Abstract
Smart Grid (SG) plays vital role to utilize electric power with high optimization through Demand Side Management (DSM). Demand Response (DR) is a key program of DSM which assist SG for optimization. Smart Home (SH) is equipped with smart appliances and communicate bidirectional with SG using Smart Meter (SM). Usually, appliances considered as working for specific time-slot and scheduler schedule them according to tariff. If actual run and power consumption of appliances are observed closely, appliances may run in phases, major tasks, sub-tasks and run continuously. In the paper, these phases have been considered to schedule the appliances using three optimization algorithms. In one way, appliances were scheduled to reduce the cost considering continuous run for given time slot according to their power load given by company’s manual. In other way, actual running of appliances with major and sub-tasks were paternalized and observed the actual consumption of load by the appliances to evaluate true cost. Simulation showed, Binary Particle Swarm Optimization (BPSO) scheduled more optimizing scheduling compared to Fire Fly Algorithm (FA) and Bacterial Frogging Algorithm (BFA). A hybrid technique of FA and GA have also been proposed. Simulation results showed that the technique performed better than GA and FA.
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References
Albadi, M.H., El-Saadany, E.F.: A summary of demand response in electricity markets. Elect. Power Syst. Res. 78(11), 1989–1996 (2008)
Jazayeri, P., Schellenberg, A., Rosehart, W.D., Doudna, J., Widergren, S., Lawrence, D., Mickey, J., Jones, S.: A survey of load control programs for price and system stability. IEEE Trans. Power Syst. 20(3), 1504–1509 (2005)
Kirschen, D.S.: Demand-side view of electricity markets. IEEE Trans. Power Syst. 18(2), 520–527 (2003)
Braithwait, S., Eakin, K.: The role of demand response in electric power market design. R. Christensen Asssociates Inc., Edison Electric Institute (2002)
Aalami, H., Yousefi, G.R., Moghadam, M.P.: Demand response model considering EDRP and TOU programs. In: Proceedings of IEEE/PES Transmission and Distribution Conference and Exposition, pp. 1–6 (2008)
Asano, H., Sagai, S., Imamura, E., Ito, K., Yokoyama, R.: Impacts of time-of-use rates on the optimal sizing and operation of cogenerationsystems. IEEE Trans. Power Syst. 7(4), 1444–1450 (1992)
Bloustein, E.: Assessment of Customer Response to Real Time Pricing. Rutgers University (2005)
Zurn, H.H., Tenfen, D., Rolim, J.G., Richter, A., Hauer, I.: Electrical energy demand efficiency efforts in Brazil, past, lessons learned, present and future: a critical review. Renew. Sustain. Energy Rev. 67(Suppl. C), 1081–1086 (2017). ISSN: 1364-0321
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)
Energy Information Administration, United States Department of Energy, Washington, https://www.eia.gov/todayinenergy/detail.cfm?id=12251. Accessed 10 Oct 2017
Shirazi, E., Jadid, S.: Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy Build. 93, 40–49 (2015)
Adika, C.O., Wang, L.: Smart charging and appliance scheduling approaches to DSM. Int. J. Electr. Power Energy Syst. 57, 232–240 (2014)
Vardakas, J.S., Zorba, N., Verikoukis, C.V.: Power demand control scenarios for SG appliances. Appl. Energy 162, 83–98 (2016)
Abushnaf, J., Rassau, A., Grnisiewicz, W.: Impact on electricity use of introducing time of use pricing to a multiuser home energy management system. Int. Trans. Electr. Energy Syst. (2015)
Bradac, Z., Kaczmarczyk, V., Fiedler, P.: Optimal scheduling of domestic appliances via MILP. Energies 8(1), 217–232 (2014)
Jovanovic, R., Bousselham, A., Bayram, S.I.: Residential Demand Response Scheduling with Consideration of Consumer Preferences. Appl. Sci. 6(1), 16 (2016)
Wen, Z., O’Neil, D., Maei, H.: Optimal demand response using device-based reinforcement learning. IEEE Trans. Smart Grid 6(5), 2312–2324 (2015)
Gao, B., Liu, X., Zhang, W., Tang, Y.: Autonomous household energy management based on a double cooperative game approach in the smart grid. Energies 8(7), 7326–7343 (2015)
Rabiee, A., Sadeghi, M., Aghaeic, J., Heidari, A.: Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties. Renew. Sustain. Energy Rev. 57, 721–739 (2016)
Rasheed, M.B., Javaid, N., Ahmad, A., Khan, Z.A., Qasim, U., Al-rajeh, N.: An efficient power scheduling scheme for residential load management in smart homes. Applied Sciences 5(4), 1134–1163 (2015)
Chiu, W.-Y., Sun, H., Poor, H.V.: Energy imbalance management using a robust pricing scheme. IEEE Trans. Smart Grid 4(2), 896–904 (2013)
Shah, S., Khalid, R., Zafar, A., Hussain, S.M., Rahim, H., Javaid, N.: An optimized priority enabled energy management system for smart homes. In: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (2017)
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Bukhsh, R., Iqbal, Z., Javaid, N., Ahmed, U., Khan, A., Khan, Z.A. (2018). Appliances Scheduling Using State-of-the-Art Algorithms for Residential Demand Response. In: Barolli, L., Xhafa, F., Javaid, N., Spaho, E., Kolici, V. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-75928-9_26
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DOI: https://doi.org/10.1007/978-3-319-75928-9_26
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