基于仿射理论的配电网供电能力的研究 *

doi:10.11985/2020.04.007

摘要/Abstract

摘要：

为了解决风光等分布式电源入网对电力系统稳定性的影响,处理好分布式电源出力波动带来的电力系统故障,利用仿射数学理论的方法建立了光伏和风机出力不确定性的区间模型,研究了复仿射潮流算法,最后结合粒子群算法进行了最大供电能力的寻优。算例验证表明,分布式电源的接入对电力系统供电能力产生了较大的影响,复仿射潮流算法下可以给出系统节点电压、负载率区间,以及最大供电能力区间,该方法为分布式电源并网对电力系统的影响提供了定量分析的依据。

关键词: 电力系统, 分布式电源, 供电能力, 复仿射潮流算法, 粒子群算法

Abstract:

In order to solve the influence of distributed power sources such as wind and sunlight on the stability of the power system, and to deal with power system failures caused by distributed power output fluctuations, an affine mathematical theory method is used to establish an interval model of photovoltaic and wind turbine output uncertainty. The complex affine power flow algorithm is studied. Finally, the optimization of the maximum power supply capacity is performed in combination with the particle swarm optimization algorithm. The verification of a numerical example shows that the access of distributed power sources has a large impact on the power supply capacity of the power system. The complex affine power flow algorithm can give the system node voltage, load rate interval, and maximum power supply interval. This method provides a basis for quantitative analysis of the influence of distributed power grids on the power system.

Key words: Power system, distributed power, power supply capability, complex affine power flow algorithm, particle swarm algorithm

中图分类号:

TM74

贾光耀, 蔡福天. 基于仿射理论的配电网供电能力的研究 ^*[J]. 电气工程学报, 2020, 15(4): 59-64.

JIA Guangyao, CAI Futian. Research on Power Supply Capability of Distribution Network Based on Affine Theory[J]. Journal of Electrical Engineering, 2020, 15(4): 59-64.

