Green Energy Usage Research Articles

Running Industrial Workflow Applications in a Software-Defined Multicloud Environment Using Green Energy Aware Scheduling Algorithm

Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows from Industrial Cyber-Physical systems, more and more data centers have been built across the globe to serve the growing needs of computing and storage. This has led to an enormous increase in energy usage by cloud data centers, which is not only a financial burden but also increases their carbon footprint. The private software defined wide area network (SDWAN) connects a cloud provider's data centers across the planet. This gives the opportunity to develop new scheduling strategies to manage cloud providers workload in a more energy-efficient manner. In this context, this article addresses the problem of scheduling data-driven industrial workflow applications over a set of private SDWAN connected data centers in an energy-efficient manner while managing tradeoff of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of nonrenewable energy by utilizing the real-world electricity prices with the availability of green energy on different cloud data centers, where the energy consumption consists of the usage of running application over multiple data centers and transferring the data among them through SDWAN. The evaluation shows that our proposed method can increase usage of green energy for the execution of industrial workflow up to 3× times with a slight increase in the cost when compared to cost-based workflow scheduling methods.

Boosting the Usage of Green Energy for EV Charging in Smart Buildings Managed by an Aggregator Through a Novel Renewable Usage Index

The growing trend of electric vehicles (EVs) and building integrated photovoltaics (BIPVs) is a promising means to reduce related climate change issues. EV loads can be managed via an aggregator to maximize the usage of green energy produced by photovoltaic units (PV) through smart charging strategies that exploit controllable EV demand connected to BIPV. Previous works have focused on the EV charging coordination in a smart BIPV, although without an optimization that encourages EV charging with the energy produced by the PV units. This paper proposes an aggregation strategy that maximizes a green energy index (GEI) for the smart charging coordination of EVs, which takes advantage of periods with high PV availability to charge the EV batteries; moreover, a post-processing stage for the GEI provides EV owners with information about the percentage of charged energy, period by period, that comes from PV generation. The results for a case study with 510 EVs integrated with 17 smart BIPVs show that the strategy effectively optimizes the usage of the energy produced by the PV units to charge the EVs, contributes to reduce non-renewable energy consumption of the building sector, and satisfies the EV owners’ energy requirements for transportation.

THE EFFECTS OF GREEN ENERGY ON ECONOMIC DEVELOPMENT, AND GREEN HUMAN RESOURCE MANAGEMENT ON NONGREEN WORK OUTCOMES IN SRI LANKA

ABSTRACT This paper combines a macroeconomic assessment of the effects of green energy in upper- income, middle-income, and lower-income developing countries, with the examination of the impact of green human resource management policies on job satisfaction, empowerment, job performance, and perceived financial performance among a sample of Sri Lankan managers. Only in upper income developing countries with industrial use of renewable energy does renewable energy output lead to economic development. Sri Lanka is a middle- income developing country. Sri Lankan managers are satisfied with green human resource management policies, which leads to superior job performance and perceptions of financial performance. The challenge is for Sri Lankan managers to view green energy as a means of advancement to economic development by supporting large-scale green energy investments in industrial production to supplement current residential green energy usage. Keywords Green Energy, Economic Development; Green Human Resource Management, Work Outcomes, Job Satisfaction, Job Performance

Framing electricity plan choices to enhance green energy usage: A choice experiment with panel data from Germany

This paper investigates the choices and accompanying biases consumers have when actively deciding between two alternative electricity plans. We follow the perspective that observed choices do not necessarily reflect true preferences over outcomes, but are affected by several behavioral tendencies or biases. Our experiment focusses on framing-effects as an important source for such observed behavior. The study is based on data from a large-scale survey experiment conducted in Germany (n = 3320). We implemented a choice experiment in two waves of the GESIS Panel and analyzed more than 16,000 binary decisions between a standard electricity plan and a supply plan based entirely on renewable energy resources. Across two waves, we manipulated the numerical information as to how the additional costs for buying a green energy plan are presented in the decision problem while keeping the monetary consequences identical between the two waves. We show that framing the consequences of a green electricity plan choice as a foregone saving yields higher rates of green choices than framing the choice of green plan as an additional expenditure. We discuss problems and merits related to the policy strategy of actively framing green energy options that extend beyond the German context.

