Abstract
Energy is the most valuable resource in every day life. However, energy demand is going high day by day. The high consumption of energy causes series of energy crisis. This problem can be handled with many optimization techniques by integrating demand side management with traditional grid. The main purpose of demand side management is to reduce the peak load and smart grid targets reduce the electric cost and load management by shifting the load from on peak hours to off peak hours. In this work, I adopt the Bacterial Foraging Algorithm (BFA) and Crow Search Algorithm (CSA). Simulation results show that our propose techniques reduce the total cost and peak average ratio by scheduling the load for 24 h. Results show that BFA is perform better than CSA and archived the objectives.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gul, M.S., Patidar, S.: Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build. 87, 155–165 (2015)
Palensky, P., Dietrich, D.: Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inf. 7(3), 381–388 (2011)
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)
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)
Chavali, P., Yang, P., Nehorai, A.: A distributed algorithm of appliance scheduling for home energy management system. IEEE Trans. Smart Grid 5(1), 282–290 (2014)
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), p. 15. IEEE (2012)
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)
Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., Niaz, I.A.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)
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)
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)
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)
Ozturk, Y., Senthilkumar, D., Kumar, S., Lee, G.: An intelligent home energy management system to improve demand response. IEEE Trans. Smart Grid 4(2), 694–701 (2012)
Samadi, P., Mohsenian-Rad, H., Wong, V.W.S., Schober, R.: Tackling the load uncertainty challenges for energy consumption scheduling in smart grid. IEEE Trans. Smart Grid 4(2), 1007–1016 (2013)
Joe-Wong, C., Sen, S., Ha, S., Chiang, M.: Optimized day-ahead pricing for smart grids with device-specific scheduling flexibility. IEEE J. Sel. Areas. Commun. 30(6), 1075–1085 (2012)
Aghaei, J., Alizadeh, M.I.: Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew. Sustain. Energy Rev. 18, 64–72 (2013)
Pina, A., Silva, C., Ferrao, P.: The impact of demand side management strategies in the penetration of renewable electricity. Energy 41(1), 128–137 (2012)
Alonso, M., Amaris, H., Alvarez-Ortega, C.: Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms. Expert Syst. Appl. 39(5), 5513–5522 (2012)
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
Tabssam, A., Pervaz, K., Saba, A., Abdeen, Z.u., Farooqi, M., Javaid, N. (2018). Demand Side Management Using Bacterial Foraging and Crow Search Algorithm Optimization Techniques. 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_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-65636-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65635-9
Online ISBN: 978-3-319-65636-6
eBook Packages: EngineeringEngineering (R0)