Demand Side Optimization in Smart Grid Using Harmony Search Algorithm and Social Spider Algorithm

  • Muhammad Junaid
  • Muhammad Hassan Rahim
  • Anwar Ur Rehman
  • Waqar Ali
  • Muhammad Awais
  • Tamour Bilal
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 13)

Abstract

Electricity is a valuable resource. With the increase of population, this valuable resource is being used inefficiently. To overcome this problem, electricity providers use various techniques like introducing different pricing schemes. In peak hours, when the usage of electricity is high, the utility increases the per unit cost. Therefore, usage of electricity in peak hours result in high electricity bills. The electricity bills can be reduced by efficiently scheduling the home appliances so that few appliances are operated during peak hours. For this purpose many techniques have been proposed. In this paper, we propose a Social Spider Algorithm (SSA) for Demand Side Management (DSM). Harmony Search Algorithm (HSA) has been adapted to evaluate the results of SSA. These algorithms schedules the appliances in such a way that the usage of electricity in peak hours is reduced. This results in reduction of electricity bill and Peak to Average Ratio (PAR).

References

  1. 1.
    Fang, X., et al.: Smart grid-the new and improved power grid: a survey. IEEE Commun. Surv. Tutor. 14(4), 944–980 (2012)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    Javaid, N., et al.: A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3), 319 (2017)CrossRefGoogle Scholar
  5. 5.
    Ma, K., et al.: Residential power scheduling for demand response in smart grid. Int. J. Electr. Power Energy Syst. 78, 320–325 (2016)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Ogunjuyigbe, A.S.O., Ayodele, T.R., Akinola, O.A.: User satisfaction-induced demand side load management in residential buildings with user budget constraint. Appl. Energy 187, 352–366 (2017)CrossRefGoogle Scholar
  9. 9.
    Bharathi, C., Rekha, D., Vijayakumar, V.: Genetic algorithm based demand side management for smart grid. Wirel. Pers. Commun. 93(2), 481–502 (2017)CrossRefGoogle Scholar
  10. 10.
    Rahim, S., et al.: Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources. Energy Build. 129, 452–470 (2016)CrossRefGoogle Scholar
  11. 11.
    Aktas, A., et al.: Experimental investigation of a new smart energy management algorithm for a hybrid energy storage system in smart grid applications. Electr. Power Syst. Res. 144, 185–196 (2017)CrossRefGoogle Scholar
  12. 12.
    Kusakana, K.: Energy management of a grid-connected hydrokinetic system under time of use tariff. Renew. Energy 101, 1325–1333 (2017)CrossRefGoogle Scholar
  13. 13.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)CrossRefGoogle Scholar
  14. 14.
    Cuevas, E., et al.: A computational intelligence optimization algorithm based on the behavior of the social-spider. In: Computational Intelligence Applications in Modeling and Control, pp. 123–146. Springer International Publishing (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Muhammad Junaid
    • 1
  • Muhammad Hassan Rahim
    • 1
  • Anwar Ur Rehman
    • 1
  • Waqar Ali
    • 1
  • Muhammad Awais
    • 1
  • Tamour Bilal
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan

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