基于分布式模型预测控制的含分布式储能有源配电网动态电压控制

doi:10.12204/j.issn.1000-7229.2021.06.012

电力建设 ›› 2021, Vol. 42 ›› Issue (6): 116-126.doi: 10.12204/j.issn.1000-7229.2021.06.012

基于分布式模型预测控制的含分布式储能有源配电网动态电压控制

李桂鑫¹, 徐科¹, 刘英英¹, 吕永青², 窦晓波², 迟福建¹

1.国网天津市电力公司,天津市 300010
2.东南大学电气工程学院,南京市 210096

收稿日期:2020-09-09 出版日期:2021-06-01 发布日期:2021-05-28
作者简介:李桂鑫(1981),男,博士,高级工程师,主要从事配电网规划、新能源及储能装置、综合能源等方面的研究工作;|徐科(1979),男,博士,高级工程师,主要研究方向为风力发电和变电站自动化系统;|刘英英(1983),女,硕士,高级工程师,主要研究方向为配电网规划设计;|吕永青(1994),男,博士研究生,主要研究方向为储能系统运行控制;|窦晓波(1979),男,博士,教授,主要研究方向为分布式电源(储能)变流器优化控制、分布式电源高渗透配电网、微电网运行控制与能量优化、智能变电站与电力通信;|迟福建(1979),男,硕士,高级工程师,研究方向为配电网新技术、配电网规划管理。
基金资助:
国网天津市电力公司科技项目“电力物联网体系下的城市配电网多层级混合储能灵活优化配置技术研究”(KJ20-1-08)

Dynamic Voltage Control Based on DMPC for Active Distribution Network with Distributed Energy Storage Systems

LI Guixin¹, XU Ke¹, LIU Yingying¹, Lü Yongqing², DOU Xiaobo², CHI Fujian¹

1. State Grid Tianjin Electric Power Company, Tianjin 300010, China
2. School of Electrical Engineering, Southeast University, Nanjing 210096, China

Received:2020-09-09 Online:2021-06-01 Published:2021-05-28
Supported by:
State Grid Tianjin Electric Power Company Research Program(KJ20-1-08)

摘要/Abstract

摘要：

针对大规模分布式电源(distributed generator, DG)接入配电网造成的电压越限问题,提出一种基于分布式模型预测控制(distributed model predictive control, DMPC)的含分布式储能有源配电网动态电压控制方法,协调配电网中大量分布式光伏(Photovoltaic, PV)、储能(energy storage, ES)等可调资源,实现电压越限的快速动态恢复。首先,通过分析分布式光伏、储能等可控资源不同的动态过程以及配电网的电压灵敏度模型,建立计及分布式电源动态过程的配电网整体控制模型;考虑到配电系统整体规模大、控制设备多,为避免对通信可靠性要求过高,通过ε分解法将整体配电网模型分解为若干子网络,降低系统的耦合关系。其次,考虑各分布式光伏、储能的有功和无功功率出力约束以及容量限制,进行含约束的分布式模型预测控制设计,并转化为二次约束二次规划(quadratically constrained quadratic program, QCQP)问题优化求解控制指令,实现配电网电压越限的快速动态恢复。最后,通过基于IEEE-33节点配电网的仿真验证了所提方法的有效性。

关键词: 分布式模型预测控制, 分布式电源, 有源配电网, 动态电压控制

Abstract:

In order to solve the voltage violation problem in a distribution network that integrates multiple distributed generators, this paper proposes a method based on distributed model predictive control for coordination of PVs and energy storage systems and for dynamic voltage regulation in a distribution network containing distributed energy storage systems. Firstly, we analysis dynamic characteristics of distributed PVs, energy storage systems and voltage sensitivity model of the distribution network, and construct the whole distribution network model considering dynamic characteristics. Considering large scale and numerous devices of distribution network, we divide it into several subsystems through ε decomposition. Then, this paper designs distributed model predictive controller containing various limits of PVs and energy storage systems and transfers this into solving a QCQP problem to optimize control instructions, and then restoring from voltage violation fast in distribution network. Finally, the simulation result in IEEE 33-node network verifies the validity of proposed method.

