计及牵引负荷相关性的随机潮流计算
|
摘要
结合随机潮流计算量化系统状态变量的概率信息,构建牵引变电所概率负荷模型,描述牵引负荷的随机性和波动性,对实现系统优化运行具有重要意义。通过蒙特卡洛算法模拟相应数据并结合非线性递减惯性权重与混沌优化的粒子群算法辨识模型参数;针对随机潮流计算输入变量的相关性控制问题,提出一种基于模拟退火算法与拉丁超立方采样的方法。仿真实验结果表明:改进的粒子群算法较普通粒子群算法拟合精度更高;改进的相关性控制方法可以不受随机变量概率密度表达式的约束,实现多个随机变量的相关性控制;计及牵引负荷样本之间的相关性对节点电压越限概率、支路功率的影响较仅考虑普通负荷相关性时更为明显,可指导系统进行概率潮流优化。
The probability load model of the traction substation was constructed to describe the randomness and volatility of traction load,combining with probabilistic power flow to calculate the probability information of systemstate variables,which is of great significance to realize optimal operation of the power system. The corresponding data were simulated by the Monte Carlo algorithm and the model parameters were identified by the particle swarm optimization with the nonlinear decreasing inertia weight and the chaos optimization. A method based on simulated annealing algorithm and Latin hypercube sampling was proposed for the correlation control problem of the input variables of the random flow calculation.The simulation results show that the improved particle swarm optimization algorithm is more accurate than the ordinary particle swarm optimization algorithm. The improved correlation control method can realize the correlation control of multiple random variables without the constraint of the probability density expression of random variables. Considering the correlation between traction load samples,the influence of node voltage overshoot probability and branch power is more obvious than that considering only the correlation of ordinary load. This method can be applied to system probabilistic power flow optimization.
The probability load model of the traction substation was constructed to describe the randomness and volatility of traction load,combining with probabilistic power flow to calculate the probability information of systemstate variables,which is of great significance to realize optimal operation of the power system. The corresponding data were simulated by the Monte Carlo algorithm and the model parameters were identified by the particle swarm optimization with the nonlinear decreasing inertia weight and the chaos optimization. A method based on simulated annealing algorithm and Latin hypercube sampling was proposed for the correlation control problem of the input variables of the random flow calculation.The simulation results show that the improved particle swarm optimization algorithm is more accurate than the ordinary particle swarm optimization algorithm. The improved correlation control method can realize the correlation control of multiple random variables without the constraint of the probability density expression of random variables. Considering the correlation between traction load samples,the influence of node voltage overshoot probability and branch power is more obvious than that considering only the correlation of ordinary load. This method can be applied to system probabilistic power flow optimization.
引文
[1]唐开林,李群湛,张丽艳,等.电气化铁道牵引网馈线电流概率分布[J].电力系统及其自动化学报,2010,22(6):12-16.TANG Kailin,LI Qunzhan,ZHANG Liyan,et al. Probability Distribution of Feeder Current of Electrified Railway Traction[J]. Proceedings of the CSU-EPSA,2010,22(6):12-16.
[2]张丽艳,李群湛,朱毅.新建电气化铁路牵引负荷预测[J].西南交通大学学报,2016,51(4):743-749.ZHANG Liyan,LI Qunzhan,ZHU Yi. Prediction of Traction Load for New Electrified Railway[J]. Journal of Southwest Jiaotong University,2016,51(4):743-749.
[3]姜咪慧,王小君,娄竟,等.牵引站馈线谐波电流的分析与预测[J].电网技术,2014,38(1):227-231.JIANG Mihui,WANG Xiaojun,LOU Jing,et al. Analysis and Prediction of Harmonic Current in Feeder of Traction Substation[J]. Power System Technolog,2014,38(1):227-231.
[4]姜咪慧.牵引负荷对地区电网电能质量的影响研究[D].北京:北京交通大学,2014:17-27.
[5]杨少兵,吴命利.电气化铁道牵引变电所负荷概率模型[J].电力系统自动化,2010,34(24):40-45.YANG Shaobing,WU Mingli. A Load Probability Model for Electrified Railway Traction Substations[J]. Automation of Electric Power System,2010,34(24):40-45.
[6]杨少兵.电气化铁道牵引负荷概率分布特性与数学模型研究[D].北京:北京交通大学,2016:29-46.
[7]李娟,杨琳,刘金龙,等.基于自适应混沌粒子群优化算法的多目标无功优化[J].电力系统保护与控制,2011,39(9):26-31.LI Juan,YANG Lin,LIU Jinlong,et al. Multi-objective Reactive Power Optimization Based on Adaptive Chaos Particle Swarm Optimization Algorithm[J]. Power System Protection and Control,2011,39(9):26-31.
[8]邱晓燕,沙熠,宁雪姣,等.计及智能电网多不确定性的多目标储能优化[J].高电压技术,2016,42(9):2715-2722.QIU Xiaoyan,SHA Yi,NING Xuejiao,et al. Multi-objective Optimization of Stored Energy Considering Multi-uncertainty in Smart grid[J]. High Voltage Engineering,2016,42(9):2715-2722.
[9]BORKOWSKA B. Probabilistic Load Flow[J]. IEEE Transactions on Power Apparatus&Systems,1974,93(3):752-759.
