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http://dx.doi.org/10.4218/etrij.15.0114.0007

Bargaining-Based Smart Grid Pricing Model for Demand Side Management Scheduling  

Park, Youngjae (Department of Computer Science & Engineering, Sogang University)
Kim, Sungwook (Department of Computer Science & Engineering, Sogang University)
Publication Information
ETRI Journal / v.37, no.1, 2015 , pp. 197-202 More about this Journal
Abstract
A smart grid is a modernized electrical grid that uses information about the behaviors of suppliers and consumers in an automated fashion to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity. In the operation of a smart grid, demand side management (DSM) plays an important role in allowing customers to make informed decisions regarding their energy consumption. In addition, it helps energy providers reduce peak load demand and reshapes the load profile. In this paper, we propose a new DSM scheduling scheme that makes use of the day-ahead pricing strategy. Based on the Rubinstein-Stahl bargaining model, our pricing strategy allows consumers to make informed decisions regarding their power consumption, while reducing the peak-to-average ratio. With a simulation study, it is demonstrated that the proposed scheme can increase the sustainability of a smart grid and reduce overall operational costs.
Keywords
Demand side management; game theory; Rubinstein-Stahl bargaining model; smart grid; two-sided price control;
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  • Reference
1 V.C. Gungor et al., "Smart Grid Technologies: Communication Technologies and Standards," IEEE Trans. Ind. Informat., vol. 7, no. 4, Sept. 2011, pp. 529-539.   DOI
2 H.K. Nguyen, J.B. Song, and Z. Han, "Demand Side Management to Reduce Peak-to-Average Ratio Using Game Theory in Smart Grid," IEEE Conf. INFOCOM Workshops, Orlando, FL, USA, Mar. 25-30, 2012, pp. 91-96.
3 T. Logenthiran, D. Srinivasan, and T.Z. Shun, "Demand Side Management in Smart Grid Using Heuristic Optimization," IEEE Trans. Smart Grid, vol. 3, no. 3, June 2012, pp. 1244-1252.   DOI
4 A.-H. Mohsenian-Rad et al., "Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid," IEEE Trans. Smart Grid, vol. 1, Dec. 2010, pp. 320-331.   DOI
5 C.J. Wong et al., "Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility," IEEE J. Sel. Areas Commun., vol. 30, no. 6, May 2012, pp. 1075-1085.   DOI
6 Y. Zhao and H. Zhao, "Study on Negotiation Strategy Power Market," Int. Conf. Power Syst. Technol., Kunming, China, vol. 3, Oct. 13-17, 2002, pp. 1335-1338.   DOI
7 W. Zhou, B. Xie, and C. Hao, "A Novel Bargaining-Based Power Allocation for Coordinated Multiple Point Transmission/ Reception," Int. Conf. Adv. Commun. Technol. Seoul, Rep. of Korea, Feb. 13-16, 2011, pp. 458-462.
8 H. Goudarzi, S. Hatami, and M. Pedram, "Demand-Side Load Scheduling Incentivized by Dynamic Energy Prices," IEEE Int. Conf. Smart Grid Commun., Brussels, Belgium, Oct. 17-20, 2011, pp. 351-356.
9 B. Shengrong and F.R. Yu, "A Game-Theoretical Scheme in the Smart Grid with Demand-Side Management: Towards a Smart Cyber-Physical Power Infrastructure," IEEE Trans. Emerg. Topics Comput., July 2013, pp. 22-32.
10 B. Xie et al., "A Novel Bargaining-Based Relay Selection and Power Allocation Scheme for Distributed Cooperative Communication Networks," IEEE Veh. Technol. Conf., Ottawa, Canada, Sept. 6-9, 2010, pp. 1-5.