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The Application of Monte Carlo Method Simulation in Wind Power Investment Project

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Advances in Computational Environment Science

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 142))

Abstract

Since wind power investment project has a long life cycle and has many risk factors, so not only the factor of initial investment cost should be taken into consideration, but also the factors influenced by cost after the project completion. To solve the problem of wind power investment, this article introduces Monte Carlo simulation, using crystal ball software to simulate and analyze the factors that make big difference in investment risk. Finally, through dynamic unit KWH cost, a evaluative indicator, selects the best one from the alternatives.

National Natural Science Foundation (70871037).

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References

  1. Butar, F.B., Lahiri, P.: On measures of uncertainty of empirical Bayes small-area estimators. Journal of Statistical Planning and Inference 112(2), 63–76 (2003)

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  2. Nobuo, F., Hisaho, N.: Monte Carlo algorithm for the double exchange model optimized for parallel computations. Computer Physics Communications 142(3), 410–412 (2001)

    Article  MATH  Google Scholar 

  3. Siddhartha, C., Federico, N., Neil, S.: Markov chain Monte Carlo methods for stochastic volatility models. Journal of Econometrics 108(2), 281–316 (2002)

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  4. Fishman, G.S.: Monte Carlo: Concepts, Algorithms, and Applications, pp. 5–7. Springer, New York (1996)

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Author information

Authors and Affiliations

  1. Dept. of Business Administration, North China Electric Power University, NCEPU, Beijing, China

    Wu Yunna, Hu Xinliang & Shen Yue

Authors
  1. Wu Yunna
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  2. Hu Xinliang
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  3. Shen Yue
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Corresponding author

Correspondence to Wu Yunna .

Editor information

Editors and Affiliations

  1. Information Engineering Research Institu, Continental Dr 100, Newark, 19713, USA

    Gary Lee

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© 2012 Springer-Verlag GmbH Berlin Heidelberg

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Yunna, W., Xinliang, H., Yue, S. (2012). The Application of Monte Carlo Method Simulation in Wind Power Investment Project. In: Lee, G. (eds) Advances in Computational Environment Science. Advances in Intelligent and Soft Computing, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27957-7_26

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  • DOI: https://doi.org/10.1007/978-3-642-27957-7_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27956-0

  • Online ISBN: 978-3-642-27957-7

  • eBook Packages: EngineeringEngineering (R0)

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