Available Green Energy Research Articles

Equity-oriented power sharing and demand response for enhancing green energy access in mixed communities

The distribution of renewable energy resources (RERs) is currently uneven and is largely influenced by their affordability across various communities. This has led to disparities in access to green energy for different community members. To address these inequalities, this study proposes an equity-oriented power-sharing mechanism that incorporates demand response (DR). The implementation of this approach will increase the availability of green energy for all members of the community in mixed neighborhoods. First, a DR program is introduced for conventional homes (i.e., homes without RERs) to maximize the consumption of RERs by shifting their load to RER excess intervals. Then, a proportionally fair mechanism is introduced to distribute the available resources among community members during times of high excess. The objective is to minimize the grid dependence of community members by reducing their reliance on purchasing power from the grid. To ensure renewable energy homes (i.e., homes with installed RERs) maintain their green energy access, additional constraints are put in place to prioritize their energy needs when compared to the non-sharing case. Three indices (green energy access, grid dependence, and cost reduction) are introduced to evaluate the performance of the proposed method in different cases: the base case, the power-sharing-only case, and the case of power-sharing with the DR. Finally, the impact of different seasons, day types, and user-defined parameters is analyzed. Simulations have shown that the proposed method can significantly increase the green energy access of conventional homes without affecting the access to green energy of renewable energy homes. It also decreases the grid dependence and the electricity cost of the entire community.

Green IT Meaning in Energy Monitoring Practices: The Case of Danish Households.

Eco-conferences like COP26 in Glasgow (UK) in 2021 have brought the debate on energy consumption and climate change to the fore. Given that a third of the energy produced worldwide is consumed in the home, it is pertinent to investigate how households use emerging technologies that allow households to monitor their energy consumption. This paper investigates how Danish households use green IT to monitor and manage their energy use and studies the related meaning householders attach to the green IT. We present qualitative data collected through interviews with 14 households, electric car owners mostly, who have adopted an application to monitor green energy availability - and its derived consumption. The paper highlights these householders' green energy monitoring practices with an emphasis on the meaning they make of the green IT application they used. Our study found that households can use more green energy without interacting continuously with the green IT application. This contrasts with a common assumption in the field of green IT design that consumers must continuously engage with the green IT to consume more green energy. We also posit that including householders in future green IT design is paramount for designing successful green IT applications. Finally, this paper calls for household energy consumption studies to view energy consumption as a service where specific practices are matched to energy sources - rather than viewing energy availability as a solitary incident.

Location-aware green energy availability forecasting for multiple time frames in smart buildings: The case of Estonia

In recent years, renewable energies (RE) have gained more attention as they provide clean and sustainable energy. Affordable and clean energy is one of the sustainable development goals (SDG-7) of the UN. Solar energy is considered to be one of the world's most abundant renewable natural resources and can certainly contribute to meeting the SDGs. The photovoltaic (PV) panel converts solar energy into electrical energy without emitting greenhouse gases. PV panels generate power based on solar radiation received at a specific location over time. As a result, PV output power is difficult to forecast. Many public/private institutes generate green energy, and balancing demand and supply is vital. This research aims to forecast PV system output power based on weather and derived features using different machine learning (ML) models. A best-fitting model is developed based on the data to accurately predict output power. Moreover, different performance metrics are used to compare and evaluate the accuracy under different ML models such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP) and Support Vector Regression (SVR). Compared to the recent research, our results demonstrate that the obtained prediction accuracy is superior, with performance gains reaching up to 14% (R-squared) in the case of the RF model and 25% (MAPE) in the case of the SVR model. The comparison result inferred a similar performance when RF and XGBoost were applied. On the other hand, the ML models MLP, SVR, and KNN give a similar prediction pattern.

Scheduling computing loads for improved utilization of solar energy

The rise in the penetration of the internet across the world has led to a rapid increase in the consumption of energy at the data centers established by leading cloud data service providers. High power consumption by these data centers [DCs] leads to high operational costs and high carbon emissions into the environment. From a sustainability point of view, the ultimate goal is to maximize the productivity and efficiency of these data centers while keeping greenhouse gas emissions to the minimum and maximize data center productivity. This goal can be achieved by better resource utilization and replacing carbon-intensive approaches of energy production with green sources of energy. Due to the limited intermittent availability of renewable sources of energy, the ideal ‘Green’ design for the DCs, should incorporate inter-operability with both renewable and non-renewable sources of energy. In this paper, we propose a ren-aware scheduler to schedule computational workload by prioritizing their execution within the duration of green energy availability on the basis of the predicted hourly green energy and workload data of DCs. Our results demonstrate that our ren-aware scheduler can increase the green energy consumption by 51% compared to the conventional randomized scheduler that distributes load without considering green energy and load. It can also reduce the total energy consumption by 25% by putting the DCs to sleep during their idle time, as it saves 4.5 times more idle energy than the randomized scheduler. Additionally, the results also demonstrate how the role of time zones of the DCs and the duration of green energy availability in them is pivotal in our ren-aware scheduler's performance.

