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Flexibility in electric power planning: Coping with demand uncertainty

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  • Gardner, Douglas T.

Abstract

Future electricity demand is a key source of uncertainty facing electric utility system planners. Flexibility offers one way to cope. Making use of the flexibility measure proposed by Hobbs, Honious and Bluestein, we first discuss what factors are of importance for determining flexibility benefits of different utility resources when dealing with demand uncertainty. A multistage stochastic electric utility planning model is then used to compare the flexibility benefits of different electric utility resources for a representative utility, Ontario Hydro. In the absence of acid-gas (i.e. NOx and SO2) emission constraints, gas combustion turbines and demand-side management (DSM) were found to offer significant flexibility benefits, while nuclear generation has a large flexibility disbenefit. With acid-gas emission constraints in place, the flexibility benefit of gas combustion turbines is greatly reduced, while those of nuclear, DSM and gas combined-cycle generation all increase. A key finding is that flexibility benefits (or disbenefits) comprise a major portion of the total expected economic benefits of different utility resources in many cases. This result implies that analyses that neglect the effects of demand uncertainty may either dramatically underestimate or overestimate the expected economic benefits of different utility resources.

Suggested Citation

  • Gardner, Douglas T., 1996. "Flexibility in electric power planning: Coping with demand uncertainty," Energy, Elsevier, vol. 21(12), pages 1207-1218.
    • Handle: RePEc:eee:energy:v:21:y:1996:i:12:p:1207-1218
    DOI: 10.1016/0360-5442(96)00061-8
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    References listed on IDEAS

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    1. Avinash K. Dixit & Robert S. Pindyck, 1994. "Investment under Uncertainty," Economics Books, Princeton University Press, edition 1, number 5474.
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    1. Malischek, Raimund & Trüby, Johannes, 2016. "The future of nuclear power in France: an analysis of the costs of phasing-out," Energy, Elsevier, vol. 116(P1), pages 908-921.
    2. Douglas T. Gardner & J. Scott Rogers, 1999. "Planning Electric Power Systems Under Demand Uncertainty with Different Technology Lead Times," Management Science, INFORMS, vol. 45(10), pages 1289-1306, October.
    3. Fürsch, Michaela & Nagl, Stephan & Lindenberger, Dietmar, 2012. "Optimization of power plant investments under uncertain renewable energy development paths - A multistage stochastic programming approach," EWI Working Papers 2012-8, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    4. Vithayasrichareon, Peerapat & MacGill, Iain F., 2012. "A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 41(C), pages 374-392.

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