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

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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 \({CO}_2\) 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.

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Acknowledgments

The authors acknowledge the support from the grant by Regione Lombardia “Metodi di integrazione delle fonti energetiche rinnovabili e monitoraggio satellitare dell’impatto ambientale”, EN-17, ID 17369.10, and grants by University of Bergamo (2010, 2011) coordinated by M. Bertocchi and M.T. Vespucci. The authors thanks the anonymous referees for advices and suggestions that greatly improve the paper.

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Correspondence to Marida Bertocchi.

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The authors acknowledge the support from the grant by Regione Lombardia “Metodi di integrazione delle fonti energetiche rinnovabili e monitoraggio satellitare dell’impatto ambientale”, EN-17, ID 17369.10, and grants by University of Bergamo (2010, 2011) coordinated by M. Bertocchi and M. T. Vespucci.

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Vespucci, M.T., Bertocchi, M., Innorta, M. et al. A stochastic model for investments in different technologies for electricity production in the long period. Cent Eur J Oper Res 22, 407–426 (2014). https://doi.org/10.1007/s10100-013-0317-4

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  • DOI: https://doi.org/10.1007/s10100-013-0317-4

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