Sustainable operation-oriented investment risk evaluation and optimization for renewable energy project: a case study of wind power in China

Abstract

Renewable energy is playing an increasingly important role in energy security and environmental protection. As China has a huge demand for renewable energy and also has abundant wind resources, it is vital that government, investors and operators work together to ensure the sustainable development of the wind energy industry. Even though China is already a leader in wind power generation with the largest installed wind power capacity in the world, it has continued to build new wind power facilities. However, due to industrial immaturity and the need for significant investment, wind power investors and operators are faced with uncertainty about the attendant risks. To achieve risk mitigation and sustainability, this paper proposes an investment risk evaluation and optimization process for Chinese wind power projects. The Monte Carlo method is first used to evaluate the investment risks, after which a multi-objective programming model is built for the optimization. A specific case in western China is examined to demonstrate the proposed methodology, with the evaluation results indicating that the project has high investment risk. Based on the case study, the key risk factors are identified and optimization suggestions given for China’s wind power projects. The proposed methodology and findings contribute to research on the planning, investment and sustainable operation of renewable energy power generation projects in other areas in China.

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Fig. 1

Source: Global Wind Energy Council

Fig. 2

Source: Global Wind Energy Council

Fig. 3

Source: CMA wind and solar energy resources assessment center

Fig. 4

Source: Chinese Wind Energy Association

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References

  1. Abdelhady, S., Borello, D., & Santori, S. (2015). Economic feasibility of small wind turbines for domestic consumers in Egypt based on the new Feed-in Tariff. Energy Procedia,75, 664–670.

    Google Scholar 

  2. Adaramola, M. S., Paul, S. S., & Oyedepo, S. O. (2011). Assessment of electricity generation and energy cost of wind energy conversion systems in north-central nigeria. Energy Conversion and Management,52(12), 3363–3368.

    Google Scholar 

  3. Akpinar, E. K., & Akpinar, S. (2004). Determination of the wind energy potential for maden–elazig, Turkey. Energy Conversion and Management,45(18), 2901–2914.

    Google Scholar 

  4. Aquila, G., Junior, P. R., Pamplona, E. O., & Queiroz, A. D. (2017). Wind power feasibility analysis under uncertainty in the Brazilian electricity market. Energy Economics,65, 127–136.

    Google Scholar 

  5. Barros, J. C., Coira, M. L., López, M. C., & Gochi, A. C. (2016). Probabilistic life-cycle cost analysis for renewable and non-renewable power plants. Energy,112, 774–787.

    Google Scholar 

  6. Caralis, G., Diakoulaki, D., Yang, P., Gao, Z., Zervos, A., & Rados, K. (2014). Profitability of wind energy investments in china using a Monte Carlo approach for the treatment of uncertainties. Renewable and Sustainable Energy Reviews,40(40), 224–236.

    Google Scholar 

  7. Chen, J., Cheng, S., & Song, M. (2017). Decomposing inequality in energy-related CO2 emissions by source and source increment: The roles of production and residential consumption. Energy Policy,107, 698–710.

    Google Scholar 

  8. China National Development and Reform Commission. (2015). Notice for Adjusting the Feed-in Tariff for photovoltaic power and wind power generation.

  9. Chinchuluun, A., & Pardalos, P. M. (2007). A survey of recent developments in multiobjective optimization. Annals of Operations Research,154(1), 29–50.

    Google Scholar 

  10. Dalabeeh, A. S. K. (2017). Techno-economic analysis of wind power generation for selected locations in Jordan. Renewable Energy,101, 1369–1378.

    Google Scholar 

  11. Dincer, F. (2011). The analysis on wind energy electricity generation status, potential and policies in the world. Renewable and Sustainable Energy Reviews,15(9), 5135–5142.

    Google Scholar 

  12. Dorvlo, A. S. (2002). Estimating wind speed distribution. Energy Conversion and Management,43(17), 2311–2318.

    Google Scholar 

  13. Falconett, I., & Nagasaka, K. (2010). Comparative analysis of support mechanisms for renewable energy technologies using probability distributions. Renewable Energy,35(6), 1135–1144.

    Google Scholar 

  14. Feng, T. T., Yang, Y. S., Yang, Y. H., & Wang, D. D. (2017). Application status and problem investigation of distributed generation in China: The case of natural gas, solar and wind resources. Sustainability,9, 1022.

    Google Scholar 

  15. Herran, D. S., Dai, H., Fujimori, S., & Masui, T. (2016). Global assessment of onshore wind power resources considering the distance to urban areas. Energy Policy,91, 75–86.

    Google Scholar 

  16. Huang, Z., Wei, Y. M., Wang, K., & Liao, H. (2017). Energy economics and climate policy modeling. Annals of Operations Research,255(1), 1–7.

