Synergistic model of power system cloud computing based on Mobile-agent

Tang, Liangrui; Li, Jiale; Wu, Runze

doi:10.1109/icnidc.2012.6418748

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…In another approach power monitoring and early warning system facilities were provided using cloud platform [12]. A mobile agent architecture for cloud based energy management system was proposed to handle customer request more efficiently [33]. In another approach a dynamic cloud based demand response model was proposed to periodically forecast demand and by dynamically managing available resources to reduce the peak demand [31].…”

Section: Related Workmentioning

confidence: 99%

Fog Paradigm for Local Energy Management Systems

Javed

Rana

Marmaras

et al. 2017

Cloud Infrastructures, Services, and IoT Systems for Smart Cities

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Cloud Computing infrastructures have been extensively deployed to support energy computation within built environments. This has ranged from predicting potential energy demand for a building (or a group of buildings), undertaking heat profile/energy distribution simulations, to understanding the impact of climate and weather on building operation. Cloud computing usage in these scenarios have benefited from resource elasticity, where the number and types of resources can change based on the complexity of the simulation being considered. Many such energy simulations require resource scalability and on-demand resources to meet the computational and data storage requirements. While there are numerous advantages of using a cloud based energy management system, there are also significant limitations. For instance, many such systems assume that the data has been pre-staged at a cloud platform prior to simulation, and do not take account of data transfer times from the building to the simulation platform.The need for supporting computation at edge resources, which can be hosted within the building itself or shared within a building complex, has become important over recent year. Additionally, network connectivity between the sensing infrastructure within a built environment and a data centre where analysis is to be carried out can be intermittent or may fail. There is therefore also a need to better understand how computation/analysis can be carried out closer to the data capture site to complement analysis that would be undertaken at the data centre. We describe how the Fog computing paradigm can be used to support some of these requirements, extending the capability of a data centre to support energy simulation within built environments.

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Section: Related Workmentioning

confidence: 99%

Fog Paradigm for Local Energy Management Systems

Javed

Rana

Marmaras

et al. 2017

Cloud Infrastructures, Services, and IoT Systems for Smart Cities

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show abstract

“…Research on data and information modeling are as follows: [14] proposed cloud-based data management model for smart grids with preserving confidentiality and privacy of data, [31] proposed layered model to do resource scheduling and synergistic management by using Mobile-agent to build power systems cloud computing platform, [32] proposed a cloudbased service-oriented architecture to develop an energy management system, [23] proposed a cloud-based smart grid information management model, [33] proposed an architecture and design of cloud-based large-scale real-time data analysis system for smart grid, [22] proposed a cloud-based SG information system and a resource optimization framework.…”

Section: Cloud-based Smart Grids Applicationsmentioning

confidence: 99%

A comprehensive assessment of cloud computing for smart grid applications: A multi-perspectives framework

Chaichi

Lavoie

Zarrin

et al. 2015

2015 Portland International Conference on Management of Engineering and Technology (PICMET)

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The IT system is one of the key infrastructures in order to add intelligent feature to power grids. Many papers anticipated a rocky road for IT system integration into power system and suggested the cloud computing adoption as a smart grid IT infrastructure to smooth the integration. Though cloud computing could overcome some of these integration challenges, it would bring some of its own. This study intends to give more insight to decision makers or utility companies to make a better decision whether to adopt cloud computing with regards to their needs or not. In the problem statement section is explained why the current approach to define advantages and disadvantages of cloud computing from selective point of views is not enough and might not be accurate, and a comprehensive assessment is required. The proposed multi-perspective framework takes all the criteria that would affect the assessment of cloud computing and put them into organizational, technical, economical, social and political categories. This framework would guide players in smart grid to better define benefits and challenges of cloud computing according to their needs.

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“…In another approach, power monitoring and early warning system facilities were provided using a cloud platform [17]. A mobile agent architecture for cloud based energy management system was proposed to handle customer request more efficiently [46]. Focusing on the energy demand a dynamic cloud based demand response model was proposed to periodically forecast demand and by dynamically managing available resources to reduce the peak demand [42].…”

Section: Introductionmentioning

confidence: 99%

Secure Data Sharing and Analysis in Cloud-Based Energy Management Systems

Anthi

Javed

Rana

et al. 2017

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Analysing data acquired from one or more buildings (through specialist sensors, energy generation capability such as PV panels or smart meters) via a cloud-based Local Energy Management System (LEMS) is increasingly gaining in popularity. In a LEMS, various smart devices within a building are monitored and/or controlled to either investigate energy usage trends within a building, or to investigate mechanisms to reduce total energy demand. However, whenever we are connecting externally monitored/controlled smart devices there are security and privacy concerns. We describe the architecture and components of a LEMS and provide a survey of security and privacy concerns associated with data acquisition and control within a LEMS. Our scenarios specifically focus on the integration of Electric Vehicles (EV) and Energy Storage Units (ESU) at the building premises, to identify how EVs/ESUs can be used to store energy and reduce the electricity costs of the building. We review security strategies and identify potential security attacks that could be carried out on such a system, while exploring vulnerable points in the system. Additionally, we will systematically categorize each vulnerability and look at potential attacks exploiting that vulnerability for LEMS. Finally, we will evaluate current counter measures used against these attacks and suggest possible mitigation strategies.

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Synergistic model of power system cloud computing based on Mobile-agent

Cited by 9 publications

References 6 publications

Fog Paradigm for Local Energy Management Systems

Fog Paradigm for Local Energy Management Systems

A comprehensive assessment of cloud computing for smart grid applications: A multi-perspectives framework

Secure Data Sharing and Analysis in Cloud-Based Energy Management Systems

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