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http://dx.doi.org/10.4218/etrij.13.0112.0625

An Optimal Power Scheduling Method Applied in Home Energy Management System Based on Demand Response  

Zhao, Zhuang (Department of Electronic Engineering, Soongsil University)
Lee, Won Cheol (Department of Electronic Engineering, Soongsil University)
Shin, Yoan (Department of Electronic Engineering, Soongsil University)
Song, Kyung-Bin (Department of Electrical Engineering, Soongsil University)
Publication Information
ETRI Journal / v.35, no.4, 2013 , pp. 677-686 More about this Journal
Abstract
In this paper, we first introduce a general architecture of an energy management system in a home area network based on a smart grid. Then, we propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price, which is transferred to an energy management controller (EMC). Referring to the DR, the EMC achieves an optimal power scheduling scheme, which is delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way possible. In our research, to avoid the high peak-to-average ratio (PAR) of power, we combine the real-time pricing model with the inclining block rate model. By adopting this combined pricing model, our proposed power scheduling method effectively reduces both the electricity cost and the PAR, ultimately strengthening the stability of the entire electricity system.
Keywords
Smart grid; energy management system; demand response; real-time pricing; inclining block rate;
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1 A.-H. Mohsenian-Rad and A. Leon-Garcia, "Optimal Residential Load Control with Price Prediction in Real-Time Electricity Pricing Environments," IEEE Trans. Smart Grid, vol. 1, no. 2, Sept. 2010, pp. 120-133.   DOI   ScienceOn
2 S. Tompros et al., "Enabling Applicability of Energy Saving Applications on the Appliances of the Home Environment," IEEE Netw., vol. 23, no. 6, Nov. 2009, pp. 8-16.   DOI   ScienceOn
3 M. Erol-Kantarci and H.T. Mouftah, "Wireless Sensor Networks for Cost-Efficient Residential Energy Management in the Smart Grid," IEEE Trans. Smart Grid, vol. 2, June 2011, pp. 314-325.   DOI   ScienceOn
4 Y.S. Son and K.D. Moon, "Home Energy Management System Based on Power Line Communication," Proc. IEEE Int. Conf. Consum. Electron., Las Vegas, NV, USA, Jan. 2010, pp. 115-116.
5 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, no. 3, Dec. 2010, pp. 320-331.   DOI   ScienceOn
6 L.D. HA et al., "Tabu Search for the Optimization of Household Energy Consumption," Proc. IEEE Int. Conf. Inf. Reuse Integration, Sept. 2006, pp. 86-92.
7 J. Chen, B. Yang, and X. Guan, "Optimal Demand Response Scheduling with Stackelberg Game Approach under Load Uncertainty for Smart Grid," IEEE 3rd Int. Conf. SmartGridComm., Tainan, Taiwan, Nov. 2012, pp. 546-551.
8 M. Inoue et al., "Network Architecture for Home Energy Management System," IEEE Trans. Consum. Electron., vol. 49, no. 3, Aug. 2003, pp. 606-613.   DOI   ScienceOn
9 Time-Based Pricing. Available: http://en.wikipedia.org/wiki/-Time-based_pricing
10 M.A.A. Pedrasa, T.D. Spooner, and I.F. MacGill, "Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services," IEEE Trans. Smart Grid, vol. 1, no. 2, Sept. 2010, pp. 134-143.   DOI   ScienceOn
11 A.-H. Mohsenian-Rad et al., "Optimal and Autonomous Incentive-Based Energy Consumption Scheduling Algorithm for Smart Grid," IEEE Conf. Innov. Smart Grid Technol., Gaithersburg, MD, USA, Jan. 2010, pp. 1-6.
12 A. Aggarwal, S. Kunta, and P.K. Verma, "A Proposed Communications Infrastructure for the Smart Grid," IEEE Conf. Innov. Smart Grid Technol., Gaithersburg, MD, USA, Jan. 2010, pp. 1-5.
13 D.Y.R. Nagesh, J.V.V. Krishna, and S.S. Tulasiram, "A Real-Time Architecture for Smart Energy Management," IEEE Conf. Innov. Smart Grid Technol., Gaithersburg, MD, USA, Jan. 2010, pp. 1-4.
14 S. Young and R. Stanic, "SmartMeter to HAN Communications," Smart Grid Australia Intelligent Networking Working Group, July 2009.
15 C.P. Rodriguez and G.J. Anders, "Energy Price Forecasting in the Ontario Competitive Power System Market," IEEE Trans. Power Syst., vol. 19, no. 1, Feb. 2004, pp. 366-374.   DOI   ScienceOn
16 A.J. Conejo et al., "Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARIMA Models," IEEE Trans. Power Syst., vol. 20, no. 2, May 2005, pp. 1035-1042.   DOI   ScienceOn
17 B.R. Szkuta, L.A. Sanabria, and T.S. Dillon, "Electricity Price Short-Term Forecasting Using Artificial Neural Networks," IEEE Trans. Power Syst., vol. 14, no. 3, Aug. 1999, pp. 851-857.
18 D.W. Bunn, "Forecasting Loads and Prices in Competitive Power Markets," Proc. IEEE, vol. 88, no. 2, Feb. 2000, pp. 163-169.   DOI   ScienceOn
19 Inclining Block Rate in British Columbia Hydro Co., Jan. 2012. http://www.bchydro.com/youraccount/content/-residential_rates.jsp
20 Real-Time Pricing for Residential Customers, Ameren Illinois Power Co., Jan. 2012. https://www2.ameren.com/retailenergy/realtimeprices.aspx
21 O. Derin and A. Ferrante, "Scheduling Energy Consumption with Local Renewable Micro-Generation and Dynamic Electricity Prices," Proc. 1st Workshop Green Smart Embedded Syst. Technol.: Infrastructures, Methods, Tools, Stockholm, Sweden, Apr. 2010.
22 J. Lu, D. Xie, and Q. Ai, "Research on Smart Grid in China," IEEE Transmission Distrib. Conf. Exposition: Asia Pacific, Seoul, Rep. of Korea, Oct. 2009, pp. 1-4.
23 L. Peretto, "The Role of Measurements in the Smart Grid Era," IEEE Instrum. Meas. Mag., vol. 13, no. 3, June 2010, pp. 22-25.
24 G. Xiong et al., "Smart (In-Home) Power Scheduling for Demand Response on the Smart Grid," IEEE PES Conf. Innov. Smart Grid Technol., Anaheim, CA, USA, Jan. 2011, pp. 1-7.
25 T.T. Kim and H.V. Poor, "Scheduling Power Consumption with Price Uncertainty," IEEE Trans. Smart Grid, vol. 2, no. 3, Sept. 2011, pp. 519-527.   DOI   ScienceOn