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A time consistent risk averse three-stage stochastic mixed integer optimization model for power generation capacity expansion

Author

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  • Pisciella, P.
  • Vespucci, M.T.
  • Bertocchi, M.
  • Zigrino, S.

Abstract

We propose a multi-stage stochastic optimization model for the generation capacity expansion problem of a price-taker power producer. Uncertainties regarding the evolution of electricity prices and fuel costs play a major role in long term investment decisions, therefore the objective function represents a trade-off between expected profit and risk. The Conditional Value at Risk is the risk measure used and is defined by a nested formulation that guarantees time consistency in the multi-stage model. The proposed model allows one to determine a long term expansion plan which takes into account uncertainty, while the LCoE approach, currently used by decision makers, only allows one to determine which technology should be chosen for the next power plant to be built.

Suggested Citation

  • Pisciella, P. & Vespucci, M.T. & Bertocchi, M. & Zigrino, S., 2016. "A time consistent risk averse three-stage stochastic mixed integer optimization model for power generation capacity expansion," Energy Economics, Elsevier, vol. 53(C), pages 203-211.
    • Handle: RePEc:eee:eneeco:v:53:y:2016:i:c:p:203-211
    DOI: 10.1016/j.eneco.2014.07.016
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    References listed on IDEAS

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    1. Francesca Maggioni & Stein Wallace, 2012. "Analyzing the quality of the expected value solution in stochastic programming," Annals of Operations Research, Springer, vol. 200(1), pages 37-54, November.
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    3. Mohammadi, Hassan, 2009. "Electricity prices and fuel costs: Long-run relations and short-run dynamics," Energy Economics, Elsevier, vol. 31(3), pages 503-509, May.
    4. Maria Teresa Vespucci & Marida Bertocchi & Laureano F. Escudero & Stefano Zigrino, 2013. "A risk averse stochastic optimization model for power generation capacity expansion," Working Papers (2013-) 1305_qum, University of Bergamo, Department of Management, Economics and Quantitative Methods.
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    Cited by:

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    2. Caunhye, Aakil M. & Cardin, Michel-Alexandre, 2018. "Towards more resilient integrated power grid capacity expansion: A robust optimization approach with operational flexibility," Energy Economics, Elsevier, vol. 72(C), pages 20-34.
    3. Nie, S. & Li, Y.P. & Liu, J. & Huang, Charley Z., 2017. "Risk management of energy system for identifying optimal power mix with financial-cost minimization and environmental-impact mitigation under uncertainty," Energy Economics, Elsevier, vol. 61(C), pages 313-329.
    4. František Zapletal & Martin Šmíd & Miloš Kopa, 2020. "Multi-stage emissions management of a steel company," Annals of Operations Research, Springer, vol. 292(2), pages 735-751, September.
    5. Daniel Velásquez-Gaviria & Andrés Mora-Valencia & Javier Perote, 2020. "A Comparison of the Risk Quantification in Traditional and Renewable Energy Markets," Energies, MDPI, vol. 13(11), pages 1-42, June.
    6. Afful-Dadzie, Anthony & Afful-Dadzie, Eric & Awudu, Iddrisu & Banuro, Joseph Kwaku, 2017. "Power generation capacity planning under budget constraint in developing countries," Applied Energy, Elsevier, vol. 188(C), pages 71-82.
    7. Andrzej Wędzik & Tomasz Siewierski & Michał Szypowski, 2019. "The Use of Black-Box Optimization Method for Determination of the Bus Connection Capacity in Electric Power Grid," Energies, MDPI, vol. 13(1), pages 1-21, December.
    8. Güner, Yusuf Emre, 2018. "The improved screening curve method regarding existing units," European Journal of Operational Research, Elsevier, vol. 264(1), pages 310-326.
    9. Schütz, Peter & Westgaard, Sjur, 2018. "Optimal hedging strategies for salmon producers," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 60-70.
    10. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2017. "Risk-based methods for sustainable energy system planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 602-615.
    11. García-Cerezo, Álvaro & Baringo, Luis & García-Bertrand, Raquel, 2021. "Robust transmission network expansion planning considering non-convex operational constraints," Energy Economics, Elsevier, vol. 98(C).
    12. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    13. Chen, Huadong & Wang, Can & Cai, Wenjia & Wang, Jianhui, 2018. "Simulating the impact of investment preference on low-carbon transition in power sector," Applied Energy, Elsevier, vol. 217(C), pages 440-455.
    14. Ioannou, Anastasia & Fuzuli, Gulistiani & Brennan, Feargal & Yudha, Satya Widya & Angus, Andrew, 2019. "Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling," Energy Economics, Elsevier, vol. 80(C), pages 760-776.

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    More about this item

    Keywords

    Price-taker producer; Power generation capacity expansion; Long term planning; Time consistency; Three-stage stochastic mixed integer optimization;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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