Cost Efficient Real Time Electricity Management Services for Green Community Using Fog

The computing devices in data centers of cloud and fog remain in continues running cycle to provide services. The long execution state of large number of computing devices consumes a significant amount of power, which emits an equivalent amount of heat in the environment. The performance of the devices is compromised in heating environment. The high powered cooling systems are installed to cool the data centers. Accordingly, data centers demand high electricity for computing devices and cooling systems. Moreover, in Smart Grid (SG) managing energy consumption to reduce the electricity cost for consumers and minimum rely on fossil fuel based power supply (utility) is an interesting domain for researchers. The SG applications are time-sensitive. In this paper, fog based model is proposed for a community to ensure real-time energy management service provision. Three scenarios are implemented to analyze cost efficient energy management for power-users. In first scenario, community’s and fog’s power demand is fulfilled from the utility. In second scenario, community’s Renewable Energy Resources (RES) based Microgrid (MG) is integrated with the utility to meet the demand. In third scenario, the demand is fulfilled by integrating fog’s MG, community’s MG and the utility. In the scenarios, the energy demand of fog is evaluated with proposed mechanism. The required amount of energy to run computing devices against number of requests and amount of power require cooling down the devices are calculated to find energy demand by fog’s data center. The simulations of case studies show that the energy cost to meet the demand of the community and fog’s data center in third scenario is 15.09% and 1.2% more efficient as compared to first and second scenarios, respectively. In this paper, an energy contract is also proposed that ensures the participation of all power generating stakeholders. The results advocate the cost efficiency of proposed contract as compared to third scenario. The integration of RES reduce the energy cost and reduce emission of CO 2 . The simulations for energy management and plots of results are performed in Matlab. The simulation for fog’s resource management, measuring processing, and response time are performed in CloudAnalyst.

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A Statistical Approach to Real-Time Updating and Automatic Scheduling of Physical Models

Volume 2: Thermal Management; Data Centers and Energy Efficient Electronic Systems ◽

10.1115/ipack2013-73042 ◽

2013 ◽

Author(s):

Huijing Jiang ◽

Xinwei Deng ◽

Vanessa Lopez ◽

Hendrik Hamann

Keyword(s):

High Resolution ◽

Real Time ◽

Physical Model ◽

Energy Management ◽

Data Center ◽

Data Centers ◽

Thermal Environment ◽

Physical Models ◽

Sensor Data ◽

Automatic Scheduling

Energy consumption of data center has increased dramatically due to the massive computing demands driven from every sector of the economy. Hence, data center energy management has become very important for operating data centers within environmental standards while achieving low energy cost. In order to advance the understanding of thermal management in data centers, relevant environmental information such as temperature, humidity and air quality are gathered through a network of real-time sensors or simulated via sophisticated physical models (e.g. computational fluid dynamics models). However, sensor readings of environmental parameters are collected only at sparse locations and thus cannot provide a detailed map of temperature distribution for the entire data center. While the physics models yield high resolution temperature maps, it is often not feasible, due to computational complexity of these models, to run them in real-time, which is ideally required for optimum data center operation and management. In this work, we propose a novel statistical modeling approach to updating physical model outputs in real-time and providing automatic scheduling for re-computing physical model outputs. The proposed method dynamically corrects the discrepancy between a steady-state output of the physical model and real-time thermal sensor data. We show that the proposed method can provide valuable information for data center energy management such as real-time high-resolution thermal maps. Moreover, it can efficiently detect systematic changes in a data center thermal environment, and automatically schedule physical models to be re-executed whenever significant changes are detected.

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Perimeter Cooling Unit and Localized Row-Based Cooling Unit Transient Air Flow Effects Modeling and Characterization in Data Centers

Volume 8B: Heat Transfer and Thermal Engineering ◽

10.1115/imece2015-51012 ◽

2015 ◽

Author(s):

Tianyi Gao ◽

James Geer ◽

Bahgat G. Sammakia ◽

Russell Tipton ◽

Mark Seymour

Keyword(s):

