2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

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

基于CPSS平行系统懒惰强化学习算法的实时发电调控

殷林飞 陈吕鹏 余涛 张孝顺

殷林飞, 陈吕鹏, 余涛, 张孝顺. 基于CPSS平行系统懒惰强化学习算法的实时发电调控. 自动化学报, 2019, 45(4): 706-719. doi: 10.16383/j.aas.c180215
引用本文: 殷林飞, 陈吕鹏, 余涛, 张孝顺. 基于CPSS平行系统懒惰强化学习算法的实时发电调控. 自动化学报, 2019, 45(4): 706-719. doi: 10.16383/j.aas.c180215
YIN Lin-Fei, CHEN Lv-Peng, YU Tao, ZHANG Xiao-Shun. Lazy Reinforcement Learning Through Parallel Systems and Social System for Real-time Economic Generation Dispatch and Control. ACTA AUTOMATICA SINICA, 2019, 45(4): 706-719. doi: 10.16383/j.aas.c180215
Citation: YIN Lin-Fei, CHEN Lv-Peng, YU Tao, ZHANG Xiao-Shun. Lazy Reinforcement Learning Through Parallel Systems and Social System for Real-time Economic Generation Dispatch and Control. ACTA AUTOMATICA SINICA, 2019, 45(4): 706-719. doi: 10.16383/j.aas.c180215

基于CPSS平行系统懒惰强化学习算法的实时发电调控

doi: 10.16383/j.aas.c180215
基金项目: 

国家自然科学基金 51477055

国家自然科学基金 51777078

详细信息
    作者简介:

    殷林飞  广西大学电气工程学院助理教授.2018年获得华南理工大学电力学院博士学位.主要研究方向为智能电网控制技术.E-mail:yinlinfei@163.com

    陈吕鹏  华南理工大学电力学院硕士研究生.2017年获得华南理工大学电力学院学士学位.主要研究方向为机器学习, 负荷预测.E-mail:chenlvpeng123@163.com

    张孝顺  汕头大学工学院副教授.2017年获得华南理工大学电力学院博士学位.主要研究方向为智能电网控制技术.E-mail:xszhang1990@sina.cn

    通讯作者:

    余涛  华南理工大学电力学院教授.2003年获得清华大学博士学位.主要研究方向为智能电网控制技术.本文通信作者.E-mail:taoyu1@scut.edu.cn

Lazy Reinforcement Learning Through Parallel Systems and Social System for Real-time Economic Generation Dispatch and Control

Funds: 

National Natural Science Foundation of China 51477055

National Natural Science Foundation of China 51777078

More Information
    Author Bio:

     Assistant professor at the College of Electrical Engineering, Guangxi University. He received his Ph. D. degree from South China University of Technology in 2018. His research interest covers control strategies of smart grid

     Master student at the School of Electric Power, South China University of Technology. He received his bachelor degree from South China University of Technology in 2017. His research interest covers machine learning and load forecasting processing

     Associate professor at Shantou University. He received his Ph. D. degree from South China University of Technology in 2017. His research interest covers control strategies of smart grid

    Corresponding author: YU Tao  Professor at the School of Electric Power, South China University of Technology. He received his Ph. D. degree in electrical engineering from Tsinghua University in 2003. His research interest covers control strategies of smart grid. Corresponding author of this paper
  • 摘要: 为解决电力系统中存在的多种时间尺度下经济调度和发电控制的协同问题,即长时间尺度下优化,短时间尺度下优化和实时控制的问题,本文提出了一种统一时间尺度的实时经济发电调度和控制框架,并为该框架提出了懒惰强化学习方法(Lazy reinforcement learning,LRL).该方法将懒惰控制器引入以人工社会——计算实验——平行执行和社会系统为基础的强化学习中,使得机组组合,经济调度,自动发电控制和发电命令调配的问题有机结合在一起,取代过去传统的发电控制框架.为了减少仿真所需的真实时间,平行系统包含多个虚拟系统和一个真实系统.仿真实验比较了懒惰学习算法,松弛人工网络以及4608种组合常规发电控制算法在IEEE新英格兰10机39节点仿真系统的控制效果.实验表明,懒惰强化学习方法的控制效果最优.仿真结果验证了懒惰强化学习方法在基于ACP和社会系统的REG框架下具有有效性和可行性.
    1)  本文责任编委 王占山
  • 图  1  传统发电调控框架

