IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v51y2015icp236-251.html
   My bibliography  Save this article

Electric sector capacity planning under uncertainty: Climate policy and natural gas in the US

Author

Listed:
  • Bistline, John E.

Abstract

This research investigates the dynamics of capacity planning and dispatch in the US electric power sector under a range of technological, economic, and policy-related uncertainties. Using a two-stage stochastic programming approach, model results suggest that the two most critical risks in the near-term planning process of the uncertainties considered here are natural gas prices and the stringency of climate policy. Stochastic strategies indicate that some near-term hedging from lower-cost wind and nuclear may occur but robustly demonstrate that delaying investment and waiting for more information can be optimal to avoid stranding capital-intensive assets. Hedging strategies protect against downside losses while retaining the option value of deferring irreversible commitments until more information is available about potentially lucrative market opportunities. These results are explained in terms of the optionality of investments in the electric power sector, leading to more general insights about uncertainty, learning, and irreversibility. The stochastic solution is especially valuable if decision-makers do not sufficiently account for the potential of climate constraints in future decades or if fuel price projections are outdated.

Suggested Citation

  • Bistline, John E., 2015. "Electric sector capacity planning under uncertainty: Climate policy and natural gas in the US," Energy Economics, Elsevier, vol. 51(C), pages 236-251.
    • Handle: RePEc:eee:eneeco:v:51:y:2015:i:c:p:236-251
    DOI: 10.1016/j.eneco.2015.07.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988315002157
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2015.07.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kanudia, Amit & Shukla, PR, 1998. "Modelling of Uncertainties and Price Elastic Demands in Energy-environment Planning for India," Omega, Elsevier, vol. 26(3), pages 409-423, June.
    2. Lawrence Cathles & Larry Brown & Milton Taam & Andrew Hunter, 2012. "A commentary on “The greenhouse-gas footprint of natural gas in shale formations” by R.W. Howarth, R. Santoro, and Anthony Ingraffea," Climatic Change, Springer, vol. 113(2), pages 525-535, July.
    3. Durand-Lasserve, Olivier & Pierru, Axel & Smeers, Yves, 2010. "Uncertain long-run emissions targets, CO2 price and global energy transition: A general equilibrium approach," Energy Policy, Elsevier, vol. 38(9), pages 5108-5122, September.
    4. Rubin, Edward S. & Yeh, Sonia & Antes, Matt & Berkenpas, Michael & Davison, John, 2007. "Use of experience curves to estimate the future cost of power plants with CO2 capture," Institute of Transportation Studies, Working Paper Series qt46x6h0n0, Institute of Transportation Studies, UC Davis.
    5. Chan, Melissa & Jones, Charles A. & Kempener, Ruud & Diaz Anadon, Laura & Logar, Nathaniel James & Narayanamurti, Venkatesh & Bunn, Matthew G. & Chan, Gabriel Angelo & Lee, Audrey, 2011. "Transforming U.S. Energy Innovation," Scholarly Articles 10594301, Harvard Kennedy School of Government.
    6. P. Massé & R. Gibrat, 1957. "Application of Linear Programming to Investments in the Electric Power Industry," Management Science, INFORMS, vol. 3(2), pages 149-166, January.
    7. James K. Hammitt & Alexander I. Shlyakhter, 1999. "The Expected Value of Information and the Probability of Surprise," Risk Analysis, John Wiley & Sons, vol. 19(1), pages 135-152, February.
    8. John Bistline & John Weyant, 2013. "Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach," Climatic Change, Springer, vol. 121(2), pages 143-160, November.
    9. Heinrich, G. & Howells, M. & Basson, L. & Petrie, J., 2007. "Electricity supply industry modelling for multiple objectives under demand growth uncertainty," Energy, Elsevier, vol. 32(11), pages 2210-2229.
    10. Koomey, Jonathan & Hultman, Nathan E., 2007. "A reactor-level analysis of busbar costs for US nuclear plants, 1970-2005," Energy Policy, Elsevier, vol. 35(11), pages 5630-5642, November.
    11. Krukanont, Pongsak & Tezuka, Tetsuo, 2007. "Implications of capacity expansion under uncertainty and value of information: The near-term energy planning of Japan," Energy, Elsevier, vol. 32(10), pages 1809-1824.
    12. Hirth, Lion, 2013. "The market value of variable renewables," Energy Economics, Elsevier, vol. 38(C), pages 218-236.
    13. Bosetti, Valentina & Tavoni, Massimo, 2009. "Uncertain R&D, backstop technology and GHGs stabilization," Energy Economics, Elsevier, vol. 31(Supplemen), pages 18-26.
    14. Bistline, John E., 2014. "Energy technology expert elicitations: An application to natural gas turbine efficiencies," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 177-187.
