Abstract
Nowadays, energy is the most valuable resource, new techniques and methods are discovered to fulfill the energy demand. These techniques and methods are very useful for Home Energy Management System (HEMS) in terms of electricity cost reduction, load balancing and power consumption. We evaluated the performance of HEMS using Grey Wolf Optimization (GWO) and Bacterial Foraging Algorithm (BFA) techniques inspired by the nature of grey wolf and bacterium respectively. For this purpose we categorize the home appliances into two classes on the bases of their power consumption pattern. Critical Peak Pricing (CPP) scheme is used to calculate the electricity bill. The load is balanced by scheduling the appliances in Peak Hours (PHs) and Off Peak Hours (OPHs) in order to reduce the cost and Peak to Average Ratio (PAR) and manage the power consumption.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhu, Z., et al.: An integer linear programming based optimization for home demand-side management in Smart Grid. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT). IEEE (2012)
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)
Zhao, Z., et al.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)
Javaid, N., et al.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)
Ma, K., et al.: Residential power scheduling for demand response in Smart Grid. Int. J. Electr. Power Energy Syst. 78, 320–325 (2016)
Rahim, S., et al.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build. 129, 452–470 (2016)
Zhou, Y., et al.: Home energy management with PSO in Smart Grid. In: 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE). IEEE (2014)
Liu, Y., et al.: Queuing-based energy consumption management for heterogeneous residential demands in Smart Grid. IEEE Trans. Smart Grid 7(3), 1650–1659 (2016)
Mahmood, D., et al.: Realistic scheduling mechanism for smart homes. Energies 9(3), 202 (2016)
Ma, J., et al.: Residential load scheduling in Smart Grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)
Ranjini, A., Zoraida, B.S.E.: Intelligent residential energy management in Smart Grid. Indian J. Sci. Technol. 9(45) (2016)
Allouhi, A., et al.: Energy consumption and efficiency in buildings: current status and future trends. J. Cleaner Prod. 109, 118–130 (2015)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Anwar ul Hassan, C.H., Khan, M.S., Ghafar, A., Aimal, S., Asif, S., Javaid, N. (2018). Energy Optimization in Smart Grid Using Grey Wolf Optimization Algorithm and Bacterial Foraging Algorithm. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-65636-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65635-9
Online ISBN: 978-3-319-65636-6
eBook Packages: EngineeringEngineering (R0)