留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

随机ADMM算法及其在电力系统凸经济调度问题中的应用

陈伟俊 罗洪林 彭建文

陈伟俊, 罗洪林, 彭建文. 随机ADMM算法及其在电力系统凸经济调度问题中的应用[J]. 应用数学和力学, 2021, 42(9): 979-988. doi: 10.21656/1000-0887.420040
引用本文: 陈伟俊, 罗洪林, 彭建文. 随机ADMM算法及其在电力系统凸经济调度问题中的应用[J]. 应用数学和力学, 2021, 42(9): 979-988. doi: 10.21656/1000-0887.420040
CHEN Weijun, LUO Honglin, PENG Jianwen. The Random ADMM and Its Application to Convex Economic Dispatch Problems of Power Systems[J]. Applied Mathematics and Mechanics, 2021, 42(9): 979-988. doi: 10.21656/1000-0887.420040
Citation: CHEN Weijun, LUO Honglin, PENG Jianwen. The Random ADMM and Its Application to Convex Economic Dispatch Problems of Power Systems[J]. Applied Mathematics and Mechanics, 2021, 42(9): 979-988. doi: 10.21656/1000-0887.420040

随机ADMM算法及其在电力系统凸经济调度问题中的应用

doi: 10.21656/1000-0887.420040
基金项目: 

国家自然科学基金(11991024

重庆市高校创新研究群体项目(CXQT20014)

11771064)

详细信息
    作者简介:

    陈伟俊(1997—),男,硕士(E-mail: 1061661769@qq.com);罗洪林(1982—),男,教授,博士,硕士生导师(通讯作者. E-mail: luohonglin_1982@163.com);彭建文(1967—),男,教授,博士,博士生导师(E-mail: jwpeng6@aliyun.com).

    通讯作者:

    罗洪林(1982—),男,教授,博士,硕士生导师(通讯作者. E-mail: luohonglin_1982@163.com)

  • 中图分类号: O231.1

The Random ADMM and Its Application to Convex Economic Dispatch Problems of Power Systems

Funds: 

The National Natural Science Foundation of China(11991024

11771064)

  • 摘要: 针对电力系统中的一类凸经济调度问题,提出了随机ADMM算法,设计了周期循环更新规则和随机选择更新规则,证明了随机ADMM算法在周期循环更新规则下的收敛性,以及得出了在随机选择更新规则下按期望收敛的结论.数值实验结果表明该方法可以有效解决电力系统中的凸经济调度问题.
  • CHEN X, LI K J, XU B, et al. Biogeography-based learning particle swarm optimization for combined heat and power economic dispatch problem[J]. Knowledge-Based Systems,2020:208: 106463.
    [2]李建. 粒子群算法在经济调度问题中的应用[J]. 工业控制计算机, 2018,31(11): 83-84. (LI Jian. Application of particle swarm optimization algorithm in economic dispatch problem[J]. Industrial Control Computer,2018,31(11): 83-84. (in Chinese))
    [3]XIONG G J, SHI D Y. Orthogonal learning competitive swarm optimizer for economic dispatch problems[J]. Applied Soft Computing,2018,66: 134-148.
    [4]CHOUHDRY Z U R, HASAN K M, RAJA M A Z. Design of reduced search space strategy based on integration of Nelder-Mead method and pattern search algorithm with application to economic load dispatch problem[J]. Neural Computing & Applications,2017,30: 3693-3705.
    [5]SEN T, MATHUR H D. A new approach to solve economic dispatch problem using a hybrid ACO-ABC-HS optimization algorithm[J]. International Journal of Electrical Power & Energy Systems,2016, 78: 735-744.
    [6]ZOU D X, STEVEN L, KONG X Y, et al. Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy[J]. Applied Energy,2019,237: 646-670.
    [7]ZAMAN F, ELSAYED S M, RAY T, et al. Configuring two-algorithm-based evolutionary approach for solving dynamic economic dispatch problems[J]. Engineering Applications of Artificial Intelligence,2016,53: 105-125.
    [8]JIN K Q, YU M, HOU G L. Sampled-data self-triggered consensus-based economic dispatch problem under switching graph[J]. Journal of Mathematical Analysis and Applications,2020,491(2): 124371.
    [9]ZHANG K K, XIONG J, DAI X G, et al. On the convergence of event-triggered distributed algorithm for economic dispatch problem[J]. International Journal of Electrical Power and Energy Systems,2020,122: 106159.
    [10]JIN R Y, QIU H F, WENG L G. Distributed discrete-time event-triggered algorithm for economic dispatch problem[J]. Pattern Recognition Letters,2020,138: 507-512.
    [11]WEI H Y, JIAO X P, MU J J. Reduced-complexity linear programming decoding based on ADMM for LDPC codes[J]. IEEE Communications Letters,2015,19(6): 909-912.
    [12]ZHOU Jianhui, LEI Yongmei. Asynchronous group-based ADMM algorithm under efficient communication structure[C]//2018 IEEE Intl Conf on Parallel & Distributed Processing With Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications. 2018: 135-140. DOI: 10.1109/BDCloud.2018.00032.
    [13]CHALISE B K. ADMM-based beamforming optimization for physical layer security in a full-duplex relay system[C]//IEEE Signal Processing Society SigPort. Brighton, UK, 2019.
    [14]JIANG A, WAN J, TANG Y, et al. ADMM-based bipartite graph approximation[C]//2019 IEEE International Conference on Acoustics, Speech and Signal Processing. Brighton, UK, 2019.
    [15]BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning,2011, 3(1): 1-122.
    [16]LI P, HU J P. An ADMM based distributed finite-time algorithm for economic dispatch problems[J]. IEEE Access,2018,6: 30969-30976.
    [17]YANG Q, CHEN G, WANG T. ADMM-based distributed algorithm for economic dispatch in power systems with both packet drops and communication delays[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(3): 842-852.
    [18]BERTSEKAS D P, TSITSIKLIS J N. Neuro-dynamic programming: an overview[C]//Proceedings of 1995 34th IEEE Conference on Decision and Control. Tucson Arizona, USA, 1995.
    [19]ALAWODE K O, JUBRIL A M, KEHINDE L O, et al. Semidefinite programming solution of economic dispatch problem with non-smooth, non-convex cost functions[J]. Electric Power Systems Research,2018,164: 178-187.
    [20]LEE S, SHIM H. Distributed algorithm for economic dispatch problem with separable losses[J]. IEEE Control Systems Letters,2019,3(3): 685-690.
  • 加载中
计量
  • 文章访问数:  58
  • HTML全文浏览量:  18
  • PDF下载量:  7
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-02-04
  • 修回日期:  2021-06-16
  • 网络出版日期:  2021-09-29

目录

    /

    返回文章
    返回