JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Optimal Energy Consumption Scheduling in Smart-Grid Considering Storage Appliance : A Game-Theoretic Approach
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Optimal Energy Consumption Scheduling in Smart-Grid Considering Storage Appliance : A Game-Theoretic Approach
Yeo, Sangmin; Lee, Deok-Joo; Kim, Taegu; Oh, Hyung-Sik;
  PDF(new window)
 Abstract
In this research, we consider a smart grid network of electricity with multiple consumers connected to a monopolistic provider. Each consumer can be informed the real time price changes through the smart meter and updates his consumption schedule to minimize the energy consumption expenditures by which the required power demand should be satisfied under the given real time pricing scheme. This real-time decision making problem has been recently studied through game-theoretic approach. The present paper contributes to the existing literature by incorporating storage appliance into the set of available household appliances which has somewhat distinctive functions compared to other types of appliances and would be regarded to play a significant role in energy consumption scheduling for the future smart grid. We propose a game-theoretic algorithm which could draw the optimal energy consumption scheduling for each household appliances including storage. Results on simulation data showed that the storage contributed to increase the efficiency of energy consumption pattern in the viewpoint of not only individual consumer but also whole system.
 Keywords
Smart grid;Energy consumption schedule;Storage appliance;Game theory;
 Language
Korean
 Cited by
 References
1.
Boyd, S. and Vandenberghe, L. (2004), Convex Optimization, Cambridge University Press, Cambridge, UK.

2.
Bu, S., Yu, F. R., and Liu, P. X. (2011), A game-theoretical decision-making scheme for electricity retailers in the smart grid with demand-side management, Proc. of 2011 IEEE International Conference on Smart Grid Communications, 387-391.

3.
Centolella, P. (2010), The integration of price responsive demand into regional transmission organization (RTO) wholesale power markets and system operations, Energy, 35(4), 1568-1574. crossref(new window)

4.
Choi, T., Ko, K., Park, S., Kim, H., and Yoon, Y. (2010), A Study on Energy Cost Saving Strategy in the Smart Grid Environment, Proc. of Summer Conf. of Korean Institute of Electrical Engineers, 517-518.

5.
Conejo, A. J., Morales, J. M., and Baringo, L. (2010), Real-time demand response model, IEEE Trans. Smart Grid, 1(3), 236-242. crossref(new window)

6.
Fudenberg, D. and Tirole, J. (1991), Game Theory, MIT Press, Cambridge, MA, USA.

7.
Gomes, A., Antunes, C. H., and Martins, A. G. (2007), A multiple objective approach to direct load control using an interactive evolutionary algorithm, IEEE Trans. Power System, 22(3), 1004-1011. crossref(new window)

8.
Kim, T., Lee, S., and Lee, S. (2010), Optimization of Home Loads scheduling in Demand Response, J. of Korean Institute of Communication and Information Sciences, 35, 1407-1415.

9.
Lee, M. (2012), The current state and the prospect of smart grid market, Research Report of Industry Risk, 2012-G-09, The Export-Import Bank of Korea.

10.
Mohsenian-Rad, A. H., Wong, V. W. S., Jatskevich, J., and Leon-Garcia, A. (2010), Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid, IEEE Trans. Smart Grid, 1(3), 320-331. crossref(new window)

11.
Moon, Y. (2014), Demand Response Real Time Pricing Model for Smart Grid Considering Consumer Behavior and Price Elasticity, J. of KORMS, 39(1), 49-67.

12.
Park, K., Lee,Y., Doh, G., and Yoo, J. (2012), Scheduling problem for energy efficiency optimization in smart grid, Proc. of Spring Joint Con. of KIIE/KORMS, 1187-1206.

13.
Ruiz, N., Cobelo, I., and Oyarzabal, J. (2009), A direct load control model for virtual power plant management, IEEE Trans. Power System, 24(2), 959-966. crossref(new window)

14.
Rosen, J. B. (1965), Existence and uniqueness of equilibrium points for concave n-person games, Econometrica, 33, 347-351.

15.
Saad, W., Han, Z., Poor, H. V., and Basar, T. (2012), Game-Theoretic Methods for the Smart Grid : An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications, IEEE Signal Processing Magazine, 29(5), 86-105. crossref(new window)

16.
Samadi, P., Mohsenian-Rad, A.-H., Schober, R., Wong, V. W. S., and Jatskevich, J. (2010), Optimal real-time pricing algorithm based on utility maximization for smart grid, Proc. of 2010 First IEEE International Conference on Smart Grid Communications, 415-420.

17.
Triki, C. and Violi, A. (2009), Dynamic pricing of electricity in retail markets, Quarterly. J. of Oper. Res., 7(1), 21-S36. crossref(new window)

18.
Vytelingum, P., Voice, T. D., Ramchurn, S. D., Rogers, A., and Jennings, N. R. (2010), Agent-based Micro-Storage Management for the Smart Grid, Proc. of the 8th Conference on Autonomous Agents And Multi-Agent Systems, 39-46.

19.
Wade, N. S., Taylor, P. C., Lang, P. D., and Jones, P. R. (2010), Evaluating the benefits of an electrical energy storage system in a future smart grid, Energy Policy, 38, 7180-7188. crossref(new window)