Datacenters powered by renewable energy: A case study for 60 degrees latitude north

Feasibility of using renewable energy to supply data centers in 60[formula omitted] north latitude

Solar energy is an inexhaustible and pollution-free energy source that plays a significant role in the sustainable provision of energy services. The main purpose of this study is to examine the potential, current situation, future strategies, and policies of solar energy, which is a renewable resource in Azerbaijan. This study is research in which qualitative methods are used and within the scope of the study, literature review, systematic approach and inductive methods were used. In the study, Azerbaijan's policy towards solar energy has been examined based on the potential sources of solar energy, the current situation and the country's future strategies. Azerbaijan is slightly behind in the production of electricity from renewable energy sources. Along with all this, the most important strategic goals for the use of renewable and alternative energy sources in Azerbaijan have been defined. Thus, by 2030, the share of renewable energy investment in the country's total energy balance to 30% has been determined as the main goal. As a result of the study, it can be said that solar energy policies will have a positive effect on the country's economy in the coming years, since Azerbaijan has a geographical location and an infrastructure system that can be delivered to the countries in need.

Optimizing energy efficiency in data center cooling towers is crucial for reducing operational costs and environmental impact. This review explores the integration of predictive maintenance and project management to achieve this goal. By leveraging predictive maintenance techniques, data center operators can anticipate and address potential issues before they lead to costly downtime or inefficiencies. Project management plays a key role in coordinating these efforts, ensuring that maintenance activities are carried out efficiently and effectively. Predictive maintenance relies on data analytics and machine learning algorithms to monitor the condition of cooling towers in real-time. By analyzing data such as temperature, pressure, and flow rates, these algorithms can detect anomalies and predict potential failures. This proactive approach allows data center operators to schedule maintenance activities during planned downtime, minimizing disruptions to operations. Project management practices, such as Agile or Waterfall methodologies, are essential for coordinating predictive maintenance efforts. Project managers oversee the planning, execution, and monitoring of maintenance activities, ensuring that they are completed on time and within budget. They also facilitate communication between stakeholders, including maintenance teams, data analysts, and management, to ensure that everyone is aligned and working towards the same goals. By integrating predictive maintenance with project management, data center operators can achieve significant improvements in energy efficiency. By addressing maintenance issues proactively, operators can reduce energy consumption, extend the lifespan of cooling equipment, and minimize downtime. Additionally, project management practices ensure that these efforts are coordinated and effective, maximizing the benefits of predictive maintenance. In conclusion, optimizing energy efficiency in data center cooling towers requires a holistic approach that combines predictive maintenance and project management. By leveraging predictive maintenance techniques and project management practices, data center operators can achieve significant improvements in energy efficiency, reduce operational costs, and enhance environmental sustainability.

This paper presents numerical simulations based on the concept of local and global dimensional analysis to reproduce the thermodynamics and aerodynamics of server-fins, i.e., detachable flat/curved fins on the inlet/outlet of a server, for enhancing energy efficiency in data centers. Server-fin designs, combinations of shapes and opening angles, are evaluated by transient analysis, vorticity analysis, temperature change rate analysis, and a modified Return Heat Index. Various numerical simulations from local to global dimensions confirm the effectiveness of the server-fins proposed herein in enhancing energy efficiency in data centers compared to their omission.

A survey on architectures and energy efficiency in Data Center Networks

The increase in energy demand due to the deployment of conventional sources makes shift towards Renewable Energy Sources. As Renewable energy cannot be tapped for continuous time, it is very convenient to add another source of energy to solar and wind energy sources. RES when added with other temporary sources of energy is called Hybrid Energy Source System. Hybrid System helps in meeting the peak demand during peak hours by using load generated at that point as main supply and the other source as secondary source. In this paper two renewable energy sources like Solar and Biomass energy is combined to form Hybrid Energy Source. In India solar energy is available abundantly and in villages where agriculture is the main source of income creates Biomass. For a particular region both these energy resources are combined and studied in this paper. The advantages of combining both the plants and the applications, usability of these sources depending upon the peak hours and the off peak hours are studied in this paper. These combinations helps us to reduce pollution, as garbage obtained from the agriculture plants will be utilized to generate power and also free availability of carbon free solar energy.

Introduction: To date, a variety of studies have been published on the topic of long term energy system transition. Most studies on future energy systems, however, have a shorter time frame or adopt a supranational focus (e.g. the Energy Roadmap, 2011 or the World Energy Outlook, 2015). It then constitutes a sincere challenge to perform a national energy system transition study with as time horizon 2050 and covering a far-reaching transformation of the energy system. VITO, together with the FPB (Federal Planning Bureau) and ICEDD, performed a study to scrutinise the transition of the Belgian national energy system towards a future mix entirely based on renewable energy sources. The focus on renewable energy sources and on building a national energy system completely running on renewable energy can be traced back to three main concerns: – Climate change: Renewable energy sources (RES) are a major instrument in the fight against climate change as RES do not release (net) greenhouse gas emissions. – Security...