GreenCloudNet++: Simulation framework for energy efficient and secure, green job scheduling in geographically distributed data centers

AbstractGeographically distributed data centers are used as backbone infrastructure to meet rapidly increasing service demands of computations and data storage in cloud computing. This increase results in high energy consumption, increased operational expenditures, and high carbon footprint which are becoming points of great concern for service providers. In this research work, we present a simulation framework named GreenCloudNet++ for simulation and evaluation of energy‐efficient, green‐aware, and secure job scheduling mechanisms for geographically distributed data centers. GreenCloudNet++ has been developed as an extension to CloudNetSim++, which is designed to simulate distributed data center architectures connected with high speed networks. The functionality of CloudNetSim++ is extended by adding hierarchical job scheduling mechanism, hierarchical statistics collection mechanism and integration mechanism for green energy. Proposed model considers availability of green energy at each data center and maximizes its utilization. It considers the amount of underutilized computational resources at individual data centers while assigning jobs to the data centers, which helps to achieve better server consolidation resulting in better energy efficiency. Proposed model also relies on network load inside each data center which helps avoiding hotspots.

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.

Toward Optimal Cost-Energy Management Green Framework for Sustainable Future Wireless Networks

The design of green cellular networking according to the traffic arrivals has the capability to reduce the overall energy consumption to a cluster in a cost-effective way. The cell zooming approach has appealed much attention that adaptively offloads the BS load demands adjusting the transmit power based on the traffic intensity and green energy availability. Besides, the researchers are focused on implementing renewable energy resources, which are considered the most attractive practices in designing energy-efficient wireless networks over the long term in a cost-efficient way in the existing infrastructure. The utilization of available solar can be adapted to acquire cost-effective and reliable power supply to the BSs, especially that sunlight is free, available everywhere, and a good alternative energy option for the remote areas. Nevertheless, planning a photovoltaic scheme necessitates viability assessment to avoid poor power supply, particularly for BSs. Therefore, cellular operators need to consider both technical and economic factors before the implementation of solar-powered BSs. This paper proposed the user-centric cell zooming policy of solar-powered cellular base stations taking into account the optimal technical criteria obtained by the HOMER software tool. The results have shown that the proposed system can provide operational expenditure (OPEX) savings of up to 47%. In addition, the efficient allocation of resource blocks (RBs) under the cell zooming technique attain remarkable energy-saving performance yielding up to 27%.

Geography-Aware Task Scheduling for Profit Maximization in Distributed Green Data Centers

Infrastructure in Distributed Green Data Centers (DGDCs) is concurrently shared by multiple different applications to flexibly provide a growing number of services to global users in a cost-effective way. A highly challenging problem is how to maximize the total profit of the DGDC provider in a market where Internet Service Provider (ISP) bandwidth price, availability of green energy, price of power grid, and revenue brought by the execution of tasks all vary with geographical locations. Unlike existing studies, this article proposes a Geography-Aware Task Scheduling (GATS) approach by considering spatial variations in DGDCs to maximize the total profit of the DGDC provider by intelligently scheduling tasks of all applications. In each time slot, the formulated profit maximization problem is solved as a convex optimization one via the interior point method. Trace-driven simulations show that GATS achieves larger total profit and higher throughput than two typical task scheduling approaches.

Toward Green IoT: Energy Solutions and Key Challenges

With the ongoing worldwide development of IoT, an unprecedented number of IoT devices imperatively consume a substantial amount of energy. IoT devices have been predicted to be the leading energy guzzler in Information and Communications Technology by 2020. In considering the finite amount of brown energy sources along with their potential harmful impacts to the climate and environment, we propose to leverage free green energy to power IoT devices and revolutionarily enable wireless charging of these devices. Specifically, we propose to green IoT in three steps, namely, ambient green energy harvesting, green energy wireless charging and green energy balancing, in which the latter step reinforces the former step to ensure the availability of green energy. We lay out the basic design principles for these three steps, shed some light on the solutions and present the corresponding challenges individually.

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.

