Auto-DR Optimal Decision-Making Method of Smart Home Based on RTP

Article Preview

Abstract:

One optimization decision method is presented in order to solve the problem of how to realize demand response according to RTP in residential electricity. This method aims at minimizing the cost of electricity and the dissatisfaction of the power consumer. Optimization decision model is built based on the classification of residential electricity load and the model is solved by genetic algorithm.The results of an example show that optimal decision-making method can help reduce the cost of electricity and be beneficial to regional power grids’ load shifting. Applying this optimization decision method to AMI can realize Auto-DR according to price signal.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

3817-3821

Citation:

Online since:

August 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Zhang Q, Wang X, Wang J, et al. Survey of Demand Response Research in Deregulated Electricity Markets [J][J]. Automation of Electric Power Systems, 2008, 3: 97-106.

Google Scholar

[2] Wang Dong-rong. Application of price⁃based demand side response in the U.S.A. [J]. POWER DEMAND SIDE MANAGEMENT, 2010, 12(4): 74-77.

Google Scholar

[3] GOLDMAN C, HOPPER N, SEZGEN O, et al. Does real-ti me pricing deliver demand response—a case study of Niagara Mohawk's large customer RTP tariff.

Google Scholar

[4] Zhao L, Ju G, Lu J. An improved genetic algorithm in multi-objective optimization and its application[J]. PROCEEDINGS-CHINESE SOCIETY OF ELECTRICAL ENGINEERING, 2008, 28(2): 96.

Google Scholar

[5] Ge J K, Qiu Y H, Wu C M, et al. Summary of genetic algorithms research[J]. Application Research of Computers, 2008, 25(10): 2911-2916.

Google Scholar

[6] BIAN Xia, MI Liang. Development on genetic algorithm theory and its applications[J] Application Research of Computers. 2010, 27(7): 2425-2429.

Google Scholar

[7] YAO Weifeng, ZHAO Junhua,WEN Fushuan et al. A Charging and Discharging Dispatching Strategy for Electric Vehicles Based on Bi-level Optimation[J]. Automation of Electric Power Systems, 2012, 36(11): 30-37.

Google Scholar

[8] Yang Wei. Optimal Scheduling for Distributed energy Resource[D]. Hefei University of Technology, (2010).

Google Scholar