    Fig.  1  Framework of conventional generation control

    图  2  两区电力系统的AGC模型

    Fig.  2  AGC model of two-area power system

    图  3  平行发电控制系统

    Fig.  3  Parallel generation control systems

    图  4  基于REG的LRL控制器的流程图

    Fig.  4  Procedures of LRL based REG controller

    图  5  平行系统下基于REG控制器的LRL算法的流程图

    Fig.  5  Procedures of LRL based REG controller under parallel systems

    图  6  新英格兰电力系统结构图

    Fig.  6  Structure of New-England power system

    图  7  光伏、电动汽车、风电、负荷曲线

    Fig.  7  Curves of photo-voltaic power (PV), electric vehicle (EV), wind power and load

    图  8  仿真统计结果

    Fig.  8  Statistical result

    图  9  仿真统计结果(频率偏差)

    Fig.  9  Statistical result of frequency deviation

    图  10  仿真统计结果(区域控制误差)

    Fig.  10  Statistical result of area control error

    图  11  平行系统频率偏差收敛曲线

    Fig.  11  Convergence curve of frequency deviation obtained by the parallel systems

    图  12  平行系统区域控制误差收敛曲线

    Fig.  12  Convergence curve of area control error obtained by the parallel systems

    表  1  频率调节方式与传统发电调控框架之间的关系

    Table  1  Relationship between regulation processes and conventional generation control framework

    传统发电控制调节方式算法类型时间间隔(s)输入输出
    UC三次调频优化算法86 400$ PD_{i, t} $$u_{i, t, j}, P_{j, t}$
    ED二次调频优化算法900 $PD_i$$P_{i, j}$
    AGC二次调频控制算法4$e_{i}, \Delta f_i$$ \Delta P_i$
    GCD二次调频优化算法4 $\Delta P_i$$\Delta P_{i, j}$
    下载: 导出CSV

    表  2  懒惰强化学习输入输出量

    Table  2  Inputs and outputs of lazy reinforcement learning

    输入输出懒惰学习强化网络懒惰强化学习
    输入量$\Delta {f_i}, {e_i}, {\bf \it {A}}$$\Delta {F'_{i, (t + 1)}}$$\Delta {f_i}, {e_i}$
    输出量${\Delta {f'_{i, (t + 1)}}}$$\Delta {P_{i, j}}, $
    $i = 1, 2, \cdots, {J_i}$
    $\Delta {P_{i, j}}, $
    $i = 1, 2, \cdots, {J_i}$
    下载: 导出CSV

    表  3  仿真所用的算法

    Table  3  Algorithms for this simulation

    序号UCEDAGCGCD
    1模拟退火算法(SAA)SAAPID控制SAA
    2多元优化(MVO)MVO滑模控制器MVO
    3遗传算法(GA)GA自抗扰控制GA
    4灰狼算法(GWO)GWO分数阶PID控制GWO
    5粒子群优化(PSO)PSO模糊逻辑控制器PSO
    6生物地理优化(BBO)BBOQ学习BBO
    7飞蛾扑火算法(MFO)MFOQ($\lambda$)学习MFO
    8鲸鱼群算法(WOA)WOAR($\lambda$)学习WOA
    9固定比例
    10松弛人工神经网络(RANN)
    11懒惰强化学习(LRL)
    下载: 导出CSV

    表  4  各对比算法的缩写

    Table  4  Abbreviation of compared algorithms

    缩写全称意义
    UCUnit commitment机组组合
    EDEconomical dispatch经济调度
    AGCAutomatic generation control自动发电控制
    GCDGeneration command dispatch发电指令调度
    RLReinforcement learning强化学习
    REGReal-time economic generation dispatch and control实时经济调度与控制
    ACPArtificial societies- computational experiments-parallel execution人工社会-计算实验-平行执行
    CPSCyber-physical system信息物理系统
    CPSSCyber-physical-social systems信息物理社会融合系统
    LRLLazy reinforcement learning懒惰强化学习
    RANNRelaxed artificial neural network松弛人工神经网络
    SAASimulated annealing algorithm模拟退火算法
    MVOMulti-verse optimizer多元优化
    GAGenetic algorithm遗传算法
    GWOGray wolf optimizer灰狼算法
    PSOParticle swarm optimization粒子群优化
    BBOBiogeography-based optimization生物地理优化
    MFOMoth-flame optimization飞蛾扑火算法
    WOAWhale optimization algorithm鲸鱼群算法
    LOOCVLeave-one-out cross-validation留一法交叉校验
    BPNNBack propagation neural network反向传播神经网络
    下载: 导出CSV

