基于模型预测控制含可再生分布式电源参与调控的配电网多时间尺度无功动态优化
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摘要
充分挖掘可再生分布式电源(RDG)动态无功电压调控能力,对改善配电系统内风电、光伏出力随机性和负荷波动导致的电压稳定性问题具有重要意义。建立了基于模型预测控制的配电网多时间尺度无功优化模型,包含日前优化调节层和实时滚动调控层。日前优化侧重于运行经济性,协调配合不同类型无功设备进行大尺度无功调节,并预留充足动态无功储备响应动态调控,降低运行风险;实时滚动调控侧重于系统运行可靠性,基于RDG、负荷超短期预测进行滚动调控,根据无功补偿需求及时决策反馈校正,逐层细化实现配电网无功电压"大幅调节"、"小幅调控"和"反馈调整",抑制不确定因素导致的电压越限。最后,通过IEEE 33节点算例进行仿真分析,验证了该文所提模型和算法可有效削弱预测误差影响,提升系统电压稳定性和电能质量。
Fully digging the dynamic reactive power and voltage regulation capability of renewable distributed generation(RDG) is of great significance for improving the voltage stability problem caused by randomness of RDG outputs and load fluctuations in the distribution system. A multi-time scale reactive power optimization model for distribution network based on model predictive control is established, which includes a day-ahead optimal regulation layer and a real-time rolling control layer. The day-ahead optimal regulation focuses on the economics of operation. Large-scale reactive power regulation is performed through the coordination and cooperation of various reactive power devices, and sufficient dynamic reactive power reserve is retained to response dynamic regulations and reduce operational risk. The real-time rolling control focuses on the stability of the system operation and is implemented based on the ultra-short-term forecast of RDG and load.According to the demand of reactive power compensation, correction of decision-making feedback is implemented timely. Then, "substantial adjustment", "small-scale regulation" and "feedback adjustment" of reactive voltage of distribution network is realized through layer-by-layer refinement to suppress voltage over-limit caused by uncertain factors. Finally, the simulation results of IEEE33-bus examples are analyzed. The results show that the model and algorithm proposed in this paper can effectively reduce the influence of prediction errors and improve the system voltage stability and power quality.
Fully digging the dynamic reactive power and voltage regulation capability of renewable distributed generation(RDG) is of great significance for improving the voltage stability problem caused by randomness of RDG outputs and load fluctuations in the distribution system. A multi-time scale reactive power optimization model for distribution network based on model predictive control is established, which includes a day-ahead optimal regulation layer and a real-time rolling control layer. The day-ahead optimal regulation focuses on the economics of operation. Large-scale reactive power regulation is performed through the coordination and cooperation of various reactive power devices, and sufficient dynamic reactive power reserve is retained to response dynamic regulations and reduce operational risk. The real-time rolling control focuses on the stability of the system operation and is implemented based on the ultra-short-term forecast of RDG and load.According to the demand of reactive power compensation, correction of decision-making feedback is implemented timely. Then, "substantial adjustment", "small-scale regulation" and "feedback adjustment" of reactive voltage of distribution network is realized through layer-by-layer refinement to suppress voltage over-limit caused by uncertain factors. Finally, the simulation results of IEEE33-bus examples are analyzed. The results show that the model and algorithm proposed in this paper can effectively reduce the influence of prediction errors and improve the system voltage stability and power quality.
引文
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[12]Zheng Yi,Li Shaoyuan,Tan Ruomu.Distributed model predictive control for on-connected microgrid power management[J].IEEE Transactions on Control Systems Technology,2018,26(3):1028-1039.
[13]Morstyn T,Hredzak B,Aguilera R P,et al.Model predictive control for distributed microgrid battery energy storage systems[J].IEEE Transactions on Control Systems Technology,2018,26(3):1107-1114.
[14]叶林,张慈杭,汤涌,等.多时空尺度协调的风电集群有功分层预测控制方法[J].中国电机工程学报,2018,38(16):1-15.Ye Lin,Zhang Cihang,Tang Yong,et al.Active power stratification predictive control approach for wind power cluster with multiple temporal and spatial scale coordination[J].Proceedings of the CSEE,2018,38(16):1-15.
[15]肖浩,裴玮,邓卫,等.分布式电源对配电网电压的影响分析及其优化控制策略[J].电工技术学报,2016,31(增刊1):203-213.Xiao Hao,Pei Wei,Deng Wei,et al.Analysis of the impact of distributed generation on distribution network voltage and its optimal control strategy[J].Transactions of China Electrotechnical Society,2016,31(S1):203-213.
[16]任佳依,顾伟,王勇,等.基于模型预测控制的主动配电网多时间尺度有功无功协调调度[J].中国电机工程学报,2018,38(5):1397-1407.Ren Jiayi,Gu Wei,Wang Yong,et al.Multi-time scale active and reactive power coordinated optimal dispatch in active distribution network based on model predictive control[J].Proceedings of the CSEE2018,38(5):1397-1407.
