Modeling of demand response programs based on market elasticity concept

  • H. JaliliEmail author
  • M. K. Sheikh-El-Eslami
  • M. Parsa Moghaddam
  • P. Siano
Original Research


Demand response programs (DRPs) are appropriate tools to improve power system operation. Applying these programs results in a reduction in reliability cost and electricity price, transmission congestion and pollution relief, and also can determine postponements in network expansion. Therefore, developing a comprehensive model for DRPs is necessary for accurate planning and encouragement of consumers to increase their participation. In this paper, by using the market elasticity concept, a comprehensive model for DRPs is developed. Market elasticity is defined as sensitivity of electricity price on the network load. The proposed model is able to increase the consumers’ participation by providing a higher awareness about their participations’ effects on their electricity cost reduction. This additional awareness is provided by creating the information about the impact of consumers’ participation on the price of the electricity market in addition to the direct impact of their participations on their cost reduction. Information about the impact of consumers’ participation on the price of the electricity market is provided by the market elasticity concept. The effectiveness of the proposed \({\rho _0}(i)\) model is demonstrated by simulation results.


DRPs Market elasticity concept Economic model of demand 




i-th period


j-th period


k-th criterion


l-th scenario


Criteria quantity


Scenarios quantity


\({\rho _0}(i)\)

Initial electricity price in i-th period

\(\rho (i)\)

Electricity price in i-th period


Initial demand in i-th period


Demand in i-th period


Price self-elasticity of demand


Price cross-elasticity of demand


Market self-elasticity


Market cross-elasticity

\(\alpha _{2}^{i},\beta _{2}^{i}\)

Demand function parameters before demand or electricity price change

\(\alpha _{1}^{i},\beta _{1}^{i}\)

Generation function parameters

\(\alpha _{3}^{i},\beta _{2}^{i}\)

Demand function parameters after demand or electricity price change

\(\Delta d(i)\)

Demand change


Customer’s benefit in i-th period


Initial customer’s income in i-th period


Customer’s income in i-th period

\(P(\Delta d(i))\)

The total amount of incentive in i-th period


Incentive of DRPs in i-th period

\(PEN(\Delta d(i))\)

The total amount of penalty in i-th period


Penalty of DRPs in i-th period


Contract level in i-th period


Scenario no.


