Wind power and thermal power turbines performance replacement optimization model based on chance constrained programming

Energy shortage and growing environmental pressure let the power industry face increasingly tough energy conservation situation; therefore, the previous condition must be adjusted to optimize performance for power generation. Our optimization purposes can be reached by researching the power generation performance for Wind & Fire turbine and introducing chance-constrained programming. While solving chance constrained programming model, the previous model is converted to its equivalent and fuzzy satisfaction theory is introducing, which can obscure multi-objective optimization model. By converting multiple objectives into a single objective, ultimately, we get the optimum results of thermal power and wind turbine power performance scheduling model, which shows that we ultimately achieve optimal results by the power generation performance replacement of wind and fire turbine.