access icon openaccess Dynamic pricing based on a cloud computing framework to support the integration of renewable energy sources

Integration of renewable energy sources into the electric grid in the domestic sector results in bidirectional energy flow from the supply side of the consumer to the grid. Traditional pricing methods are difficult to implement in such a situation of bidirectional energy flow and they face operational challenges on the application of price-based demand side management programme because of the intermittent characteristics of renewable energy sources. In this study, a dynamic pricing method using real-time data based on a cloud computing framework is proposed to address the aforementioned issues. The case study indicates that the dynamic pricing captures the variation of energy flow in the household. The dynamic renewable factor introduced in the model supports consumer oriented pricing. A new method is presented in this study to determine the appropriate level of photovoltaic (PV) penetration in the distribution system based on voltage stability aspect. The load flow study result for the electric grid in Kerala, India, indicates that the overvoltage caused by various PV penetration levels up to 33% is within the voltage limits defined for distribution feeders. The result justifies the selected level of penetration.

Inspec keywords: demand side management; distributed power generation; pricing; cloud computing; load flow; renewable energy sources; power engineering computing

Other keywords: voltage stability; dynamic pricing; electric grid; cloud computing framework; bidirectional energy flow; India; distribution system; photovoltaic penetration; renewable energy sources; dynamic renewable factor; consumer oriented pricing; price based demand side management programme

Subjects: Power system management, operation and economics; Power engineering computing; Distributed power generation; Internet software

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • 2. Dupont, B., Tant, J., Belmans, R.: ‘Automated residential demand response based on dynamic pricing’. Proc. IEEE PES Innovative Smart grid Technologies (ISGT), Europe, October 2012, pp. 17.
    11. 11)
      • 8. Xinkun, J., Zijun, H., Zongqi, L.: ‘Multi agent based cloud architecture of Smart grid’, Energy Procedia, 2011, 12, pp. 6066.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • 17. Liu, Y., Bebic, J., Kroposki, B., Debedout, J., Ren, W.: ‘Distributed system voltage performance analysis for high penetration PV’. Proc. IEEE Energy, Atlanta, November 2008, pp. 11341139.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • 6. Shahidehpour, M., Wang, Y.: ‘Communication and control in electric power systems’ (John Wiley & Sons, New Jersey, 2003, 2nd edn.).
    21. 21)
    22. 22)
      • 7. Lohrmann, B., Kao, O.: ‘Processing smart meter data streams in cloud’. Proc. IEEE PES Innovative Smart grid Technologies (ISGT), Europe, December 2011, pp. 17.
    23. 23)
    24. 24)
      • 11. Central Electricity Regulatory Commission (CERC), Guidelines for tariff determination. Available at http://www.cercind.gov.in/, accessed October 2012.
    25. 25)
      • 16. Rusitschka, S., Eger, K., Gerdes, C.: ‘Smart grid data cloud: a model for utilizing cloud computing in the smart grid domain’. IEEE Int. Conf. on Smart Grid Communications, Gaithersburg, October 2010, pp. 483488.
    26. 26)
      • 15. ‘Cloud Computing-Key Enabler of Smart grid’, Global Data's report, 2011, www.globaldata.com.
    27. 27)
      • 13. Jaimol, T., Ashok, S., Jose, T.L.: ‘Hybrid pricing strategy for solar energy’. Proc. Sustainable Energy and Intelligent Systems (SEISCON), Chennai, July 2011, pp. 121125.
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