Energy efficiency (EE), energy consumption cost and environmental impact are vibrant challenges to cloud computing and data centers. Reducing energy consumption and emissions of carbon dioxide (CO2) in data centers represent open areas and driving force for future research work on green data centers. Our Literature review reveals that there are currently several energy efficiency frameworks for data centers which combine a green IT architecture with specific activities and procedures that led to decrease the impact on environment and less CO2 emissions. The current available frameworks have some pros and cons that is the reason why there is an urgent need for an integrated criterion for selecting and adopting energy efficiency framework for data centers. The required energy efficiency framework criteria should also consider the social network applications as a vital related factor in elevating energy consumption, as well as high potential for better energy efficiency in data centers. Additionally, in this paper, we highlighted the importance of the identification of efficient and effective energy efficiency metric that can be used for the measurement and determination of the value of data centers efficiency and their performance combined with sound and empirically validated integrated EE framework.

A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea

Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy.

Over the past decade, Multi-Criteria Decision Making (MCDM) has become a foundational tool in energy system design. Numerous technical methodologies and algorithms are available for evaluating and designing energy systems, optimizing either single or multiple criteria. MCDM aids decision-makers (DMs) in solving complex problems, such as site and supplier selection, ranking, and assessment. This paper provides a comprehensive review of both historical and contemporary MCDM techniques referenced in the literature. India is a rapidly developing and highly populated country with a high demand for energy to sustain its economic growth. To meet this demand, the Indian government want to achieve a target to increase the share of renewable energy upto 40% of the total installed capacity by year 2030. This target is a clear indication of the potential for hybrid renewable energy in India. We select solar and wind energy sources among various renewable energy sources based on various study that choose the optimal site selection criteria among them. we provide an overview of recent research on the application of MCDM for the selection of renewable hybrid power plants and identify that social factor is one of the main criteria that play a crucial role in site selection. Several MCDM methods have been applied to the selection of renewable hybrid power plants and compare them for optimal result. The use of sustainable MCDM techniques can help in the evaluation of the suitability of the sites based on multiple criteria. It increases the flexibility and sensitivity of the decisionmaking efforts in such type of approach. This approach allows to comprehensively and effectively assess and tackle the impending issues in the field of renewable energy. These techniques can use in site selection in other industry.

Renewable energy sources like wind and solar energies can be combined to increase the total power generation and thereby increase the efficiency of the system. The combination also provides a means to overcome the intermittent nature of the solar and wind renewable energy sources, since one source can be used for power generation when the other is not available. AC-DC converters are used to convert the alternating voltage of the wind generator to a constant DC value which can be used to charge the batteries or later converted to AC voltage to drive AC loads. A Maximum Power Point Tracking (MPPT) system using a boost converter is designed to extract maximum possible power from the sun when it is available. This method provides better harmonic reduction since Harmonic content is detrimental for the generator lifespan, heating issues, and efficiency. Simulations are carried out in PSIM software and MATLAB.' Keywords: Boost converter, hybrid system, MATLAB, MPPT, PSIM software, renewable energy

Promotional policy and perspectives of usage renewable energy in Lithuania

The rapid revolution of cloud computing model is accompanied by huge amounts of energy consumed by the cloud data centers. So, enhancing the energy efficiency of those data centers has become a major challenge. This paper tackles the problem of enhancing the energy consumption of cloud data centers by proposing a novel virtual machine placement strategy. The proposed strategy suits both static and dynamic placement process. It aims to better utilize the involved physical machines which host the virtual machines. As different types of jobs do not intensively use the compute and/or non-compute resources in the hosted physical machine, virtual machines allocated to the jobs of different types are placed on the same physical machine where possible. The paper presents a mathematical formulation of the virtual machine placement process based on the Multiple Choice Knapsack Problem which is a generalization of the classical Knapsack Problem. The performance evaluation of the proposed strategy shows that it can enhance the energy efficiency of the cloud data centers by trying to minimize the number of the involved physical machines which host the virtual machines, and by optimally utilizing the involved physical machines.

The solar and wind renewable energy sources are gaining popularity to encourage green energy into the power system. The cost of generation of solar and wind energy sources are decreasing and competing with conventional coal-based generation. Therefore, it is very important to integrate these renewable sources into the power system. Integrating Solar and wind energy sources require to solve the uncertainty problem. Both the solar and wind energy generation is uncertain and not controllable. In this paper, sliding window optimal ARIMA forecasting algorithm is proposed to solve the uncertainty associated with solar and wind sources. The proposed forecasting method is used on the data collected from National Renewable Energy Laboratory website.