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A stochastic model for investments in different technologies for electricity production in the long period

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  • Maria Vespucci
  • Marida Bertocchi
  • Mario Innorta
  • Stefano Zigrino

Abstract

We present a single stage stochastic mixed integer linear model for determining the optimal mix of different technologies for electricity generation, ranging from coal, nuclear and combined cycle gas turbine to hydroelectric, wind and photovoltaic, taking into account the existing plants, the cost of investment in new plants, maintenance costs, purchase and sale of $${CO}_2$$ CO 2 emission trading certificates and green certificates, in order to satisfy regulatory requirements. The power producer is assumed to be a price-taker. Stochasticity of future fuel prices, which affect the generation variable costs, is included in the model by means of a set of scenarios. The main contribution of the paper, beyond considering stochasticity in the future fuel prices, is the introduction of CVaR risk measure in the objective function in order to limit the possibility of low profits in bad scenarios with a fixed confidence level. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Maria Vespucci & Marida Bertocchi & Mario Innorta & Stefano Zigrino, 2014. "A stochastic model for investments in different technologies for electricity production in the long period," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(2), pages 407-426, June.
    • Handle: RePEc:spr:cejnor:v:22:y:2014:i:2:p:407-426
    DOI: 10.1007/s10100-013-0317-4
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    References listed on IDEAS

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    1. Enzo Sauma & Shmuel Oren, 2006. "Proactive planning and valuation of transmission investments in restructured electricity markets," Journal of Regulatory Economics, Springer, vol. 30(3), pages 358-387, November.
    2. 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.
    3. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    4. Enzo Sauma & Shmuel Oren, 2006. "Proactive planning and valuation of transmission investments in restructured electricity markets," Journal of Regulatory Economics, Springer, vol. 30(3), pages 261-290, November.
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    Cited by:

    1. Heinz Stigler & Udo Bachhiesl & Gernot Nischler & Gerald Feichtinger, 2016. "ATLANTIS: techno-economic model of the European electricity sector," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(4), pages 965-988, December.
    2. 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.
    3. 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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