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参考文献 20

[1]	孙健. 我国供电可靠性管理的现状分析与展望[J]. 供用电, 2015,32(11):1-5.
	SUN Jian. Analysis and prospect of power supply reliability management in China[J]. Supply and Power, 2015,32(11):1-5.
[2]	王成山, 李鹏, 于浩. 智能配电网的新形态及其灵活性特征分析与应用[J]. 电力系统自动化, 2018,42(10):13-21.
	WANG Chengshan, LI Peng, YU Hao. A new form of intelligent distribution network and its flexible characteristics analysis and application[J]. Automation of Electric Power Systems, 2018,42(10):13-21.
[3]	陶顺, 陈鹏伟, 肖湘宁, 等. 智能配电网不确定性建模与供电特征优化技术综述[J]. 电工技术学报, 2017,32(10):77-91.
	TAO Shun, CHEN Pengwei, XIAO Xiangning, et al. Summarization of uncertainty modeling and optimization of power supply characteristics for intelligent distribution networks[J]. Transactions of China Electrotechnical Society, 2017,32(10):77-91.
[4]	王守相, 葛磊蛟, 张齐, 等. 配电网分布式能源接纳能力影响因素分析[J]. 供用电, 2016,33(4):2-7,63.
	WANG Shouxiang, GE Leihuan, ZHANG Qi, et al. Analysis of influencing factors of distributed energy acceptance capacity of distribution network[J]. Supply and Electricity, 2016,33(4):2-7,63.
[5]	陶维青, 李嘉茜, 丁明, 等. 分布式电源并网标准发展与对比[J]. 电气工程学报, 2016,11(4):1-8,46.
	TAO Weiqing, LI Jiaqian, DING Ming, et al. Development and comparison of grid-connected standards for distributed power sources[J]. Transactions of the Chinese Society of Electrical Engineering, 2016,11(4):1-8,46.
[6]	叶林, 张亚丽, 李强, 等. 采用分段离散化和高斯混合模型的多场景概率潮流计算[J]. 电力系统自动化, 2017,41(12):131-137.
	YE Lin, ZHANG Yali, LI Qiang, et al. Multi-scenario probabilistic power flow calculation using piecewise discretization and Gaussian mixture model[J]. Automation of Electric Power Systems, 2017,41(12):131-137.
[7]	AI Xiaomeng, WEN Jinyu, WU Tong, et al. A discrete point estimate method for probabilistic load flow based on the measured data of wind power[J]. IEEE Transactions on Industry Applications, 2013,49(5):2244-2252. doi: 10.1109/TIA.2013.2262254
[8]	MATOS M A, EDUARDO G. The fuzzy power flow revisited[J]. IEEE Transactions on Power Systems, 2008,23(1):213-218. doi: 10.1109/TPWRS.2007.913686
[9]	廖小兵, 刘开培, 张亚超, 等. 基于区间泰勒展开的不确定性潮流分析[J]. 电工技术学报, 2018,33(4):750-758.
	LIAO Xiaobing, LIU Kaipei, ZHANG Yachao, et al. Uncertain power flow analysis based on interval Taylor expansion[J]. Transactions of China Electrotechnical Society, 2018,33(4):750-758.
[10]	陈鹏伟, 肖湘宁, BARAN M. 计及分布式电源无功特性的弱环配电网复仿射潮流算法[J]. 电力系统自动化, 2019,43(7):59-77.
	CHEN Pengwei, XIAO Xiangning, BARAN M. Weak loop distribution network complex affine power flow algorithm considering distributed power reactive power characteristics[J]. Automation of Electric Power Systems, 2019,43(7):59-77.
[11]	王成山, 王守相. 基于区间算法的配电网三相潮流计算及算例分析[J]. 中国电机工程学报, 2002(3):59-63.
	WANG Chengshan, WANG Shouxiang. Three-phase power flow calculation and example analysis of distribution network based on interval algorithm[J]. Proceedings of the CSEE, 2002(3):59-63.
[12]	韩亮, 王守相. 含光伏风电的基于仿射算法的配电三相潮流计算[J]. 电网技术, 2013,37(12):3413-3418.
	HAN Liang, WANG Shouxiang. Calculation of three-phase power flow based on affine algorithm with photovoltaic wind power[J]. Power System Technology, 2013,37(12):3413-3418.
[13]	邵振国, 王树洪. 一种采用复仿射区间潮流的有源配电网多目标无功优化方法[J]. 中国电机工程学报, 2017,37(17):4956-4965,5215.
	SHAO Zhenguo, WANG Shuhong. A multi-objective reactive power optimization method for active distribution networks using complex affine interval power flow[J]. Proceedings of the CSEE, 2017,37(17):4956-4965,5215.
[14]	王守相, 韩亮. DG出力不确定性对配电网影响力分析的复仿射数学方法[J]. 中国电机工程学报, 2014,34(31):5507-5515. doi: 10.13334/j.0258-8013.pcsee.2014.31.007
	WANG Shouxiang, HAN Liang. A complex affine mathematical method for analyzing the influence of DG output uncertainty on distribution network[J]. Proceedings of the CSEE, 2014,34(31), 5507-5515.
[15]	韩亮. 计及间歇式电源不确定性的电力系统区间—仿射分析方法[D]. 天津:天津大学, 2014.
	HAN Liang. Power system interval considering intermittent power source uncertainty:Affine analysis method[D]. Tianjin:Tianjin University, 2014.
[16]	王蕾, 赵艳雷, 吕东飞. 考虑静态安全约束的分布式电源准入容量与最佳接入位置计算[J]. 供用电, 2019,36(5):34-41,65.
	WANG Lei, ZHAO Yanlei, LÜ Dongfei. Calculation of admission capacity and optimal access location of distributed power sources considering static security constraints[J]. Power Supply, 2019,36(5):34-41,65.
[17]	兰佳, 徐斌, 刘红新, 等. 基于粒子群算法的分布式电源接入位置及容量寻优[J]. 供用电, 2016,33(10):81-85.
	LAN Jia, XU Bin, LIU Hongxin, et al. Optimizing the location and capacity of distributed power supply based on particle swarm optimization[J]. Supply and Power, 2016,33(10):81-85.
[18]	赵金焕, 马平. 计及DG功率不确定性的配电网多目标无功优化[J]. 电气工程学报, 2020,15(3):65-71.
	ZHAO Jinhuan, MA Ping. Multi-objective reactive power optimization of distribution network considering DG power uncertainty[J]. Journal of Electrical Engineering, 2020,15(3):65-71.
[19]	张骁. 基于粒子群算法的变电站工程项目多目标优化研究[D]. 北京:华北电力大学, 2018.
	ZHANG Xiao. Research on multi-objective optimization of substation engineering projects based on particle swarm optimization[D]. Beijing:North China Electric Power University, 2018.
[20]	LIU X. Impact of beta-distributed wind power on economic load dispatch[J]. Electric Power Components and Systems, 2011,39(8):768-779. doi: 10.1080/15325008.2010.541412

节点		类型		装机容量/kW
10		光伏		90
15		风力		70
21		风力		50
25		风力		150
28		光伏		200
32	其他分布式电源		60

节点	分布式电源类型	相对影响力(%)
10	光伏	6.56
15	风力	3.26
21	风力	3.32
25	风力	31.6
28	光伏	50.34
32	其他电源	4.89

类型	最大供电能力/kW
传统算法下的供电能力	1 246
复仿射算法下的供电能力	1 218~1 275