[17]Edrah M,Lo K L,Anaya L O.Reactive power control of DFIG wind turbines for power oscillation damping under a wide range of operating conditions[J].IETGeneration Transmission and Distribution,2016,10(15):3777-3785.
[18]王成山,宋关羽,李鹏,等.考虑分布式电源运行特性的有源配电网智能软开关SOP规划方法[J].中国电机工程学报,2017,37(7):1889-1897.Wang Chengshan,Song Guanyu,Li Peng,et al.Optimal configuration of soft open point for active distribution network considering the characteristics of distributed generation[J].Proceedings of the CSEE2017,37(7):1889-1897.
[19]张涛,张东方,王凌云,等.计及电动汽车充电模式的主动配电网多目标优化重构[J].电力系统保护与控制,2018,46(8):1-9.Zhang Tao,Zhang Dongfang,Wang Lingyun,et al.Multi-objective optimization of active distribution network reconfiguration considering electric vehicle charging mode[J].Power System Protection and Control,2018,46(8):1-9.
[20]刘方,杨秀,时珊珊,等.基于序列运算的微网经济优化调度[J].电工技术学报,2015,30(20):227-237.Liu Fang,Yang Xiu,Shi Shanshan,et al.Economic operation of micro-grid based on sequence operation[J].Transactions of China Electrotechnical Society,2015,30(20):227-237.
[21]冯凯,应展烽,陈汹,等.计及线路热惯性效应的模型预测控制安全经济调度模型[J].电工技术学报,2018,33(8):1875-1883.Feng Kai,Ying Zhanfeng,Chen Xiong,et al.Model predictive control security economic dispatch model considering transmission line thermal inertia effect[J]Transactions of China Electrotechnical Society,2018,33(8):1875-1883.
[22]张忠,王建学.采用模型预测控制的微电网有功无功联合实时调度方法[J].中国电机工程学报,2016,36(24):6743-6750.Zhang Zhong,Wang Jianxue.An active and reactive power joint real-time dispatch approach for microgrid using model predictive control[J].Proceedings of the CSEE,2016,36(24):6743-6750.
[23]郑翔,赵凤展,杨仁刚,等.基于大数据的低压配电网无功运行评价体系[J].电网技术,2017,41(1):272-278.Zheng Xiang,Zhao Fengzhan,Yang Rengang,et al An evaluation system to reactive power operation in LV distribution network based on big data[J].Power System Technology,2017,41(1):272-278.
[2]康重庆,姚良忠.高比例可再生能源电力系统的关键科学问题与理论研究框架[J].电力系统自动化,2017,41(9):2-11.Kang Chongqing,Yao Liangzhong.Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J].Automation of Electric Power Systems,2017,41(9):2-11.
[3]姚良忠,朱凌志,周明,等.高比例可再生能源电力系统的协同优化运行技术展望[J].电力系统自动化,2017,41(9):36-43.Yao Liangzhong,Zhu Lingzhi,Zhou Ming,et al.Prospects of coordination and optimization for power systems with high proportion of renewable energy[J].Automation of Electric Power Systems,2017,41(9):36-43.
[4]邢志斌,韦钢,贺静,等.基于图论的主动配电网双阶段电压控制[J].电工技术学报,2017,32(1):40-47.Xing Zhibin,Wei Gang,He Jing,et al.A dual-stage voltage control method in active distribution network based on graph theory[J].Transactions of China Electrotechnical Society,2017,32(1):40-47.
[5]范士雄,蒲天骄,刘广一,等.主动配电网中分布式发电系统接入技术及其进展[J].电工技术学报,2016,31(增刊2):92-101.Fan Shixiong,Pu Tianjiao,Liu Guangyi,et al.Technologies and its trends of grid integration of distributed generation in active distribution network[J].Transactions of China Electrotechnical Society,2016,31(S2):92-101.
[6]蔡宇,林今,宋永华,等.基于模型预测控制的主动配电网电压控制[J].电工技术学报,2015,30(23):42-49.Cai Yu,Lin Jin,Song Yonghua,et al.Voltage control strategy in active distribution network based on model predictive control[J].Transactions of China Electrotechnical Society,2015,30(23):42-49.
[7]曾博,杨煦,张建华.考虑可再生能源跨区域消纳的主动配电网多目标优化调度[J].电工技术学报,2016,31(22):148-158.Zeng Bo,Yang Xu,Zhang Jianhua.Multi-objective optimization for active distribution network scheduling considering renewable energy harvesting across regions[J].Transactions of China Electrotechnical Society,2016,31(22):148-158.
[8]张晓朝,段建东,石祥宇,等.利用DFIG无功能力的分散式风电并网有功最大控制策略研究[J].中国电机工程学报,2017,37(7):2001-2009.Zhang Xiaochao,Duan Jiandong,Shi Xiangyu,et al.Research of maximum control strategy with dispersed wind power considering reactive capability of DFIG[J].Proceedings of the CSEE,2017,37(7):2001-2009.
