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As a large-scale regulating power source, pumped storage power station is of great significance for the safe and stable operation of power system. Pumped storage power plant project has a large investment, long construction period, involving capital, environment, manpower and other aspects of resource consumption, easy to have a significant impact on the regional economy, scientific and effective investment cost analysis and prediction of pumped storage power plant project investment planning is crucial. This paper first analyzes the cost composition of pumped storage power plant, identifies the internal and external influencing factors of each cost element, and screens out the key factors. Afterwards, the key factors are used as inputs, and regression analysis is performed in several ways and the regression effects are compared. The optimal regression method is chosen to predict the cost trend of pumped storage investment, and the prediction results provide a reference for future investment planning of pumped storage power plants.

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A Model for Forecasting Investment Trends in Pumped Storage Power Plants that Takes into Account Both Internal and External Conditions

  • 会议论文

  • 发表日期：

    2025年

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  • XiuJie Bian,
  • WenQiang Zhao,
  • YuYang Li

摘要

As a large-scale regulating power source, pumped storage power station is of great significance for the safe and stable operation of power system. Pumped storage power plant project has a large investment, long construction period, involving capital, environment, manpower and other aspects of resource consumption, easy to have a significant impact on the regional economy, scientific and effective investment cost analysis and prediction of pumped storage power plant project investment planning is crucial. This paper first analyzes the cost composition of pumped storage power plant, identifies the internal and external influencing factors of each cost element, and screens out the key factors. Afterwards, the key factors are used as inputs, and regression analysis is performed in several ways and the regression effects are compared. The optimal regression method is chosen to predict the cost trend of pumped storage investment, and the prediction results provide a reference for future investment planning of pumped storage power plants.