Evaluation of Distributed Generation Impact on Reliability of a Distribution System using DIgSILENT PowerFactory

As an effective supplement to the centralized fossil fuel based traditional generation, Distributed Generation (DG) has become an effective alternative choice and has been rapidly increasing since past few years due to growing demand for electricity and the new policies of governing bodies for usage of green energy. In overall power system, distribution systems are more vulnerable to faults and reliability aspects of such systems becomes an important issue. With higher penetration of DG into the distribution network, it will be necessary to study the impact of such generation on the various aspects of distribution system. Thus, increase in rate of penetration DGs into the distribution system on one side and increased faults in distribution network on another side, will make the study of impact of DG integration on distribution system reliability an interesting topic of research. The present work focuses on evaluation of impacts of integration of such DGs on reliability of local distribution network, typically in an urban scenario By using the simulation method using DIgSILENT PowerFactory software, the impacts of integration of DG in terms of enhancement in distribution system reliability indices and reduction in system losses for different scenarios are studied and presented in this paper. Based on the simulation results obtained and after analysis of the distribution system, overall results are summarized by focusing on the installation of suitable capacity of DG and the location of DG which are important factors affecting the system losses and system reliability indices.

Optimized Energy Cost and Carbon Emission-Aware Virtual Machine Allocation in Sustainable Data Centers

Cloud data center’s total operating cost is conquered by electricity cost and carbon tax incurred due to energy consumption from the grid and its associated carbon emission. In this work, we consider geo-distributed sustainable datacenter’s with varying on-site green energy generation, electricity prices, carbon intensity and carbon tax. The objective function is devised to reduce the operating cost including electricity cost and carbon cost incurred on the power consumption of servers and cooling devices. We propose renewable-aware algorithms to schedule the workload to the data centers with an aim to maximize the green energy usage. Due to the uncertainty and time variant nature of renewable energy availability, an investigation is performed to identify the impact of carbon footprint, carbon tax and electricity cost in data center selection on total operating cost reduction. In addition, on-demand dynamic optimal frequency-based load distribution within the cluster nodes is performed to eliminate hot spots due to high processor utilization. The work suggests optimal virtual machine placement decision to maximize green energy usage with reduced operating cost and carbon emission.

Energy Consumption and Emissions of Diesel-CNG Dual Fuel Engine at High Load Operation

Global warming and energy sustainability issues are among the major world concern. Malaysian National Green Technology Policy 2009 and Thailand Power Development Plan 2015-2036 (PDP 2015) were launched to enhance the green and sustainable energy usage. Meanwhile in the transportation sector, National Automotive Policy (NAP) has been implemented and revised to enhance the usage of the green energy, in order to achieve a low carbon emission and energy efficient vehicle. Researchers keep striving to find alternative solutions to power vehicles by cleaner energy efficiently. Compressed Natural Gas (CNG) has lower carbon emission and higher energy density compared to common petroleum fuel. It provides an opportunity to power the vehicle cleanly. Thus, it has been used as an alternative for fueling gasoline engine. However, CNG fuel is difficult to be applied on diesel engine. Unlike gasoline engine, diesel engine does not have spark plug and its fuel is combusted through compression in cylinder. Since CNG has high octane number, it is difficult to self-ignite in diesel engine.Therefore, Diesel-CNG Dual Fuel (DDF) system is applied. The system use CNG as part fuel and certain amount of diesel pilot fuel is injected into the cylinder to ignite the combustion. DDF engine may potentially reduce Carbon Dioxide (CO2) emission. However, high fuel consumption and Nitrogen Oxide (NOX) emission have been observed at high load engine operation due to improper fuel ratio. In this study, four ratios of DDF were tested and compared with 100% diesel: 90D10G, 80D20G, 70D30G, 60D40G. It was found that each of the fuel ratio behaved differently in terms of brake specific energy consumption (BSEC) and exhaust emissions.

