Abstract
The rise of energy demand is an alarming situation for mankind as it can lead towards a crisis. This problem can be easily tackled by assimilating Demand Side Management (DSM) with traditional grid by means of bi-directional communication between utility companies and consumers. This study evaluates the performance of Home Energy Management System (HEMS) using meta-heuristic optimization techniques: Genetic Algorithm (GA) and Crow Search Algorithm (CSA). The appliances are classified in three sets on the basis of their electrical energy consumption pattern. Moreover, the Real Time Pricing (RTP) scheme is used for power bill control. The core aims of this paper are to minimize electrical energy cost and consumption by scheduling of appliances, decline in peak to average ratio, while getting the best out of user comfort. Besides, simulation results illustrate that there is a trade-off between waiting time and electricity cost. The outcomes also indicate that CSA perform better as compared to GA in relation to cost.
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
References
Kuzlu, M., Pipattanasomporn, M., Rahman, S.: Communication network requirements for major smart grid applications in HAN, NAN and WAN. Comput. Netw. 67, 74–88 (2014)
Graditi, G., Di Silvestre, M.L., Gallea, R., Riva Sanseverino, E.: Heuristic-based shiftable loads optimal management in smart micro-grids. IEEE Trans. Industr. Inf. 11, 271–280 (2015)
Siano, P.: Demand response and smart grids: a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)
Erdinc, O., Paterakis, N.G., Mendes, T.D.P., Bakirtzis, A.G., Catalao, J.P.S.: Smart household operation considering bi-directional EV and ESS utilization by real-time pricing-based DR. IEEE Trans. Smart Grid 6, 1281–1291 (2015)
Karanfil, F., Li, Y.: Electricity consumption and economic growth: exploring panel-specific differences. Energy Policy 82, 264–277 (2015)
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.B., Javaid, N., Ahmad, A., Khan, Z.A., Qasim, U., Alrajeh, N.: An efficient power scheduling scheme for residential load management in smart homes. Appl. Sci. 5(5), 1134–1163 (2015)
Ullah, I., Javaid, N., Khan, Z.A., Qasim, U., Khan, Z.A., Mehmood, S.A.: An incentive based optimal energy consumption scheduling algorithm for residential users. Procedia Comput. Sci. 52, 851–857 (2015)
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, 1802–1812 (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 2013, 1391–1400 (2013). https://doi.org/10.1109/TSG.2013.2251018
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 Build. 129, 452–470 (2016)
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 (2012)
Ma, J., Chen, H., Song, L., Li, Y.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7, 771–784 (2015)
Ma, K., Yao, T., Yang, J., Guan, X.: Residential power scheduling for demand response in smart grid. Int. J. Electr. Power Energy Syst. 78, 320–325 (2016)
Al Balas, F.A., Mardini, W., Khamayseh, Y., Bani-Salameh, A.K.: Improved appliance coordination scheme with waiting time in smart grids. Int. J. Adv. Comput. Sci. Appl. 7(4) (2016)
Safdarian, A., Fotuhi-Firuzabad, M., Lehtonen, M.: Optimal residential load management in smart grids: a decentralized framework. IEEE Trans. Smart Grid 7, 1836–1845 (2016)
Achicanoy M., W.O., Jimenez, J.B.: Electricity demand modeling for rural residential housing: a case study in Colombia. In: 2015 IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM), pp. 614–618. IEEE, October 2015
Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side management for smart grid. Wireless Pers. Commun. 93, 481–502 (2017)
Awais, M., Javaid, N., Shaheen, N., Iqbal, Z., Rehman, G., Muhammad, K., Ahmad, I.: An efficient genetic algorithm based demand side management scheme for smart grid. In: 2015 18th International Conference on Network-Based Information Systems, pp. 351–356. IEEE, September 2015
Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)
Kavousi-Fard, A.: A hybrid accurate model for tidal current prediction. IEEE Trans. Geosci. Remote Sens. 55, 112–118 (2017)
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
Khan, M. et al. (2018). Residential Demand Side Management in Smart Grid Using Meta-Heuristic Techniques. 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_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-69835-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69834-2
Online ISBN: 978-3-319-69835-9
eBook Packages: EngineeringEngineering (R0)