Journal of Regulatory Economics

, Volume 47, Issue 3, pp 239–272 | Cite as

Using deferrable demand in a smart grid to reduce the cost of electricity for customers

  • Wooyoung Jeon
  • Alberto J. LamadridEmail author
  • Jung Youn Mo
  • Timothy D. Mount
Original Article


The primary purpose of this paper is to evaluate the benefits of distributed storage capacity in the form of deferrable demand managed centrally by a system operator, and in particular, to determine the savings in the total annual cost of supplying electricity for a system that has a substantial amount of variable generation from wind turbines. Since the objective of a centrally controlled system is to minimize the expected daily operating costs subject to the availability of generating units and storage capacity, the basic economic question is whether the savings in the annual system cost of supply, including the capital cost of installed generating capacity, can offset the capital cost of installing deferrable demand capacity. The analysis uses a new multi-period model of a power grid that treats stochastic generation explicitly and determines the optimum hourly commitment of conventional generators and the charging/discharging of deferrable demand needed to maintain the reliability of supply. A simulation example shows that deferrable demand can reduce system costs by (1) shifting demand from expensive peak periods to less expensive off-peak periods, (2) providing ramping services to mitigate the variability of wind generation, and (3) reducing the amount of installed peaking capacity needed for System Adequacy and the associated capital costs. If customers pay rates for electricity that reflect the true system costs of supplying their patterns of purchases from the grid, customers with deferrable demand will pay lower bills for electricity and their savings will be substantially more than the cost of installing deferrable demand devices. The results also show that if customers pay typical flat rates for electric energy, the economic incentives for installing deferrable demand are perverse.


Power system economics Energy storage Pricing and rate design 



The authors would like to thank Ray D. Zimmerman, Carlos E. Murillo-Sanchez, Robert J. Thomas, Michael Crew and other participants at the Eastern and Western Rutgers Conferences organized by the Center for Research in Regulated Industries at the Rutgers Business School-Newark and New Brunswick for their comments and input. We also thank two anonymous reviewers for their constructive suggestions. This research was supported by the Lehigh Faculty Innovation Grant, the US Department of Energy through the Consortium for Electric Reliability Technology Solutions (CERTS), the Power Systems Engineering Research Center (PSERC), an NSF I/UCRC, and by NSF-Project 64581 “Cyber-Physical Energy Systems: Foundations for Smart Grids Supporting Intelligent Dependable Energy and Active Load.” The authors are responsible for all conclusions presented.


