IDEAS home Printed from
   My bibliography  Save this paper

Integrating short-term demand response into long-term investment planning


  • De Jonghe, C.
  • Hobbs, B. F.
  • Belmans, R.


Planning models have been used for many years to optimize generation investments in electric power systems. More recently, these models have been extended in order to treat demand-side management on an equal footing. This paper stresses the importance of integrating short-term demand response to time-varying prices into those investment models. Three different methodologies are suggested to integrate short-term responsiveness into a long-term model assuming that consumer response can be modelled using price-elastic demand and that generators behave competitively. First, numerical results show that considering operational constraints in an investment model results in less inflexible base load capacity and more mid-range capacity that has higher ramp rates. Then, own-price and cross-price elasticities are included in order to incorporate consumers’ willingness to adjust the demand profile in response to price changes. Whereas own-price elasticities account for immediate response to price signals, cross-price elasticities account for shifting loads to other periods. As energy efficiency programs sponsored by governments or utilities also influence the load profile, the interaction of energy efficiency expenditures and demand response is also modelled. In particular, reduced responsiveness to prices can be a side effect when consumers have become more energy efficient. Comparison of model results for a single year optimization with and without demand response shows the peak reduction and valley filling effects of response to real-time prices for an illustrative example with a large amount of wind power injections. Additionally, increasing demand elasticity increases the optimal amount of installed wind power capacity. This suggests that demand-side management can result in environmental benefits not only through reducing energy use, but also by facilitating integration of renewable energy.

Suggested Citation

  • De Jonghe, C. & Hobbs, B. F. & Belmans, R., 2011. "Integrating short-term demand response into long-term investment planning," Cambridge Working Papers in Economics 1132, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1132

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Oikonomou, Vlasis & Rietbergen, Martijn & Patel, Martin, 2007. "An ex-ante evaluation of a White Certificates scheme in The Netherlands: A case study for the household sector," Energy Policy, Elsevier, vol. 35(2), pages 1147-1163, February.
    2. Bushnell, James, 2010. "Building Blocks: Investment in Renewable and Non-Renewable Technologies," Staff General Research Papers Archive 31546, Iowa State University, Department of Economics.
    3. Kenneth Gillingham & Richard G. Newell & Karen Palmer, 2009. "Energy Efficiency Economics and Policy," Annual Review of Resource Economics, Annual Reviews, vol. 1(1), pages 597-620, September.
    4. Hobbs, Benjamin F. & Nelson, Sushil K., 1989. "Assessing conservation payments: Least-cost, least-rates, or most-value?," The Electricity Journal, Elsevier, vol. 2(6), pages 28-39, July.
    5. Benjamin F. Hobbs, 1991. "The "Most Value" Test: Economic Evaluation of Electricity Demand-Side Management Considering Customer Value," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 67-92.
    6. De Jonghe, Cedric & Delarue, Erik & Belmans, Ronnie & D'haeseleer, William, 2009. "Interactions between measures for the support of electricity from renewable energy sources and CO2 mitigation," Energy Policy, Elsevier, vol. 37(11), pages 4743-4752, November.
    7. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    8. Richard Green & Nicholas Vasilakos, 2011. "The Long-term Impact of Wind Power on Electricity Prices and Generating Capacity," Discussion Papers 11-09, Department of Economics, University of Birmingham.
    9. Palmer, Karen & Burtraw, Dallas, 2005. "Cost-effectiveness of renewable electricity policies," Energy Economics, Elsevier, vol. 27(6), pages 873-894, November.
    10. van der Weijde, A.H. & Hobbs, B.F., 2011. "Planning electricity transmission to accommodate renewables: Using two-stage programming to evaluate flexibility and the cost of disregarding uncertainty," Cambridge Working Papers in Economics 1113, Faculty of Economics, University of Cambridge.
    11. Maddaloni, Jesse D. & Rowe, Andrew M. & van Kooten, G. Cornelis, 2009. "Wind integration into various generation mixtures," Renewable Energy, Elsevier, vol. 34(3), pages 807-814.
    12. Delarue, Erik & De Jonghe, Cedric & Belmans, Ronnie & D'haeseleer, William, 2011. "Applying portfolio theory to the electricity sector: Energy versus power," Energy Economics, Elsevier, vol. 33(1), pages 12-23, January.
    13. Lijesen, Mark G., 2007. "The real-time price elasticity of electricity," Energy Economics, Elsevier, vol. 29(2), pages 249-258, March.
    14. Severin Borenstein, 2005. "The Long-Run Efficiency of Real-Time Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 93-116.
    15. Hobbs, Benjamin F., 1995. "Optimization methods for electric utility resource planning," European Journal of Operational Research, Elsevier, vol. 83(1), pages 1-20, May.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. repec:eee:rensus:v:80:y:2017:i:c:p:603-619 is not listed on IDEAS
    2. Batas Bjelić, Ilija & Rajaković, Nikola & Ćosić, Boris & Duić, Neven, 2013. "Increasing wind power penetration into the existing Serbian energy system," Energy, Elsevier, vol. 57(C), pages 30-37.
    3. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2016. "The impact of operations anticipations on the quality of stochastic location-allocation models," Omega, Elsevier, vol. 62(C), pages 19-33.
    4. Neves, Diana & Pina, André & Silva, Carlos A., 2015. "Demand response modeling: A comparison between tools," Applied Energy, Elsevier, vol. 146(C), pages 288-297.
    5. Batas Bjelić, Ilija & Rajaković, Nikola, 2015. "Simulation-based optimization of sustainable national energy systems," Energy, Elsevier, vol. 91(C), pages 1087-1098.
    6. Xiao, Jingjie, 2013. "Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming," MPRA Paper 58696, University Library of Munich, Germany.

    More about this item


    Wind power generation; power generation planning; load management; energy efficiency;

    JEL classification:

    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cam:camdae:1132. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jake Dyer). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.