    表  5  机组参数表

    Table  5  Parameters of the generators

    机组编号30373931323334353638
    机组最小连续开机时间$T_j^{\mathrm{min-up}}$ (h)8855633111
    机组最小连续关机时间$T_j^{\mathrm{min-down}}$ (h)8855633111
    机组最大出力$P_j^{\min}$ (MW)4554551301301628085555555
    机组最小出力$P_j^{\max}$ (MW)1501502020252025101010
    热启动成本$SU_{\mathrm{H}, j}$ (t/(MW $\cdot$ h))4 5005 000550560900170260303030
    冷启动成本$SU_{\mathrm{C}, j}$ (t/(MW $\cdot$ h))9 00010 0001 1001 1201 800340520606060
    冷启动时间$T_j^{\mathrm{cold}}$ (h)5544422000
    ED成本系数$a_j$0.6750.450.5630.5630.450.5630.5630.3370.3150.287
    ED成本系数$b_j$360240299299240299299181168145
    ED成本系数$c_j$11 2507 5109 3909 3907 5109 3909 3905 5305 2505 270
    ED排放系数$\alpha _j$3.3751.1251.6891.5761.171.5761.5760.6740.630.574
    ED排放系数$\beta _j$1 800600897837624837837362404290
    ED排放系数$\gamma _j$56 25018 77028 17026 29019 53026 29026 29011 06013 80010 540
    下载: 导出CSV

    表  6  机组组合问题参数表

    Table  6  Parameters for unit commitment problem

    UC问题的负荷时段(h)123456789101112
    UC问题的负荷值$PD_t$ (WM)7007508509501 0001 1001 1501 2001 3001 4001 4501 500
    UC问题的旋转备用$SR_t$ (WM)70758595100110115120130140145150
    UC问题的负荷时段(h)131415161718192021222324
    UC问题的负荷值$PD_t$ (WM)1 4001 3001 2001 0501 0001 1001 2001 4001 3001 100900800
    UC问题的旋转备用$SR_t$ (WM)1401301201051001101201401301109080
    下载: 导出CSV

    表  7  UC算法仿真结果统计

    Table  7  Statistic of simulation results obtained by the UC

    算法ACE1 (MW) $\Delta f_1$ (Hz)ACE2 (MW)$\Delta f_2$ (Hz)ACE3 (MW)$\Delta f_3$ (Hz)
    SAA573.89040.038235258.77980.037525 527.97461.3137
    MVO575.36720.038274259.92650.0375585 532.62021.3154
    GA603.43910.041805258.64840.0410416 052.28061.4428
    GWO616.0640.043454257.61070.0426536 290.08431.5017
    PSO575.71720.038264260.35430.0375555 535.16441.3159
    BBO574.27690.038213259.3490.0374995 522.56911.3131
    MFO569.71590.037685259.14990.0369845 441.34871.2932
    WOA645.59060.047207255.82460.046396 844.85091.6369
    RANN553.40320.039963224.17480.0390835 431.28441.2907
    LRL441.92250.010254389.99050.00956121 023.19190.23743
    下载: 导出CSV

    表  8  ED算法仿真结果统计

    Table  8  Statistic of simulation results obtained by the ED algorithms

    算法ACE1 (MW) $\Delta f_1$ (Hz)ACE2 (MW)$\Delta f_2$ (Hz)ACE3 (MW)$\Delta f_3$ (Hz)
    SAA587.84140.039976258.27670.0392345 777.57551.3756
    MVO588.1770.039978258.51250.0392455 782.35671.3768
    GA589.40910.040193257.63350.0394795 818.98091.3856
    GWO587.65470.039959258.09230.0392285 780.46641.3763
    PSO587.8580.039915258.81110.0391825 771.29241.3741
    BBO588.01980.039924258.92110.0391925 770.46081.3739
    MFO588.18360.039988258.49480.039255 778.8441.3759
    WOA588.69740.040103257.71130.0393875 805.40461.3823
    RANN553.40320.039963224.17480.0390835 431.28441.2907
    LRL441.92250.010254389.99050.00956121 023.19190.23743
    下载: 导出CSV

