Abstract
Smart grid plays a significant role in decreasing of electricity consumption cost through Demand Side Management (DSM). Smart homes, a part of smart grid contributes a lot in minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to scheduling of home appliances. This scheduling problem is considered as an optimization problem. Meta-heuristic algorithms have attracted increasing attention in last few years for solving optimization problems. Hence, in this study we propose an efficient scheme in Home Energy Management System (HEMS) using Genetic Algorithm (GA) and Cuckoo search algorithm to solve optimization problem. The proposed scheme is implemented on a single smart home and a smart building; comprising of thirty smart homes. Real Time Pricing (RTP) signals are used in term of electricity cost estimation for both single smart home and a smart building. Experimental results demonstrate the extremely effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and Peak to Average Ratio (PAR) minimization. Moreover, our proposed scheme obtains the desired tradeoff between electricity cost and user waiting time.
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Fuselli, D., De Angelis, F., Boaro, M., Squartini, S., Wei, Q., Liu, D., Piazza, F.: Action dependent heuristic dynamic programming for home energy resource scheduling. Int. J. Electr. Power Energy Syst. 48, 148–160 (2013)
Evangelisti, S., Lettieri, P., Clift, R., Borello, D.: Distributed generation by energy from waste technology: a life cycle perspective. Process Saf. Environ. Prot. 93, 161–172 (2015)
Khalid, A., Javaid, N., Mateen, A., Khalid, B., Khan, Z.A., Qasim, U.: Demand side management using hybrid bacterial foraging and genetic algorithm optimization techniques. In: 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), pp. 494–502. IEEE (2016)
Albadi, M.H., El-Saadany, E.F.: A summary of demand response in electricity markets. Electr. Power Syst. Res. 78(11), 1989–1996 (2008)
Avci, M., Erkoc, M., Rahmani, A., Asfour, S.: Model predictive HVAC load control in buildings using real-time electricity pricing. Energy Build. 60, 199–209 (2013)
Yang, J., Zhang, G., Ma, K.: Matching supply with demand: a power control and real time pricing approach. Int. J. Electr. Power Energy Syst. 61, 111–117 (2014)
Hu, W., Chen, Z., Bak-Jensen, B.: Optimal operation strategy of battery energy storage system to real-time electricity price in Denmark. In: 2010 IEEE Power and Energy Society General Meeting, pp. 1–7. IEEE (2010)
Tascikaraoglu, A., Boynuegri, A.R., Uzunoglu, M.: A demand side management strategy based on forecasting of residential renewable sources: a smart home system in Turkey. Energy Build. 80, 309–320 (2014)
Department of energy and climate change. Demand side response in the domestic sector - a literature review of major trial (2012)
Bradac, Z., Kaczmarczyk, V., Fiedler, P.: Optimal scheduling of domestic appliances via MILP. Energies 8(1), 217–232 (2014)
Agnetis, A., de Pascale, G., Detti, P., Vicino, A.: Load scheduling for household energy consumption optimization. IEEE Trans. Smart Grid 4(4), 2364–2373 (2013)
Mahmood, A., Javaid, N., Khan, N.A., Razzaq, S.: An optimized approach for home appliances scheduling in smart grid. In: 2016 19th International Multi-Topic Conference (INMIC), pp. 1–5. IEEE (2016)
Mary, G.A., Rajarajeswari, R.: Smart grid cost optimization using genetic algorithm. Int. J. Res. Eng. Technol. 3(07), 282–287 (2014)
Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side management for smart grid. Wireless Pers. Commun. 93(2), 481–502 (2017)
Setlhaolo, D., Xia, X., Zhang, J.: Optimal scheduling of household appliances for demand response. Electr. Power Syst. Res. 116, 24–28 (2014)
Samadi, P., Wong, V.W.S., Schober, R.: Load scheduling and power trading in systems with high penetration of renewable energy resources. IEEE Trans. Smart Grid 7(4), 1802–1812 (2016)
Ullah, I., Javaid, N., Khan, Z.A., Qasim, U., Khan, Z.A., Mehmood, S.A.: An incentive-based optimal energy consumption scheduling algorithm for residential users. Procedia Comput. Sci. 52, 851–857 (2015)
Whitley, D.: A genetic algorithm tutorial. Stat. Comput. 4(2), 65–85 (1994)
Yang, X.-S., Deb, S.: Cuckoo search via Lvy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 210–214. IEEE (2009)
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Aslam, S. et al. (2018). An Efficient Home Energy Management Scheme Using Cuckoo Search. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_15
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DOI: https://doi.org/10.1007/978-3-319-69835-9_15
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