Reactive Power Optimization of Power System with Electric Energy Router Applying Modified Teaching-Learning Algorithm

doi:10.12204/j.issn.1000-7229.2022.06.012

Abstract

Abstract:

In this paper,an improved teaching and learning algorithm based on roulette wheel selection and adaptive Corsi variation strategy is proposed to solve the convergence and diversity problems of the traditional intelligent algorithm and it is applied to the reactive power optimization of power system with electric energy router (EER). The algorithm introduces roulette selection method in the learning phase to improve the learning efficiency of the group, and adopts adaptive Cauchy mutation strategy after the teaching to enhance the diversity of the class population, which avoids falling into local optimal solutions in the iterative process. Then, a reactive power optimization model is established with the objective function of minimizing active network loss and voltage deviation. The modified IEEE RTS-79 standard test system is used as an example for simulation analysis. The results show that the improved algorithm has both convergence and diversity, which has better optimization effect than the traditional algorithm.

Key words: electric energy router (EER), teaching-learning algorithm, roulette wheel selection, adaptive Cauchy mutation, reactive power optimization

CLC Number:

TM711

CONG Fanping, ZHOU Jianping, MAO Dajun, QI Guoqing, HUANG Zufan. Reactive Power Optimization of Power System with Electric Energy Router Applying Modified Teaching-Learning Algorithm[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(6): 110-118.

Figures/Tables 14

Fig.1 General topology structure of the EER

Fig.2 Power electronic structure of the EER ports

Fig.3 Schematic diagram of roulette wheel selection

Fig.4 Process of population’s evolution

Fig.5 Modified IEEE RTS-79 test system

Fig.6 Output data of the generators

Fig.7 Port parameter settings of EER

Fig.8 Comparison of the operating status of the original system and the EER system

