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Mitigating market risk for wind power providers via financial risk exchange

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  • Shin, Hunyoung
  • Baldick, Ross

Abstract

When wind power producers (WPPs) participate in forward electricity markets, they become exposed to real-time (RT) market risks from uncertain generation outputs and highly volatile RT market prices. This joint volume-price risk causes a risk-averse WPP to sell less energy than the expected generation, which discourages the WPP from fully enjoying the benefits of participating in forward electricity markets. In order to mitigate volume-price risks from the RT market, this paper proposes a financial instrument referred to as a risk exchange (REX) that enables the WPPs to trade random net payments from uncertain prices and generation outputs, after the day-ahead market is cleared. A negotiation for the REX is modeled by a bargaining game based on a conflict of interest in determining the REX amounts. Both Nash and Rubinstein's bargaining game models are addressed to analyze the REX bargaining game. It is shown that there is a unique outcome of the game which can be achieved by using a pure strategy. Moreover, a central planner who aims to minimize the aggregated risks of the WPPs is explored. Numerical examples demonstrate that the REX is able to reduce RTM risks successfully and encourages the WPPs to sell more energy to the DAM. Since the REX is not limited by physical constraints in power systems, it can be traded by the WPPs exposed to different locational marginal prices (LMPs).

Suggested Citation

  • Shin, Hunyoung & Baldick, Ross, 2018. "Mitigating market risk for wind power providers via financial risk exchange," Energy Economics, Elsevier, vol. 71(C), pages 344-358.
  • Handle: RePEc:eee:eneeco:v:71:y:2018:i:c:p:344-358
    DOI: 10.1016/j.eneco.2018.02.012
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    1. Rubinstein, Ariel, 1982. "Perfect Equilibrium in a Bargaining Model," Econometrica, Econometric Society, vol. 50(1), pages 97-109, January.
    2. Severin Borenstein & James Bushnell & Christopher R. Knittel & Catherine Wolfram, 2008. "Inefficiencies And Market Power In Financial Arbitrage: A Study Of California'S Electricity Markets," Journal of Industrial Economics, Wiley Blackwell, vol. 56(2), pages 347-378, June.
    3. Boroumand, Raphaël Homayoun & Goutte, Stéphane & Porcher, Simon & Porcher, Thomas, 2015. "Hedging strategies in energy markets: The case of electricity retailers," Energy Economics, Elsevier, vol. 51(C), pages 503-509.
    4. Ketterer, Janina C., 2014. "The impact of wind power generation on the electricity price in Germany," Energy Economics, Elsevier, vol. 44(C), pages 270-280.
    5. Koichiro Ito & Mar Reguant, 2016. "Sequential Markets, Market Power, and Arbitrage," American Economic Review, American Economic Association, vol. 106(7), pages 1921-1957, July.
    6. Casimir Lorenz & Clemens Gerbaulet, 2014. "New Cross-Border Electricity Balancing Arrangements in Europe," Discussion Papers of DIW Berlin 1400, DIW Berlin, German Institute for Economic Research.
    7. Boroumand, Raphaël Homayoun & Goutte, Stéphane & Porcher, Simon & Porcher, Thomas, 2015. "Hedging strategies in energy markets: The case of electricity retailers," Energy Economics, Elsevier, vol. 51(C), pages 503-509.
    8. Nash, John, 1950. "The Bargaining Problem," Econometrica, Econometric Society, vol. 18(2), pages 155-162, April.
    9. Bowden, Nicholas & Hu, Su & Payne, James, 2009. "Day-Ahead Premiums on the Midwest ISO," The Electricity Journal, Elsevier, vol. 22(2), pages 64-73, March.
    10. Pineda, S. & Conejo, A.J., 2012. "Managing the financial risks of electricity producers using options," Energy Economics, Elsevier, vol. 34(6), pages 2216-2227.
    11. Woo, C.K. & Horowitz, I. & Moore, J. & Pacheco, A., 2011. "The impact of wind generation on the electricity spot-market price level and variance: The Texas experience," Energy Policy, Elsevier, vol. 39(7), pages 3939-3944, July.
    12. Falsafi, Hananeh & Zakariazadeh, Alireza & Jadid, Shahram, 2014. "The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programming," Energy, Elsevier, vol. 64(C), pages 853-867.
    13. Pousinho, H.M.I. & Mendes, V.M.F. & Catalão, J.P.S., 2011. "A risk-averse optimization model for trading wind energy in a market environment under uncertainty," Energy, Elsevier, vol. 36(8), pages 4935-4942.
    14. Hadsell, Lester, 2008. "Day-Ahead Premiums on the New England ISO," The Electricity Journal, Elsevier, vol. 21(4), pages 51-57, May.
    15. Farahmand, H. & Doorman, G.L., 2012. "Balancing market integration in the Northern European continent," Applied Energy, Elsevier, vol. 96(C), pages 316-326.
    16. Varkani, Ali Karimi & Daraeepour, Ali & Monsef, Hassan, 2011. "A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets," Applied Energy, Elsevier, vol. 88(12), pages 5002-5012.
    17. Broeer, Torsten & Fuller, Jason & Tuffner, Francis & Chassin, David & Djilali, Ned, 2014. "Modeling framework and validation of a smart grid and demand response system for wind power integration," Applied Energy, Elsevier, vol. 113(C), pages 199-207.
    18. McAfee R. Preston & Vincent Daniel, 1993. "The Declining Price Anomaly," Journal of Economic Theory, Elsevier, vol. 60(1), pages 191-212, June.
    19. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    20. Zugno, Marco & Morales, Juan Miguel & Pinson, Pierre & Madsen, Henrik, 2013. "A bilevel model for electricity retailers' participation in a demand response market environment," Energy Economics, Elsevier, vol. 36(C), pages 182-197.
    21. Francis A. Longstaff & Ashley W. Wang, 2004. "Electricity Forward Prices: A High-Frequency Empirical Analysis," Journal of Finance, American Finance Association, vol. 59(4), pages 1877-1900, August.
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    Cited by:

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    2. Nitesh Kumar Singh & Chaitali Koley & Sadhan Gope & Subhojit Dawn & Taha Selim Ustun, 2021. "An Economic Risk Analysis in Wind and Pumped Hydro Energy Storage Integrated Power System Using Meta-Heuristic Algorithm," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    3. Ruhang Xu & Zhilin Liu & Zhuangzhuang Yu, 2019. "Exploring the Profitability and Efficiency of Variable Renewable Energy in Spot Electricity Market: Uncovering the Locational Price Disadvantages," Energies, MDPI, vol. 12(14), pages 1-30, July.
    4. Lucy, Zachary & Kern, Jordan, 2021. "Analysis of fixed volume swaps for hedging financial risk at large-scale wind projects," Energy Economics, Elsevier, vol. 103(C).

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    More about this item

    Keywords

    Electricity markets; Wind power; Risk hedging; Bargaining game;
    All these keywords.

    JEL classification:

    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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