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Derived Demand and Capacity Planning Under Uncertainty

Author

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  • Eduardo Marco Modiano

    (Pontifícia Universidade Católica do Rio de Janeiro, Brazil)

Abstract

This paper is concerned with the derivation of demand functions for primary resources in an economic environment with uncertainty. This issue is critical when resource markets must decide, in the face of uncertainty, whether to expand capacity and/or to introduce new technologies. The models proposed are primarily data oriented. They explore two alternative behavioral models of action under uncertainty: here-and-now and wait-and-see . We present an application to the U.S. energy sector's demand for coal and the related expansion of capacity of coal-fired electricity generation plants.

Suggested Citation

  • Eduardo Marco Modiano, 1987. "Derived Demand and Capacity Planning Under Uncertainty," Operations Research, INFORMS, vol. 35(2), pages 185-197, April.
    • Handle: RePEc:inm:oropre:v:35:y:1987:i:2:p:185-197
    DOI: 10.1287/opre.35.2.185
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    Cited by:

    1. Logan, Douglas M., 1990. "5.4. Decision analysis in engineering-economic modeling," Energy, Elsevier, vol. 15(7), pages 677-696.
    2. Stephan Nagl, Michaela Fursch, and Dietmar Lindenberger, 2013. "The Costs of Electricity Systems with a High Share of Fluctuating Renewables: A Stochastic Investment and Dispatch Optimization Model for Europe," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    3. Kjetil Haugen & Stein Wallace, 2006. "Stochastic programming: Potential hazards when random variables reflect market interaction," Annals of Operations Research, Springer, vol. 142(1), pages 119-127, February.
    4. Stephan Nagl & Michaela Fürsch & Dietmar Lindenberger, 2013. "The Costs of Electricity Systems with a High Share of Fluctuating Renewables: A Stochastic Investment and Dispatch Optimization Model for Europe," The Energy Journal, , vol. 34(4), pages 151-180, October.
    5. C A Poojari & C Lucas & G Mitra, 2008. "Robust solutions and risk measures for a supply chain planning problem under uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(1), pages 2-12, January.
    6. Nagl, Stephan & Fürsch, Michaela & Lindenberger, Dietmar, 2012. "The costs of electricity systems with a high share of fluctuating renewables - a stochastic investment and dispatch optimization model for Europe," EWI Working Papers 2012-1, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).

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