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计及相关性的含下垂控制型及间歇性电源的孤岛微电网电压稳定概率评估 被引量:16

Probabilistic Evaluation of Voltage Stability for Islanded Microgrids With Droop-controlled and Intermittent Distributed Generations Considering Correlation
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摘要 针对含下垂控制型和间歇性分布式电源(distributed generation,DG)的孤岛微电网(islanded microgrid,IMG),提出一种基于无迹变换技术的计及随机变量相关性的静态电压稳定概率评估模型和方法。首先,计及下垂控制DG和负荷的电压频率静特性建立了IMG的稳态模型,并以负荷裕度为指标,运用非线性规划法建立其静态电压稳定性分析的模型;然后,根据无迹变换的原理将概率分析问题转化为确定性优化问题,并采用IPOPT内点法优化工具求解。在概率评估模型中考虑了间歇性微电源输出功率的随机性和相关性以及负荷的不确定性和相关性。采用中国西北某风电场采集的风速数据及IEEE33节点系统构造IMG算例系统,并以基于Nataf变换的蒙特卡罗法的结果为基准,验证该文模型和方法的有效性,分析了其相关性对负荷裕度的期望和标准差的影响,并通过算例数据验证了算法对随机变量的不同概率分布类型、参数及相关性的适应性。 To assess the static voltage stability of islanded microgrids(IMGs) with droop-controlled and intermittent distributed generations(DGs),a probabilistic evaluation method of voltage stability based on unscented transformation technique was proposed.The static model of islanded microgrid was presented considering the static voltage and frequency characteristics of the droop-controlled DGs and loads.Then load margin was taken as the static voltage stability index and nonlinear programming method was applied to develop the evaluation model.Unscented transformation technique was utilized to transform stochastic optimization problem to deterministic optimization problem,which was solved by interior point method of the IPOPT toolbox.The randomness of out power of the intermittent DGs and load fluctuation was considered in the model.The calculation results of the proposed method were verified by those of Monte Carlo method with Nataf transformation on an IMG modified from IEEE 33-bus system.Moreover the adaptability of the proposed method to the correlation,the probability distribution types and parameters of random variables was testified.
出处 《中国电机工程学报》 EI CSCD 北大核心 2018年第4期1065-1074,共10页 Proceedings of the CSEE
基金 国家自然科学基金项目(51677151) 陕西省教育厅专项科研计划项目(16JK1548) 西安理工大学科技创新计划项目(2016CX044)~~
关键词 微电网 下垂控制 静态电压稳定 概率评估 无迹变换 非线性规划 microgrid droop-controlled static voltagestability probabilistic evaluation unscented transformation nonlinear programming
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