Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

  • Ghulam Hafeez
  • Rabiya Khalid
  • Abdul Wahab Khan
  • Malik Ali Judge
  • Zafar Iqbal
  • Rasool Bukhsh
  • Asif Khan
  • Nadeem Javaid
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 13)

Abstract

In the smart grid (SG) users in residential sector adopt various load scheduling methods to manage their consumption behavior with specific objectives. In this paper, we focus on the problem of load scheduling under utility and rooftop photovoltaic (PV) units. We adopt genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), and proposed genetic wind driven optimization (GWDO) algorithm to schedule the operation of interruptible appliances (IA) and non interruptible appliances (Non-IA) in order to reduce electricity cost and peak to average ratio (PAR). For energy pricing combined real time pricing (RTP) and inclined block rate (IBR) is used because in case of only RTP their is possibility of building peaks during off peak hours that may damage the entire power system. The proposed algorithm shift load from peak consumption hours to off peak hours and to hours with high generation from rooftop PV units. For practical consideration, we also take into consideration pricing scheme, rooftop PV units, and ESS in our system model, and analyze their impacts on electricity cost and PAR. Simulation results show that our proposed scheduling algorithm can affectively reflect and affect users consumption behavior and achieve the optimal electricity cost and PAR.

References

  1. 1.
    Hurlbut, D.: State clean energy practices: renewable portfolio standards. Nat. Renew. Energy Lab., Golden, CO, USA, Technical report NREL/TP-670-43512 (2008)Google Scholar
  2. 2.
    Roselund, C., Bernhardt, J.: Lessons Learned Along Europe’s Road to Renewables. IEEE Spectrum (2015)Google Scholar
  3. 3.
    Ma, J., Chen, H.H., Song, L., Li, Y.: Residential load scheduling in smart grid: a cost efficiency perspective. IEEE Trans. Smart Grid 7(2), 771–784 (2016)Google Scholar
  4. 4.
    Solar Energy. https://en.wikipedia.org/wiki/Solar_energy. Accessed 9 Mar 2017
  5. 5.
    Shirazi, E., Jadid, S.: Optimal residential appliance scheduling under dynamic pricing scheme via HEMDAS. Energy Build. 93, 40–49 (2015)CrossRefGoogle Scholar
  6. 6.
    Häberlin, H.: Analysis of loss mechanisms in crystalline silicon modules in outdoor operation. In: Photovoltaics System Design and Practice, pp. 538–542. Wiley, West Sussex (2012)Google Scholar
  7. 7.
    Liu, Y., Yuen, C., Huang, S., Hassan, N.U., Wang, X., Xie, S.: Peak-to-average ratio constrained demand-side management with consumer’s preference in residential smart grid. IEEE J. Sel. Top. Sig. Process. 8(6), 1084–1097 (2014)CrossRefGoogle Scholar
  8. 8.
    Adika, C.O., Wang, L.: Smart charging and appliance scheduling approaches to demand side management. Int. J. Electr. Power Energy Syst. 57, 232–240 (2014)CrossRefGoogle Scholar
  9. 9.
    Shirazi, E., Zakariazadeh, A., Jadid, S.: Optimal joint scheduling of electrical and thermal appliances in a smart home environment. Energy Conv. Manage. 106, 181–193 (2015)CrossRefGoogle Scholar
  10. 10.
    Ogwumike, C., Short, M., Abugchem, F.: Heuristic optimization of consumer electricity costs using a generic cost model. Energies 9(1), 6 (2015)CrossRefGoogle Scholar
  11. 11.
    Chen, C., Nagananda, K.G., Xiong, G., Kishore, S., Snyder, L.V.: A communication-based appliance scheduling scheme for consumer-premise energy management systems. IEEE Trans. Smart Grid 4(1), 56–65 (2013)CrossRefGoogle Scholar
  12. 12.
    Samadi, P., Mohsenian-Rad, H., Wong, V.W., Schober, R.: Tackling the load uncertainty challenges for energy consumption scheduling in smart grid. IEEE Trans. Smart Grid 4(2), 1007–1016 (2013)CrossRefGoogle Scholar
  13. 13.
    Adika, C.O., Wang, L.: Autonomous appliance scheduling for household energy management. IEEE Trans. Smart Grid 5(2), 673–682 (2014)CrossRefGoogle Scholar
  14. 14.
    Ziadi, Z., Taira, S., Oshiro, M., Funabashi, T.: Optimal power scheduling for smart grids considering controllable loads and high penetration of photovoltaic generation. IEEE Trans. Smart Grid 5(5), 2350–2359 (2014)CrossRefGoogle Scholar
  15. 15.
    Chen, L., Li, N., Low, S., Doyle, J.: Two market models for demand response in power networks. In: Proceedings of 1st IEEE International Conference on Smart Grid Communications, pp. 397–402, October 2010Google Scholar
  16. 16.
    Ye, F., Qian, Y., Hu, R.Q.: A real-time information based demand-side management system in smart grid. IEEE Trans. Parall. Distrib. Syst. 27(2), 329–339 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ghulam Hafeez
    • 1
  • Rabiya Khalid
    • 1
  • Abdul Wahab Khan
    • 1
  • Malik Ali Judge
    • 1
  • Zafar Iqbal
    • 2
  • Rasool Bukhsh
    • 1
  • Asif Khan
    • 1
  • Nadeem Javaid
    • 1
  1. 1.COMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.PMAS Agriculture UniversityRawalpindiPakistan

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