Large Signal Stability Analysis of DC Microgrid System Considering Dynamic Characteristics of Constant Power Load and Energy Storage System
Liu Xinbo, Gao Zhuo
Collaborative Innovation Center of Electric Vehicles in Beijing College Electrical and Control Engineering North China University of Technology Beijing 100144 China
Abstract：In DC microgrids, the loads are interfaced through power electronic converters. These loads behave as constant power loads (CPLs) with the closed control loop. When the voltage changes, these loads exhibit negative impedance characteristics and may cause instability of the system. Consequently, large signal stability analysis of DC microgrids is very necessary. This paper analyzed the large signal stability of DC microgrid, based on the negative impedance characteristics of the constant power load and the charging and discharging characteristics of the energy storage unit. Brayton-Moser’s mixed potential function is used to obtain the large signal stability criteria. According to the criterion, the influence of the DC bus voltage, the power of constant power loads and the charging and discharging power of the storage units on the system stability are all quantitatively analyzed. The criteria are very simple and easy to be used, and provide design basis for ensure the stability of DC microgrid. The experiment and simulation results indicate the system can maintain stable under large disturbance conditions.
刘欣博, 高卓. 考虑恒功率负载与储能单元动态特性的直流微电网系统大信号稳定性分析[J]. 电工技术学报, 2019, 34(zk1): 292-299.
Liu Xinbo, Gao Zhuo. Large Signal Stability Analysis of DC Microgrid System Considering Dynamic Characteristics of Constant Power Load and Energy Storage System. Transactions of China Electrotechnical Society, 2019, 34(zk1): 292-299.
[1] 朱永利, 李莉, 宋亚奇, 等. ODPS平台下的电力设备监测大数据存储与并行处理方法[J]. 电工技术学报, 2017, 32(9): 199-210. Zhu Yongli, Li Li, Song Yaqi, et al.Storage and parallel processing of big data of power equipment condition monitoring on ODPS platform[J]. Transa- ctions of China Electrotechnical Society, 2017, 32(9): 199-210. [2] Wang Yi, Chen Qixin, Hong Tao, et al.Review of smart meter data analytics: applications, metho- dologies, and challenges[J]. IEEE Transactions on Smart Grid, 2018, DOI: 10.1109/TSG.2018.2818167. [3] 吴金浩, 杨秀媛, 孙骏. 基于主成分分析法的风电功率短期组合预测[J]. 电气技术, 2016, 17(7): 41-47. Wu Jinhao, Yang Xiuyuan, Sun Jun.The combination forecasting model for wind farm power based on PCA[J]. Electrical Engineering, 2016, 17(7): 41-47. [4] 邹经鑫, 陈伟根, 万福, 等. 油纸绝缘老化拉曼光谱特征量提取及诊断方法[J]. 电工技术学报, 2018, 33(5): 1133-1142. Zou Jingxin, Chen Weigen, Wan Fu, et al.The raman spectral feature extraction and diagnosis of oil-paper insulation ageing[J]. Transactions of China Electro- technical Society, 2018, 33(5): 1133-1142. [5] Frey B J,Dueck D.Clustering by passing messages between data points[J]. Science, 2007, 315(5814): 972-976. [6] Lin J, Keogh E, Lonardi S, et al.A symbolic representation of time series, with implications for streaming algorithms[C]//ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2003: 2-11. [7] Wang Yi, Chen Qixin, Kang Chongqing, et al.Clustering of electricity consumption behavior dynamics toward big data applications[J]. IEEE Transactions on Smart Grid, 2016, 7(5): 2437-2447. [8] Pham N D, Le Q L, Dang T K.Two novel adaptive symbolic representations for similarity search in time series databases[C]//Web Conference, IEEE, Busan, South Korea, 2010: 181-187. [9] Ahmed A M, Bakar A A, Hamdan A R.Harmony Search algorithm for optimal word size in symbolic time series representation[C]//Data