Sustainability modelling and green energy optimisation in microgrid powered distributed FogMicroDataCenters in rural area

Government now-a-days are looking forward to achieve “Energy for All” standards. Countries are committing themselves to reduce carbon emissions. Recently, every IT industry giants, who typically own and operate huge Data Centers (DC) are looking forward to achieve “100% usage of green energy to power their datacenters, contribute to the utility grid in equivalence to their electrical power consumptions”. Electrification and gradual decarbonisation still continue as significant, global concern. Hence it is pretty clear that need for MicroGrid (MG) based facilities is actually a mandatory. But intermittent-characteristic (unreliability/unavailability) of Renewable-Energy (RE) generating sources (like Solar Panels, Wind Turbines) regularly hinder both the stake holders-Government and IT industry from accomplishing such social and economic initiatives. Hence SmartMicroGrid (SMG) connected with utility grid are envisioned to support powering industries inorder to avoid future power outages. Interconnected industry4.0 factories where, actually the smart-management interms of two-way power supply and two-way communication network is foreseen. They are highly dependent on modern, integrated, Information and Communication technologies (IoT sensors, Fog and Cloud computing etc.). In this paper we have focussed on two objectives. Firstly our objective is to identify, optimum renewable-generation-capacities inorder to minimise investment cost of a microgrid. Secondly our objective is to maximize usage of more clean energy for applications running on FogMicroDataCenter (fogMDC), powered by microgrids and controlled by Sofware Defined Networking (SDN). We have used realistic meteorological data of Tamil nadu, India for our proposed work. To the best of our knowledge, this is the first work proposed for two different scenarios, performance modelling and economic modelling aiding MG powered FogMDCs. Proposed economic modelling work is related to finding, the optimum sizing of distributed energy resources and financial cost. Proposed performance modelling is about, the clean energy usages in micro grid powered fogMDC for operations, supported by SDN for efficient distributed Virtual Machine (VM) based resource utilization to guarantee QoS in time sensitive IoT applications.

Managing Energy Consumption in Distributed Data Centers using Genetic algorithm

This work shows a multi-target approach for planning vitality utilization in server farms thinking about customary and environmentally friendly power vitality information sources. Cloud computing is a developing innovation. Cloud computing offers administrations such as IaaS, SaaS, PaaS and it gives computing resources through virtualization over data network. Data center consumes huge amount of electrical energy in which it releases very high amount of carbon-di-oxide. The foremost critical challenge in cloud computing is to implement green cloud computing with the help of optimizing energy utilization. The carbon footprint is lowered while minimizing the operating cost. We know that renewable energies that are produced on-site are highly variable and unpredictable but usage of green energy is very important for the mankind using huge amount of single sourced brown energy is not suggested, so our algorithm which evolves genetically and gives practical solution in order to use renewable energy.

Unraveling the Water Adsorption Mechanism in the Mesoporous MIL-100(Fe) Metal–Organic Framework

Adsorption-based heat transfer (AHT) devices are promising alternatives for green energy production and (re)usage; however, they are still limited by the low performance of their benchmark adsorben...

Feasibility study on green transportation

This is a research report on a green transportation project. Green transportation relies on renewable energy sources rather than fossil fuels currently used extensively. Sustainability assumes low impacts on environment and balancing current and future needs. We are investigating introduction of electrical vehicles (EV) and issues which influence that EVs are rapidly becoming a mainstay in vehicle product lines. These issues are related to the remaining available reserves of fossil fuels, important for the further use of Internal Combustion Engines (ICE), from one side, and on the other side, availability and reserves of lithium and other metals, required for EV batteries and motors, which makes impacts on swiftness of green transportation introduction. This transition to EVs is characterized by the convergence of mobility and energy what can bring significant benefits to the entire society. There are also policy implications which direct the usage of green energy for EV charging and development of related infrastructure. Finally, we present the economics of green transportation and business models which ensure its sustainability.

Exploring the Consumer Attitude of Building-Attached Photovoltaic Equipment Using Revised Technology Acceptance Model

Climate change affects agriculture, the water supply, health, and the sustainability of the environment, and is largely due to greenhouse gases produced by human activities and power production. In order to reduce greenhouse gas emissions, the usage of renewable green energy must be promoted. The International Energy Agency showed that renewables have surpassed coal as the largest source of installed power capacity; half a million solar panels are installed every day around the world. The Taiwanese government has planned to block its fourth nuclear power plant, and is closing Taiwan’s three operating nuclear power plants since solar energy is the best way to solve power shortages. This study defined a solar-energy building as a Building-Attached Photovoltaic (BAPV) system in which the solar modules can be attached to and detached from the building without any structural damage; then, we proposed the Technology Acceptance Model (TAM) to forecast and explain public acceptance of BAPV. Last, we explored consumers’ intentions to use the BAPV systems and their purchasing behavior. The analytical results are fairly consistent with the proposed hypotheses. We find that when perceived ease of use (PEOU) is the antecedent of perceived usefulness (PU) and attitude toward using (AT), the model fit shows a positive influence. However, when PEOU directly affects AT, it shows a negative influence. These two opposing results show that consumers lack an awareness of PEOU. In order to get a positive AT, PEOU and PU have to be synergized. Further, the PEOU also has a negative influence on the antecedent of AT, but positively affects purchasing behavior.

