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

Xu Lei (), Tang Shiyun, Deng Yanfei and Yuan Yuan
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Xu Lei: Xihua University
Tang Shiyun: Xihua University
Deng Yanfei: Southwest University for Nationalities
Yuan Yuan: Sichuan Electric Power Design & Consulting Co., Ltd

Annals of Operations Research, 2020, vol. 290, issue 1, No 11, 223-241

Abstract: 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.

Keywords: Investment risk evaluation; MOP optimization; Renewable power generation; Sustainable operation; Wind power project (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10479-018-2878-z

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