Abstract
The tremendous evolution of the technology has empowered the energy consumers to receive real-time information regarding electricity consumption prices with the help of two way communication between the main grid and the smart meter. We have proposed evolutionary optimization techniques such as; genetic algorithm (GA) and teaching-learning base algorithm (TLBO) in this paper. The aforementioned algorithms are exploited to find out an optimal schedule for every appliance based on real-time pricing (RTP) signal. It enables the real-time automation of smart home appliances considering the economic criteria of each smart home. Our scheduling strategy shifts the extra load exceeding the threshold limit to the hours where the electricity pricing is low. In this way, we can reduce electricity cost while considering the user comfort by reducing delay and peak to average ratio (PAR).
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References
DOE, U.: US Household Electricity Report. Energy Information Administration (EIA) Regional Energy Profile (2005)
Albadi, M.H., El-Saadany, E.F.: A summary of demand response in electricity markets. Electr. Power Syst. Res. 78(11), 1989–1996 (2008)
de la Torre, S., Arroyo, J.M., Conejo, A.J., Contreras, J.: Price maker self-scheduling in a pool-based electricity market: a mixed-integer LP approach. IEEE Trans. Power Syst. 17(4), 1037–1042 (2002)
Tsui, K.M., Chan, S.C.: Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans. Smart Grid 3(4), 1812–1821 (2012)
Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear programming based optimization for home demand-side management in smart grid. In: Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pp. 1–5. IEEE, January 2012
Kriett, P.O., Salani, M.: Optimal control of a residential microgrid. Energy 42(1), 321–330 (2012)
Pretorius, H.M., Delport, G.J.: Scheduling of cogeneration facilities operating under the real-time pricing agreement. In: IEEE International Symposium on Industrial Electronics, 1998, Proceedings. ISIE 1998, vol. 2, pp. 390–395. IEEE, July 1998
Derin, O., Ferrante, A.: Scheduling energy consumption with local renewable micro-generation and dynamic electricity prices. In: First Workshop on Green and Smart Embedded System Technology: Infrastructures, Methods, and Tools, April 2010
Zhang, J., Wu, Y., Guo, Y., Wang, B., Wang, H., Liu, H.: A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints. Appl. Energy 183, 791–804 (2016)
Yi, P., Dong, X., Iwayemi, A., Zhou, C., Li, S.: Real-time opportunistic scheduling for residential demand response. IEEE Trans. Smart Grid 4(1), 227–234 (2013)
Nadeem, Z., Javaid, N., Malik, A.W., Iqbal, S.: Scheduling appliances with GA, TLBO, FA, OSR and their hybrids using chance constrained optimization for smart homes. Energies 11(4), 888 (2018)
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Nadeem, Z., Javaid, N., Malik, A.W., Jamil, A., Fatima, I., Khalid, M.U. (2019). Towards Real-Time Opportunistic Scheduling of the Home Appliances Using Evolutionary Techniques. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_73
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DOI: https://doi.org/10.1007/978-3-319-93659-8_73
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