A Hybrid Algorithm based on Computational Intelligence Methods for House Energy Management in Presence of Electric Vehicle


Electronics and Computer Engineering Department, Hakim Sabzevari University, Sabzevar, Iran


This paper has proposed an energy management system for domestic house applications considering electric vehicle. The first step is linked to optimal and adaptive scheduling of appliances and is defined as a multi objective function model with some fuzzy coefficient and owner comfort factors. The important aspect of this tool is to help customers respond to real time electricity price. In the second step, emergency demand response program (EDRP) modeling and the possibility of energy exchange has been investigated. In the third step, the electric vehicle charging planning has been investigated as adaptive and uncertainty. In energy management part, specific scenarios is simulated which reflect 60.96% of energy consumption reduction and the optimal scheduling of electric vehicle charging too. In the emergency demand response part two specific scenarios are simulated which reflect 66.77% of energy consumption reduction and peak shaving of 1 kW per house.


[1] H. Farhangi, "The path of the smart grid," IEEE power and energy magazine, vol. 8, pp. 18-28, 2010.
[2] United States. Department of Energy, and Spencer Abraham," National transmission grid study," US Department of Energy, 2002.
[3] Netbeheer Nederland, "The Road to a Sustainable and Efficient Energy Supply: Smart Grids Roadmap," Version 11 February 2012.
[4] A. Faruqui, R. Hledik, S. Newell and H. Pfeifenberger, "The power of 5 percent," The Electricity Journal, vol. 20, pp. 68-77, 2007.
[5] Q. QDR, "Benefits of demand response in electricity markets and recommendations for achieving them," US Dept. Energy, Washington, DC, USA, Tech. Rep, 2006.
[6] B. Insight, "Smart Metering in Western Europe," M2M research series, 2009.
[7] Ghassemi, A., S. Bavarian, and L. Lampe. "Cognitive radio for smart grid communications." Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on. IEEE, 2010.
[8] Federal Energy Regulatory Commission, "Assessment of Demand Response and Advanced Metering", Staff Report, August 2006 (Revised December 2008), https://www.ferc.gov/legal/staff-reports/demand-response.pdf.
[9] M. Pipattanasomporn, M. Kuzlu, and S. Rahman, "An algorithm for intelligent home energy management and demand response analysis," IEEE Transactions on Smart Grid, vol. 3, pp. 2166-2173, 2012.
[10] Y. Guo, M. Pan, Y. Fang, and P. P. Khargonekar, "Coordinated energy scheduling for residential households in the smart grid", Smart Grid Communications (SmartGridComm), IEEE Third International Conference on, pp. 121-126, 2012.
[11] L. P. Qian, Y. J. A. Zhang, J. Huang, and Y. Wu, "Demand response management via real-time electricity price control in smart grids," IEEE Journal on Selected Areas in Communications, vol. 31, pp. 1268-1280, 2013.
[12] B. Qela and H. Mouftah, "Peak load curtailment in a smart grid via fuzzy system approach," IEEE Transactions on Smart Grid, vol. 5, pp. 761-768, 2014.
[13] B. Liu and Q. Wei, “Home energy control algorithm research based on demand response programs and user comfort”, Proc. 2nd Int. Conf. Meas. Inf. Control, Harbin, China, pp. 995–999, 2013.
[14] M. Tasdighi, H. Ghasemi, and A. Rahimi-Kian, "Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling," IEEE Transactions on Smart Grid, vol. 5, pp. 349-357, 2014.
[15] F. De Angelis, M. Boaro, D. Fuselli, S. Squartini, F. Piazza, and Q. Wei, "Optimal home energy management under dynamic electrical and thermal constraints," IEEE Transactions on Industrial Informatics, vol. 9, pp. 1518-1527, 2013.
