optimal configurations with wind and solar generation, backed up by battery storage and onsite gas generation. The results show that off-grid generation could provide lower cost and carbon emissions for each of Europe’s data centre hotspots in Frankfurt, London, Amsterdam, Paris, and Dublin. This paper compares each generation configuration to grid equivalent systems and an onsite gas-only generation solution. The results showed that each hybrid renewable generation configuration had a reduced levelized cost of energy (LCOE) and reduced CO2eq emissions compared to that of its grid and gas-only equivalent. Previous literature does not consider the economic implications caused by a mismatch between generation and consumption. Therefore, this paper introduces a new metric to evaluate and compare the economic performance of each microgrid, the levelized cost of energy utilised (LCOEu) which gives the levelized cost of energy for a given microgrid considering only the energy which is consumed by the data centre. The LCOEu across all sites was found to be between 70 and 102 GBP/MWh with emissions between 0.021 and 0.074 tCO2eq/MWh.

Data centers are growing consumers of energy and emitters of greenhouse gases (GHG) worldwide. This paper examines data center power and locational workload management as strategies for energy savings and GHG emissions reduction. The case study examined focuses on GHG emissions from the electricity grid supply in a controlled small-scale computer cluster experiment and location (Philadelphia, PA). Virtualization is a technique that consolidates multiple online services onto fewer computing resources within a data center and deploys computing resources only as needed. The method can be applied not only within a data center, but also among multiple data centers in different locations, thereby taking advantage of deploying data centers that are linked to “low-carbon” electricity grids. Understanding the interaction between data center location and real time power consumption is critical to optimizing computer cluster usage, since demand during certain times of the day may rely on coal as the marginal source. Using the power savings results generated from the virtualization experiments performed on a small computer cluster at Drexel University, and power supply from the electricity grid serving the data center over a 24hour day during a peak electricity summer month, we examine the time of day for shifting data center workloads in order to minimize GHG emissions.

Background: Data Centre industry activity in Asia Pacific is experiencing significant growth, driven by the increasing adoption of cloud technology and data-driven solutions. Telkom Group faces the biggest challenges in data centre development in the form of compliance with increasingly stringent sustainability regulations, increasing demand for skilled and experienced talent, as well as the challenge of adopting green innovation as a solution to sustainability issues. Data Centre Managers need to encourage talent who are capable of developing innovations to reduce carbon emissions, as well as the need for very high electrical power.Purpose: The purpose of this study is to examine how data center performance is impacted by green innovation and digital talent development. It also looks at how green innovation may help data center companies become more competitive, increase operational sustainability, and improve energy efficiency in the face of regulatory obstacles and a lack of skilled digital workers.Design/methodology/approach: The research uses Partial Least Square (PLS) analysis using SmartPLS software to test various related variables. Data collection used a survey method which included in-depth interviews and questionnaires. Respondents are Data Center Managers, Digital Talent Development Experts, Data Center Operational and Technical Teams, and Experts and Consultants in the Field of Green Technology and Data Centers.Findings/Result: By increasing energy efficiency and reducing negative impacts on the environment, green innovation greatly impacts data centre performance. Data centres can significantly reduce their energy consumption and carbon emissions by implementing renewable energy and efficient cooling systems. This research emphasizes the importance of green innovation and digital transformation to improve data centre performance, and emphasizes the positive benefits of green innovation on data centre performance. The research also shows that incorporating environmentally friendly practices and advanced technologies into data centre operational strategies can provide major benefits in terms of energy efficiency, resource efficiency and cost efficiencyConculusions: Through modern technology, carbon reduction, and energy efficiency, digital transformation and green innovation greatly improve data center performance. Reliability, cost effectiveness, and sustainability are increased by integrating automation, efficient cooling, and renewable energy. According to this survey, green innovation is essential for streamlining operations and encouraging eco-friendly data center practices.Originality/value (State of the art): This study highlights the unique role of green practices in boosting energy efficiency, operational sustainability, and competitiveness in the data center business despite regulatory and talent shortages by using renewable energy and modern technologies. Keywords: data centre performance, digital transformation, digital talent, green innovation, green energy

Two data centers in this study were within a co-location facility located on the sixth floor of a multi-story building in downtown Los Angeles, California. The facility had 37,758 gross square feet floor area with 2-foot raised-floors in the data services area. The two data centers were designated as the west data center (DC No.18) and the east data center (DC No.19). The study found that 56% of the overall electric power was consumed by sixth floor critical loads in both data centers, 33% of the power was consumed by HVAC systems, 3% of the power was consumed by UPS units, 3% of the power was for generator losses, and the remaining 5% was used by lighting and miscellaneous loads in the building. The power density of installed computer loads (rack load) in the two data centers was 20 W/ft{sup 2} and 56 W/ft{sup 2}, respectively. The power density was relatively lower in DC No.18 compared to other data centers previously studied. In addition, HVAC to IT power demand ratio was 0.6 in DC No.18 in this study, and was 0.4 in DC No.19. Two out of three chillers were running at a low partial load, making the operation very energy inefficient. The operation and control of the chillers and air-handling units should be optimized while providing sufficient cooling to the data centers. Although arranging hot aisle/cold aisle design to separate airflow streams would be difficult in such a co-location data center, optimizing air distribution should be pursued. General recommendations for improving overall data center energy efficiency include improving the design, operation, and control of mechanical systems serving the data centers with various critical loads in place. This includes chiller operation, chilled water system, AHUs, airflow management and control in data centers. Additional specific recommendations or considerations to improve energy efficiency are provided in this report.

