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A Dynamic Programming Model for the Expansion of Electric Power Systems

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  • E. R. Petersen

    (Queen's University, Kingston, Ontario)

Abstract

This paper describes a dynamic programming model that has been developed to determine an optimal expansion plan for the generating capacity of an electric power system. The optimization model determines the least-cost mix of capacity between hydro, nuclear, thermal and peaking turbine plants, the size of the plants to add to the system, and the timing of these additions. We show how the computational requirements of this four-state-variable, four-decision-variable problem can be substantially reduced, resulting in a computationally feasible model. The techniques developed are applicable to a large class of capital budgeting problems under uncertainty. Reference is also made to the actual application of the model and an example is presented.

Suggested Citation

  • E. R. Petersen, 1973. "A Dynamic Programming Model for the Expansion of Electric Power Systems," Management Science, INFORMS, vol. 20(4-Part-II), pages 656-664, December.
    • Handle: RePEc:inm:ormnsc:v:20:y:1973:i:4-part-ii:p:656-664
    DOI: 10.1287/mnsc.20.4.656
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    Cited by:

    1. Lee, Chia-Yen & Charles, Vincent, 2022. "A robust capacity expansion integrating the perspectives of marginal productivity and capacity regret," European Journal of Operational Research, Elsevier, vol. 296(2), pages 557-569.
    2. Pandey, Rahul, 2002. "Energy policy modelling: agenda for developing countries," Energy Policy, Elsevier, vol. 30(2), pages 97-106, January.
    3. Kim, Hansung & Lee, Hwarang & Koo, Yoonmo & Choi, Dong Gu, 2020. "Comparative analysis of iterative approaches for incorporating learning-by-doing into the energy system models," Energy, Elsevier, vol. 197(C).
    4. Ritzenhofen, Ingmar & Birge, John R. & Spinler, Stefan, 2016. "The structural impact of renewable portfolio standards and feed-in tariffs on electricity markets," European Journal of Operational Research, Elsevier, vol. 255(1), pages 224-242.

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