Optimization scheduling model incorporating multivariate trapezoidal fuzzy parameters under wind power fluctuation patterns classification

With the rapid growth of grid-integrated renewable energy capacity, accurately characterizing its uncertainties has become essential for power system scheduling decision-making. To address this issue, based on the multivariate trapezoidal fuzzy parameters for reshaping the uncertainty of renewable energy, this paper proposes a low-carbon economic optimization scheduling method that considers fuzzy chance constraints. First, the distribution characteristics of forecast errors under different wind power fluctuation patterns are analyzed. Based on conventional single trapezoidal fuzzy parameters, a multivariate trapezoidal fuzzy parameter selection model is proposed according to the classification of wind power fluctuation patterns. Secondly, based on the analysis of green certificate-carbon joint trading mechanism, the optimization scheduling model is constructed with the system's total operating cost as the objective, considering multivariate trapezoidal fuzzy parameters under fuzzy chance constraints. Finally, through case studies, compared to traditional models, the scheduling scheme under the proposed model reduces the total system cost by 19.63 %, carbon emissions by 19.56 %, and the renewable energy curtailment rate increases by 3.62 %. The results demonstrate that the proposed model lowers the risk aversion level, while showing significant potential in improving both system economy and low-carbon performance.