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

Author

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

Abstract

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
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    Cited by:

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    2. Haas, J. & Cebulla, F. & Cao, K. & Nowak, W. & Palma-Behnke, R. & Rahmann, C. & Mancarella, P., 2017. "Challenges and trends of energy storage expansion planning for flexibility provision in low-carbon power systems – a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 603-619.
    3. Neves, Diana & Pina, André & Silva, Carlos A., 2015. "Demand response modeling: A comparison between tools," Applied Energy, Elsevier, vol. 146(C), pages 288-297.
    4. Sajad Aliakbari Sani & Olivier Bahn & Erick Delage & Rinel Foguen Tchuendom, 2022. "Robust Integration of Electric Vehicles Charging Load in Smart Grid’s Capacity Expansion Planning," Dynamic Games and Applications, Springer, vol. 12(3), pages 1010-1041, September.
    5. Michal Kaut & Kjetil Midthun & Adrian Werner & Asgeir Tomasgard & Lars Hellemo & Marte Fodstad, 2014. "Multi-horizon stochastic programming," Computational Management Science, Springer, vol. 11(1), pages 179-193, January.
    6. Batas Bjelić, Ilija & Rajaković, Nikola, 2015. "Simulation-based optimization of sustainable national energy systems," Energy, Elsevier, vol. 91(C), pages 1087-1098.
    7. Ioana Bejan & Carsten Lynge Jensen & Laura M. Andersen & Lars Gårn Hansen, 2019. "The hidden cost of real time electricity pricing," IFRO Working Paper 2019/03, University of Copenhagen, Department of Food and Resource Economics.
    8. 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.
    9. 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.
    10. 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.

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    More about this item

    Keywords

    Wind power generation; power generation planning; load management; energy efficiency;
    All these keywords.

    JEL classification:

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

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