    表  9  AGC算法仿真结果统计

    Table  9  Statistic of simulation results obtained by the AGC algorithms

    算法ACE1 (MW) $\Delta f_1$ (Hz)ACE2 (MW)$\Delta f_2$ (Hz)ACE3 (MW)$\Delta f_3$ (Hz)
    PID控制591.30810.040435257.5180.0397175 854.01021.3939
    滑动模式控制器590.73350.040374257.44950.0396565 844.72911.3916
    自抗扰控制591.37710.040424257.67730.0397075 853.04881.3937
    分数阶PID控制591.10070.040437257.30690.0397155 852.74781.3936
    模糊逻辑控制591.9510.040504257.60240.0397815 863.47851.3963
    Q学习591.36030.040452257.45720.0397275 855.13391.3942
    Q($\lambda$)学习591.07720.040419257.44210.0396965 849.97051.393
    R($\lambda$)学习591.72820.040494257.4690.039775 862.78321.3961
    RANN553.40320.039963224.17480.0390835 431.28441.2907
    LRL441.92250.010254389.99050.00956121 023.19190.23743
    下载: 导出CSV

    表  10  GCD算法仿真结果统计

    Table  10  Statistic of simulation results obtained by the GCD algorithms

    算法ACE1 (MW) $\Delta f_1$ (Hz)ACE2 (MW)$\Delta f_2$ (Hz)ACE3 (MW)$\Delta f_3$ (Hz)
    SAA591.30810.040435257.5180.0397175 854.01021.3939
    MVO590.73350.040374257.44950.0396565 844.72911.3916
    GA591.37710.040424257.67730.0397075 853.04881.3937
    GWO591.10070.040437257.30690.0397155 852.74781.3936
    PSO591.9510.040504257.60240.0397815 863.47851.3963
    BBO591.36030.040452257.45720.0397275 855.13391.3942
    MFO591.07720.040419257.44210.0396965 849.97051.393
    WOA591.72820.040494257.4690.039775 862.78321.3961
    固定比例509.03910.028801282.03320.0276093 973.7430.94347
    RANN553.40320.039963224.17480.0390835 431.28441.2907
    LRL441.92250.010254389.99050.00956121 023.19190.23743
    下载: 导出CSV
  • [1] 王宗杰, 郭志忠, 王贵忠, 吴志琪.高比例可再生能源电网功率平衡的实时调度临界时间尺度研究.中国电机工程学报, 2017, 37(S1):39-46 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb2017z1005

    Wang Zong-Jie, Guo Zhi-Zhong, Wang Gui-Zhong, Wu Zhi-Qi. On the critical timescale of real-time dispatch considering power balancing under power systems with high proportional intermittent power sources. Proceedings of the CSEE, 2017, 37(S1):39-46 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb2017z1005
    [2] Liang Z T, Liang J, Zhang L, Wang C F, Yun Z H, Zhang X. Analysis of multi-scale chaotic characteristics of wind power based on Hilbert-Huang transform and Hurst analysis. Applied Energy, 2015, 159:51-61 doi: 10.1016/j.apenergy.2015.08.111
    [3] 覃岭, 林济铿, 戴赛, 王海林, 郑卫红.基于改进轻鲁棒优化模型的风、火机组组合.中国电机工程学报, 2016, 36(15):4108-4118 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb201615011

    Qin Ling, Lin Ji-Keng, Dai Sai, Wang Hai-Lin, Zheng Wei-Hong. Improved light robust optimization model based wind-thermal unit commitment. Proceedings of the CSEE, 2016, 36(15):4108-4118 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb201615011
    [4] 陈典, 钟海旺, 夏清.基于全成本电价的安全约束经济调度.中国电机工程学报, 2016, 36(5):1190-1199 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb201605003

