Coordinated AGC Algorithm for Distributed Multi-region Multi-energy Micro-network Group

被引：0

作者：

Xi L. ^{[1
]}

Zhou L.-P. ^{[1
]}

机构：

[1] College of Electrical Engineering and New Energy, China Three Gorges University, Yichang

来源：

Xi, Lei (xilei2014@163.com) | 1818年 / Science Press卷 / 46期

基金：

中国国家自然科学基金;

关键词：

Automatic generation control (AGC); Comprehensive energy; Double Q-learning; Multi-energy microgrid; Reinforcement learning;

D O I：

10.16383/j.aas.c200105

中图分类号：

学科分类号：

摘要：

Comprehensive energy multi-region coordination is the development trend of the power grid, and the core question is what method to use for multi-region coordination. Based on the integration of the qualification trace and dual Q-learning in Q (σ), this paper proposes a multi-agent collaborative control algorithm for multi-region and multi-energy micro-grid group, named DQ (σ, λ), to avoid high exploration value of traditional reinforcement learning actions. At the same time of evaluation, the distributed multi-region collaboration is obtained. Simulations of the improved IEEE two area load frequency control model and the three area multi-energy microgrid group automatic generation control (AGC) model show that the proposed algorithm has fast convergence and better dynamic performance than traditional methods, and can achieve distributed Synergy of regional multi-energy microgrid groups. Copyright © 2020 Acta Automatica Sinica. All rights reserved.

引用

页码：1818 / 1830

页数：12

共 33 条

[1] Meng L X, Savaghebi M, Andrad F, Vasquez J C, Guerrero J M, Graells M., Microgrid central controller development and hierarchical control implementation in the intelligent microgrid lab of Aalborg University, Proceedings of the 2015 IEEE Applied Power Electronics Conference and Exposition (APEC), pp. 2585-2592, (2015)
[2] Brijesh P, Jiju K, Dhanesh P R, Joseph A., Microgrid for sustainable development of remote villages, Proceedings of the 2019 IEEE Region 10 Conference, pp. 2433-2438, (2019)
[3] Wang J, Cisse B M, Brown D, Crabb A., Development of a microgrid control system for a solar-plus-battery microgrid to support a critical facility, Proceedings of the 2017 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1-5, (2017)
[4] Suyanto H, Irawati R., Study trends and challenges of the development of microgrids, Proceedings of the 6th IEEE International Conference on Advanced Logistics and Transport (ICALT), pp. 160-164, (2017)
[5] Behera A, Panigrahi T K, Ray P K, Sahoo A K., A novel cascaded PID controller for automatic generation control analysis with renewable sources, IEEE/CAA Journal of Automatica Sinica, 6, 6, pp. 1438-1451, (2019)
[6] Jagatheesan K, Anand B, Samanta S, Dey N, Ashour A S, Balas V E., Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm, IEEE/CAA Journal of Automatica Sinica, 6, 2, pp. 503-515, (2019)
[7] Zhao Xi- Lin, Lin Zhen-Yu, Fu Bo, He Li, Xu Guang-Hui, Application of predictive optimization PID method to AGC of power system with windy power, Journal of Power System and Automation, 31, pp. 16-22, (2019)
[8] Xie Ping-Ping, Li Yin-Hong, Liu Xiao-Juan, Shi Dong-Yuan, Duan Xian-Zhong, Optimal PI/PID controller design of AGC based on social learning adaptive bacteria foraging algorithm for interconnected power grids, Proceedings of the Chinese Society of Electrical Engineering, 36, 20, pp. 5440-5448, (2016)
[9] Arya Y., A novel CFFOPI-FOPID controller for AGC performance enhancement of single and multi-area electric power systems, ISA Transactions, 100, pp. 126-135, (2020)
[10] Xi L, Yu L, Xu Y C, Wang S X, Chen X., A novel multi-agent DDQN-AD method-based distributed strategy for automatic generation control of integrated energy systems, IEEE Transactions on Sustainable Energy, (2019)

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