Optimal Cooperative Power Allocation for Energy-Harvesting-Enabled Relay Networks

In this paper, we present a new power allocation scheme for a decode-and-forward (DF) relaying-enhanced cooperative wireless system. While both source and relay nodes may have limited traditional brown power supply or fixed green energy storage, the hybrid source node can also draw power from the surrounding radio frequency (RF) signals. In particular, we assume a deterministic RF energy harvesting (EH) model under which the signals transmitted by the relay serve as the renewable energy source for the source node. The amount of harvested energy is known for a given transmission power of the forwarding signal and channel condition between the source and relay nodes. To maximize the overall throughput while meeting the constraints imposed by the non-sustainable energy sources and the renewable energy source, an optimization problem is formulated and solved. Based on different harvesting efficiency and channel condition, closed form solutions are derived to obtain the optimal source and relay power allocation jointly. It is shown that instead of demanding high on-grid power supply or high green energy availability, the system can achieve compatible or higher throughput by utilizing the harvested energy.

Exploiting Renewable Sources: When Green SLA Becomes a Possible Reality in Cloud Computing

While the proliferation of cloud services have greatly impacted our society, how green are these services is yet to be answered. Although, demand escalation for green services has grown due to societal awareness, the approaches to provide green services and establish Green SLAs remain oblivious for cloud or infrastructure providers. The main challenge for cloud provider is to manage Green SLAs with their customers while satisfying their business objectives, such as maximizing profits by lowering expenditure for green energy. Since, Green SLA needs to be proposed based on the presence of green energy, the intermittent nature of renewable sources makes it difficult to be achieved. In response, this paper presents a scheme for green energy management in the presence of explicit and implicit integration of renewable energy in data center. More specifically we propose three contributions: i) we introduce the concept of virtualization of green energy to address the uncertainty of green energy availability, ii) we extend the Cloud Service Level Agreement (CSLA) language to support Green SLA by introducing two new threshold parameters and iii) we introduce greenSLA algorithm which leverages the concept of virtualization of green energy to provide per interval specific Green SLA . Experiments were conducted with real workload profile from PlanetLab and server power model from SPECpower to demonstrate that, Green SLA can be successfully established and satisfied without incurring higher cost.

Orchestrating connectivity services to support elastic operations in datacenter federations

Datacenter federations are able to manage appropriately the green energy resources available in each datacenter (DC) thanks to their geographically distributed infrastructure, thus reducing energy expenditure. Scheduling algorithms can compute virtual machine migration, transferring huge amounts of raw data from one DC to another to minimize operational costs and ensuring a certain Quality of Experience. Because green energy availability greatly depends on weather conditions, in this work we present a statistical model to improve green solar energy availability estimation accuracy and we use it in a mixed integer linear programming formulation to compute optimal virtual machine placement. Optical connections can be used to provide connectivity services of enough capacity to support those migrations. In particular, elastic optical networks can provide connections with multi-granular bitrate, which can be adapted on demand. DC resource managers can request optical connections and control their capacity. However, that scheme involves the resource managers to implement algorithms and interfaces to deal with network specifics and complexity. To solve that issue, in this paper we propose coordinating transfer-based inter-DC connectivity services; inter-DC connectivity is requested in terms of volume of data and completion time. We analyze cost savings when each connectivity model is applied in a DC federation. For the sake of a compelling analysis, exhaustive simulation experiments are carried out considering realistic scenarios. Results show that the notification-based model can save up to 20 % of energy costs and more than 40 % of communication costs in the evaluated scenarios.

Reducing the expenses of geo-distributed data centers with portable containerized modules

In order to maximize their profits, big IT companies need to reduce the operating expenses (OpEx) and capital expenses (CapEx) of their geo-distributed data centers. To reduce OpEx, recent studies have proposed algorithms that dynamically dispatch workload among different data center sites, to exploit the variability in local electricity price and green energy availability. However, the amount of OpEx savings achieved by workload dispatching is often limited by the local computing capacity. To reduce CapEx, data centers try to minimize the needed increases in size based on estimated workload growth. However, workload overestimation would result in a high CapEx, while underestimation would hurt business increases.In this paper, we propose ExContainer, a novel strategy that leverages portable containerized modules to minimize both OpEx and CapEx for geo-distributed data centers. With ExContainer, containers are allocated monthly to different sites and workload is dispatched hourly, to minimize total OpEx. To cut CapEx, ExContainer finds the best trade-off between the degree of capacity expansion and the number of needed new containers to handle the workload peak. We evaluate ExContainer with real-world traces. Our experimental results show that compared to state-of-the-art solutions, ExContainer allows significant reduction in both OpEx and CapEx.

