孙国强,左岩,黄蔓云,孙康,王齐辉
SUN Guoqiang,ZUO Yan,HUANG Manyun,SUN Kang,WANG Qihui

• 1:河海大学电气与动力工程学院

摘要(Abstract)：

随着新能源在电力系统中的渗透率逐步增加，其发电的波动性和间歇性对电力系统的安全、稳定、经济运行带来了严峻挑战。针对高比例新能源并网场景下，两阶段序贯预测-决策调度方式存在的预测精度与决策收益不匹配的问题，提出了一种基于半端到端框架的预测与决策一体化的最优线路开断方法。通过引入MPA（海洋捕食者算法）优化预测模型组合权重，将预测与决策过程耦合，形成以系统运行成本为训练目标的闭环结构，从而实现预测模型的优化。最后，使用改进的IEEE 30节点系统和IEEE 118节点系统进行算例分析，验证了所提方法在降低系统运行成本、减少弃光量和机组再调度功率、提高新能源消纳能力等方面的有效性。
With growing penetration of renewable energy in power systems, its inherent volatility and intermittency pose significant challenges to system security, stability, and economic operation. To address the mismatch between prediction accuracy and decision-making benefits in conventional two-stage sequential forecast-decision-making scheduling under high renewable integration scenarios, this paper proposes an integrated prediction-decisionmaking framework for optimal transmission line switching(TLS) based on a semi-end-to-end architecture. By employing the Marine predator algorithm(MPA) to optimize the weighting of prediction model ensembles, the method couples forecasting and decision-making-making processes into a closed-loop structure trained with system operational cost as the objective, so as to optimize the prediction model. Case studies on modified IEEE 30-bus and 118-bus systems demonstrate the method's effectiveness in reducing operational costs, decreasing PV curtailment and generator rescheduling power, and enhancing renewable energy accommodation capability.

关键词(KeyWords)： 半端到端;输电线路开断;高比例新能源;预测决策一体化
semi-end-to-end;TLS;high proportion renewable energy;integrated prediction and decision-making

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(U24B2088)

作者(Author): 孙国强,左岩,黄蔓云,孙康,王齐辉
SUN Guoqiang,ZUO Yan,HUANG Manyun,SUN Kang,WANG Qihui

DOI: 10.19585/j.zjdl.202510007

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