基于图表示学习和特征引导的电力系统运行场景生成方法  

A Power System Operating Scenario Generation Method Based on Graph Representation Learning and Feature Guidance

作　　者：陈志威 吴毓峰 潘振宁[1] 余涛[2] 刘前进[1] 黄文琦 侯佳萱 CHEN Zhiwei;WU Yufeng;PAN Zhenning;YU Tao;LIU Qianjin;HUANG Wenqi;HOU Jiaxuan(School of Electric Power Engineering,South China University of Technology,Guangzhou 510641,Guangdong Province,China;Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid(South China University of Technology),Guangzhou 510641,Guangdong Province,China;CSG Digital Grid Research Institute,Guangzhou 510700,Guangdong Province,China)

机构地区：[1]华南理工大学电力学院,广东省广州市510641 [2]广东省电网智能量测与先进计量企业重点实验室(华南理工大学),广东省广州市510641 [3]南方电网数字电网研究院有限公司,广东省广州市510700

出　　处：《中国电机工程学报》2024年第24期9521-9532,I0002,共13页PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING

基　　金：国家自然科学基金项目(U2066212,52207105)。

摘　　要：随着海量新能源接入,电力系统的随机性显著增大,运行场景数据分布弥散且不均匀,导致现有场景生成方法适用性降低。传统依赖于人工经验和模型的场景生成方法更关注于场景的某种特征而忽略场景的数据分布。基于数据驱动场景生成方法则侧重于描述场景的数据分布,而一些具有小概率、高风险的运行场景则容易被忽视。基于此,该文首次提出基于图表示学习和特征引导的电力系统运行场景生成方法。首先,以图的形式实现电网运行特征的深度提取和挖掘,提高生成场景的多样性。其次,通过数据和知识融合的方式,将调度人员关注的特征通过知识的形式嵌入模型中,在尽量维持其分布特征的前提下,实现自定义特征引导的运行场景生成。在考虑运行风险的高比例新能源电力系统中的验证结果表明,相比于传统的场景生成方法,提出的模型在生成运行场景的同时提高了指定特征运行场景的生成效率,保持了生成场景一致性的同时提高了场景的多样性,为电力系统调度中机器辅助决策提供了更完备的数据支撑。With the massive integration of new energy sources,the randomness of the power system has increased significantly,and the data distribution of the operation scenarios is scattered and uneven,which reduces the applicability of the existing scenario generation methods.Traditional scenario generation based on human experience and models pays more attention to some characteristics of the scenario and ignores the data distribution of the scenario.Data-driven scenario generation methods focus on describing the data distribution of the scenario,while some operation scenarios with low probability and high risk are easily overlooked.To address this issue,this research paper presents a novel approach for scenario generation,combining graph representation learning and feature-guided operation scenario generation based on the graph representation of grid operation scenarios.The proposed method mines grid operation features and incorporates the desired features into the model through data and knowledge fusion,ensuring feature-guided operation scenario generation while maintaining and preserving the distributional characteristics as far as possible.Ultimately,the verification results obtained from a system that incorporates a substantial amount of renewable energy sources and considers the operational risks of the power grid demonstrate that the proposed model outperforms the traditional scenario generation method.The proposed model not only enhances the generation efficiency of specific operational scenarios,but also ensures the consistency and diversity of the generated scenarios.Moreover,this paper provides comprehensive data support for machine-aided decision-making in power system dispatching.

关 键 词：图表示学习 生成式对抗网络 特征引导 调度场景生成 

分 类 号：TM32[电气工程—电机]