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Financial and operational decisions in the electricity sector: Contract portfolio optimization with the conditional value-at-risk criterion

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  • Yau, Sheena
  • Kwon, Roy H.
  • Scott Rogers, J.
  • Wu, Desheng

Abstract

The restructuring of electricity markets around the world have caused increased volatility and uncertainty of the price power. As a result, providers of power now face increased uncertainty and risk in the operational and financial decisions related to procurement. Providers must seek optimal ways to deliver the required volume of power to retailers and end users while managing risk. We consider a mixed-integer programming model for a power providing agent that jointly considers the problem of selecting custom electricity contracts and finding the optimal procurement strategy of meeting contract obligations under spot price uncertainty. A two-stage stochastic integer programming (SIP) model with a conditional value-at-risk (CVaR) constraint to incorporate risk aversion is developed. Computational results are presented that demonstrates the CVaR approach and the results are compared with a corresponding expected cost minimization approach. The SIP model with CVaR will allow acceptance of contracts at lower prices compared to an approach based on a corresponding risk-neutral model as a hedge against uncertainty and mis-specified arbitrage.

Suggested Citation

  • Yau, Sheena & Kwon, Roy H. & Scott Rogers, J. & Wu, Desheng, 2011. "Financial and operational decisions in the electricity sector: Contract portfolio optimization with the conditional value-at-risk criterion," International Journal of Production Economics, Elsevier, vol. 134(1), pages 67-77, November.
    • Handle: RePEc:eee:proeco:v:134:y:2011:i:1:p:67-77
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    References listed on IDEAS

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    1. Wei, Yi-Ming & Mi, Zhi-Fu & Huang, Zhimin, 2015. "Climate policy modeling: An online SCI-E and SSCI based literature review," Omega, Elsevier, vol. 57(PA), pages 70-84.
    2. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    3. Tamar Gadrich & Emil Bashkansky & Ričardas Zitikis, 2015. "Assessing variation: a unifying approach for all scales of measurement," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1145-1167, May.
    4. Tasuku Soma & Yuichi Yoshida, 2023. "Online risk-averse submodular maximization," Annals of Operations Research, Springer, vol. 320(1), pages 393-414, January.
    5. Lynch & John Curtis, 2016. "The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis," Applied Economics, Taylor & Francis Journals, vol. 48(2), pages 133-151, January.
    6. Somayeh Moazeni & Thomas F. Coleman & Yuying Li, 2016. "Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy," Annals of Operations Research, Springer, vol. 237(1), pages 99-120, February.
    7. Alexandru V. Asimit & Raluca Vernic & Ricardas Zitikis, 2016. "Background Risk Models and Stepwise Portfolio Construction," Methodology and Computing in Applied Probability, Springer, vol. 18(3), pages 805-827, September.
    8. Yucekaya, A., 2022. "Electricity trading for coal-fired power plants in Turkish power market considering uncertainty in spot, derivatives and bilateral contract market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    9. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    10. Jose Arreola Hernandez & Sang Hoon Kang & Seong‐Min Yoon, 2022. "Nonlinear spillover and portfolio allocation characteristics of energy equity sectors: Evidence from the United States and Canada," Review of International Economics, Wiley Blackwell, vol. 30(1), pages 1-33, February.
    11. Soleimani, Hamed & Govindan, Kannan, 2014. "Reverse logistics network design and planning utilizing conditional value at risk," European Journal of Operational Research, Elsevier, vol. 237(2), pages 487-497.
    12. Lu, Jie & Gupte, Akshay & Huang, Yongxi, 2018. "A mean-risk mixed integer nonlinear program for transportation network protection," European Journal of Operational Research, Elsevier, vol. 265(1), pages 277-289.
    13. Mohsen Zamani & Mahdi Abolghasemi & Seyed Mohammad Seyed Hosseini & Mir Saman Pishvaee, 2019. "Considering pricing and uncertainty in designing a reverse logistics network," Papers 1909.11633, arXiv.org.
    14. Somayeh Moazeni & Thomas Coleman & Yuying Li, 2016. "Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy," Annals of Operations Research, Springer, vol. 237(1), pages 99-120, February.

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