Key words: distributed model predictive control, distributed generator, active distribution network, dynamic voltage control

中图分类号:

TM73

李桂鑫, 徐科, 刘英英, 吕永青, 窦晓波, 迟福建. 基于分布式模型预测控制的含分布式储能有源配电网动态电压控制[J]. 电力建设, 2021, 42(6): 116-126.

LI Guixin, XU Ke, LIU Yingying, Lü Yongqing, DOU Xiaobo, CHI Fujian. Dynamic Voltage Control Based on DMPC for Active Distribution Network with Distributed Energy Storage Systems[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(6): 116-126.

图/表 29

图1 分布式光伏动态模型

Fig.1 Dynamic model of distributed PV。。

图2 光伏Q(V)调节特性

Fig.2 Q-V characteristic of PV

图3 分布式储能动态模型

Fig.3 Dynamic model of distributed ES

图4 分布式电源影响区域示意图

Fig.4 Influence zone of distributed generators

图5 模型预测控制原理图

Fig.5 Schematic diagram of MPC

图6 基于IEEE-33节点有源配电网仿真试验拓扑

Fig.6 Active distribution simulation test topology based on IEEE 33-node system

图7 场景1下各分布式光伏有功功率出力波形

Fig.7 Active power output of PVs in case 1

图8 场景1下IEEE-33节点拓扑内部分节点电压

Fig.8 Voltage of some nodes in IEEE 33-node system in case 1

图9 场景1下施加控制后IEEE-33节点拓扑内部分节点电压

Fig.9 Voltages of some nodes in IEEE 33-node system after control in case 1

图10 场景1下分布式储能有功功率出力波形

Fig.10 Waveform of active power of energy storages in case 1

图11 场景1下分布式储能无功功率出力波形

Fig.11 Waveform of reactive power of energy storages in case 1

图12 场景1下分布式光伏无功功率出力波形

Fig.12 Waveform of reactive power of PVs in case 1

图13 场景2下各分布式光伏有功功率出力波形

Fig.13 Active power output of PVs in case 2

图14 场景2下IEEE-33节点拓扑内部分节点电压

Fig.14 Voltages of some nodes in IEEE 33-node system in case 2

图15 场景2下施加控制后IEEE-33节点拓扑内部分节点电压

Fig.15 Voltage of some nodes in IEEE 33-node system after control in case 2

图16 场景2下分布式储能有功功率出力波形

Fig.16 Waveform of active power of energy storage in case 2

图17 场景2下分布式储能无功功率出力波形

Fig.17 Waveform of reactive power of energy storage in case 2

图18 场景2下分布式光伏无功功率出力波形

Fig.18 Waveform of reactive power of PVs in case 2

表A1 IEEE-33节点配电网负荷参数

Table A1 Load parameter of IEEE 33-node distribution network

表A2 IEEE-33节点配电网线路参数

Table A2 Line parameters of IEEE 33-node distribution network

表A3 各分布式电源配置

Table A3 Configuration of DGs

图B1 场景1下区域协同交互功率波形

Fig.B1 Waveform of interactive powers between coordinating areas in case 1

图B2 场景1下区域1设备动态过程控制量变化波形

Fig.B2 Waveform of control sequences of devices during dynamic process in area 1 in case1

图B3 正向潮流下区域2设备动态过程控制量变化波形

Fig.B3 Waveform of control sequences of devices during dynamic process in area 2 in case 1

图B4 场景1下区域3设备动态过程控制量变化波形

Fig.B4 Waveform of control sequences of devices during dynamic process in area 3 in case 1

图B5 场景2下区域协同功率波形

Fig.B5 Waveform of powers between coordinating areas in case 2

图B6 场景2下区域1设备动态过程控制量变化波形

Fig.B6 Waveform of control sequences of devices during dynamic process in area 1 in case 2

图B7 场景2下区域2设备动态过程控制量变化波形

Fig.B7 Waveform of control sequences of devices during dynamic process in area 2 in case 2

图B8 场景2下区域3设备动态过程控制量变化波形

Fig.B8 Waveform of control sequences of devices during dynamic process in area 3 in case 2

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基于分布式模型预测控制的含分布式储能有源配电网动态电压控制

Dynamic Voltage Control Based on DMPC for Active Distribution Network with Distributed Energy Storage Systems

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