全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于分布式多步回溯Q(λ)学习的复杂电网最优潮流算法

, PP. 185-192

Keywords: 最优潮流,Q(λ,),学习,多目标优化,分布式强化学习

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对传统最优潮流算法对复杂多目标函数的不适应性以及常规算法难以满足大规模电网计算实时性的要求,本文中提出一种新颖的基于复杂电网分区的最优潮流分布式Q(λ)学习算法,该算法无须对最优潮流数学模型进行辅助处理,不依赖于对象模型,其内部各Agent使用标准的多步Q(λ)算法独立承担各分区子系统的学习任务,通过统一协作从而形成整体意义上的最优,并在IEEE118节点等标准算例中进行了验证,取得了良好的效果,为解决复杂电网多目标最优潮流问题提供了一种新的可行、有效的方法。

References

[1]  赵晋泉, 侯志俭, 吴际舜. 改进最优潮流牛顿算法有效性的对策研究[J]. 中国电机工程学报, 1999, 19(12): 70-75.
[2]  Zhong Yu, Gu Guochang, Zhang Rubo. Survey of distributed reinforcement learning algorithms in multi-agent systems[J]. Control Theory & Applications, 2003, 20(3): 317-322.
[3]  Xu Chuanpu, Yang Libing, Liu Fubin. Discuss on the Union implementation scheme of energy conservation measures and electricity marketability methods[J]. Automatic of Electric Power Systems, 2007, 31(23): 99-103.
[4]  Vlachogiannis J G, Hatziagyriou N D. Reinforcement learning for reactive power control[J]. IEEE Transactions on Power Systems, 2004, 19(3): 1317-1325.
[5]  邱晓燕, 张子健, 李兴源. 基于改进遗传内点法的电网多目标无功优化[J]. 电网技术, 2009, 33(13): 27-31.
[6]  Zhong Yu, Gu Guochang, Zhang Rubo. Research on the architectures of distributed reinforcement learning systems[J]. Computer Engineering and Applications, 2003, 39(11): 111-113.
[7]  Jing Peng, Williams R J. Incremental multi-step Q-learning[J]. Machine Leaning, 1996(22): 283-290.
[8]  Richard S Sutton, Andrew G Barto. Reinforcement learning: an introduction[M]. Cambridge: MIT Press, 1998.
[9]  Yu Tao, Zhou Bin, Zhen Weiguo. Application and development of reinforcement learning theory in power systems[J]. Power System Protection and Control, 2009, 37(14): 122-128.
[10]  Kim B H, Baldick R. Coarse-grained distributed optimal power flow[J]. IEEE Transactions on Power Systems, 1997, 12(2): 932-939.
[11]  David I Sun, Bruce Ashley, Brian Brewer , et al. Optimal power flow by newton approach[J]. IEEE Transactions on Power Apparatus and Systems, 1984, 103(10): 2864-2880.
[12]  韦化, 李滨, 杭乃善, 等. 大规模水-火电力系统最优潮流的现代内点算法实现[J]. 中国电机工程学报, 2003, 23(6): 13-18.
[13]  Wei Hua, Li Bin, Hang Naishan, et al. An implementation of interior point algorithm for large-scale hydro-thermal optimal power flow problems[J]. Proceedings of the CSEE, 2003, 23(6): 13-18.
[14]  Zhao Jinquan, Hou Zhijian, Wu Jishun. Some new strategies for improving the effectiveness of newton optimal power flow algorithm[J]. Proceedings of the CSEE, 1999, 19(12): 70-75.
[15]  周明, 孙树栋. 遗传算法原理及应用[M]. 北京: 国防工业出版社, 1999.
[16]  Luonan Chen, Hideki Suzuki, Kazuo Katou. Mean field theory for optimal power flow[J]. IEEE Transactions on Power Systems, 1997, 12(4): 1481-1486.
[17]  李晓梅, 莫则尧. 可扩展并行算法的设计与分析[M]. 北京: 国防工业出版社, 2000.
[18]  潘哲龙, 张伯明, 孙宏斌, 等. 分布计算的遗传算法中无功优化中的应用[J]. 电力系统自动化, 2001, 6(13): 37-41.
[19]  Pan Zhelong, Zhang Boming, Sun Hongbin et al. A distributid genetic algorithm for reactive power optimization[J]. Automaticon of Electric Power Systems, 2001, 6(13): 37-41.
[20]  Batut J, Renaud A. Daily generation scheduling optimization with transmission constraints[J]. IEEE Transactions on Power Systems, 2000, 7(3): 982-989.
[21]  程新功, 厉吉文, 曹立霞, 等. 电力系统最优潮流的分布式并行算法[J]. 电力系统自动化, 2003, 27(24): 23-27.
[22]  Cheng Xingong, Li Jiwen, Cao Lixia, et al. Distribution and parallel optimal power flow solution of electric power systems[J]. Automation of Electric Power Systems, 2003, 27(24): 23-27.
[23]  李强. 分布式优化算法的算法研究[D]. 北京: 华北电力大学, 2006.
[24]  仲宇, 顾国昌, 张汝波. 多智能体系统中的分布式强化学习研究现状[J]. 控制理论与应用, 2003, 20(3): 317-322.
[25]  胥传普, 杨立兵, 刘福斌. 关于节能降耗与电力市场联合实施方案的探讨[J]. 电力系统自动化, 2007, 31(23): 99-103.
[26]  Qiu Xiaoyan, Zhang Zijian, Li Xinyuan. Multi- objective reactive power optimization based on improved genetic-interior point algorithm[J]. Power System Technology, 2009, 33(13): 27-31.
[27]  仲宇, 顾国昌, 张汝波. 分布式强化学习的体系结构研究[J]. 计算机工程与应用, 2003, 39(11): 111-113.
[28]  Watkins J C H, Dayan Peter. Q-learning[J]. Machine Leaning, 1992(8): 279-292.
[29]  张汝波. 强化学习理论及应用[M]. 哈尔滨: 哈尔滨工程大学出版社, 2001.
[30]  余涛, 周斌, 甄卫国. 强化学习理论在电力系统中的应用及展望[J]. 电力系统保护与控制, 2009, 37(14): 122-128.
[31]  刘明波, 谢敏, 赵维兴. 大电网最优潮流计算[M]. 北京: 科学出版社. 2010.
[32]  Deb K, Pratap A, Agarwal S. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[33]  H L Liao, Q H Wu, L Jiang. Multi-objective optimization by reinforcement learning for power system dispatch and voltage stability[C]. Proceedings of IEEE PES Conference on Innovative Smart Grid Technologies Europe, Gothenburg, Sweden, 2010: 1-8.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133