Abstract
In this modern world, the demand of energy rises exponentially, that makes it a valuable resource. New techniques and methods are being developed to solve the problem of energy crisis in residential areas. The strategy to handle this problem is by integrating the demand side management (DSM) with smart grid (SG). DSM enables the consumer to schedule their load profile effectively in order to reduce electricity cost and power peak creation, referred as peak-to-average ratio (PAR). This paper evaluates the performance of home energy management system (HEMS) using meta-heuristic techniques; harmony search algorithm (HSA) and flower pollination algorithm (FPA). In this regard, a single home is considered with smart appliances classified as automatically operated appliances (AOAs) and manually operated appliances (MOAs). Moreover, critical peak pricing (CPP) is used as a price signal. In this paper, emphasis is placed on the cost minimization and load scheduling by shifting the load between off-peak and on-peak hours, while considering the user comfort. Simulation results shows that the performance of FPA is better in terms of cost and PAR reduction, whereas there exists a trade-offs between electricity cost and user comfort level.
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
Gelazanskas, L., Gamage, K.A.A.: Demand side management in smart grid: a review and proposals for future direction. Sustain. Cities Soc. 11, 22–30 (2014)
Siano, P.: Demand response and smart grids a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)
Javaid, N., et al.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)
Rahim, S., et al.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build. 129, 452–470 (2016)
Cherukuri, S.H.C., Saravanan, B.: A novel energy management algorithm for reduction of main grid dependence in future smart grids using electric springs. Sustain. Energy Technol. Assess. 21, 1–12 (2017)
Soares, J., et al.: A stochastic model for energy resources management considering demand response in smart grids. Electr. Power Syst. Res. 143, 599–610 (2017). 73
Shi, W., et al.: Real-time energy management in microgrids. IEEE Trans. Smart Grid 8(1), 228–238 (2017). K. Elissa, Title of paper if known, unpublished
Yorozu, Y., Hirano, M., Oka, K., Tagawa, Y.: Electron spectroscopy studies on magneto-optical media and plastic substrate interface. IEEE Transl. J. Magn. Jpn. 2, 740–741 (1987). (Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982)
Yu, C.-N., Mirowski, P., Ho, T.K.: A sparse coding approach to household electricity demand forecasting in smart grids. IEEE Trans. Smart Grid 8(2), 738–748 (2017)
Hazra, J., Das, K., Seetharam, D.P.: Smart grid congestion management through demand response. In: 2012 IEEE Third International Conference on Smart Grid Communications (Smart Grid Comm). IEEE (2012)
Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M.: Optimal residential load management in smart grids: a decentralized framework. IEEE Trans. Smart Grid 7(4), 1836–1845 (2016)
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)
Zhu, Z., et al.: An integer linear programming based optimization for home demand-side management in smart grid. In: Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES. IEEE (2012)
Gem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)
Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) Unconventional Computation and Natural Computation. LNCS, vol. 7445, pp. 240–249. Springer, Berlin (2012)
Yang, X.S.: Harmony search as a metaheuristic algorithm. In: Music-Inspired Harmony Search Algorithm, pp. 1–14. Springer, Berlin (2009)
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
Tariq, M., Khalid, A., Ahmad, I., Khan, M., Zaheer, B., Javaid, N. (2018). Load Scheduling in Home Energy Management System Using Meta-Heuristic Techniques and Critical Peak Pricing Tariff. 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_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-69835-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69834-2
Online ISBN: 978-3-319-69835-9
eBook Packages: EngineeringEngineering (R0)