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基于强化学习的多微电网分布式二次优化控制
作者:
作者单位:

1.南京理工大学自动化学院,江苏省南京市 210094;2.国网江苏省电力有限公司电力科学研究院,江苏省南京市 211103;3.国网浙江省电力有限公司嘉兴供电公司,浙江省嘉兴市 314000

摘要:

针对微电网中分布式电源下垂一次控制产生的系统频率和电压静态偏差问题,提出了一种基于强化学习就地反馈方法的分布式二次优化控制,利用本地信息可兼顾频率恢复和电压调整的需求。首先,针对微电网经济性、频率及电压控制需求和各分布式电源综合性能(环境效益、经济效益、技术效益),定义了本地奖励,协调多微电网的频率恢复和电压调节。其次,针对电网实际运行情况,在满足供需平衡的同时,使用多智能体强化学习算法对全局奖励反馈优化修正,使各分布式电源协同出力渐近消除频率偏差,保证微电网的稳定运行。最后,通过实例验证了所提出控制的有效性和适应性。

关键词:

基金项目:

国家自然科学基金资助项目(51607036);江苏省自然科学基金资助项目(BK20160674)。

通信作者:

作者简介:

沈珺(1995—),女,硕士研究生,主要研究方向:强化学习、电力系统分布式控制。E-mail: 893266233@qq.com
柳伟(1985—),男,通信作者,博士,副教授,硕士生导师,主要研究方向:微电网/主动配电网分布式控制及优化、配电网电压控制、智能配电网实时仿真、人工智能在电力系统中的应用。E-mail: wliu@njust.edu.cn
李虎成(1987—),男,硕士,工程师,主要研究方向:电网调度自动化、电网运行与控制技术。E-mail: lihucheng68@163.com


Reinforcement Learning Based Distributed Secondary Optimal Control for Multiple Microgrids
Author:
Affiliation:

1.School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;2.Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China;3.Jiaxing Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Jiaxing 314000, China

Abstract:

Aiming at the static deviation problems of system frequency and voltage caused by primary control of distributed generators in microgrid, a distributed secondary optimal control based on reinforcement learning local feedback method is proposed, which addresses the need of frequency recovery and voltage adjustment by using the local information. Firstly, according to the demand of microgrid economy, frequency and voltage control and the comprehensive performance of distributed generators (environmental benefit, economic benefit and technical benefit), a local reward is defined to coordinate the frequency recovery and voltage regulation of multiple microgrids. Secondly, according to the actual operation of the power grid, the multi-agent reinforcement learning algorithm is used to optimize and modify the global reward feedback, while satisfying the balance between supply and demand, so that the frequency deviation can be eliminated asymptotically and the stable operation of the microgrid can be guaranteed. Finally, the effectiveness and adaptability of the proposed control are verified by the analysis of case study.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. 51607036) and Jiangsu Provincial Natural Science Foundation of China (No. BK20160674).
引用本文
[1]沈珺,柳伟,李虎成,等.基于强化学习的多微电网分布式二次优化控制[J].电力系统自动化,2020,44(5):198-206. DOI:10.7500/AEPS20190521007.
SHEN Jun, LIU Wei, LI Hucheng, et al. Reinforcement Learning Based Distributed Secondary Optimal Control for Multiple Microgrids[J]. Automation of Electric Power Systems, 2020, 44(5):198-206. DOI:10.7500/AEPS20190521007.
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  • 收稿日期:2019-05-21
  • 最后修改日期:2019-09-18
  • 录用日期:
  • 在线发布日期: 2020-03-08
  • 出版日期:
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