摘要
电力系统安全约束经济调度(security-constrained economic dispatch,SCED)需要考虑海量N-1场景,安全约束数量大。然而,在实际电力系统应用中SCED模型起作用约束比例低,为减少计算负担,目前工业界普遍根据人工经验预先设置起作用约束集,存在不全和不准的问题。当前,数据驱动方法可直接建立SCED输入输出的映射关系,有望打破其计算瓶颈。但电力系统运行方式复杂多变,某些运行场景下历史样本极少,需解决小样本场景下的电网数据驱动分析难题。为此,提出面向小样本的数据驱动安全约束经济调度快速计算方法。首先,为降低小样本下复杂SCED模型的学习难度,提出基于边际机组特征聚类的样本预分类方法,拆解SCED模型输入输出复杂映射关系。然后,采用适合小样本学习特性的高斯过程(Gaussian process,GP),建立SCED替代模型,预先辨识起作用约束集,提出基于高斯过程的SCED快速计算框架。最后,在IEEE 30节点和IEEE118节点标准测试系统的仿真结果表明,所提方法能够具有良好的小样本场景适应性,可显著提高SCED的计算效率。
Security-constrained economic dispatch(SCED)in power systems needs to consider a large number of N-1scenarios with massive security constraints.However,the SCED model has few active constraints in the actual power system applications.To reduce the computational burden,the industrial community generally set up the active constraints according to the artificial experience,which is inaccurate and incomplete.Recently,the data-driven method has been implemented to directly establish the mapping relationship between SCED input and output,which is expected to break its computing bottleneck.But the operation mode of the power system is complex and changeable,and there are few historical samples in some operation scenarios.It is necessary to solve the problem of data-driven analysis in small sample scenarios of power systems.On this basis,this paper proposed a fast data-driven security-constrained economic dispatch algorithm with small samples.Firstly,to reduce the learning difficulty of the complex SCED model with small samples,a sample pre-classification method based on marginal unit feature clustering was proposed to split the complex functional relationship between the input and output of SCED.Then,this paper used Gaussian process(GP),which is suitable for small sample learning,to establish SCED surrogate model and identify the active constraint set in advance.A fast computing framework of SCED was proposed based on Gaussian process.Finally,the simulation results performed on IEEE 30-bus and IEEE 118-bus standard systems demonstrate that the proposed method could significantly improve the computational efficiency of SCED with small sample requirements.
作者
朱正春
杨知方
余娟
谢永胜
雷星雨
杨燕
ZHU Zhengchun;YANG Zhifang;YU Juan;XIE Yongsheng;LEI Xingyu;YANG Yan(State Key Laboratory of Power Transmission Equipment&System Security and New Technology(Chongqing University),Shapingba District,Chongqing 400044,China;State Grid Xinjiang Electric Power Corporation,Urumqi 830011,Xinjiang Uygur Autonomous Region,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2022年第12期4430-4440,共11页
Proceedings of the CSEE
基金
国家重点研发计划项目(2021YFE0191000)
国家自然科学基金项目(52077016)
电力系统国家重点实验室资助课题(SKLD20KZ01)。
关键词
安全约束经济调度
小样本
高斯过程
约束辨识
security-constrained economic dispatch
small sample
Gaussian process
constraints identification
