LAUSR

Abstract Uncertainties are often involved in the investment decision making of the coal-fired power plant. They are often characterized by diversity and fuzziness. In order to minimize their adverse effects on the evaluation of investment, we proposed a novel combined thermal power plant investment decision-making model. This model takes the Net Present Value(NPV) as the objective function, and takes the predictions of revenue and cost as the center. With regard to revenue prediction, Self-adapting Intelligent Grey prediction Model (SIGM) combined with Interval Grey number Prediction Model based on the Triangular whitenization weight function(IGPM_T) model is employed to forecast the changing tendency of the on-grid price, which is the decisive factor for revenue of power plant. With respect to cost prediction, Ito stochastic process theory is utilized to simulate the variation tendency of the operating cost of power generation. In order to acquire the key drift and floating coefficients in the formula of Ito process, the combinatorial simulation experiment is conducted, and by visual comparison, rational coefficient combination is determined. Finally, the reliability and validity of the proposed combined model are verified through an example of a coal-fired power plant, and results show that the proposed model can provide satisfactory evaluation for thermal power plant investment.