Fresh water is one of the few resources which is scarce and has no replacement; it is also closely coupled to energy consumption. Fresh water usage for power generation and other cooling applications is well known and accounts for 40% of total freshwater withdrawal in the U. S[1]. A significant amount of energy is embedded in the consumption of water for conveyance, treatment and distribution of water. Waste water treatment plants also consume a significant amount of energy. For example, water distribution systems and water treatment plants consume 1.3MWh and 0.5MWh[2], respectively, for every million gallons of water processed. Water consumption in data centers is often overlooked due to low cost impact compared to energy and other consumables. With the current trend towards local onsite generation[3], the role of water in data centers is more crucial than ever. Apart from actual water consumption, the impact of embedded energy in water is only beginning to be considered in water end-use analyses conducted by major utilities[4]. From a data center end-use perspective, water usage can be characterized as direct, for cooling tower operation, and indirect, for power generation to operate the IT equipment and cooling infrastructure[5]. In the past, authors have proposed and implemented metrics to evaluate direct and indirect water usage using an energy-based metric. These metrics allow assessment of water consumption at various power consumption levels in the IT infrastructure and enable comparison with other energy efficiency metrics within a data center or among several data centers[6]. Water consumption in data centers is a function of power demand, outside air temperature and water quality. While power demand affects both direct and indirect water consumption, water quality and outside air conditions affect direct water consumption. Water from data center infrastructure is directly discharged in various forms such as water vapor and effluent from cooling towers. Classification of direct water consumption is one of the first steps towards optimization of water usage. Subsequently, data center processes can be managed to reduce water intake and discharge. In this paper, we analyze water consumption from data center cooling towers and propose techniques to reuse and reduce water in the data center.

Maximizing the revenues of data centers in regulation market by coordinating with electric vehicles

In this paper, we address the problem of Data Centers (DCs) energy efficiency considering their integration into the electrical and thermal grids by emphasizing the role of the DC Digital Twin model in DC flexibility management. Due to their high digitization and controllable energy systems, the DCs can act as flexible assets, being able to dynamically adapt their energy profiles and valuable energy services. We present a flexibility management solution that is using a Digital Twin model of DC systems to determine action plans for shifting energy load. DC monitored data is acquired by integration with existing DC infrastructure management (DCIM) while energy predictions are computed for DC energy demand, energy flexibility, and heat generation. The flexibility optimization plans for DC operation are determined and enforced after DC manager validation via DCIM integration. Five energy services are identified as suitable to be provided by the DC with the help of described flexibility management solution: energy trading for increasing profit, grid congestion management by decreasing DC energy demand, scheduling by increasing DC energy demand to consume as much as possible the renewable available in the local grid, power factor compensation and sell heat on demand.

Green energy paradigm has been gaining popularity in the computing system from the software, hardware, infrastructure and application perspectives. Within that concept, data center greening is of utmost importance at the moment since data centers are one of the most energy conserving elements. Data centers are seen as the technology era's black energy-swallowing secret. Reducing energy consumption at data centers can reduce carbon footprint effect tremendously. Not addressing the issue immediately will lead to significant energy usage by data centers and will hinder the growth of data centers. The call for sustainable energy efficient data center leads to venturing into data center green computing. The green computing concept can be achieved by using several methods adopted by researchers including renewable energy, virtualization through cloud computing, proper cooling system, identifying suitable location to harvest energy whilst reducing the need for air-conditioning and employing suitable networking and information technology infrastructure. This paper focuses into several approaches used by researches to reduce energy consumption at data centers while deploying efficient database management system. This paper differs from others in the literature by giving some suitable solutions by looking into a hybrid model for green computing in data centers.

Scalable data centers – Power generation and delivery challenges and solutions

Reliability, availability, and life-cycle cost (LCC) analysis of combined cooling, heating and power (CCHP) integration to data centers considering electricity and cooling supplies

Data center is considered as major contributor for CO2 emission in ICT sector. Power optimization approaches supported with power usage metrics were proposed and discussed in literatures. Recently, engaging green energy resources to power data center were proposed toward reducing data centers' carbon footprints, called renewable-based data center. Apparently, most of these proposals were mainly considering various techniques to predict the renewable availability. Although utilizing green resources in data center would reduce the carbon footprints, the green energy has its temporal limitation on the around the clock operation. In this short paper, we are proposing a temporal power model for green energy usage in data center. The model is combining the power usage in data center and the green power supplied. Using the total derivative, it rates the instantaneous change of carbon footprints. A mathematical approach using the total derivative was applied to a PUE-based power model to achieve the instantaneous green cost of a request in data center. This model is applicable to accurately capture the power usage and carbon footprints per request. In addition, our model is expandable to precisely calculate the per request green incentive.