基于高风险场景的关键输电断面极限优化模型  

Optimization Model for Critical Transmission Interface Limits Based on High-risk Scenarios

作　　者：范宏 刘琳琳 田书欣 FAN Hong;LIU Linlin;TIAN Shuxin(Shanghai Electric Power University,Shanghai 200090,China)

机构地区：[1]上海电力大学,上海200090

出　　处：《智慧电力》2025年第4期1-10,共10页Smart Power

基　　金：国家重点研发计划基金资助项目(2022YFB2402800)。

摘　　要：为应对高比例新能源接入引发的电力系统运行风险,提出了一种基于高风险运行场景的关键输电断面极限功率(TTC)优化模型。首先,基于改进半不变量法与概率潮流计算生成母线电压及线路功率数据,结合主导模式振荡中心和安全稳定参与因子(SSI)构建高风险场景库;利用支持向量机(SVM)筛选高风险场景,并通过欧氏距离权重降维法提升计算效率与风险预测准确性。其次,引用拉丁超立方抽样法(LHS)量化不同极限功率下的弃电风险及电压/潮流安全约束,建立兼顾输电能力最大化与多风险约束的优化模型。最后,设计粒子群-遗传混合算法求解模型,以IEEE 39节点系统验证了模型在提升断面传输极限与风险控制方面的有效性。To address operational risks in power systems caused by high penetration of renewable energy integration,this paper proposes a total transfer capability(TTC)optimization model for critical transmission interfaces based on high-risk operational scenarios.First,bus voltage and line power data are generated using an improved semi-invariant method combined with probabilistic power flow calculations.A high-risk scenario library is constructed by integrating dominant mode oscillation centers and security and stability involvement factors(SSI).Support vector machine(SVM)is employed to screen high-risk scenarios,and Euclidean distance weighted-based dimensionality reduction is applied to enhance computational efficiency and risk prediction accuracy.Second,Latin hypercube sampling(LHS)is introduced to quantify renewable energy curtailment risks and voltage/power flow security constraints under different TTC limits.An optimization model is established to maximize transmission capacity while balancing multiple risk constraints.Finally,a particle swarm-genetic hybrid algorithm is designed for solving the model,and its effectiveness in improving transmission interface limits and risk control is validated using the IEEE 39-node system.

关 键 词：高风险运行场景 安全稳定参与因子 支持向量机法 拉丁超立方抽样 

分 类 号：TM721[电气工程—电力系统及自动化]