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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.
Rasool Bukhsh; Muhammad Umar Javed; Aisha Fatima; Nadeem Javaid; Muhammad Shafiq; Jin-Ghoo Choi. Cost Efficient Real Time Electricity Management Services for Green Community Using Fog †. Energies 2020, 13, 3164 .
AMA StyleRasool Bukhsh, Muhammad Umar Javed, Aisha Fatima, Nadeem Javaid, Muhammad Shafiq, Jin-Ghoo Choi. Cost Efficient Real Time Electricity Management Services for Green Community Using Fog †. Energies. 2020; 13 (12):3164.
Chicago/Turabian StyleRasool Bukhsh; Muhammad Umar Javed; Aisha Fatima; Nadeem Javaid; Muhammad Shafiq; Jin-Ghoo Choi. 2020. "Cost Efficient Real Time Electricity Management Services for Green Community Using Fog †." Energies 13, no. 12: 3164.
The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.
Rasool Bukhsh; Nadeem Javaid; Zahoor Ali Khan; Farruh Ishmanov; Muhammad Khalil Afzal; Zahid Wadud. Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid. Energies 2018, 11, 3345 .
AMA StyleRasool Bukhsh, Nadeem Javaid, Zahoor Ali Khan, Farruh Ishmanov, Muhammad Khalil Afzal, Zahid Wadud. Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid. Energies. 2018; 11 (12):3345.
Chicago/Turabian StyleRasool Bukhsh; Nadeem Javaid; Zahoor Ali Khan; Farruh Ishmanov; Muhammad Khalil Afzal; Zahid Wadud. 2018. "Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid." Energies 11, no. 12: 3345.
The influence of Information Communication and Technology (ICT) in power systems necessitates Smart Grid (SG) with monitoring and real-time control of electricity consumption. In SG, huge requests are generated from the smart homes in residential sector. Thus, researchers have proposed cloud based centralized and fog based semi-centralized computing systems for such requests. The cloud, unlike the fog system, has virtually infinite computing resources; however, in the cloud, system delay is the challenge for real-time applications. The prominent features of fog are; awareness of location, low latency, wired and wireless connectivity. In this paper, the impact of longer delay of cloud in SG applications is addressed. We proposed a cloud-fog based system for efficient processing of requests coming from the smart homes, their quick response and ultimately reduced cost. Each smart home is provided with a 5G based Home Energy Management Controller (HEMC). Then, the 5G-HEMC communicates with the High Performance Fog (HPF). The HPFs are capable of processing energy consumers’ huge requests. Virtual Machines (VMs) are installed on physical systems (HPFs) to entertain the requests using First Come First Service (FCFS) and Ant Colony Optimization (ACO) algorithms along with Optimized Response Time Policy (ORTP) for the selection of potential HPF for efficient processing of the requests with maximum resource utilization. It is analysed that size and number of virtual resources affect the performance of the computing system. In the proposed system model, micro grids are introduced in the vicinity of energy consumers for uninterrupted and cost optimized power supply. The impact of the number of VMs on the performance of HPFs is analysed with extensive simulations with three scenarios.
Rasool Bakhsh; Nadeem Javaid; Itrat Fatima; Majid Iqbal Khan; Khaled. A. Almejalli. Towards Efficient Resource Utilization Exploiting Collaboration between HPF and 5G Enabled Energy Management Controllers in Smart Homes. Sustainability 2018, 10, 3592 .
AMA StyleRasool Bakhsh, Nadeem Javaid, Itrat Fatima, Majid Iqbal Khan, Khaled. A. Almejalli. Towards Efficient Resource Utilization Exploiting Collaboration between HPF and 5G Enabled Energy Management Controllers in Smart Homes. Sustainability. 2018; 10 (10):3592.
Chicago/Turabian StyleRasool Bakhsh; Nadeem Javaid; Itrat Fatima; Majid Iqbal Khan; Khaled. A. Almejalli. 2018. "Towards Efficient Resource Utilization Exploiting Collaboration between HPF and 5G Enabled Energy Management Controllers in Smart Homes." Sustainability 10, no. 10: 3592.
Smart grid plays a significant role in decreasing of electricity consumption cost through Demand Side Management (DSM). Smart homes, a part of smart grid contributes a lot in minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to scheduling of home appliances. This scheduling problem is considered as an optimization problem. Meta-heuristic algorithms have attracted increasing attention in last few years for solving optimization problems. Hence, in this study we propose an efficient scheme in Home Energy Management System (HEMS) using Genetic Algorithm (GA) and Cuckoo search algorithm to solve optimization problem. The proposed scheme is implemented on a single smart home and a smart building; comprising of thirty smart homes. Real Time Pricing (RTP) signals are used in term of electricity cost estimation for both single smart home and a smart building. Experimental results demonstrate the extremely effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and Peak to Average Ratio (PAR) minimization. Moreover, our proposed scheme obtains the desired tradeoff between electricity cost and user waiting time.
Sheraz Aslam; Rasool Bukhsh; Adia Khalid; Nadeem Javaid; Ibrar Ullah; Itrat Fatima; Qadeer Ul Hasan. An Efficient Home Energy Management Scheme Using Cuckoo Search. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2017, 167 -178.
AMA StyleSheraz Aslam, Rasool Bukhsh, Adia Khalid, Nadeem Javaid, Ibrar Ullah, Itrat Fatima, Qadeer Ul Hasan. An Efficient Home Energy Management Scheme Using Cuckoo Search. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2017; ():167-178.
Chicago/Turabian StyleSheraz Aslam; Rasool Bukhsh; Adia Khalid; Nadeem Javaid; Ibrar Ullah; Itrat Fatima; Qadeer Ul Hasan. 2017. "An Efficient Home Energy Management Scheme Using Cuckoo Search." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 167-178.