[9]李振坤,鲍新雨,邵宇鹰,等.考虑多种调压措施的分布式光伏消纳能力研究[J].电力系统保护与控制,2018,46(8):10-16.Li Zhenkun,Bao Xinyu,Shao Yuying,et al.Studying accommodation ability of distributed photovoltaic considering various voltage regulation measures[J].Power System Protection and Control,2018,46(8):10-16.
[10]严干贵,王茂春,穆钢,等.双馈异步风力发电机组联网运行建模及其无功静态调节能力研究[J].电工技术学报,2008,23(7):98-104.Yan Gangui,Wang Maochun,Mu Gang,et al.Modeling of grid-connected doubly-fed induction generator for reactive power static regulation capacity study[J].Transactions of China Electrotechnical Society,2008,23(7):98-104.
[11]韩平平,张佳琪,张晓安.电力市场下含双馈电机风电场的电力系统无功优化[J].电网技术,2017,41(1):171-177.Han Pingping,Zhang Jiaqi,Zhang Xiaoan.Reactive power optimization for power system containing DFIG wind farm in electricity market environment[J].Power System Technology,2017,41(1):171-177.
[12]Zheng Yi,Li Shaoyuan,Tan Ruomu.Distributed model predictive control for on-connected microgrid power management[J].IEEE Transactions on Control Systems Technology,2018,26(3):1028-1039.
[13]Morstyn T,Hredzak B,Aguilera R P,et al.Model predictive control for distributed microgrid battery energy storage systems[J].IEEE Transactions on Control Systems Technology,2018,26(3):1107-1114.
[14]叶林,张慈杭,汤涌,等.多时空尺度协调的风电集群有功分层预测控制方法[J].中国电机工程学报,2018,38(16):1-15.Ye Lin,Zhang Cihang,Tang Yong,et al.Active power stratification predictive control approach for wind power cluster with multiple temporal and spatial scale coordination[J].Proceedings of the CSEE,2018,38(16):1-15.
[15]肖浩,裴玮,邓卫,等.分布式电源对配电网电压的影响分析及其优化控制策略[J].电工技术学报,2016,31(增刊1):203-213.Xiao Hao,Pei Wei,Deng Wei,et al.Analysis of the impact of distributed generation on distribution network voltage and its optimal control strategy[J].Transactions of China Electrotechnical Society,2016,31(S1):203-213.
[16]任佳依,顾伟,王勇,等.基于模型预测控制的主动配电网多时间尺度有功无功协调调度[J].中国电机工程学报,2018,38(5):1397-1407.Ren Jiayi,Gu Wei,Wang Yong,et al.Multi-time scale active and reactive power coordinated optimal dispatch in active distribution network based on model predictive control[J].Proceedings of the CSEE2018,38(5):1397-1407.
[17]Edrah M,Lo K L,Anaya L O.Reactive power control of DFIG wind turbines for power oscillation damping under a wide range of operating conditions[J].IETGeneration Transmission and Distribution,2016,10(15):3777-3785.
[18]王成山,宋关羽,李鹏,等.考虑分布式电源运行特性的有源配电网智能软开关SOP规划方法[J].中国电机工程学报,2017,37(7):1889-1897.Wang Chengshan,Song Guanyu,Li Peng,et al.Optimal configuration of soft open point for active distribution network considering the characteristics of distributed generation[J].Proceedings of the CSEE2017,37(7):1889-1897.
[19]张涛,张东方,王凌云,等.计及电动汽车充电模式的主动配电网多目标优化重构[J].电力系统保护与控制,2018,46(8):1-9.Zhang Tao,Zhang Dongfang,Wang Lingyun,et al.Multi-objective optimization of active distribution network reconfiguration considering electric vehicle charging mode[J].Power System Protection and Control,2018,46(8):1-9.
[20]刘方,杨秀,时珊珊,等.基于序列运算的微网经济优化调度[J].电工技术学报,2015,30(20):227-237.Liu Fang,Yang Xiu,Shi Shanshan,et al.Economic operation of micro-grid based on sequence operation[J].Transactions of China Electrotechnical Society,2015,30(20):227-237.
[21]冯凯,应展烽,陈汹,等.计及线路热惯性效应的模型预测控制安全经济调度模型[J].电工技术学报,2018,33(8):1875-1883.Feng Kai,Ying Zhanfeng,Chen Xiong,et al.Model predictive control security economic dispatch model considering transmission line thermal inertia effect[J]Transactions of China Electrotechnical Society,2018,33(8):1875-1883.
[22]张忠,王建学.采用模型预测控制的微电网有功无功联合实时调度方法[J].中国电机工程学报,2016,36(24):6743-6750.Zhang Zhong,Wang Jianxue.An active and reactive power joint real-time dispatch approach for microgrid using model predictive control[J].Proceedings of the CSEE,2016,36(24):6743-6750.
[23]郑翔,赵凤展,杨仁刚,等.基于大数据的低压配电网无功运行评价体系[J].电网技术,2017,41(1):272-278.Zheng Xiang,Zhao Fengzhan,Yang Rengang,et al An evaluation system to reactive power operation in LV distribution network based on big data[J].Power System Technology,2017,41(1):272-278.