Demand side management scheme in smart grid with cloud computing approach using stochastic dynamic programming

Reka, S. Sofana; Ramesh, V.

doi:10.1016/j.pisc.2016.04.024

Cited by 38 publications

(13 citation statements)

References 5 publications

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“…In addition, there are also some limitations of cloud, i.e., latency, security and downtime problems. In order to ameliorate the latency, reliability, and resiliency of the services, one new strategy is presented in [24], in which fog platform is utilized by considering the energy management as a service. This strategy also discusses two energy management prototypes: home energy management prototype and MG energy management prototype for electricity bill and delay minimization.…”

Section: Methodologies Regarding Fog Based Architecturementioning

confidence: 99%

Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework

Javaid

Saba

et al. 2019

Energies

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In this work, a new orchestration of Consumer to Fog to Cloud (C2F2C) based framework is proposed for efficiently managing the resources in residential buildings. C2F2C is a three layered framework consisting of cloud layer, fog layer and consumer layer. Cloud layer deals with on-demand delivery of the consumer’s demands. Resource management is intelligently done through the fog layer because it reduces the latency and enhances the reliability of cloud. Consumer layer is based on the residential users and their electricity demands from the six regions of the world. These regions are categorized on the bases of the continents. Two control parameters are considered: clusters of buildings and load requests, whereas four performance parameters are considered: Request Per Hour (RPH), Response Time (RT), Processing Time (PT) and cost in terms of Virtual Machines (VMs), Microgrids (MGs) and data transfer. These parameters are analysed by the round robin algorithm, equally spread current execution algorithm and our proposed algorithm shortest job first. Two scenarios are used in the simulations: resource allocation using MGs and resource allocation using MGs and power storage devices for checking the effectiveness of the proposed work. The simulation results of the proposed technique show that it has outperformed the previous techniques in terms of the above-mentioned parameters. There exists a tradeoff in the PT and RT as compared to cost of VM, MG and data transfer.

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Section: Methodologies Regarding Fog Based Architecturementioning

confidence: 99%

Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework

Javaid

Saba

et al. 2019

Energies

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“…The problem was subject to a predefined equipment rating, preferable time interval and operating period. In [33], a peak to average ratio minimization was achieved by demand side management and considering cloud computing. In [34], the cost of peak to average ratio was minimized considering energy scheduling through a game theory.…”

Section: Peak To Average Ratio Minimizationmentioning

confidence: 99%

Enabling electricity access: revisiting load models for AC‐grid operation ‐ Part I

Al-Sumaiti

Salama

Moursi

et al. 2019

IET Generation, Transmission & Distribution

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Meeting electricity demand in remote communities and non-electrified regions in the poor developing world is a challenge. Power generation is in shortage compared to electricity demand. Electric utilities either would enforce grid's zonal load curtailment or not electrify regions. Controlling electricity demand can play a vital role in enabling electricity access; however, weather uncertainty drives electricity demand variability. This paper provides an overview of current demand side management research, identify research gaps and propose a more promising approach to enable electricity access. Also, it proposes manipulating appliances models to fit their operation in applications where power supply shortage is an issue such. The proposed work considers the effect of the probabilistic nature of weather and meeting AC grid codes of operation.

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“…Demand for a resource varies and is uncontrollable. 29 Maintaining an efficient DSM is very complicated as many variations may occur. This is not that much of an issue when we consider only the industrial image processing aspect.…”

Section: Demand-side Management (Dsm): This Ismentioning

confidence: 99%

Machine learning–based automated image processing for quality management in industrial Internet of Things

Rahmatov

Paul

Saeed

et al. 2019

International Journal of Distributed Sensor Networks

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The aim of this article is to automate quality control once a product, essentially a central processing unit system, is manufactured. Creating a model that helps in quality control, increases efficiency and speed of production by rejecting abnormal products automatically is vital. A widely used technology for this is to use industrial image processing that is based on the use of special cameras or imaging systems installed within the production line. In this article, we propose a highly efficient model to automate central processing unit system production lines in an industry such that images of the production lines are scanned and any abnormalities in their assembly are pointed out by the model and information about this is transferred to the system administrator via a cyber-physical cloud system network. A machine learning-based approach is used for proper classification. This model not only focuses on just the abnormalities but also helps in configuring the angles from which images of the production are taken, and our methods show 92% accuracy.

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Demand side management scheme in smart grid with cloud computing approach using stochastic dynamic programming

Cited by 38 publications

References 5 publications

Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework

Intelligent Resource Allocation in Residential Buildings Using Consumer to Fog to Cloud Based Framework

Enabling electricity access: revisiting load models for AC‐grid operation ‐ Part I

Machine learning–based automated image processing for quality management in industrial Internet of Things

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