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Assessment of the potential of multifarious demand response programs in reducing transformer loss of life

Nonika Loitongbam ORCID logo EMAIL logo , Kumar Raja Gadham and T. Ghose


Transformer may enter the ageing cycle sooner if it is loaded more than the rated value for longer periods of time in its life cycle. This paper exploits demand response as a way to improve transformer life by reducing the hottest spot temperature (HST) which is caused due to better load profile. The aim of the paper is to investigate the impact of various types of price-based and incentive-based demand response programs (DRPs) on the transformer life and other attributes like energy consumption, peak to average ratio, etc. Entropy method is used to determine the weights of multi-attributes in a multi-attribute decision-making (MADM) model formed by the various attributes and the multifarious demand response programs. Using these weights, the various DRPs are ranked using Program Ranking Index to assist the utility in deciding which DRP is to be employed. IEEE transformer model is used to calculate the transformer ageing for two cases with and without demand response programs. The simulation results validate the effectiveness of demand response in mitigating transformer loss of life. Furthermore, the economic and technical benefits of employing demand response are quantified.

Corresponding author: Nonika Loitongbam, Department of Electrical Engineering, MIT, Takyelpat, Imphal, Manipur, India, E-mail:

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.


1. on Power Distribution in India.pdf.Search in Google Scholar

2. in Google Scholar

3. Zhang, X, Gockenbach, E, Wasserberg, V, Borsi, H. Estimation of the lifetime of the electrical components in distribution networks. IEEE Trans Power Deliv Jan 2007;22:515–22. in Google Scholar

4. Shahbazi, B, Vadiati, M. Transformer condition monitoring system for smart grid. Proc ICCIA 2011:32–7.10.1109/ICCIAutom.2011.6356626Search in Google Scholar

5. Chatterjee, A, Sarkar, R, Roy, NK, Kumbhakar, P. Online monitoring of transformers using gas sensor fabricated by nanotechnology. Eur Trans Electr Power 2012;23:867–75. in Google Scholar

6. Poyser, TD. An on-line microprocessor based transformer analysis system to improve the availability and utilization of power transformers. IEEE Trans Power Apparatus Syst Apr 1983;PAS-102:957–62. in Google Scholar

7. Dominelli, N, Rao, A, Kundur, P. Life extension and condition assessment: techniques for an aging utility infrastructure. IEEE Power Energy Mag May 2006;4:24–35. in Google Scholar

8. Douglass, DA, Lawry, DC, Edris, AA, Bascom, EC. Dynamic thermal ratings realize circuit load limits. IEEE Comput Appl Power Jan 2000;13:38–44. in Google Scholar

9. IEEE guide for loading mineral-oil-immersed power transformers and step-voltage regulators IEEE. IEEE Standard C57 2011 :91. in Google Scholar

10. Power transformers part 7: loading guide for oil-immersed power transformers. International Standard IEC 2005:60076–7. in Google Scholar

11. Jain, AK. Demand response of an industrial buyer considering congestion and LMP in day-ahead electricity market. Int J Emerg Elec Power Syst June 2019;20. in Google Scholar

12. Jain, AK, Srivastava, SC. Price responsive demand management of an industrial buyer in day-ahead electricity market. International Journal of Emerging Electric Power Systems 2017;18. in Google Scholar

13. Mansouria, SA, Ahmarinejada, A, Ansariana, M, Javadib, MS, Catalao, JPS. Stochastic planning and operation of energy hubs considering demand response programs using Benders decomposition approach. Electrical Power and Energy Systems September 2020;120:106030. in Google Scholar

14. Aalami, HA, Parsa Moghaddam, M, Yousefi, GR. Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy Jan 2010;87:243–50. in Google Scholar

15. Aalami, HA, Parsa Moghaddam, M, Yousefi, GR. Modeling and prioritizing demand response programs in power markets. Elec Power Syst Res Apr 2010;80:426–35. in Google Scholar

16. Jargstorf, J, Vanthournout, K, De Rybel, T, Van Hertem, D. Effect of demand response on transformer life time expectation. In: Proc. ISGT Europe 2012:1–8.10.1109/ISGTEurope.2012.6465805Search in Google Scholar

17. Humayun, M, Degefa, MZ, Safdarian, A, Lehtonen, M. Utilization improvement of transformers using demand response. IEEE Trans Power Deliv Feb 2015;30:202–10. in Google Scholar

18. Humayun, M, Safdarian, A, Degefa, MZ, Lehtonen, M. Demand response for operational life extension and efficient capacity utilization of power transformers during contingencies. IEEE Trans Power Syst Jul 2015;30:2160–9. in Google Scholar

19. U.S Department of Energy. Energy policy act of 2005. section 1252; February 2006. [Online]. Available from: in Google Scholar

20. IEA. Strategic plan for the IEA demand-side management program 2008–2012. [Online]. Available from: in Google Scholar

21. FERC. Assessment of demand response and advanced metering. Staff Rep. AD06-2-000; 2006. [Online]. Available from: in Google Scholar

22. Kirschen, D, Strbac, G, Cumperayot, P, de Paiva Mendes, D. Factoring the elasticity of demand in electricity prices. IEEE Trans Power Syst May 2000;15:612–7. in Google Scholar

23. Kirschen, DS, Strbac, G. Fundamentals of power system economics. Hoboken, NJ: Wiley; 2004.10.1002/0470020598Search in Google Scholar

24. Schweppe, FC, Caramanis, MC, Tabors, RD, Bohn, RE. Spot pricing of electricity. Boston, MA: Kluwer; 1989.10.1007/978-1-4613-1683-1Search in Google Scholar

25. Troffaes, MCM, Sahlin, U. Imprecise swing weighting for multi-attribute utility elicitation based on partial preferences. PMLR: Proceedings of Machine Learning Research 2017;62:333–45. in Google Scholar

26. Roszkowska, E. Rank ordering criteria weighting methods – a comparative overview. Optimum Studia Ekonomiczne nr 2013;5:14–33. in Google Scholar

27. Diakoulaki, D, Mavrotas, G, Papayannakis, L. Determining objective weights in multiple criteria problems: the critic method. Comput Oper Res Aug 1995;22:763–70. in Google Scholar

28. Ustinovičius, L. Determining integrated weights of attributes. Statyba-Civil Engineering 2001;7:321–6. in Google Scholar

29. Nigim, K, Munier, N, Green, J. Pre feasibility MCDM tools to aid communities in prioritizing local viable renewable energy sources. Renew Energy September 11 2004;29:1775–91. in Google Scholar

30. in Google Scholar

31. in Google Scholar

Received: 2020-04-16
Accepted: 2020-08-15
Published Online: 2020-09-14

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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