[16] T. Huang and D. Liu, "A self-learning scheme for residential energy system control and management," Neural Computing and Applications, vol. 22, pp. 259-269, 2013.
[17] مهدی تورانی، محمدرضا آقاابراهیمی، حمیدرضا نجفی، «برنامه‌ریزی شارژ و دشارژ خودروهای الکتریکی در ریزشبکه بر پایه مسافرت روزانه خودروها»، مجله مهندسی برق دانشگاه تبریز، دوره 46، شماره 4، صفحه 65-76، زمستان 1395.
[18] جمشید آقایی، سیداحسان باقری، سجاد شفیعی، طاهر نیکنام، سیدمحسن باقری، «بررسی پاسخ‌گویی شبکه توزیع هوشمند به عملکرد خودروهای الکتریکی هیبریدی قابل اتصال به شبکه»، مجله مهندسی برق دانشگاه تبریز، دوره 47، شماره 1، صفحه 11-20، بهار 1396.
[19] K. C. Sou, J. Weimer, H. Sandberg, and K. H. Johansson, "Scheduling smart home appliances using mixed integer linear programming", 50th IEEE Conference on Decision and Control and European Control Conference, pp. 5144-5149, 2011.
[20] S. Nistor, J. Wu, M. Sooriyabandara, and J. Ekanayake, "Cost optimization of smart appliances", Innovative Smart Grid Technologies (ISGT Europe), 2nd IEEE PES International Conference and Exhibition on, pp. 1-5, 2011.
[21] M. Rastegar, M. Fotuhi-Firuzabad, and F. Aminifar, "Load commitment in a smart home," Applied Energy, vol. 96, pp. 45-54, 2012.
[22] J. M. Lujano-Rojas, C. Monteiro, R. Dufo-Lopez, and J. L. Bernal-Agustín, "Optimum residential load management strategy for real time pricing (RTP) demand response programs," Energy Policy, vol. 45, pp. 671-679, 2012.
[23] A.-H. Mohsenian-Rad and A. Leon-Garcia, "Optimal residential load control with price prediction in real-time electricity pricing environments," IEEE transactions on Smart Grid, vol. 1, pp. 120-133, 2010.
[24] S. Gottwalt, W. Ketter, C. Block, J. Collins, and C. Weinhardt, "Demand side management—A simulation of household behavior under variable prices," Energy Policy, vol. 39, pp. 8163-8174, 2011.
[25] K. Clement-Nyns, E. Haesen, and J. Driesen, "The impact of charging plug-in hybrid electric vehicles on a residential distribution grid," IEEE Transactions on Power Systems, vol. 25, pp. 371-380, 2010.
[26] K. J. Baker and R. M. Rylatt, "Improving the prediction of UK domestic energy-demand using annual consumption-data," Applied Energy, vol. 85, pp. 475-482, 2008.
[27] G. K. Tso and K. K. Yau, "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, vol. 32, pp. 1761-1768, 2007.
[28] N. A. Burney, "Socioeconomic development and electricity consumption A cross-country analysis using the random coefficient method", Energy Economics, vol. 17, pp. 185-195, 1995.
[29] M. E. Wijaya and T. Tezuka, "Policy-Making for Households Appliances-Related Electricity Consumption in Indonesia-A Multicultural Country", Open Journal of Energy Efficiency, vol.2, pp.53-64, 2013.
[30] R. J. Cebula, "Recent evidence on determinants of per residential customer electricity consumption in the US: 2001-2005", Journal of Economics and Finance, vol. 36, pp. 925-936, 2012.
[31] M. M. Sahebi, E. A. Duki, M. Kia, A. Soroudi, and M. Ehsan, "Simultanous emergency demand response programming and unit commitment programming in comparison with interruptible load contracts," IET generation, transmission & distribution, vol. 6, pp. 605-611, 2012.
[32] S. Rajakaruna, F. Shahnia, and A. Ghosh, Plug in electric vehicles in smart grids: Springer, 2015.
[33] Collia, Demetra V., Joy Sharp, and Lee Giesbrecht. "The 2001 national household travel survey: A look into the travl patterns of older Americans." Journal of safety research 34, no. 4, pp. 461-470, 2003.
[34] J. González, R. Alvaro, C. Gamallo, M. Fuentes, J. Fraile-Ardanuy, L. Knapen, and D. Janssens, "Determining electric vehicle charging point locations considering drivers’ daily activities," Procedia Computer Science, vol. 32, pp. 647-654, 2014.