Analysis on TD-LTE wireless private network capacity meeting the interaction demand of future smart grid

AbstractAlong with the usage of green energy, electricity, and the advanced innovation of energy, new power services will grow explosively, such as distributed photovoltaic, electric vehicle charging pile and so on. As a result, the interaction mode ‘source-net-load–storage’ will change. Aiming to promote the power grid operation and service level, it is required to improve the ability of distribution network control and awareness, especially the ability of real-time operating state collection in the low voltage power grid (380 kv / 220 kv). Power wireless private network based on TD-LTE is one of the most important technologies to realize the power terminal access and data collection which can solve the ‘last mile’ problem of terminal access. However, the wireless communication capacity is limited by the bandwidth, frequency, technology, etc. In this paper, we analyze the requirements of power grid basic services for TD-LTE wireless private network. And the basic services include power utilization inform...

Investigating Energy Consumption and Performance Trade-Off for Interactive Cloud Application

With the ever growing demand and popularity of cloud based services, data centers have to urgently face energy consumption issue. Similar to other large consumers of power, data centers find themselves increasingly pressured to reduce their carbon footprint. In response, cloud providers have started to set sustainability goals to reduce carbon emissions by using renewable sources to their services. Traditionally, batch processing cloud applications are deadline oriented, hence they can be easily adapted with the different green energy profile. Whereas, interactive cloud applications are imposed with several performance criteria. This paper, the first of its kind, investigates a thorough analysis of energy consumption and performance trade-off by allowing smart usage of green energy for interactive cloud application. Moreover, we propose an auto-scaler, named SaaScaler, that implements several control loop based application controllers to satisfy different performance (i.e., response time, availability, and user experience) and resource aware metrics (i.e., quality of energy). Based on extensive experiments with RUBiS benchmark and real workload traces using single compute node in Openstack/Grid’5000, results suggest that 13 percent brown energy consumption can be reduced without deprovisioning any physical or virtual resources at IaaS layer while 29 percent more users can access the application by dynamically adjusting capacity requirements. Furthermore, our investigation verifies that the energy consumption deviates as little as .07 percent when our approach is scaled using several physical nodes.

An online electricity cost budgeting algorithm for maximizing green energy usage across data centers

With the sky-rocketing development of Internet services, the power usage in data centers has been significantly increasing. This ever increasing energy consumption leads to negative environmental impact such as global warming. To reduce their carbon footprints, large Internet service operators begin to utilize green energy. Since green energy is currently more expensive than the traditional brown one, it is important for the operators to maximize the green energy usage subject to their desired long-term (e.g., a month) cost budget constraint. In this paper, we propose an online algorithm GreenBudget based on the Lyapunov optimization framework. We prove that our algorithm is able to achieve a delicate tradeoff between the green energy usage and the enforcement of the cost budget constraint, and a control parameter V is the knob to arbitrarily tune such a tradeoff. We evaluate GreenBudget utilizing real-life traces of user requests, cooling efficiency, electricity price and green energy availability. Experimental results demonstrate that under the same cost budget constraint, GreenBudget can increase the green energy usage by 11.55% compared with the state-of-the-art work, without incurring any performance violation of user requests.

Optimizing Green Energy, Cost, and Availability in Distributed Data Centers

Integrating renewable energy and ensuring high availability are two major requirements for geo-distributed data centers. Availability is ensured by provisioning spare capacity across the data centers to mask data center failures (either partial or complete). We propose a mixed integer linear programming formulation for capacity planning while minimizing the total cost of ownership (TCO) for highly available, green, distributed data centers. We minimize the cost due to power consumption and server deployment while targeting a minimum usage of green energy. Solving our model shows that capacity provisioning considering green energy integration not only lowers carbon footprint but also reduces the TCO. Results show that up to 40% green energy usage is feasible with a marginal increase in the TCO compared with the other cost-aware models.