Thermal Management ◽

Data Center ◽

Data Centers ◽

Cooling System ◽

Air Flow ◽

Cooling Systems ◽

Dynamic Thermal Management ◽

Hybrid Cooling ◽

Cooling Unit ◽

Cooling Air

Cooling power constitutes a large portion of the total electrical power consumption in data centers. Approximately 25%∼40% of the electricity used within a production data center is consumed by the cooling system. Improving the cooling energy efficiency has attracted a great deal of research attention. Many strategies have been proposed for cutting the data center energy costs. One of the effective strategies for increasing the cooling efficiency is using dynamic thermal management. Another effective strategy is placing cooling devices (heat exchangers) closer to the source of heat. This is the basic design principle of many hybrid cooling systems and liquid cooling systems for data centers. Dynamic thermal management of data centers is a huge challenge, due to the fact that data centers are operated under complex dynamic conditions, even during normal operating conditions. In addition, hybrid cooling systems for data centers introduce additional localized cooling devices, such as in row cooling units and overhead coolers, which significantly increase the complexity of dynamic thermal management. Therefore, it is of paramount importance to characterize the dynamic responses of data centers under variations from different cooling units, such as cooling air flow rate variations. In this study, a detailed computational analysis of an in row cooler based hybrid cooled data center is conducted using a commercially available computational fluid dynamics (CFD) code. A representative CFD model for a raised floor data center with cold aisle-hot aisle arrangement fashion is developed. The hybrid cooling system is designed using perimeter CRAH units and localized in row cooling units. The CRAH unit supplies centralized cooling air to the under floor plenum, and the cooling air enters the cold aisle through perforated tiles. The in row cooling unit is located on the raised floor between the server racks. It supplies the cooling air directly to the cold aisle, and intakes hot air from the back of the racks (hot aisle). Therefore, two different cooling air sources are supplied to the cold aisle, but the ways they are delivered to the cold aisle are different. Several modeling cases are designed to study the transient effects of variations in the flow rates of the two cooling air sources. The server power and the cooling air flow variation combination scenarios are also modeled and studied. The detailed impacts of each modeling case on the rack inlet air temperature and cold aisle air flow distribution are studied. The results presented in this work provide an understanding of the effects of air flow variations on the thermal performance of data centers. The results and corresponding analysis is used for improving the running efficiency of this type of raised floor hybrid data centers using CRAH and IRC units.

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Minimization of Energy Using Heuristic Resource Allocation and Migration for Cloud Computing

International Journal of Knowledge and Systems Science ◽

10.4018/ijkss.2021010106 ◽

2021 ◽

Vol 12 (1) ◽

pp. 74-83

Author(s):

Manjunatha S. ◽

Suresh L.

Keyword(s):

Cloud Computing ◽

Data Center ◽

Large Scale ◽

Data Centers ◽

Service Providers ◽

Cost Effective ◽

Cooling Systems ◽

Operational Costs ◽

Cloud Computing Service ◽

And Migration

Data center is a cost-effective infrastructure for storing large volumes of data and hosting large-scale service applications. Cloud computing service providers are rapidly deploying data centers across the world with a huge number of servers and switches. These data centers consume significant amounts of energy, contributing to high operational costs. Thus, optimizing the energy consumption of servers and networks in data centers can reduce operational costs. In a data center, power consumption is mainly due to servers, networking devices, and cooling systems, and an effective energy-saving strategy is to consolidate the computation and communication into a smaller number of servers and network devices and then power off as many unneeded servers and network devices as possible.

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Operation Scheduling of Household Load, EV and BESS Using Real Time Pricing, Incentive Based DR and Peak Power Limiting Strategy

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2018-0186 ◽

2018 ◽

Vol 20 (1) ◽

Author(s):

Sandeep Kakran ◽

Saurabh Chanana

Keyword(s):

Real Time ◽

Energy Management ◽

Cost Reduction ◽

Management System ◽

Peak Power ◽

Base Case ◽

Energy Management System ◽

Electricity Cost ◽

Home Energy Management ◽

Home Energy Management System

Abstract Demand response (DR) programs have become powerful tools of the smart grids, which provide opportunities for the end-use consumers to participate actively in the energy management programs. This paper investigates impact of different DR strategies in a home-energy management system having consumer with regular load, electric vehicle (EV) and battery-energy storage system (BESS) in the home. The EV is considered as a special type of load, which can also work as an electricity generation source by discharging the power in vehicle-to-home mode during high price time. BESS and a small renewable energy source in form of rooftop photovoltaic panels give a significant contribution in the energy management of the system. As the main contribution to the literature, a mixed integer linear programming based model of home energy management system is formulated to minimize the daily cost of electricity consumption under the effect of different DR programs; such as real time price based DR program, incentive based DR program and peak power limiting DR program. Finally, total electricity prices are analysed in the case studies by including different preferences of the household consumer under mentioned DR programs. A total of 26.93 % electricity cost reduction is noticed with respect to base case, without peak limiting DR and 19.93 % electricity cost reduction is noticed with respect to base case, with peak limiting DR.