Mining and Opti- mization, IEEE, Putrajaya, Malaysia, 2011: 57-62. [10] Bondu A, Boullé M, Cornuéjols A.Symbolic repre- sentation of time series: a hierarchical coclustering formalization[C]//International Workshop on Advanced Analytics and Learning on Temporal Data, Springer International Publishing, Porto, Portugal, 2015: 3-16. [11] Rechy-Ramírez F, Mesa H G A, Mezura-Montes E, et al. Times series discretization using evolutionary pro- gramming[C]//International Conference on Artificial Intelligence: Advances in Soft Computing, Springer- Verlag, Guadalajara, Mexico, 2011: 225-234. [12] Marquez-Grajales A, Acosta-Mesa H G, Mezura- Montes E. An adaptive symbolic discretization scheme for the classification of tempo ral datasets using NSGA-II[C]//IEEE International Autumn Meeting on Power, Electronics and Computing, IEEE, Ixtapa, Mexico, 2017: 1-8. [13] Fuad M M M. Variable-chromosome-length genetic algorithm for time series discretization[M]. Database and Expert Systems Applications. Springer Inter- national Publishing, Regensburg, Germany, 2016: 418-425. [14] Acosta-Mesa H G, Cruz-Ramírez N, García-Lopez D A. Entropy based linear approximation algorithm for time series discretization. advances in artificial intelligence and applications[J]. Research in Com- puters, 2008, 32: 214-224. [15] Aghabozorgi S, Shirkhorshidi A S, Wah T Y.Time- series clustering-a decade review[J]. Information Systems, 2015, 53(C): 16-38. [16] Al-Otaibi R, Jin N, Wilcox T, et al.Feature construction and calibration for clustering daily load curves from smart-meter data[J]. IEEE Transactions on Industrial Informatics, 2017, 12(2): 645-654. [17] 李欣然, 徐振华, 宋军英, 等. 基于功率空间的分时段负荷模型参数在线修正[J]. 电工技术学报, 2012, 27(8): 147-156. Li Xinran, Xu Zhenhua, Song Junying, et al.On-line revising algorithm for load model parameters of substation in different daily periods based on the measured active power[J]. Transactions of China Electrotechnical Society, 2012, 27(8): 147-156. [18] 刘思, 李林芝, 吴浩, 等. 基于特性指标降维的日负荷曲线聚类分析[J]. 电网技术, 2016, 40(3): 797-803. Liu Si, Li Linzhi, Wu Hao, et al.Cluster analysis of daily load curves using load pattern indexes to reduce dimensions[J]. Power System Technology, 2016, 40(3): 797-803. [19] 龚钢军, 陈志敏, 陆俊, 等. 智能用电用户行为分析的聚类优选策略[J]. 电力系统自动化, 2018, 42(2): 58-63. Gong Gangjun, Chen Zhimin, Lu Jun, et al.Clustering optimization strategy for electricity consumption behavior analysis in smart grid[J]. Automation of Electric Power Systems, 2018, 42(2): 58-63. [20] 沈小军, 周冲成, 吕洪. 基于运行数据的风电机组间风速相关性统计分析[J]. 电工技术学报, 2017, 32(16): 265-274. Shen Xiaojun, Zhou Chongcheng, Lü Hong.Statistical analysis of wind speed correlation between wind turbines based on operational data[J]. Transa- ctions of China Electrotechnical Society, 2017, 32(16): 265-274. [21] 孙建军, 张世泽, 曾梦迪, 等. 考虑分时电价的主动配电网柔性负荷多目标优化控制[J]. 电工技术学报, 2018, 33(2): 401-412. Sun Jianjun, Zhang Shize, Zeng Mengdi, et al.Multi-objective optimal control for flexible load in active distribution network considering time-of-use tariff[J]. Transactions of China Electrotechnical Society, 2018, 33(2): 401-412. [22] 黄俊辉, 汪惟源, 王海潜, 等. 基于模拟退火遗传算法的交直流系统无功优化与电压控制研究[J]. 电力系统保护与控制, 2016, 44(10): 37-43. Huang Junhui, Wang Weiyuan, Wang Haiqian, et al.Study of hybrid genetic algorithm and annealing algorithm on reactive power optimization and voltage control in AC/DC transmission system[J]. Power System Protection and Control, 2016, 44(10): 37-43. [23] Commission for Energy Regulation. CER Smart Metering Project[EB/OL]. 2012, http://www.ucd.ie/ issda/data/commissionforenergyregulationcer/
2019, Vol. 34 