Table 1 Calculation results of EER’s port parameter in different scenarios

Fig.9 Reactive power compensation capacity of capacitor banks

Table 2 Statistical results of reactive power optimization

Fig.10 Active power loss of IEEE RTS-79 system

Fig.11 Voltage’s deviation of IEEE RTS-79 system

Fig.12 Node voltages of IEEE RTS-79 system

References 23

[1]	SHE X, WANG F, BURGOS R, et al. Solid state transformer interfaced wind energy system with integrated active power transfer, reactive power compensation and voltage conversion functions[C]// 2012 IEEE Energy Conversion Congress and Exposition. IEEE, 2012: 3140-3147.
[2]	SHI J J, GOU W, YUAN H, et al. Research on voltage and power balance control for cascaded modular solid-state transformer[J]. IEEE Transactions on Power Electronics, 2011, 26(4): 1154-1166.
[3]	SHE X, HUANG A. Solid state transformer in the future smart electrical system[C]// 2013 IEEE Power & Energy Society General Meeting. IEEE, 2013: 1-5.
[4]	殷展, 杜仁平, 姜黎明, 等. 基于电能路由器的交直流混合配电网潮流优化控制[J]. 哈尔滨理工大学学报, 2021, 26(4): 20-27.
	YIN Zhan, DU Renping, JIANG Liming, et al. Power flow optimizaiton control in AC/DC hybrid distribution network based on power router[J]. Journal of Harbin University of Science and Technology, 2021, 26(4): 20-27.
[5]	HUANG L, ZONG C X, YANG X, et al. Power flow calculation of distribution network with multiple energy routers[J]. IEEE Access, 2020, 9: 23489-23497.
[6]	LIU B, WU W H, ZHOU C X, et al. An AC-DC hybrid multi-port energy router with coordinated control and energy management strategies[J]. IEEE Access, 2019, 7: 109069-109082.
[7]	CHEN Y N, WANG P, ELASSER Y, et al. Multicell reconfigurable multi-input multi-output energy router architecture[J]. IEEE Transactions on Power Electronics, 2020, 35(12): 13210-13224.
[8]	LIU Y S, CHEN X, WU Y, et al. Enabling the smart and flexible management of energy prosumers via the energy router with parallel operation mode[J]. IEEE Access, 2020, 8: 35038-35047.
[9]	MIAO J Q, ZHANG N, KANG C Q. Generalized steady-state model for energy router with applications in power flow calculation[C]// 2016 IEEE Power and Energy Society General Meeting. IEEE, 2016: 1-5.
[10]	HUANG L, ZONG C X, YANG X, et al. Power flow calculation of distribution network with multiple energy routers[J]. IEEE Access, 2020, 9: 23489-23497.
[11]	郭靖, 李可军, 王景山, 等. 含能源路由器的交直流网络潮流计算模型及可行解求取[J]. 电力系统自动化, 2018, 42(13): 85-93.
	GUO Jing, LI Kejun, WANG Jingshan, et al. Power flow calculation model and feasible solution of AC-DC network with energy routers[J]. Automation of Electric Power Systems, 2018, 42(13): 85-93.
[12]	张晓英, 张艺, 王琨, 等. 基于改进NSGA-Ⅱ算法的含分布式电源配电网无功优化[J]. 电力系统保护与控制, 2020, 48(1): 55-64.
	ZHANG Xiaoying, ZHANG Yi, WANG Kun, et al. Reactive power optimization of distribution network with distributed generations based on improved NSGA-Ⅱ algorithm[J]. Power System Protection and Control, 2020, 48(1): 55-64.
[13]	陈国发, 张文庆, 刘锋, 等. 基于改进蜂群算法的含DG配网多目标无功优化[J]. 智慧电力, 2019, 47(3): 97-103.
	CHEN Guofa, ZHANG Wenqing, LIU Feng, et al. Multi-objective reactive power optimization of distribution network with DG based on improved bee colony algorithm[J]. Smart Power, 2019, 47(3): 97-103.
[14]	杨蕾, 吴琛, 黄伟, 等. 含高比例风光新能源电网的多目标无功优化算法[J]. 电力建设, 2020, 41(7): 100-109.
	YANG Lei, WU Chen, HUANG Wei, et al. Pareto-based multi-objective reactive power optimization for power grid with high-penetration wind and solar renewable energies[J]. Electric Power Construction, 2020, 41(7): 100-109.
[15]	QU X H, LIU B, LI Z Y, et al. A novel improved teaching-learning based optimization for functional optimization[C]// 2016 12th IEEE International Conference on Control and Automation.IEEE, 2016: 939-943.
[16]	ZHAI J C, QIN Y P, ZHAO Z. A self-reflection teaching-learning-based optimization algorithm and its application[C]// 2019 Chinese Control and Decision Conference (CCDC). IEEE, 2019: 1446-1451.
[17]	童楠, 符强, 钟才明. 基于自主学习行为的教与学优化算法[J]. 计算机应用, 2018, 38(2): 443-447.
	TONG Nan, FU Qiang, ZHONG Caiming. Improved teaching-learning-based optimization algorithm based on self-learning mechanism[J]. Journal of Computer Applications, 2018, 38(2): 443-447.
[18]	CHEN Y P, LI S Z, LIN X M. Fast hog feature computation based on CUDA[C]// 2011 IEEE International Conference on Computer Science and Automation Engineering. IEEE, 2011: 748-751.
[19]	HIRABAYASHI M, KATO S, EDAHIRO M, et al. GPU implementations of object detection using HOG features and deformable models[C]// 2013 IEEE 1st International Conference on Cyber-Physical Systems, Networks, and Applications. IEEE, 2013: 106-111.
[20]	MIAO J Q, ZHANG N, KANG C Q, et al. Steady-state power flow model of energy router embedded AC network and its application in optimizing power system operation[J]. IEEE Transactions on Smart Grid, 2018, 9(5): 4828-4837.
[21]	郑超, 周孝信, 李若梅, 等. VSC-HVDC稳态特性与潮流算法的研究[J]. 中国电机工程学报, 2005, 25(6): 1-5.
	ZHENG Chao, ZHOU Xiaoxin, LI Ruomei, et al. Study on the steady characteristic and algorithm of power flow for VSC-HVDC[J]. Proceedings of the CSEE, 2005, 25(6): 1-5.
[22]	李红伟, 蒋嘉焱, 刘青卓, 等. 基于改进教与学算法的配网多目标无功优化[J]. 控制工程, 2020, 27(5): 878-883.
	LI Hongwei, JIANG Jiayan, LIU Qingzhuo, et al. Multi-objective reactive power optimization of distribution network based on improved teachIng learning based optimization[J]. Control Engineering of China, 2020, 27(5): 878-883.
[23]	GRIGG C, WONG P, ALBRECHT P, et al. The IEEE reliability test system-1996: A report prepared by the reliability test system task force of the application of probability methods subcommittee[J]. IEEE Transactions on Power Systems, 1999, 14(3): 1010-1020.