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An Implementation of Demand Side Energy Management for Refined Load Control

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1092-1093.409 ◽

2015 ◽

Vol 1092-1093 ◽

pp. 409-413

Author(s):

Xiao Ming Jin ◽

Xue Lin Zhao ◽

Kun Qi Jia ◽

Guang Yu He

Keyword(s):

Energy Saving ◽

Energy Management ◽

Energy Demand ◽

Air Conditioner ◽

Demand Side ◽

Zigbee Network ◽

Remote Controller ◽

Cost Efficient ◽

End Use ◽

And Control

Demand Side Energy Management System (DSEMS) manages energy demand by controlling end-use appliances in a refined, energy-saving, cost-efficient and user-friendly way. The DSEMS runs on an Android tablet computer, which serves as energy gateway to communicate with two types of controllers via ZigBee network. Smart sockets connected to the ZigBee network will monitor and control plug-in loads and IR remote controller (IRRC) for air-conditioner temperature setting. The proposed system has been installed in an apartment with over 24 rooms as a paradigm, which proved that the DSEMS can realize the autonomous energy-saving via real-time surveillance and control on household appliances.

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Expanded Assessment of a Practical Thermally Aware Energy-Optimized Load Placement Strategy for Open-Aisle, Air-Cooled Data Centers

Journal of Electronic Packaging ◽

10.1115/1.4024945 ◽

2013 ◽

Vol 135 (3) ◽

Cited By ~ 2

Author(s):

Dustin W. Demetriou ◽

H. Ezzat Khalifa

Keyword(s):

Real Time ◽

Data Center ◽

Data Centers ◽

Thermal Environment ◽

Inlet Temperature ◽

Airflow Rate ◽

Time Data ◽

Improve Energy Efficiency ◽

Air Temperatures ◽

Holistic Optimization

This paper expands on the work presented by Demetriou and Khalifa (Demetriou and Khalifa, 2013, “Thermally Aware, Energy-Based Load Placement in Open-Aisle, Air-Cooled Data Centers,” ASME J. Electron. Packag., 135(3), p. 030906) that investigated practical IT load placement options in open-aisle, air-cooled data centers. The study found that a robust approach was to use real-time temperature measurements at the inlet of the racks to remove IT load from the servers with the warmest inlet temperature. By considering the holistic optimization of the data center load placement strategy and the cooling infrastructure optimization, for a range of data center IT utilization levels, this study investigated the effect of ambient temperatures on the data center operation, the consolidation of servers by completely shutting them off, a complementary strategy to those presented by Demetriou and Khalifa (Demetriou and Khalifa, 2013, “Thermally Aware, Energy-Based Load Placement in Open-Aisle, Air-Cooled Data Centers,” ASME J. Electron. Packag., 135(3), p. 030906) for increasing the IT load beginning with servers that have the coldest inlet temperature and finally the development of load placement rules via either static (i.e., during data center benchmarking) or dynamic (using real-time data from the current thermal environment) allocation. In all of these case studies, by using a holistic optimization of the data center and associated cooling infrastructure, a key finding has been that a significant amount of savings in the cooling infrastructure's power consumption is seen by reducing the CRAH's airflow rate. In many cases, these savings can be larger than providing higher temperature chilled water from the refrigeration units. Therefore, the path to realizing the industry's goal of higher IT equipment inlet temperatures to improve energy efficiency should be through both a reduction in air flow rate and increasing supply air temperatures and not necessarily through only higher CRAH supply air temperatures.

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Exploring Energy Management on GPUs in Game Architectures

Journal of Interactive Systems ◽

10.5753/jis.2014.646 ◽

2014 ◽

Vol 5 (2) ◽

pp. 1

Author(s):

Marcelo Zamith ◽

Luis Valente ◽

Mark Joselli ◽

José Ricardo Silva Junior ◽

Esteban Clua ◽

...

