计及多峰概率分布的电力系统鲁棒优化调度策略OA

In order to enhance the low-carbon operational capability of the power system under the fluctuations of renewable energy output and load uncertainty,this paper proposes a robust scheduling strategy for power systems based on Gaussian mixture model and confidence interval decision theory.First,in order to describe the characteristics of source-load uncertainty more accurately,the paper compares various uncertainty modeling methods and selects the Gaussian mixture model capable of characterizing multi-peak features to construct the source-load uncertainty model,and then,based on confidence interval decision theory,constructs a robust optimization model with the objective of minimizing system operating costs.Finally,the paper uses the Python programming language to perform simulation verification.The results show that the proposed method exhibits better economic efficiency and robustness under scenarios of renewable energy fluctuations and load uncertainty,effectively enhancing the system's low-carbon scheduling capability.