In order to explore the trading mechanism between flexibility resources and power market in the new power system, the article firstly elaborates the characteristics of flexibility resources in the new power system, and constructs a power spot market price prediction model optimized based on PSO algorithm and LS-SVM algorithm. In order to clarify the coupling relationship between flexibility resources and power market trading mechanism, a two-layer optimization model of coupled carbon-green certificate-consumption volume of power market trading in the previous day is constructed, and an arithmetic example analysis is carried out to analyze the application effect of the model. The results show that the PSO optimization LS-SVM model proposed in this paper is acceptable for day-ahead market electricity price prediction, and its prediction error is much lower than that of other comparative models, which shows that it can make effective prediction for the electricity market. This paper solves the multi-objective optimization problem of system economic benefits, energy saving and emission reduction benefits, and obtains a total of 83,607,000 yuan of optimization costs for the previous day’s clearing, which is 177,000 yuan more than the difference of 8,537,700 yuan of the system cost of considering the economic benefits objective alone, and the difference of the total cost is not significant, which indicates that the multi-objective optimization function constructed in this paper is able to ensure the economic benefits of the system operation, and achieve the environmental benefits on the basis of maximization, to achieve the effect of energy saving and emission reduction.
