针对风电不确定性及风电场间的相关性对电力系统的影响,同时考虑维持电力系统的安全稳定性和经济性,提出一种含风电并网的机会约束暂态稳定约束最优潮流（CCTSCOPF）方法。首先,在风电不确定性分析的基础上,结合风电场间的相关性,采用基于密度峰值聚类的高斯混合模型（DPC-GMM）对风电出力概率分布进行建模,精确刻画风电出力的概率特性,为模型构建提供关键依据;其次,结合机会约束理论构建CCTSCOPF模型,并通过无迹变换法处理模型中的随机变量,从而推导输出变量的概率信息,实现不确定性问题的确定性转化;然后,利用斑马优化算法（ZOA）对CCTSCOPF模型进行求解,提高搜索效率和解的质量,以有效应对复杂的优化问题;最后,在改进的IEEE 39节点系统上进行算例分析,证实了所提方法的准确性和适用性。
Abstract
The impact of wind power uncertainty and the correlation between wind farms on the power system is analyzed while maintaining the system's security, stability, and economic efficiency. A chance constrained transient stability constrained optimal power flow （CCTSCOPF） method incorporating grid-connected wind power is proposed. Initially, building upon the analysis of wind power uncertainty and accounting for the correlation among wind farms, a Gaussian mixture model based on the density peak clustering algorithm （DPC-GMM） is employed to model the probabilistic distribution of wind power output. This approach accurately captures the probabilistic characteristics of wind power generation, thus providing a crucial foundation for model development. Subsequently, a CCTSCOPF model is formulated within the framework of chance constraints, and the unscented transform method is applied to address the stochastic variables present in the model. This facilitates the derivation of probabilistic information for the output variables, thereby enabling the deterministic transformation of the uncertainty problem. Next, the zebra optimization algorithm （ZOA） is applied to solve the CCTSCOPF model, enhancing both the search efficiency and the quality of the solutions, effectively tackling the complexities of the optimization problem. Finally, case studies based on the enhanced IEEE 39-bus system are conducted, validating the accuracy and applicability of the proposed methodology.
关键词
风电 /
不确定性 /
暂态稳定性 /
最优潮流 /
机会约束 /
无迹变换
Key words
wind power /
uncertainty /
transient stability /
optimal power flow /
chance constrained /
unscented transformation
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基金
国家自然科学基金（52407118）; 梯级水电站运行与控制湖北省重点实验室（三峡大学）开放基金课题（2023KJX06）
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