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A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries

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  • Vithayasrichareon, Peerapat
  • MacGill, Iain F.

Abstract

This paper presents a novel decision-support tool for assessing future generation portfolios in an increasingly uncertain electricity industry. The tool combines optimal generation mix concepts with Monte Carlo simulation and portfolio analysis techniques to determine expected overall industry costs, associated cost uncertainty, and expected CO2 emissions for different generation portfolio mixes. The tool can incorporate complex and correlated probability distributions for estimated future fossil-fuel costs, carbon prices, plant investment costs, and demand, including price elasticity impacts. The intent of this tool is to facilitate risk-weighted generation investment and associated policy decision-making given uncertainties facing the electricity industry. Applications of this tool are demonstrated through a case study of an electricity industry with coal, CCGT, and OCGT facing future uncertainties. Results highlight some significant generation investment challenges, including the impacts of uncertain and correlated carbon and fossil-fuel prices, the role of future demand changes in response to electricity prices, and the impact of construction cost uncertainties on capital intensive generation. The tool can incorporate virtually any type of input probability distribution, and support sophisticated risk assessments of different portfolios, including downside economic risks. It can also assess portfolios against multi-criterion objectives such as greenhouse emissions as well as overall industry costs.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:enepol:v:41:y:2012:i:c:p:374-392
    DOI: 10.1016/j.enpol.2011.10.060
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    as
    1. Geoffrey Rothwell, 2006. "A Real Options Approach to Evaluating New Nuclear Power Plants," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 37-53.
    2. Huang, Yun-Hsun & Wu, Jung-Hua, 2008. "A portfolio risk analysis on electricity supply planning," Energy Policy, Elsevier, vol. 36(2), pages 627-641, February.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Marrero, Gustavo A. & Ramos-Real, Francisco Javier, 2010. "Electricity generation cost in isolated system: The complementarities of natural gas and renewables in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2808-2818, December.
    5. Richard Green, 2008. "Carbon Tax or Carbon Permits: The Impact on Generators Risks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 67-90.
    6. Spinney, Peter J & Watkins, G Campbell, 1996. "Monte Carlo simulation techniques and electric utility resource decisions," Energy Policy, Elsevier, vol. 24(2), pages 155-163, February.
    7. Madlener, Reinhard & Wenk, Christioph, 2008. "Efficient Investment Portfolios for the Swiss Electricity Supply Sector," FCN Working Papers 2/2008, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    8. van der Weijde, A.H. & Hobbs, B.F., 2011. "Planning electricity transmission to accommodate renewables: Using two-stage programming to evaluate flexibility and the cost of disregarding uncertainty," Cambridge Working Papers in Economics 1113, Faculty of Economics, University of Cambridge.
    9. Gardner, Douglas T., 1996. "Flexibility in electric power planning: Coping with demand uncertainty," Energy, Elsevier, vol. 21(12), pages 1207-1218.
    10. Feretic, Danilo & Tomsic, Zeljko, 2005. "Probabilistic analysis of electrical energy costs comparing: production costs for gas, coal and nuclear power plants," Energy Policy, Elsevier, vol. 33(1), pages 5-13, January.
    11. H. Brett Humphreys & Katherine T. McClain, 1998. "Reducing the Impacts of Energy Price Volatility Through Dynamic Portfolio Selection," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 107-131.
    12. Shimon Awerbuch, 2006. "Portfolio-Based Electricity Generation Planning: Policy Implications For Renewables And Energy Security," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 11(3), pages 693-710, May.
    13. Gotham, Douglas & Muthuraman, Kumar & Preckel, Paul & Rardin, Ronald & Ruangpattana, Suriya, 2009. "A load factor based mean-variance analysis for fuel diversification," Energy Economics, Elsevier, vol. 31(2), pages 249-256, March.
    14. Wright, Evelyn L. & Belt, Juan A.B. & Chambers, Adam & Delaquil, Pat & Goldstein, Gary, 2010. "A scenario analysis of investment options for the Cuban power sector using the MARKAL model," Energy Policy, Elsevier, vol. 38(7), pages 3342-3355, July.
    15. Mondal, Md. Alam Hossain & Boie, Wulf & Denich, Manfred, 2010. "Future demand scenarios of Bangladesh power sector," Energy Policy, Elsevier, vol. 38(11), pages 7416-7426, November.
    16. Lee, Shun-Chung & Shih, Li-Hsing, 2010. "Renewable energy policy evaluation using real option model -- The case of Taiwan," Energy Economics, Elsevier, vol. 32(Supplemen), pages 67-78, September.
    17. Laurikka, Harri, 2006. "Option value of gasification technology within an emissions trading scheme," Energy Policy, Elsevier, vol. 34(18), pages 3916-3928, December.
    18. Fan, Shu & Hyndman, Rob J., 2011. "The price elasticity of electricity demand in South Australia," Energy Policy, Elsevier, vol. 39(6), pages 3709-3719, June.
    19. Roques, Fabien A. & Newbery, David M. & Nuttall, William J., 2008. "Fuel mix diversification incentives in liberalized electricity markets: A Mean-Variance Portfolio theory approach," Energy Economics, Elsevier, vol. 30(4), pages 1831-1849, July.
    20. Roques, F.A. & Nuttall, W.J. & Newbery, D.M., 2006. "Using Probabilistic Analysis to Value Power Generation Investments Under Uncertainty," Cambridge Working Papers in Economics 0650, Faculty of Economics, University of Cambridge.
    21. Cai, Wenjia & Wang, Can & Wang, Ke & Zhang, Ying & Chen, Jining, 2007. "Scenario analysis on CO2 emissions reduction potential in China's electricity sector," Energy Policy, Elsevier, vol. 35(12), pages 6445-6456, December.
    22. Wang, Ke & Wang, Can & Lu, Xuedu & Chen, Jining, 2007. "Scenario analysis on CO2 emissions reduction potential in China's iron and steel industry," Energy Policy, Elsevier, vol. 35(4), pages 2320-2335, April.
    23. Rafaj, Peter & Kypreos, Socrates, 2007. "Internalisation of external cost in the power generation sector: Analysis with Global Multi-regional MARKAL model," Energy Policy, Elsevier, vol. 35(2), pages 828-843, February.
    24. 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.
    25. Williams, J.H. & Ghanadan, R., 2006. "Electricity reform in developing and transition countries: A reappraisal," Energy, Elsevier, vol. 31(6), pages 815-844.
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