    15. Robert Howarth & Renee Santoro & Anthony Ingraffea, 2012. "Venting and leaking of methane from shale gas development: response to Cathles et al," Climatic Change, Springer, vol. 113(2), pages 537-549, July.
    16. John R. Birge & Charles H. Rosa, 1996. "Incorporating Investment Uncertainty into Greenhouse Policy Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 79-89.
    17. Haewon McJeon & Jae Edmonds & Nico Bauer & Leon Clarke & Brian Fisher & Brian P. Flannery & Jérôme Hilaire & Volker Krey & Giacomo Marangoni & Raymond Mi & Keywan Riahi & Holger Rogner & Massimo Tavon, 2014. "Limited impact on decadal-scale climate change from increased use of natural gas," Nature, Nature, vol. 514(7523), pages 482-485, October.
    18. Wilkerson, Jordan & Larsen, Peter & Barbose, Galen, 2014. "Survey of Western U.S. electric utility resource plans," Energy Policy, Elsevier, vol. 66(C), pages 90-103.
    19. Bistline, John E., 2014. "Natural gas, uncertainty, and climate policy in the US electric power sector," Energy Policy, Elsevier, vol. 74(C), pages 433-442.
    20. Usher, Will & Strachan, Neil, 2012. "Critical mid-term uncertainties in long-term decarbonisation pathways," Energy Policy, Elsevier, vol. 41(C), pages 433-444.
    21. Albert Madansky, 1960. "Inequalities for Stochastic Linear Programming Problems," Management Science, INFORMS, vol. 6(2), pages 197-204, January.
    22. Richard Van Noorden, 2010. "Carbon sequestration: Buried trouble," Nature, Nature, vol. 463(7283), pages 871-873, February.
    23. John P. Weyant, Francisco C. de la Chesnaye, and Geoff J. Blanford, 2006. "Overview of EMF-21: Multigas Mitigation and Climate Policy," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 1-32.
    24. Kanudia, Amit & Loulou, Richard, 1998. "Robust responses to climate change via stochastic MARKAL: The case of Quebec," European Journal of Operational Research, Elsevier, vol. 106(1), pages 15-30, April.
    25. Shlyakhter, Alexander I. & Kammen, Daniel M. & Broido, Claire L. & Wilson, Richard, 1994. "Quantifying the credibility of energy projections from trends in past data : The US energy sector," Energy Policy, Elsevier, vol. 22(2), pages 119-130, February.
    26. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    27. Peck, Stephen C. & Teisberg, Thomas J., 1993. "Global warming uncertainties and the value of information: an analysis using CETA," Resource and Energy Economics, Elsevier, vol. 15(1), pages 71-97, March.
    28. Hu, Ming-Che & Hobbs, Benjamin F., 2010. "Analysis of multi-pollutant policies for the U.S. power sector under technology and policy uncertainty using MARKAL," Energy, Elsevier, vol. 35(12), pages 5430-5442.
    29. Lion Hirth, 2013. "The Market Value of Variable Renewables. The Effect of Solar and Wind Power Variability on their Relative Price," RSCAS Working Papers 2013/36, European University Institute.
    30. Alexis Hannart & Michael Ghil & Jean-Louis Dufresne & Philippe Naveau, 2013. "Disconcerting learning on climate sensitivity and the uncertain future of uncertainty," Climatic Change, Springer, vol. 119(3), pages 585-601, August.
    31. Donald L. Keefer, 1994. "Certainty Equivalents for Three-Point Discrete-Distribution Approximations," Management Science, INFORMS, vol. 40(6), pages 760-773, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Healey, Stephen & Jaccard, Mark, 2016. "Abundant low-cost natural gas and deep GHG emissions reductions for the United States," Energy Policy, Elsevier, vol. 98(C), pages 241-253.
    2. Chenxi Xiang & Xinye Zheng & Feng Song & Jiang Lin & Zhigao Jiang, 2023. "Assessing the roles of efficient market versus regulatory capture in China’s power market reform," Nature Energy, Nature, vol. 8(7), pages 747-757, July.
    3. Bistline, John E., 2016. "Energy technology R&D portfolio management: Modeling uncertain returns and market diffusion," Applied Energy, Elsevier, vol. 183(C), pages 1181-1196.
    4. John E. Bistline & Francisco Chesnaye, 2017. "Banking on banking: does “when” flexibility mask the costs of stringent climate policy?," Climatic Change, Springer, vol. 144(4), pages 597-610, October.
    5. Chen, H. & Chyong CK. & Kang, J-N. & Wei Y-M., 2018. "Economic dispatch in the electricity sector in China: potential benefits and challenges ahead," Cambridge Working Papers in Economics 1836, Faculty of Economics, University of Cambridge.
    6. Guerra, Omar J. & Tejada, Diego A. & Reklaitis, Gintaras V., 2019. "Climate change impacts and adaptation strategies for a hydro-dominated power system via stochastic optimization," Applied Energy, Elsevier, vol. 233, pages 584-598.
    7. Mowers, Matthew & Mignone, Bryan K. & Steinberg, Daniel C., 2023. "Quantifying value and representing competitiveness of electricity system technologies in economic models," Applied Energy, Elsevier, vol. 329(C).
    8. Bistline, John E. & Comello, Stephen D. & Sahoo, Anshuman, 2018. "Managerial flexibility in levelized cost measures: A framework for incorporating uncertainty in energy investment decisions," Energy, Elsevier, vol. 151(C), pages 211-225.
