基于改进协同量子粒子群算法的多微网负荷频率控制

doi:10.12204/j.issn.1000-7229.2023.07.010

电力建设 ›› 2023, Vol. 44 ›› Issue (7): 87-97.doi: 10.12204/j.issn.1000-7229.2023.07.010

基于改进协同量子粒子群算法的多微网负荷频率控制

方仍存¹(), 桑子夏¹(), 刘知行²(), 樊华²(), 王丹²(), 毛承雄²()

1.国网湖北省电力有限公司经济技术研究院,武汉市 430077
2.强电磁工程与新技术国家重点实验室(华中科技大学),武汉市 430074

收稿日期:2022-08-08 出版日期:2023-07-01 发布日期:2023-06-25
通讯作者: 刘知行(1997),男,硕士研究生,主要研究方向为负荷频率控制、微电网能量管理,E-mail:m202071489@hust.edu.cn
作者简介:方仍存(1980),男,博士,高级工程师,主要研究方向为配电网规划、能源经济,E-mail:57863010@qq.com;
桑子夏(1984),男,博士,高级工程师,主要研究方向为配电网规划、大功率电力电子设备,E-mail:spadegadget@hotmail.com;
樊华(1990),男,博士,主要研究方向为电力系统优化、微电网群联络线功率控制,E-mail:fanhua@hust.edu.cn;
王丹(1977),男,博士,教授,主要研究方向为电力系统运行与控制、大功率电力电子技术在电力系统中的应用,E-mail:wangdan@mail.hust.edu.cn;
毛承雄(1964),男,博士,教授,博导,主要研究方向为大型同步发电机励磁控制、大功率电力电子技术在电力系统中的应用、电力系统物理模拟及数模混合仿真接口系统,E-mail:cxmao@hust.edu.cn。
基金资助:
国网湖北省电力有限公司科技项目(52153820000H)

Load-Frequency Control of Multi-Microgrid Systems Based on Improved Cooperative Quantum-Behaved Particle Swarm Optimization

FANG Rengcun¹(), SANG Zixia¹(), LIU Zhixing²(), FAN Hua²(), WANG Dan²(), MAO Chengxiong²()

1. State Grid Hubei Economic Research Institute, Wuhan 430077,China
2. State Key Laboratory of Advance Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074,China

Received:2022-08-08 Online:2023-07-01 Published:2023-06-25

摘要/Abstract

摘要：

网络化通信是智能电网的重要组成部分,但其带来的通信时滞导致维持多微网系统频率稳定更加困难,因此有必要研究网络化时滞下多微网互联系统的负荷频率控制问题。针对该问题,文章提出基于改进协同量子粒子群算法的分数阶PID控制器参数优化方法,以提升网络化通信时滞下多微网互联系统控制器的控制性能。首先,建立了具备风储特性的多微网互联系统仿真模型,并在模型中考虑了网络化通信时滞。其次,借鉴协同与混沌的思想对量子粒子群算法进行改进,优化了算法在粒子群多样性及高维度适应性等方面的性能,使之适用于高维度空间寻优问题。最后,借助MATLAB仿真实验分别从不同时滞下的稳定极限、阶跃扰动下的动态性能、长时间随机扰动下的CPS指标三个方面验证了所提优化方法的可行性与有效性。

关键词: 负荷频率控制, 分数阶PID控制器, 量子粒子群算法, 联络线功率控制, 时滞鲁棒性

Abstract:

Networked communication is an important component of smart grids. However, the communication delay caused by networked communication further increases the difficulty of maintaining the frequency stability of multi-microgrid systems. Therefore, it is necessary to study the load-frequency control of multi-microgrid interconnected systems with networked delays. To address this problem, a parameter optimization method for a fractional-order proportional-integral-derivative controller based on an improved cooperative quantum-behaved particle swarm optimization algorithm was proposed to improve the performance of controllers under networked communication delays. First, a multi-microgrid interconnected system model with wind and storage characteristics was established. Second, the idea of coordination and chaos was used to improve the quantum-behaved particle swarm optimization to make it suitable for high-dimensional space optimization and optimize the performance of the algorithm in terms of particle swarm diversity and high-dimensional adaptability. Finally, the feasibility and effectiveness of the optimization method were verified from three aspects: the stability limit under different time delays, the dynamic performance under step disturbances, and the CPS index under random disturbances.

Key words: load-frequency control, fractional-order PID controller, quantum-behaved particle swarm optimization, tie-line power control, time-delay robustness

中图分类号:

TM732

方仍存, 桑子夏, 刘知行, 樊华, 王丹, 毛承雄. 基于改进协同量子粒子群算法的多微网负荷频率控制[J]. 电力建设, 2023, 44(7): 87-97.

FANG Rengcun, SANG Zixia, LIU Zhixing, FAN Hua, WANG Dan, MAO Chengxiong. Load-Frequency Control of Multi-Microgrid Systems Based on Improved Cooperative Quantum-Behaved Particle Swarm Optimization[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(7): 87-97.

图/表 20

图1 多微网互联系统示意图

Fig.1 Schematic diagram of multi-microgrid system

图2 微型燃气轮机的频率控制模型

Fig.2 Frequency control model of micro-turbine

图3 电池储能系统传递函数模型

Fig.3 Transfer function model of energy storage

图4 微电网LFC系统模型

Fig.4 LFC model of microgrid

图5 三微网LFC系统结构

Fig.5 LFC system structure of three microgrids

图6 控制器参数优化流程图

Fig.6 Controller parameter optimization flow chart

图7 优化结果迭代曲线

Fig.7 Iterative curve of optimal results

表1 0.3 s通信时滞下的优化结果

Table 1 Optimization effect under 0.3 s communication delay

图8 0.3 s通信时滞下系统的动态性能

Fig.8 Dynamic performance with 0.3 s communication delay

表2 0.3 s通信时滞下的时滞稳定极限

Table 2 Delay margin under 0.3 s communication delay

表3 1 s时滞下的优化结果

Table 3 Result of simulation under 1 s delay

图9 1 s时滞下的迭代曲线

Fig.9 Iterative curve under 1 s delay

图10 微电网频率及联络线功率偏差变化曲线

Fig.10 Variation curve of microgrid frequency and tie line power deviation

表4 动态性能指标

Table 4 Dynamic performance index

图11 不同时滞下ICQPSO-FOPID实验组Δf1变化曲线

Fig.11 Frequency deviation curve of ICQPSO-FOPID group Δf1 under different delay

表5 时滞稳定极限

Table 5 Delay margin

表6 CPS1指标

Table 6 CPS1 index

表A1 模型参数

Table A1 Parameters of the model

图A1 不同时滞下Δf1变化曲线

Fig.A Frequency deviation curve of Δf1under different delay

图A2 随机扰动

Fig.A2 Random disturbance

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基于改进协同量子粒子群算法的多微网负荷频率控制

Load-Frequency Control of Multi-Microgrid Systems Based on Improved Cooperative Quantum-Behaved Particle Swarm Optimization

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