摘要
为降低鲁棒优化方法的保守性和随机优化方法的复杂性,增强电力系统应对偶发线路故障扰动的能力,提出了一种分布不确定性条件下的N-k安全准则的分布鲁棒机组组合模型。根据有限历史样本数据,在满足一定置信水平的条件下,基于非精确Dirichlet模型方法构造包含真实N-k故障概率分布的模糊集,用以描述输电线路故障概率分布的不确定性。首先,通过对模糊集中最劣概率分布的辨识,将原始的分布鲁棒优化调度问题转化为确定性概率分布条件下的两阶段鲁棒优化决策模型。然后,采用列与约束生成算法对模型进行处理,利用Big-M法、分段线性化技术和对偶原理对主问题和子问题进行转化,得到混合整数线性规划模型,有效降低了模型求解难度。最后,整合4种N-k不确定集合,对IEEE 14节点及IEEE 118节点系统进行算例分析,验证了所提方法的有效性。
In order to reduce the conservatism of the robust optimization methods and the complexity of the stochastic optimization methods,and enhance the ability of power system to deal with occasional line fault disturbance,this paper proposes a distributed robust unit commitment model with N-k safety criterion under uncertain distribution.According to the limited historical sample data,under the condition of satisfying a certain confidence level,a fuzzy set containing the real N-k fault probability distribution is constructed based on imprecise Dirichlet model(IDM)to describe the uncertainty of transmission line fault probability distribution.Firstly,by identifying the worst probability distribution in the fuzzy set,the original distribution robust optimization scheduling problem is transformed into a two-stage robust optimization decision model under the deterministic probability distribution conditions.Then,the column and constraint generation(C&CG)algorithm is used to process the model,the main problem and subproblem are transformed by using Big-M method,segment linearization technique and duality principle.And a mixed-integer linear programming problem(MILP)model is obtained,which effectively reduces the difficulty in solving the model.Finally,four kinds of N-k uncertain sets are integrated,and case studies on IEEE 14-bus system and IEEE 118-bus system demonstrate the efficiency of the proposed method.
作者
吉兴全
郝晴
张玉敏
韩学山
杨明
张旋
JI Xingquan;HAO Qing;ZHANG Yumin;HAN Xueshan;YANG Ming;ZHANG Xuan(College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China;Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong University),Jinan 250061,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2022年第2期56-64,共9页
Automation of Electric Power Systems
基金
国家自然科学基金青年基金资助项目(52107111)。
