Assessment of Communications Infrastructure and Protocols for Smart Grid Metering by Game Theory Concept

Vikas Khare, Cheshta J. Khare

Abstract


Smart grid dependent on various class of innovation, for example, PC based remote control and computerization of power or energy grid and in another way, it would be a way in which transmission & distribution are controlled by computer and information technology based system. In this paper game theory concept is used for assessment and analysis of communication infrastructure and protocols for smart grid metering. The paper presents concepts of game theory inneighborhood area Network and home area network based smart metering. Further smart metering communication, meter acting as an interface and controller acting as an interface is optimized with the help of game theory based gambit software.


Full Text:

PDF

References


Sayed K. & Gabbar H.A. SCADA and smart energy grid control automation. Smart Energy Grid Engineering. 2017:pp. 481-514.

Ruland K. C. & Sassmannshausen J.End-to-end-authentication in smart grid control. Smart Energy Grid Engineering. 2017: pp. 453-479.

Ali S.M. & Jawad M.Wide area smart grid architectural model and control: A survey. Renewable and Sustainable Energy Reviews. October 2016; 64: pp. 311-328.

Patteeuw D. & Lieve H. Combined design and control optimization of residential heating systems in a smart-grid context. Energy and Buildings. December 2016; 133: pp. 640-657.

Gao D., Yongjun S. & Yuehong L.A robust demand response control of commercial buildings for smart grid under load prediction uncertainty. Energy. December 2015; 93(Part 1):pp. 275-283.

Reka S.S. & Ramesh V. Industrial demand side response modelling in smart grid using stochastic optimisation considering refinery process. Energy and Buildings. 2016;127: pp. 84-94.

Saad W.& Han Z. Game theoretic method for the smart grid.IEEE Signal Processing Magazine, Special Issue on Signal Processing Techniques for the Smart Grid. 2012.

Ahat M.& Amar S.B.Smart grid & optimization.American Journal of Operations Research. 2013; 3: pp. 196-206.

Hang J.S.& Kim M.Game theory based approach for energy routing in a smart grid network.Journal of computer networks and communications. 2016: pp. 1-8.

Khare V., Nema S. & Baredar P. Application of game theory in solar wind hybrid energy system. International journal of electrical and electronics engineering research (IJEEER). December 2012; 2(4): pp. 25-32.

Khare V., Nema S. & Baredar P.Game theory based cournot’s model of solar wind hybrid renewable energy system.2013 International Conference on Renewable Energy and Sustainable Energy [ICRESE‘13], IEEE. 2013: pp. 7-11.

Khare V., Nema S. & Baredar P. Optimization of Hydrogen based hybrid renewable energy system using HOMER, BB BC AND GAMBIT. International Journal of Hydrogen Energy, Elsevier. October 2016; 41(38): pp. 16743–16751.

M. M. Fouda, Z. M. Fadlullah, N. Kato, R. Lu, &X. Shen. A Lightweight Message Authentication Scheme for Smart Grid Communications.IEEE Transactions on Smart Grid. August 2011. Digital Object Identifier: 10.1109/TSG.2011.2160661.

Mohsenian-Rad V. W. S. Wong, J. Jatskevich, R. Schober, & A. Leon-Garcia. Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid.IEEE Trans. on Smart Grid. December 2010;1(3): pp.320-331.

D. E. Charilas&A. D. Panagopoulos. A survey on game theory applications in wireless networks. Computer Networks. December 2010;54(18): pp. 3421-3430.

T. Agarwal&S. Cui. Noncooperative Games for Autonomous Consumer Load Balancing over Smart Grid.Avaibalein http://arxiv.org/abs/1104.3802.

P. Aristidou, A. Dimeas, & N. Hatziargyriou. Microgrid Modelling and Analysis Using Game Theory Methods. in Energy-Efficient Computing and Networking, Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering. 2011;54(1): pp. 12-19.

M. Alamaniotis, R. Gao, &L. Tsoukalas. Towards an Energy Internet: A Game-Theoretic Approach to Price-Directed Energy Utilization.In Proc. 1st International ICST Conf. on E Energy, Athens, Greece. October 2010: pp. 3-10.

Vytelingum et al.Agent-based Micro-Storage Management for the Smart Grid.In Proc 9th International Conf. on Autonomous Agents and Multiagent Systems (AAMAS’10). May 1014, 2010, Toronto, Canada.

C. Ibars, M. Navarro& L. Giupponi. Distributed Demand Management in Smart Grid with a Congestion Game.In Proc. 1st IEEE International Conf. on Smart Grid Communications (Smart Grid Comm), Gaithersburg, Maryland, USA. October 2010.


Refbacks

  • There are currently no refbacks.