高比例可再生能源的多区域电力系统负荷频率自抗扰控制

doi:10.3969/j.issn.2097-0706.2022.10.005

摘要/Abstract

摘要：

由于可再生能源的波动性和随机性，多区域可再生能源电力系统的负荷频率变化复杂且难以控制。为了改善电力系统的稳定性，针对多区域可再生能源电力系统的负荷频率控制问题提出了一种基于多目标遗传算法的串级自抗扰控制方法。该方法通过设计线性自抗扰控制并引入其串级结构来提高系统的抗干扰能力，借助多目标遗传算法对设计的控制器参数进行优化以减小频率和联络线功率偏差，达到更好的控制效果。此外，进行不同控制策略下电力系统的仿真和对比，结果表明所设计的控制系统具有很好的动态性能，加入负荷扰动后能够快速消除干扰并维持系统稳定。仿真结果验证了所设计控制器的有效性。

关键词: 可再生能源, 多区域电力系统, 负荷频率控制, 自抗扰控制, 多目标遗传算法, 新型电力系统, 碳中和

Abstract:

Due to the fluctuation and randomness of renewable energy，the load frequency of a multi-area renewable energy power system is varied， complex and difficult to be controlled. In order to improve the stability of power systems，a cascade active disturbance rejection control method based on multi-objective genetic algorithm for the load frequency control of multi-area renewable energy power systems is proposed. The method can boost the anti-disturbance performance of the system by introducing cascade structure to linear active disturbance rejection control， and reduce the deviation of the frequency and tie-line power by taking multi-objective genetic algorithm to optimize the parameters of the designed controller. In addition，the simulation results of the power system under different control strategies are compared. The results show that the designed control system has good dynamic performance，and can quickly eliminate the disturbance and maintain the stability of the system. Simulation results verify the effectiveness of the designed controller.

Key words: renewable energy, multi-area power system, load frequency control, active disturbance rejection control, multi-objective genetic algorithm, new power system, carbon neutrality

中图分类号:

TK01:TP273

李朋真, 刘艳红, 吴振龙. 高比例可再生能源的多区域电力系统负荷频率自抗扰控制[J]. 综合智慧能源, 2022, 44(10): 33-41.

LI Pengzhen, LIU Yanhong, WU Zhenlong. Active disturbance rejection control on load frequency of multi-area power systems with high-proportion renewable energy[J]. Integrated Intelligent Energy, 2022, 44(10): 33-41.

图/表 18

图1

图2

图3

图4

图5

表1

优化的串级ADRC参数

系统	位置	$ω c$	$b$	$ω o$
光伏系统	外环	4.776 4	-96.556 1	0.439 0
光伏系统	内环	0.540 0	16.000 0	33.800 0
火电系统	外环	7.079 7	4.000 0	1.800 0
火电系统	内环	2.000 0	0.600 0	100.000 0
风电系统	外环	0.448 9	-2.565 3	6.478 8
风电系统	内环	3.964 0	4.573 0	0.634 5

表1

图6

图7

图8

表2

不考虑风电系统的IAE指标

控制策略	场景	$I A E f 1$	$I A E f 2$	$I A E P t i e$	$I A E t o t a l$
COA:PDn-PI	1	0.018 0	0.022 1	0.006 4	0.046 5
COA:PDn-PI	2	0.063 5	0.231 3	0.108 9	0.403 7
Cascaded ADRC	1	0.013 7	0.013 1	0.009 7	0.036 5
Cascaded ADRC	2	0.007 2	0.132 3	0.056 9	0.196 4
MOGA:CADRC	1	0.010 5	0.010 7	0.008 2	0.029 4
MOGA:CADRC	2	0.007 3	0.141 8	0.039 2	0.188 3

表2

图9

图10

图11

表3

考虑风电系统的IAE指标

控制策略	场景	$I A E f 1$	$I A E f 2$	$I A E P t i e$	$I A E t o t a l$
COA:PDn-PI	1	0.011 2	0.010 4	0.001 7	0.023 3
COA:PDn-PI	2	0.151 2	0.158 7	0.037 2	0.347 1
Cascaded ADRC	1	0.013 6	0.012 6	0.009 8	0.036 0
Cascaded ADRC	2	0.007 2	0.132 3	0.056 9	0.196 4
MOGA:CADRC	1	0.010 5	0.010 9	0.008 3	0.029 7
MOGA:CADRC	2	0.007 3	0.141 8	0.039 2	0.188 3

表3

图12

图13

图14

图15

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