Keyword(s):

Real Time ◽

Energy Management ◽

Energy Demand ◽

Interactive Systems ◽

Multiple Gpus ◽

Processing Power ◽

Physical Simulations ◽

Consumption Energy ◽

Time Systems ◽

Near Future

CPUs and GPUs have been evolving rapidly over time regarding their capabilities and processing power. This has opened many new possibilities for interactive and real time systems, such as more sophisticated scene realism, more precise and complex artificial intelligence, and better physical simulations. However, these improvements come at a cost: increase of energy consumption. Energy management in interactive and real time architectures have not been receiving much attention over the years, but this issue is likely to become important in the near future due to the increasing energy demand and consumption required by top-notch game applications(especially regarding the mobile and portable consoles). In this paper we introduce the concept of intelligent energy management for games and interactive systems and address the aforementioned issue through these contributions: 1) an investigation of works related to energy management (in general); 2) implementations of feasibility tests for energy management on GPUs; and 3) a novel game architecture with energy management, using multiple GPUs.

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An Efficient Technique-Based Distributed Energy Management for Hybrid MG System: A Hybrid SBLA-CGO Technique

10.21203/rs.3.rs-166971/v1 ◽

2021 ◽

Author(s):

T Logeswaran ◽

Francis H Shajin ◽

Paulthurai Rajesh

Keyword(s):

Renewable Energy ◽

Energy Management ◽

Energy Demand ◽

Present Method ◽

Perfect Match ◽

Energy System ◽

Photovoltaic System ◽

Present System ◽

Prediction Errors ◽

Electricity Cost

Abstract This manuscript presents an optimal energy management on microgrid (MG) connected to the grid that chooses the energy scheduling based on the proposed method. The present method is the joint implementation of the Side-Blotched Lizard Algorithm (SBLA) and the Chaos Game Optimization Algorithm (CGO) and hence it is named as SBLA-CGO method. Here, the MG system contains a photovoltaic system (PV), wind turbine (WT), battery storage (BS) and fuel cell (FC). Constantly, the necessary load demand of MG system connected to the grid is measured with SBLA method. The CGO increases the perfect match of MG with the expected load requirement. Moreover, renewable energy forecasting errors are evaluated twice by MG energy management for minimizing the control. Through the operation of MG schedule of several RES to decrease the electricity cost using the first method. Balancing the energy flow and minimize the effects of prediction errors according to the rule presented as planned power reference is second method. The main aim of the present method is evaluated with connection of fuel cost, the variation of energy per hour of the electrical grid, the cost of operation and maintenance of MG system connected to the network. According to RES, the energy demand and SOC of the storage elements are the conditions. Renewable energy system units use batteries as energy sources to allow them to operate continuously on stable and sustainable power generation. The analysis of the present method is analyzed by comparing with the other systems. The results of the comparison assess the strength of the present system and confirm their potential for solving the issues.

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Energy Optimization in Smart Urban Buildings Using Bio-Inspired Ant Colony Optimization

10.21203/rs.3.rs-989640/v1 ◽

2021 ◽

Author(s):

Fakhri Alam Khan ◽

Kifayat Ullah ◽

Atta ur Rahman ◽

Sajid Anwar

Keyword(s):

Ant Colony Optimization ◽

Real Time ◽

Energy Management ◽

Management System ◽

Ant Colony ◽

Consumption Pattern ◽

Energy Management System ◽

Electricity Cost ◽

Home Appliances ◽

Home Energy Management

Abstract Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where Demand Response (DR) strategy is used to modify the nature of demand of consumer. In return, utility pay incentives to the consumer. The increasing load demand in residential area and irregular electricity load profile have encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. In order to meet the electricity demand of the consumers, the energy consumption pattern of a consumer is maintained through scheduling the appliances in day-ahead and real-time bases. In this paper we propose a hybrid algorithm Bacterial foraging Ant colony optimization is proposed (HB-ACO) which contain both BFA and ACO properties. Primary objectives of scheduling is to shift load from On-peak hour to Off-peak hours to reduce electricity cost and peak to average ratio. A comparison of these algorithms is also presented in terms of performance parameters electricity cost, reduction of PAR and user comfort in term of waiting time. The proposed techniques are evaluated using two pricing scheme time of use and critical peak pricing. The HB-ACO shows better performance as compared to ACO and BFA which is evident from the simulation results Moreover the concept of coordination among home appliances is presented for real time scheduling. We consider this is knapsack problem and solve it through Ant colony optimization algorithm.

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