[10]于晗,钟志勇,黄杰波,等.采用拉丁超立方采样的电力系统概率潮流计算方法[J].电力系统自动化,2009,33(21):32-35.YU Han,ZHONG Zhiyong,HUANG Jiebo,et al. A Probabilistic Load Flow Calculation Method with Latin Hypercube Sampling[J]. Automation of Electric Power System,2009,33(21):32-35.
[11]陈雁,文劲宇,程时杰.考虑输入变量相关性的概率潮流计算方法[J].中国电机工程学报,2011,31(22):80-87.CHEN Yan,WEN Jinyu,CHENG Shijie. Probabilistic Load Flow Analysis Considering Dependencies among Input Random Variables[J]. Proceedings of the CSEE,2011,31(22):80-87.
[12]陈璨,吴文传,张伯明,等.考虑光伏出力相关性的配电网概率潮流[J].电力系统自动化,2015,39(9):41-47.CHEN Can,WU Wenchuan,ZHANG Boming,et al. Probabilistic Load Flow of Distribution Network Considering Correlated Photovoltaic Power Output[J]. Automation of Electric Power System,2015,39(9):41-47.
[13]李俊芳,张步涵.基于进化算法改进拉丁超立方抽样的概率潮流计算[J].中国电机工程学报,2011,31(25):90-96.LI Junfang,ZHANG Buhan. Probabilistic Load Flow Based on Improved Latin Hypercube Sampling with Evolutionary Algorithm[J]. Proceedings of the CSEE,2011,31(25):90-96.
[2]张丽艳,李群湛,朱毅.新建电气化铁路牵引负荷预测[J].西南交通大学学报,2016,51(4):743-749.ZHANG Liyan,LI Qunzhan,ZHU Yi. Prediction of Traction Load for New Electrified Railway[J]. Journal of Southwest Jiaotong University,2016,51(4):743-749.
[3]姜咪慧,王小君,娄竟,等.牵引站馈线谐波电流的分析与预测[J].电网技术,2014,38(1):227-231.JIANG Mihui,WANG Xiaojun,LOU Jing,et al. Analysis and Prediction of Harmonic Current in Feeder of Traction Substation[J]. Power System Technolog,2014,38(1):227-231.
[4]姜咪慧.牵引负荷对地区电网电能质量的影响研究[D].北京:北京交通大学,2014:17-27.
[5]杨少兵,吴命利.电气化铁道牵引变电所负荷概率模型[J].电力系统自动化,2010,34(24):40-45.YANG Shaobing,WU Mingli. A Load Probability Model for Electrified Railway Traction Substations[J]. Automation of Electric Power System,2010,34(24):40-45.
[6]杨少兵.电气化铁道牵引负荷概率分布特性与数学模型研究[D].北京:北京交通大学,2016:29-46.
[7]李娟,杨琳,刘金龙,等.基于自适应混沌粒子群优化算法的多目标无功优化[J].电力系统保护与控制,2011,39(9):26-31.LI Juan,YANG Lin,LIU Jinlong,et al. Multi-objective Reactive Power Optimization Based on Adaptive Chaos Particle Swarm Optimization Algorithm[J]. Power System Protection and Control,2011,39(9):26-31.
[8]邱晓燕,沙熠,宁雪姣,等.计及智能电网多不确定性的多目标储能优化[J].高电压技术,2016,42(9):2715-2722.QIU Xiaoyan,SHA Yi,NING Xuejiao,et al. Multi-objective Optimization of Stored Energy Considering Multi-uncertainty in Smart grid[J]. High Voltage Engineering,2016,42(9):2715-2722.
[9]BORKOWSKA B. Probabilistic Load Flow[J]. IEEE Transactions on Power Apparatus&Systems,1974,93(3):752-759.
[10]于晗,钟志勇,黄杰波,等.采用拉丁超立方采样的电力系统概率潮流计算方法[J].电力系统自动化,2009,33(21):32-35.YU Han,ZHONG Zhiyong,HUANG Jiebo,et al. A Probabilistic Load Flow Calculation Method with Latin Hypercube Sampling[J]. Automation of Electric Power System,2009,33(21):32-35.
[11]陈雁,文劲宇,程时杰.考虑输入变量相关性的概率潮流计算方法[J].中国电机工程学报,2011,31(22):80-87.CHEN Yan,WEN Jinyu,CHENG Shijie. Probabilistic Load Flow Analysis Considering Dependencies among Input Random Variables[J]. Proceedings of the CSEE,2011,31(22):80-87.
[12]陈璨,吴文传,张伯明,等.考虑光伏出力相关性的配电网概率潮流[J].电力系统自动化,2015,39(9):41-47.CHEN Can,WU Wenchuan,ZHANG Boming,et al. Probabilistic Load Flow of Distribution Network Considering Correlated Photovoltaic Power Output[J]. Automation of Electric Power System,2015,39(9):41-47.
[13]李俊芳,张步涵.基于进化算法改进拉丁超立方抽样的概率潮流计算[J].中国电机工程学报,2011,31(25):90-96.LI Junfang,ZHANG Buhan. Probabilistic Load Flow Based on Improved Latin Hypercube Sampling with Evolutionary Algorithm[J]. Proceedings of the CSEE,2011,31(25):90-96.