Abstract
In this paper, an energy management controller (EMC) is designed using three optimization techniques: harmony search algorithm (HSA), firefly algorithm (FA) and enhanced differential evolution (EDE). The objectives of this work are to minimize electricity cost as well as peak to average ratio (PAR) while maintaining the user comfort (UC). Critical peak pricing (CPP) is used for the calculation of electricity bill. The trade-off between UC and electricity cost is exploited in such a way that a stability is achieved among UC and electricity price that is preferred by the consumer. Reduction in PAR is beneficial for both consumer and utility as it provides stability to the electric grid.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1–5. IEEE, January 2012
Zhang, D., Evangelisti, S., Lettieri, P., Papageorgiou, L.G.: Economic and environmental scheduling of smart homes with microgrid: DER operation and electrical tasks. Energy Convers. Manag. 110, 113–124 (2016)
Jaramillo, L.B., Weidlich, A.: Optimal microgrid scheduling with peak load reduction involving an electrolyzer and flexible loads. Appl. Energy 169, 857–865 (2016)
Zhao, Z., Lee, W.C., Shin, Y., Song, K.-B.: An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 4(3), 1391–1400 (2013)
Rajarajeswari, R., Vijayakumar, K., Modi, A.: Demand side management in smart grid using optimization technique for residential, commercial and industrial load. Indian J. Sci. Technol. 9(43) 2016
Rahim, S., Javaid, N., Ahmad, A., Khan, S.A., Khan, Z.A., Alrajeh, N., Qasim, U.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Buildings 129, 452–470 (2016)
Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side management for smart grid. Wirel. Pers. Commun. 93(2), 481–502 (2017)
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)
Muratori, M., Rizzoni, G.: Residential demand response: dynamic energy management and time-varying electricity pricing. IEEE Trans. Power Syst. 31(2), 1108–1117 (2016)
Liu, Y., Yuen, C., Rong, Y., Zhang, Y., Xie, S.: Queuing-based energy consumption management for heterogeneous residential demands in smart grid. IEEE Trans. Smart Grid 7(3), 1650–1659 (2016)
Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)
Rasheed, M., Javaid, N., Ahmad, A., Khan, Z., Qasim, U., Alrajeh, N.: An efficient power scheduling scheme for residential load management in smart homes. Appl. Sci. 5(4), 1134–1163 (2015)
Abushnaf, J., Rassau, A., Górnisiewicz, W.: Impact on electricity use of introducing time-of-use pricing to a multi-user home energy management system. Int. Trans. Electr. Energy Syst. 26(5), 993–1005 (2016)
Northwest Power & Conservation Council. https://www.nwcouncil.org/. Accessed 09 Apr 2017
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)
Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78 (2010)
Arafa, M., Sallam, E.A., Fahmy, M.: An enhanced differential evolution optimization algorithm. In: 2014 Fourth International Conference on Digital Information and Communication Technology and it’s Applications (DICTAP), pp. 216–225. IEEE (2014)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
Zafar, A., Shah, S., Khalid, R., Hussain, S.M., Rahim, H., Javaid, N.: A meta - heuristic home energy management system
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
Abideen, Z.U., Jamshaid, F., Zahra, A., Rehman, A.U., Razzaq, S., Javaid, N. (2018). Meta-Heuristic and Nature Inspired Approaches for Home Energy Management. In: Barolli, L., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-65521-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-65521-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65520-8
Online ISBN: 978-3-319-65521-5
eBook Packages: EngineeringEngineering (R0)