On Green-Energy-Powered Cognitive Radio Networks

A green-energy-powered cognitive radio (CR) network is capable of liberating the wireless access networks from spectral and energy constraints. The limitation of the spectrum is alleviated by exploiting cognitive networking in which wireless nodes sense and utilize the spare spectrum for data communications, whereas dependence on the traditional unsustainable energy is assuaged by adopting energy harvesting through which green energy can be harnessed to power wireless networks. Green-energy-powered CR increases the network availability and thus extends emerging network applications. Designing green CR networks is challenging. It requires not only the optimization of dynamic spectrum access but also the optimal utilization of green energy. This paper surveys the energy-efficient CR techniques and the optimization of green-energy-powered wireless networks. Existing works on energy-aware spectrum sensing, management, and sharing are investigated in detail. The state of the art of the energy-efficient CR-based wireless access network is discussed in various aspects, such as relay and cooperative radio and small cells. Envisioning green energy as an important energy resource in the future, network performance highly depends on the dynamics of the available spectrum and green energy. As compared with the traditional energy source, the arrival rate of green energy, which highly depends on the environment of the energy harvesters, is rather random and intermittent. To optimize and adapt the usage of green energy according to the opportunistic spectrum availability, we discuss research challenges in designing CR networks that are powered by energy harvesters.

A Simple Power Management Scheme with Enhanced Stability for a Solar PV/Wind/Fuel Cell/Grid Fed Hybrid Power Supply Designed for Industrial Loads

This paper proposes a new power conditioner topology with an intelligent power management controller that integrates multiple renewable energy sources such as solar energy, wind energy, and fuel cell energy with battery and AC grid supply as backup to make the best use of their operating characteristics with better reliability than that could be obtained by single renewable energy source based power supply. The proposed embedded controller is programmed to perform MPPT for solar PV panel and WTG, SOC estimation and battery, maintaining a constant voltage at PCC and power flow control by regulating the reference currents of the controller in an instantaneous basis. The instantaneous variation in reference currents of the controller enhances the controller response as it accommodates the effect of continuously varying solar insolation and wind speed in the power management. It also prioritizes the sources for consumption to achieve maximum usage of green energy than grid energy. The simulation results of the proposed power management system with real-time solar radiation and wind velocity data collected from solar centre, KEC, and experimental results for a sporadically varying load demand are presented in this paper and the results are encouraging from reliability and stability perspectives.

On Optimizing Green Energy Utilization for Cellular Networks with Hybrid Energy Supplies

Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to achieve green cellular networks. With the development of green energy technologies, BSs are able to be powered by green energy in order to reduce the on-grid energy consumption, thus reducing the CO2 footprints. In this paper, we envision that the BSs of future cellular networks are powered by both on-grid energy and green energy. We optimize the energy utilization in such networks by maximizing the utilization of green energy, and thus saving on-grid energy. The optimal usage of green energy depends on the characteristics of the energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. We decompose the problem into two sub-problems: the multi-stage energy allocation problem and the multi-BSs energy balancing problem. We propose algorithms to solve these sub-problems, and subsequently solve the green energy optimization problem. Simulation results demonstrate that the proposed solution achieves significant on-grid energy savings.

Green wireless communication networks [Editor's note

The increasing usage of conventional energy sources such as oil and coal poses a wide variety of environmental issues, including climate changes, air pollution, and acid rain. At the same time, the prices of conventional energy sources are soaring while the world's energy consumption is ever increasing. In order to provide sufficient, environment-friendly, and lowcost energy to sustain economic growth and raise living standards across the world, innovation and development of alternative energy sources are required. Green energy, which refers to the eco-friendly renewable energy replenished from natural resources such as sunlight, wind, and geothermal heat, is expected to be a promising alternative to conventional energy sources. According to the 2012 Renewables Global Status Report (GSR), the development of green energy techniques has dramatically reduced the cost of green-powered electricity. For instance, the prices of photovoltaic modules and onshore wind turbines have dropped by 50 percent and close to 10 percent, respectively. Both the decreasing cost and the environmental concerns motivate the usage of green energy. In 2011, renewable sources supplied 16.7 percent of global energy consumption, with an increasing share of renewable energy and decreasing utilization of traditional energy. Moreover, investment in renewables has increased by 17 percent to a record of $257 billion in spite of the widening financial crisis in Europe, and 118 countries reached renewable energy targets.