Elements of normalized decision matrix


Elements of decision matrix


Best solution/Worst solution


Weight of k-th criterion


Priority coefficient in TOPSIS method


Consumer’s welfare parameter


Distance between each scenario and the best solution/worst solution



  1. Aalami HA (2010) Demand response modeling based on demand price elasticity coefficients (In Persian). Ph.D. dissertation, Department of Electrical and Computer Engineering, Tarbiat Modares University, TehranGoogle Scholar
  2. Aalami HA, Yousefi GR, Parsa MM (2008) Demand response model considering EDRP and TOU programs. In: IEEE, PES, T&D ConferenceGoogle Scholar
  3. Aalami HA, Parsa Moghaddam M, Yousefi GR (2010a) Demand response modeling considering interruptible/curtailable loads and capacity market programs. Appl Energy 87:243–250CrossRefGoogle Scholar
  4. Aalami HA, Parsa Moghaddam M, Yousefi GR (2010b) Modeling and prioritizing demand response programs in power markets. Electr Power Syst Res 80:426–435CrossRefGoogle Scholar
  5. Aalami HA, Parsa Moghaddam M, Yousefi GR (2015) Evaluation of nonlinear models for time-based rates demand response programs. Int J Electr Power Energy Syst 65:282–290CrossRefGoogle Scholar
  6. Aghajani GR, Shayanfar HA, Shayegani H (2015) Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers Manag 106:308–321CrossRefGoogle Scholar
  7. Brosdahl DJC, Carpenter JM (2010) Consumer knowledge of the environmental impacts of textile and apparel production, concern for the environment, and environmentally friendly consumption behavior. J Textile Appar Technol Manag 6:1–9Google Scholar
  8. Chan R (2001) Determinants of Chinese consumers’ green purchase behavior. Psychol Mark 18:389–399CrossRefGoogle Scholar
  9. Chan K (1999) Market segmentation of green consumers in Hong Kong. J Int Consum Mark 12:7–24Google Scholar
  10. Cirio D, Demartini G, Massucco S, Monni A, Scaler P, Silvestvo F, Vimercati G (2003) Load control for improving system security and economics. In: IEEE, Power Tech Conference, pp 1–8Google Scholar
  11. Deng R, Yang Z, Chen J, Asr NY, Chow MY (2014a) Residential energy consumption scheduling: a coupled-constraint game approach. IEEE Trans Smart Grid 5:1340–1350CrossRefGoogle Scholar
  12. Deng R, Yang Z, Chen J, Chow MY (2014b) Load scheduling with price uncertainty and temporally-coupled constraints in smart grid. IEEE Trans Power Syst 29:2823–2834CrossRefGoogle Scholar
  13. Deng R, Yang Z, Chow MY, Chen J (2015) A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans Industr Inf 11:570–582CrossRefGoogle Scholar
  14. Fan Z (2011) Distributed demand response and user adaption in smart grid. In: IEEE International Symposium on Integrated Network Management, pp 726–729Google Scholar
  15. Faruqui A, Sergici S (2010) Household response to dynamic pricing of electricity: a survey of 15 experiments. J Regul Econ 38:193–225CrossRefGoogle Scholar
  16. IEA (2009) Strategic plan for the IEA-demand side management program 2004–2009.
  17. Iran Ministry of Energy Statistical information of energy balance (2010)
  18. Iran Power Industry Statistics (2015)
  19. Ishak S, Zabil NFM (2012) Impact of consumer awareness and knowledge to consumer effective behavior. Asian Soc Sci 8:108–114Google Scholar
  20. Karthikeyan S, Jacob Ragled I, Kothari D (2013) A review on market power in deregulated electricity market. Electr Power Energy Syst 48:139–147CrossRefGoogle Scholar
  21. Liang YP (2012) The relationship between consumer product involvement, product knowledge and impulsive buying behavior. Soc Behav Sci 57:325–330CrossRefGoogle Scholar
  22. Marwan M, Ledwich G, Ghosh A (2014) Demand-side response model to avoid spike of electricity price. J Process Control 24:782–789CrossRefGoogle Scholar
  23. Mohajeryami S, Schwarz P, Teimourzadeh Baboli P (2015) Including the behavioral aspects of customers in demand response model: Real time pricing versus peak time rebate. In: North American Power Symposium (NAPS)Google Scholar
  24. Na L, Lijun C, Dahleh MA (2015) Demand response using linear supply function bidding. IEEE Trans Smart Grid 6:1827–1838CrossRefGoogle Scholar
  25. Pillay A, Karthikeyan S, Kothari D (2015) Congestion management in power systems—a review. Electr Power Energy Syst 70:83–90CrossRefGoogle Scholar
  26. Saaty T (1980) The analytic hierarchy processes. McGraw Hill, New YorkzbMATHGoogle Scholar
  27. Safamehr H, Rahimi Kian A (2015) A cost-efficient and reliable energy management of a micro-grid using intelligent demand response program. Energy 91:283–293CrossRefGoogle Scholar
  28. Schweppe FC, Caramanis MC, Tabors RD, Bohn RE (1989) Spot pricing of electricity. Kluwer Academic Publishers, DordrechtGoogle Scholar
  29. Smith V, Kiesling L (2005) A market-based model for ISO-sponsored demand response programs. In: A white paper prepared for the multi-client studyGoogle Scholar
  30. U. S. Department of Energy (2006) Benefits of demand response in electricity markets and recommendations for achieving them. Section 1252 of the report. Energy policy act of 2005Google Scholar
  31. Vardakas JS, Zorba N, Verikoukis V (2015) A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Commun Surv Tutor 17:152–178CrossRefGoogle Scholar
  32. Ying L, Boong Loong N, Trayer M, Lingjia L (2012) Automated residential demand response: algorithmic implications of pricing models. IEEE Trans Smart Grid 3:1712–1721CrossRefGoogle Scholar
  33. Yousefi A, Aalami HA, Shayesteh E, Parsa Moghaddam M (2008) Enhancement of spinning reserve capacity by means of optimal utilization of EDRP program. In: Proceeding of the Fourth IASTED International Conference, Power and Energy SystemsGoogle Scholar
  34. Yu N, Yu J (2006) Optimal TOU decision considering demand response model. In: IEEE International Conference on Power System Technology, pp 1–5Google Scholar
  35. Zakariazadeh A, Homaee O, Jadid S, Siano P (2014a) A new approach for real time voltage control using demand response in an automated distribution system. Appl Energy 114:157–166CrossRefGoogle Scholar
  36. Zakariazadeh A, Jadid S, Siano P (2014b) Multi-objective scheduling of electric vehicles in smart distribution system. Energy Convers Manag 79:43–53CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Roudehen BranchIslamic Azad UniversityRoudehenIran
  2. 2.Faculty of Electrical & Computer EngineeringTarbiat Modares UniversityTehranIran
  3. 3.University of SalernoSalernoItaly

Personalised recommendations