Cloud computing for energy management in smart grid - an application survey

The smart grid is the emerging energy system wherein the application of information technology, tools and techniques that make the grid run more efficiently. It possesses demand response capacity to help balance electrical consumption with supply. The challenges and opportunities of emerging and future smart grids can be addressed by cloud computing. To focus on these requirements, we provide an in-depth survey on different cloud computing applications for energy management in the smart grid architecture. In this survey, we present an outline of the current state of research on smart grid development. We also propose a model of cloud based economic power dispatch for smart grid.

[1]  Ali Mohammad Ranjbar,et al.  A cloud computing framework on demand side management game in smart energy hubs , 2015 .

[2]  Sehyun Park,et al.  Cloud Computing-Based Building Energy Management System with ZigBee Sensor Network , 2012, 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[3]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

[4]  Xi Fang,et al.  Evolving Smart Grid Information Management Cloudward: A Cloud Optimization Perspective , 2013, IEEE Transactions on Smart Grid.

[5]  Sebnem Rusitschka,et al.  Smart Grid Data Cloud: A Model for Utilizing Cloud Computing in the Smart Grid Domain , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[6]  P. Mell,et al.  The NIST Definition of Cloud Computing , 2011 .

[7]  Ali Mohammad Ranjbar,et al.  Integrated Demand Side Management Game in Smart Energy Hubs , 2015, IEEE Transactions on Smart Grid.

[8]  Mario Gerla,et al.  Energy Service Interface: Accessing to Customer Energy Resources for Smart Grid Interoperation , 2013, IEEE Journal on Selected Areas in Communications.

[9]  Ching-Hsien Hsu,et al.  Implementation of Smart Power Management and Service System on Cloud Computing , 2012, 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing.

[10]  Xuan Li,et al.  Pricing and peak aware scheduling algorithm for cloud computing , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[11]  Nada Golmie,et al.  NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 1.0 , 2010 .

[12]  Albert Y. Zomaya,et al.  Energy Conscious Scheduling for Distributed Computing Systems under Different Operating Conditions , 2011, IEEE Transactions on Parallel and Distributed Systems.

[13]  N.D. Hatziargyriou,et al.  A Stability Algorithm for the Dynamic Analysis of Inverter Dominated Unbalanced LV Microgrids , 2007, IEEE Transactions on Power Systems.

[14]  Irina Branovic,et al.  Smart power grid and cloud computing , 2013 .

[15]  Enyew Sileshi Gebretsadik,et al.  Cloud computing for monitoring and controlling of distributed energy generations , 2014, 2014 49th International Universities Power Engineering Conference (UPEC).

[16]  Andrey Brito,et al.  Low Cost Energy Forecasting for Smart Grids Using Stream Mine 3G and Amazon EC2 , 2014, 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing.

[17]  Sehyun Park,et al.  Intelligent cloud home energy management system using household appliance priority based scheduling based on prediction of renewable energy capability , 2012, IEEE Transactions on Consumer Electronics.

[18]  T. Rajeev,et al.  A cloud computing approach for power management of microgrids , 2011 .

[19]  Sherali Zeadally,et al.  Cloud-Assisted Context-Aware Vehicular Cyber-Physical System for PHEVs in Smart Grid , 2017, IEEE Systems Journal.

[20]  Berthold Bitzer,et al.  Cloud computing framework for smart grid applications , 2013, 2013 48th International Universities' Power Engineering Conference (UPEC).

[21]  Yun Zhu,et al.  Research on power dispatching automation system based on cloud computing , 2012, IEEE PES Innovative Smart Grid Technologies.

[22]  Alex Hebra The reality of generating and transmitting ultrahigh voltage power, Part 1 , 2012, IEEE Instrumentation & Measurement Magazine.

[23]  M. Vetterli,et al.  Wireless Sensor Networks for Environmental Monitoring: The SensorScope Experience , 2008, 2008 IEEE International Zurich Seminar on Communications.

[24]  George K. Karagiannidis,et al.  Big Data Analytics for Dynamic Energy Management in Smart Grids , 2015, Big Data Res..

[25]  Young-Jin Kim,et al.  Cloud-based demand response for smart grid: Architecture and distributed algorithms , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[26]  Lei Zhang,et al.  Research and Application on the Cloud-Computing-Based Power Dispatching IT Architecture , 2011, 2011 Asia-Pacific Power and Energy Engineering Conference.

[27]  Sudip Misra,et al.  Cloud Computing Applications for Smart Grid: A Survey , 2015, IEEE Transactions on Parallel and Distributed Systems.

[28]  Hamed Mohsenian Rad,et al.  Energy and Performance Management of Green Data Centers: A Profit Maximization Approach , 2013, IEEE Transactions on Smart Grid.

[29]  A. Ukil Towards networked smart digital sensors: A review , 2008, 2008 34th Annual Conference of IEEE Industrial Electronics.

[30]  Yan Li,et al.  Cloud Service based intelligent power monitoring and early-warning system , 2012, IEEE PES Innovative Smart Grid Technologies.

[31]  S. K. Muthusundar,et al.  A sustainable renewable energy mix option for the secluded society , 2014 .

[32]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[33]  S. Ashok,et al.  Dynamic load-shifting program based on a cloud computing framework to support the integration of renewable energy sources , 2015 .

[34]  Deepak Kumar,et al.  MOSOA-Based Multiobjective Design of Power Distribution Systems , 2017, IEEE Systems Journal.