Energy-aware joint management of networks and Cloud infrastructures

Fueled by the massive adoption of Cloud services, the CO2 emissions of the Information and Communication Technology (ICT) systems are rapidly increasing. Overall service centers and networks account for 2–4% of global CO2 emissions and it is expected they can reach up to 10% in 5–10years. Service centers and communication networks have been managed independently so far, but the new generation of Cloud systems can be based on a strict integration of service centers and networking infrastructures. Moreover, geographically-distributed service centers are being widely adopted to keep Cloud services close to end users and to guarantee high performance. The geographical distribution of the computing facilities offers many opportunities for optimizing energy consumption and costs by means of a clever distribution of the computational workload exploiting different availability of renewable energy sources, but also different time zones and hourly energy pricing. Energy and cost savings can be pursued by dynamically allocating computing resources to applications at a global level, while communication networks allow to assign flexibly load requests and to move data.Even if in the last years a quite large research effort has been devoted to the energy efficiency of service centers and communication networks, limited work has been done for exploring the opportunities of integrated approaches able to exploit possible synergies between geographically distributed service centers and networks for accessing and interconnecting them. In this paper we propose an optimization framework able to jointly manage the use of brown and green energy in an integrated system and to guarantee quality requirements. We propose an efficient and accurate problem formulation that can be solved for real-size instances in few minutes to optimality. Numerical results, on a set of randomly generated instances and a case study representative of a large Cloud provider, show that the availability of green energy have a big impact on optimal energy management policies and that the contribution of the network is far from being negligible.

SEED: solar energy‐aware efficient scheduling for data centers

SUMMARYIt is well known that data centers are consuming a large amount of energy that incurs significant financial and environmental costs. Recently, there has been an increasing interest in utilizing green energy for data centers, where green energy sources include solar and wind. This paper studies the crucial problem of maximizing the utilization of green energy through scheduling complex jobs in data centers in order to reduce the use of traditional brown energy. However, it is highly challenging for data centers to make use of green energy. First, the availability of typical green energy is variable to dynamic changes of natural environments, for example, weather. Second, although predictions can be made for the future availability of green energy, it is inevitable that such predictions have errors. Third, jobs are associated with strict deadlines, and it is required that jobs are completed before their deadlines. Finally, because the reliability in a data center relies upon temperature, the awareness of temperature should be taken into account while maximizing the green energy. In this paper, we consider online scheduling of jobs whose arrivals to the data center system dynamically. In addition, we explicitly take the power consumption of switches into account when scheduling jobs onto computing nodes. Two solar energy‐aware algorithms called SEEDMin and SEEDMax have been proposed. Then, we extend SEED to RSEED with the awareness of reliability. To evaluate the effectiveness of the proposed algorithms, comprehensive simulations have been conducted, and the proposed algorithms are compared with other state‐of‐art algorithms. Experimental results demonstrate that both SEEDMin and SEEDMax can significantly increase the utilization of solar energy without violating job deadlines and overall energy budget. The amount of solar energy utilized by SEEDMin and SEEDMax is 33.4%and35.3% larger than that of two traditional scheduling algorithms, MinMin and MinMax, respectively. Also, it can be seen that RSEED greatly improves the reliability by decreasing the temperature. Copyright © 2013 John Wiley & Sons, Ltd.

Using Datacenter Simulation to Evaluate Green Energy Integration

Many simulators are available to evaluate performance and power tradeoffs in datacenters. The authors use one such simulator to demonstrate that by accurately provisioning green energy availability for longer time intervals, green energy prediction can improve overall energy efficiency.

Utilizing green energy prediction to schedule mixed batch and service jobs in data centers

As brown energy costs grow, renewable energy becomes more widely used. Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16]. In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production. This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.

Lightpath scheduling and routing for green data centres

Optical networking technologies enable data centres to be located near sources of green energy (i.e., renewable energy). Since some green energy sources are intermittent and are not always available, we need to dynamically connect distribution networks to the green energy powered data centres. On the other hand, the availability of green energy is reasonably predictable, and we are thus able to schedule connectivity to data centres in advance. We propose a WDM network planning model, which allows lightpaths to slide within their desired timing windows with no penalty on the optimization objective, and to slide beyond their desired timing windows with a deteriorating "green-level". Our simulation results show the tradeoffs between the consumption of brown energy (i.e., energy generated by carbon-intensive means), the capability of providing required connectivity to data centres, network resource utilization, and overall operation objective.