Electricity Price-Driven Renewable Energy Station Value Evaluation Model: An Analysis of Economic Impact and Decision-Making Strategies

This paper proposes a new energy station value evaluation and optimization model driven by electricity price, which aims to more accurately reflect the impact of electricity price fluctuations on the economic value of new energy projects. By comprehensively considering meteorological factors, economic development, power consumption, power rationing and operating costs, a random variable model based on time series analysis and machine learning method is constructed, and Monte Carlo simulation technology is used for cash flow prediction and pricing analysis. The empirical research results show that this model can effectively improve the accuracy and stability of new energy project valuation, reduce the cognitive gap between investors and developers in market transactions, and promote the scientific and transparency of investment decisions. This study provides a powerful tool support for financial investment decision-making in the field of new energy and proposes that the combination of market trading mechanism and dynamic optimization model can be further studied in the future to better adapt to the complex and volatile market environment.