  1. Allen, E., Lang, J., & Ilic, M. (2008). A combined equivalenced-electric, economic, and market representation of the Northeastern power coordinating council U.S. electric power system. IEEE Transactions on Power Systems, 23(3), 896–907.CrossRefGoogle Scholar
  2. CASE 07-M-0548 - Proceeding on motion of the commission regarding and energy efficiency portfolio: (Order issued and effective december 26, 2013). (2013).
  3. Castro, L. D., & Dutra, J. (2013). Paying for the smart grid, energy economics, 40, Supplement 1(0), S74–S84. Supplement issue: Fifth Atlantic workshop in energy and environmental economics.
  4. Chen, J., Mount, T. D., Thorp, J. S., & Thomas, R. J. (2005). Location-based scheduling and pricing for energy and reserves: A responsive reserve market proposal. Journal of Decision Support Systems, 40(3–4), 563–577.CrossRefGoogle Scholar
  5. Cuomo, A. (2014). Governor Cuomo announces fundamental shift in utility regulation, Technical report, State of New York, executive chamber.
  6. EVAPCO. (2007). Thermal ice storage–application and design guide, Technical report , EVAPCO Inc.Google Scholar
  7. EVFR (2012). FirstRate, Technical report, Energy visuals, Inc.
  8. Hunt, M., Heinemeier, K., Hoeschele, M., & Weitzel, E. (2010). HVAC energy efficiency maintenance study. CALMAC: Technical report.Google Scholar
  9. Jeon, W., Mo, J. Y., & Mount, T. D. (2015). Developing a smart grid that customers can afford: The impact of deferrable demand. The Energy Journal, 36(4).
  10. Keane, A., Milligan, M., Dent, C. J., Hasche, B., D’Annunzio, C., Dragoon, K., et al. (2011). Capacity value of wind power. IEEE Transactions on Power Systems, 26(2), 564–572.CrossRefGoogle Scholar
  11. Kumar, N., Besuner, P. M., Lefton, S. A., Agan, D. D., & Hilleman, D. D. (2012). Power plant cycling costs, Technical report, Intertek APTECH.
  12. Lamadrid, A. J., Mount, T., Jeon, W., & Lu, H. (2014). Barriers to increasing the role of demand resources in electricity markets. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 2314–2324). IEEE.Google Scholar
  13. Lew, D., Brinkman, G., Kumar, N., Besuner, P., Agan, D., & Lefton, S. (2012). Impacts of wind and solar on emissions and wear and tear of fossil-fueled generators, in power and energy society general meeting, 2012 IEEE, pp. 1–8.Google Scholar
  14. Lew, D., Brinkman, G., Kumar, N., Lefton, S., Jordan, G., & Venkataraman, S. (2013). Finding flexibility: Cycling the conventional fleet. Power and Energy Magazine IEEE, 11(6), 20–32.CrossRefGoogle Scholar
  15. Lively, M. (2010). Short run marginal cost pricing for fast responses on the Smart Grid, in innovative smart grid technologies (ISGT), 2010, pp. 1–6.Google Scholar
  16. Mount, T., & Lamadrid, A. J. (2010). Are existing ancillary service markets adequate with high penetrations of variable generation?, in PES general meetings, pp. 1–9.Google Scholar
  17. Mount, T., Maneevitjit, S., Lamadrid, A., Thomas, B., & Zimmerman, R. (2012). The hidden system costs of wind generation in a deregulated electricity market. The Energy Journal, 33(1), 161–186.CrossRefGoogle Scholar
  18. Murillo-Sanchez, C., Zimmerman, R., Anderson, C., & Thomas, R. (2013). Secure planning and operations of systems with stochastic sources, energy storage, and active demand. IEEE Transactions on Smart Grid, 4(4), 2220–2229.CrossRefGoogle Scholar
  19. NERC (2010). Reliability standards for the bulk electric systems of North America, 116–390 Village Road, Princeton, NJ, 08540: North American Electric Reliability Corporation.
  20. Norgaard, P., & Hottlinen, H. (2004), A multi-turbine power curve approach, in Nordic wind power conference,, pp. 1–5
  21. NREL. (2010). Eastern wind integration and transmission study, Technical report, EnerNex Corporation, The National Renewable Energy Laboratory, 1617 Cole Boulevard. Colorado: Golden. 80401.Google Scholar
  22. Outhred, H. (1998). A review of electricity industry restructuring in Australia, Electric Power Systems Research, 44(1), 15–25.
  23. Rahimi, F., & Ipakchi, A. (2010). Demand response as a market resource under the smart grid paradigm. IEEE Transactions on Smart Grid, 1(1), 82–88.CrossRefGoogle Scholar
  24. Zimmerman, R. D., Murillo-Sanchez, C. E., & Thomas, R. J. (2011). MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on Power Systems, 26(1), 12–19.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Wooyoung Jeon
    • 1
  • Alberto J. Lamadrid
    • 2
    Email author
  • Jung Youn Mo
    • 3
  • Timothy D. Mount
    • 4
  1. 1.Korea Energy Economics InstituteUlsanSouth Korea
  2. 2.Department of EconomicsLehigh UniversityBethlehemUSA
  3. 3.Korea Institute for Industrial Economics and TradeSejongKorea
  4. 4.Dyson School of Applied Economics and ManagementCornell UniversityIthacaUSA

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