access icon free Demand response-based enhanced LRIC pricing framework

Increasing penetration of intermittent renewable generators (RGs) results in variable generation. This is likely to create congestion of varying quantum and temporal distribution in distribution networks. The existing distribution network charging methodologies like long-run incremental cost (LRIC) offer location-specific signal to users and charge customers reflecting their use of system. These methodologies can be modified using contribution factor to reflect users demand coincident with peak network demand. RGs are encouraged by relieving them from such contribution-based pricing signals. Congestion caused by intermittent RG could be mitigated by utilising demand customers’ flexibility. This study incorporates short-term demand-side response (DSR) signal for demand customers to manage variability caused by RG in the modified LRIC pricing model. These short-term DSR signals in the form of peak/off-peak charge offers, in conjunction with demand elasticity, helps to assess customer response. This result in modified load profile for various class customers connected at the nodes, which is used for evaluating network charges. The proposed approach is applied to a practical Indian reference network with consideration of distributed generation. Results from modified LRIC pricing model encourage users to change their short-term consumption and help network owner to alleviate congestion and minimise network investment.

Inspec keywords: pricing; distributed power generation; demand side management; distribution networks

Other keywords: Indian reference network; distributed generation; quantum distribution; users demand coincident; long-run incremental cost; enhanced LRIC pricing framework; temporal distribution; intermittent renewable generators; location-specific signal; distribution networks; contribution-based pricing signals; short-term DSR signals; demand-side response

Subjects: Power system management, operation and economics; Distribution networks

References

    1. 1)
      • 7. Santos, P.E.S., Lima, J.W.M., Leme, R.C., et al: ‘Distribution charges for consumers and micro generation considering load elasticity sensitivity’, Energy Econ., 2012, 34, (2), pp. 468475.
    2. 2)
      • 2. Gans, W., Alberini, A., Longo, A.: ‘Smart meter devices and the effect of feedback on residential electricity consumption: evidence from a natural experiment in northern Ireland’, Energy Econ., 2013, 36, pp. 729743.
    3. 3)
      • 4. Benefits of demand response in electricity markets and recommendations for achieving them’ (US Department of Energy, 2006). Available at http://eetd.lbl.gov.
    4. 4)
      • 25. The own price elasticity of demand for electricity in NEM regions’ Tech. rep., (National Institute of Economic and Industry Research, 2007), National Electricity Market Management Company.
    5. 5)
      • 8. Taylor, T.N., Schwarz, P.M.: ‘The long-run effects of a time-of-use demand charge’, RAND J. Econ., 1990, 21, (3), pp. 431445.
    6. 6)
      • 9. Nelson, T., Orton, F.: ‘A new approach to congestion pricing in electricity markets: improving user pays pricing incentives’, Energy Econ., 2013, 40, (4), pp. 17.
    7. 7)
      • 1. Brandstätt, C., Brunekreeft, G., Friedrichsen, N.: ‘Locational signals to reduce network investments in smart distribution grids. What works and what not?’, Util. Policy, 2011, 19, (5), pp. 244254.
    8. 8)
      • 20. Gu, C., Yuan, C., Li, F., et al: ‘Risk management in use-of-system tariffs for network users’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 46834691.
    9. 9)
      • 21. Verzijlbergh, R.A., De Vries, L.J., Lukszo, Z.: ‘Renewable energy sources and responsive demand. Do we need congestion management in the distribution grid?’, IEEE Trans. Power Syst., 2014, 29, (5), pp. 21192128.
    10. 10)
      • 24. Bhakar, R., Padhy, N.P., Gupta, H.O.: ‘Reference network development for distribution network pricing’. Proc. IEEE PES Transmission and Distribution Conf. and Exposition, New Orleans, Louisiana, April 2010.
    11. 11)
      • 17. Sharma, A., Bhakar, R., Tiwari, H.P.: ‘Smart network pricing based on long run incremental cost pricing model’. Proc. Eighteen National Power Syst. Conf., Guwahati, December 2014.
    12. 12)
      • 12. Gu, C., Yang, W., Song, Y., et al: ‘Distribution network pricing for uncertain load growth using fuzzy set theory’, IEEE Trans. Smart Grid, 2016, 7, (4), pp. 19321940.
    13. 13)
      • 10. Pérez-Arriaga, I.J., Smeers, Y.: ‘Guidelines on tariff setting’, in Leveque, F. (Ed.): ‘Transport pricing of electricity networks’ (Kluwer Academic Publishers, Boston, 2003, 1st edn.).
    14. 14)
      • 18. Retail tariff model’ (Frontier Economics, 2012). Available at http://www.aemc.gov.au.
    15. 15)
      • 6. Hogan, W.W.: ‘Fairness and dynamic pricing: comments’, Electr. J., 2010, 23, (6), pp. 2835.
    16. 16)
      • 19. Iskin, I., Daim, T., Kayakutlu, G., et al: ‘Exploring renewable energy pricing with analytic network process – comparing a developed and a developing economy’, Energy Econ., 2012, 34, (4), pp. 882891.
    17. 17)
      • 16. Li, F., Marangon-Lima, J.W., Rudnick, H., et al: ‘Distribution pricing: are we ready for the smart grid?’, IEEE Power Energy Mag., 2015, 13, (4), pp. 7886.
    18. 18)
      • 5. Álvarez, C., Alcázar, M., Escrivá, G., et al: ‘Technical and economical tools to assess customer demand response in the commercial sector’, Energy Convers. Manage., 2009, 50, (10), pp. 26052612.
    19. 19)
      • 3. Wang, Y., Yang, W., Liu, T.: ‘Appliances considered demand response optimisation for smart grid’, IET Gener. Transm. Dis., 2017, 11, (4), pp. 856864.
    20. 20)
      • 23. Bhakar, R., Padhy, N.P., Gupta, H.O.: ‘Development of a flexible distribution reference network’. Proc. IEEE Power Engineering Society General Meeting, Minneapolis, July 2010.
    21. 21)
      • 14. Li, F., Tolley, D.: ‘Long-run incremental cost pricing based on unused capacity’, IEEE Trans. Power Syst., 2007, 22, (4), pp. 16831689.
    22. 22)
      • 15. Li, F., Tolley, D., Padhy, N.P., et al: ‘Network benefits from introducing an economic methodology for distribution charging’ (Department of Electronic & Electrical Engineering University of Bath, 2005) [Online]. Available at http://www.ofgem.gov.uk/.
    23. 23)
      • 11. The costs and benefits of demand management’ (Productivity Commission, Canberra, 2013). Available at http://www.pc.gov.au.
    24. 24)
      • 22. Rutz, W.L., Becker, M., Wicks, F.E.: ‘Treatment of elastic demand in generation planning’, IEEE Trans. Power Appar. Syst., 1985, PAS-104, (11), pp. 30923097.
    25. 25)
      • 13. Li, F.: ‘The benefit of a long-run incremental pricing methodology to future network development’. Proc. IEEE Power Engineering Society General Meeting, Tampa, June 2007.
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