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A New Memory Updation Heuristic Scheme for Energy Management System in Smart Grid

  • Waleed Ahmad
  • Nadeem JavaidEmail author
  • Sajjad Khan
  • Maria Zuraiz
  • Tayyab Awan
  • Muhammad Amir
  • Raza Abid Abbasi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)

Abstract

In the last decade, high energy demand is observed due to increase in population. Due to high demand of energy, numerous challenges in the existing power systems are raised i.e., robustness, stability and sustainability. This work is focused for the residential sector Energy Management System (EMS), especially for the smart homes. An EMS is proposed which shifts the electricity load from high price to low price hours. To fulfill the high load demand of electricity consumers, we have proposed a new Memory Updation Heuristic Scheme (MUHS), which efficiently schedule the appliance from on peak to off peak hours. The objective of our new scheme MUHS is to automate the EMS. The significance of our new proposed MUHS scheme shown the efficiency by reducing Cost, Peak to Average Ratio (PAR) and increase User Comfort (UC) by balancing the load demand in peak times.

Keywords

Smart homes Demand side management Energy Management System Appliance scheduling Critical Peak Pricing 

References

  1. 1.
    Cena, G., Valenzano, A., Vitturi, S.: Hybrid wired/wireless networks for real-time communications. IEEE Ind. Electron. Mag. 2(1), 8–20 (2008)CrossRefGoogle Scholar
  2. 2.
    Ganji Tanha, M.: Security constrained unit commitment reserve determination in joint energy and ancillary services auction (Doctoral dissertation)Google Scholar
  3. 3.
    Strbac, G.: Demand side management: benefits and challenges. Energy Policy 36(12), 4419–4426 (2008)CrossRefGoogle Scholar
  4. 4.
    Gellings, C.W.: The concept of demand-side management for electric utilities. Proc. IEEE 73(10), 1468–1470 (1985)CrossRefGoogle Scholar
  5. 5.
    Chaabene, M., Ben Ammar, M., Elhajjaji, A.: Fuzzy approach for optimal energy management of a domestic photovoltaic panel. Appl. Energy 84(10), 992–1001 (2007)CrossRefGoogle Scholar
  6. 6.
    Pradhan, V., Balijepalli, V.M., Khaparde, S.A.: An effective model for demand response management systems of residential electricity consumers. IEEE Syst. J. 10(2), 434–445 (2016)CrossRefGoogle Scholar
  7. 7.
    Khan, M.A., Javaid, N., Mahmood, A., Khan, Z.A., Alrajeh, N.: A generic demand side management model for smart grid. Int. J. Energy Res. 39(7), 954–964 (2015)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Agnetis, A., de Pascale, G., Detti, P., Vicino, A.: Load scheduling for household energy consumption optimization. IEEE Trans. Smart Grid 4(4), 2364–2373 (2013)CrossRefGoogle Scholar
  10. 10.
    Bradac, Z., Kaczmarczyk, V., Fiedler, P.: Optimal scheduling of domestic appliances via MILP. Energies 8(1), 217–232 (2014)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Yalcintas, M., Hagen, W.T., Kaya, A.: An analysis of load reduction and load shifting techniques in commercial and industrial buildings under dynamic electricity pricing schedules. Energy Build. 88, 15–24 (2015)CrossRefGoogle Scholar
  13. 13.
    Khan, A., Javaid, N., Khan, M.I.: Time and device based priority induced comfort management in smart home within the consumer budget limitation. Sustainable cities and society (2018)Google Scholar
  14. 14.
    Khalid, A., et al.: Cuckoo search optimization technique for multi-objective home energy management. In: International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. Springer, Cham (2017)Google Scholar
  15. 15.
    Samuel, O., Javaid, N., Ashraf, M., Ishmanov, F., Afzal, M., Khan, Z.: Jaya-based optimization method with high dispatchable distributed generation for residential microgrid. Energies 11(6), 1513 (2018)CrossRefGoogle Scholar
  16. 16.
    Khalid, A., Javaid, N., Guizani, M., Alhussein, M., Aurangzeb, K., Ilahi, M.: Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings. IEEE Access 6, 19509–19529 (2018)CrossRefGoogle Scholar
  17. 17.
    Marzband, M., et al.: Real time experimental implementation of optimum energy management system in stand alone microgrid by using multi-layer and colony optimization. Int. J. Electr. Power Energy Syst. 75, 265–274 (2016)CrossRefGoogle Scholar
  18. 18.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)CrossRefGoogle Scholar
  19. 19.
    Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Waleed Ahmad
    • 1
  • Nadeem Javaid
    • 1
    Email author
  • Sajjad Khan
    • 1
  • Maria Zuraiz
    • 2
  • Tayyab Awan
    • 3
  • Muhammad Amir
    • 1
  • Raza Abid Abbasi
    • 1
  1. 1.COMSATS UniversityIslamabadPakistan
  2. 2.COMSATS University Islamabad, Abbotabad CampusAbbotabadPakistan
  3. 3.National University of Science and TechnologyIslamabadPakistan

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