    Google Scholar 

  17. Hwang, C. L., Paidy, S. R., Yoon, K., & Masud, A. M. (1980). Mathematical programming with multiple objectives: a tutorial. Computers & Operations Research,7(1), 5–31.

    Google Scholar 

  18. Justus, C. G., Hargraves, W. R., Mikhail, A., & Graber, D. (1978). Methods for estimating wind speed frequency distributions. Journal of Applied Meteorology,17, 350–353.

    Google Scholar 

  19. Kim, Y. J. (2017). Monte Carlo vs. fuzzy Monte Carlo simulation for uncertainty and global sensitivity analysis. Sustainability,9(4), 539.

    Google Scholar 

  20. Li, C. B., Lu, G. S., & Wu, S. (2013). The investment risk analysis of wind power project in china. Renewable Energy,50(3), 481–487.

    Google Scholar 

  21. Malekpoor, H., Chalvatzis, K., Mishra, N., Mehlawat, M., Zafirakis, D., & Song, M. (2017). Integrated grey relational analysis and multi objective grey linear programming for sustainable electricity generation planning. Annals of Operations Research. https://doi.org/10.1007/s10479-017-2566-4.

    Article  Google Scholar 

  22. Mandal, P., Zareipour, H., & Rosehart, W. D. (2015). Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNS. International Journal of Energy Research,38(13), 1654–1666.

    Google Scholar 

  23. Ming, Z., Zhang, K., & Dong, J. (2013). Overall review of china’s wind power industry: status quo, existing problems and perspective for future development. Renewable & Sustainable Energy Reviews,24(C), 379–386.

    Google Scholar 

  24. Ministry of Finance of China & State Administration of Taxation. (2016). Notice for the value-added tax of wind power generation.

  25. Mohammadi, K., & Mostafaeipour, A. (2013). Economic feasibility of developing wind turbines in Aligoodarz, Iran. Energy Conversion and Management,76(30), 645–653.

    Google Scholar 

  26. Pereira, E. S., Pinho, J. T., Galhardo, M. B., & Macêdo, W. N. (2014). Methodology of risk analysis by Monte Carlo method applied to power generation with renewable energy. Renewable Energy,69(3), 347–355.

    Google Scholar 

  27. Song, M., Peng, J., Wang, J., & Zhao, J. (2017). Environmental efficiency and economic growth of China: A ray slack-based model analysis. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2017.03.073.

    Article  Google Scholar 

  28. Song, M., & Wang, S. (2017a). Measuring environment-biased technological progress considering energy saving and emission reduction. Process Safety and Environmental Protection. https://doi.org/10.1016/j.psep.2017.08.042.

    Article  Google Scholar 

  29. Song, M., & Wang, S. (2017b). Participation in global value chain and green technology progress: Evidence from big data of Chinese enterprises. Environmental Science and Pollution Research,24(2), 1648–1661.

    Google Scholar 

  30. Sun, S., Liu, F., Xue, S., Zeng, M., & Zeng, F. (2015). Review on wind power development in China: Current situation and improvement strategies to realize future development. Renewable and Sustainable Energy Reviews,45, 589–599.

    Google Scholar 

  31. Ulgen, K., & Hepbasli, A. (2002). Determination of Weibull parameters for wind energy analysis of İzmir, Turkey. International Journal of Energy Research,26(6), 495–506.

    Google Scholar 

  32. Xie, B. C., Fan, Y., & Qu, Q. Q. (2012). Does generation form influence environmental efficiency performance? An analysis of China’s power system. Applied Energy,96, 261–271.

    Google Scholar 

  33. Yang, M., Nguyen, F., T’Serclaes, P. D., & Buchner, B. (2010). Wind farm investment risks under uncertain CDM benefit in China. Energy Policy,38(3), 1436–1447.

    Google Scholar 

  34. Zhao, Z., Chang, R., & Chen, Y. (2016). What hinder the further development of wind power in china?—A socio-technical barrier study. Energy Policy,88, 465–476.

    Google Scholar 

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Acknowledgements

The work was supported by the Program of National Natural Science Foundation of China under Grant No. (71401136 and 71503216), the Fundamental Research Funds for the Central Universities,Southwest University for Nationalities under Grant (No. 2016NZYQN13), and the System Science and Enterprise Development Research Center Project under Grant (No. xq15c02). The authors are indebted to the editors and reviewers for their valuable comments and suggestions.

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Lei, X., Shiyun, T., Yanfei, D. et al. Sustainable operation-oriented investment risk evaluation and optimization for renewable energy project: a case study of wind power in China. Ann Oper Res 290, 223–241 (2020). https://doi.org/10.1007/s10479-018-2878-z

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Keywords

  • Investment risk evaluation
  • MOP optimization
  • Renewable power generation
  • Sustainable operation
  • Wind power project