    9. Jin, S.W. & Li, Y.P. & Nie, S. & Sun, J., 2017. "The potential role of carbon capture and storage technology in sustainable electric-power systems under multiple uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 467-480.
    10. Wei, Yi-Ming & Chen, Hao & Chyong, Chi Kong & Kang, Jia-Ning & Liao, Hua & Tang, Bao-Jun, 2018. "Economic dispatch savings in the coal-fired power sector: An empirical study of China," Energy Economics, Elsevier, vol. 74(C), pages 330-342.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bistline, John E., 2014. "Natural gas, uncertainty, and climate policy in the US electric power sector," Energy Policy, Elsevier, vol. 74(C), pages 433-442.
    2. Bistline, John E., 2016. "Energy technology R&D portfolio management: Modeling uncertain returns and market diffusion," Applied Energy, Elsevier, vol. 183(C), pages 1181-1196.
    3. Parkinson, Simon C. & Djilali, Ned, 2015. "Long-term energy planning with uncertain environmental performance metrics," Applied Energy, Elsevier, vol. 147(C), pages 402-412.
    4. John Bistline & John Weyant, 2013. "Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach," Climatic Change, Springer, vol. 121(2), pages 143-160, November.
    5. Seljom, Pernille & Tomasgard, Asgeir, 2015. "Short-term uncertainty in long-term energy system models — A case study of wind power in Denmark," Energy Economics, Elsevier, vol. 49(C), pages 157-167.
    6. Fodstad, Marte & Crespo del Granado, Pedro & Hellemo, Lars & Knudsen, Brage Rugstad & Pisciella, Paolo & Silvast, Antti & Bordin, Chiara & Schmidt, Sarah & Straus, Julian, 2022. "Next frontiers in energy system modelling: A review on challenges and the state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Kim, Hansung & Cheon, Hyungkyu & Ahn, Young-Hwan & Choi, Dong Gu, 2019. "Uncertainty quantification and scenario generation of future solar photovoltaic price for use in energy system models," Energy, Elsevier, vol. 168(C), pages 370-379.
    8. Santen, Nidhi R. & Anadon, Laura Diaz, 2016. "Balancing solar PV deployment and RD&D: A comprehensive framework for managing innovation uncertainty in electricity technology investment planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 560-569.
    9. James H. Merrick & John E. T. Bistline & Geoffrey J. Blanford, 2021. "On representation of energy storage in electricity planning models," Papers 2105.03707, arXiv.org, revised May 2021.
    10. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    11. Nahmmacher, Paul & Schmid, Eva & Pahle, Michael & Knopf, Brigitte, 2016. "Strategies against shocks in power systems – An analysis for the case of Europe," Energy Economics, Elsevier, vol. 59(C), pages 455-465.
    12. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    13. Collins, Seán & Deane, John Paul & Poncelet, Kris & Panos, Evangelos & Pietzcker, Robert C. & Delarue, Erik & Ó Gallachóir, Brian Pádraig, 2017. "Integrating short term variations of the power system into integrated energy system models: A methodological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 839-856.
    14. Mowers, Matthew & Mignone, Bryan K. & Steinberg, Daniel C., 2023. "Quantifying value and representing competitiveness of electricity system technologies in economic models," Applied Energy, Elsevier, vol. 329(C).
    15. Bistline, John & Blanford, Geoffrey & Mai, Trieu & Merrick, James, 2021. "Modeling variable renewable energy and storage in the power sector," Energy Policy, Elsevier, vol. 156(C).
    16. Jåstad, Eirik Ogner & Trotter, Ian M. & Bolkesjø, Torjus Folsland, 2022. "Long term power prices and renewable energy market values in Norway – A probabilistic approach," Energy Economics, Elsevier, vol. 112(C).
    17. Oliver Gregor Gorbach & Jessica Thomsen, 2022. "Comparing the Energy System of a Facility with Uncertainty about Future Internal Carbon Prices and Energy Carrier Costs Using Deterministic Optimisation and Two-Stage Stochastic Programming," Energies, MDPI, vol. 15(10), pages 1-39, May.
    18. Peters, Jeffrey C., 2017. "Natural gas and spillover from the US Clean Power Plan into the Paris Agreement," Energy Policy, Elsevier, vol. 106(C), pages 41-47.
    19. Keppler, Jan Horst & Quemin, Simon & Saguan, Marcelo, 2022. "Why the sustainable provision of low-carbon electricity needs hybrid markets," Energy Policy, Elsevier, vol. 171(C).
    20. Christoph Wolter & Henrik Klinge Jacobsen & Lorenzo Zeni & Georgios Rogdakis & Nicolaos A. Cutululis, 2020. "Overplanting in offshore wind power plants in different regulatory regimes," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(3), May.

    More about this item

    Keywords

    Electricity; Uncertainty; Stochastic programming; Climate policy; Risk management;
    All these keywords.

    JEL classification:

    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

    Statistics

    Access and download statistics

    Corrections

    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:eee:eneeco:v:51:y:2015:i:c:p:236-251. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.