    Chen Dian, Zhong Hai-Wang, Xia Qing. Security constrained economic dispatch based on total cost price. Proceedings of the CSEE, 2016, 36(5):1190-1199 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb201605003
    [5] 范刘洋, 汪可友, 吴巍, 李国杰, 葛延峰.多时间尺度的电力系统鲁棒调度研究.电网技术, 2017, 41(5):1576-1582 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dwjs201705030

    Fan Liu-Yang, Wang Ke-You, Wu Wei, Li Guo-Jie, Ge Yan-Feng. A study of multi-time scale robust schedule and dispatch methodology. Power System Technology, 2017, 41(5):1576-1582 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dwjs201705030
    [6] 胡林, 申建建, 唐海.考虑复杂约束的水电站AGC控制策略.中国电机工程学报, 2017, 37(19):5643-5654 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb201719013

    Hu Lin, Shen Jian-Jian, Tang Hai. Automatic generation control strategies of hydropower plant considering complex constraints. Proceedings of the CSEE, 2017, 37(19):5643-5654 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb201719013
    [7] Yu T, Wang Y M, Ye W J, Zhou B, Chan K W. Stochastic optimal generation command dispatch based on improved hierarchical reinforcement learning approach. IET Generation. Transmission and Distribution, 2011, 5(8):789-797 doi: 10.1049/iet-gtd.2010.0600
    [8] Zhang X S, Yu T, Yang B, Li L. Virtual generation tribe based robust collaborative consensus algorithm for dynamic generation command dispatch optimization of smart grid. Energy, 2016, 101:34-51 doi: 10.1016/j.energy.2016.02.009
    [9] 张孝顺, 余涛, 唐捷.基于分层相关均衡强化学习的CPS指令优化分配算法.电力系统自动化, 2015, 39(8):80-86 http://d.old.wanfangdata.com.cn/Periodical/dlxtzdh201508013

    Zhang Xiao-Shun, Yu Tao, Tang Jie. Optimal CPS command dispatch based on hierarchically correlated equilibrium reinforcement learning. Automation of Electric Power Systems, 2015, 39(8):80-86 http://d.old.wanfangdata.com.cn/Periodical/dlxtzdh201508013
    [10] Abass Y A, Al-Awami A T, Jamal T. Integrating automatic generation control and economic dispatch for microgrid real-time optimization. In:Proceedings of the 2016 IEEE Power and Energy Society General Meeting (PESGM). Boston, USA:IEEE, 2016. 1-5
    [11] Yang M, Wang M Q, Cheng F L, Lee W J. Robust economic dispatch considering automatic generation control with affine recourse process. International Journal of Electrical Power and Energy Systems, 2016, 81:289-298 doi: 10.1016/j.ijepes.2016.02.033
    [12] Li N, Zhao C H, Chen L J. Connecting automatic generation control and economic dispatch from an optimization view. IEEE Transactions on Control of Network Systems, 2016, 3(3):254-264 doi: 10.1109/TCNS.2015.2459451
    [13] 唐捷, 张泽宇, 程乐峰, 张孝顺, 余涛.基于CEQ(λ)强化学习算法的微电网智能发电控制.电测与仪表, 2017, 54(1):39-45 doi: 10.3969/j.issn.1001-1390.2017.01.007

    Tang Jie, Zhang Ze-Yu, Cheng Le-Feng, Zhang Xiao-Shun, Yu Tao. Smart generation control for micro-grids based on correlated equilibrium Q(λ) learning algorithm. Electrical Measurement and Instrumentation, 2017, 54(1):39-45 doi: 10.3969/j.issn.1001-1390.2017.01.007
    [14] 余涛, 周斌, 陈家荣.基于多步回溯Q(λ)学习的互联电网随机最优CPS控制.电工技术学报, 2011, 26(6):179-186 http://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201106029.htm

    Yu Tao, Zhou Bin, Chan Ka-Wing. Stochastic optimal CPS control for interconnected power grids using multi-step backtrack Q(λ) learning. Transactions of China Electrotechnical Society, 2011, 26(6):179-186 http://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201106029.htm
    [15] 王飞跃.人工社会、计算实验、平行系统—关于复杂社会经济系统计算研究的讨论.复杂系统与复杂性科学, 2004, 1(4):25-35 doi: 10.3969/j.issn.1672-3813.2004.04.002

    Wang Fei-Yue. Artificial societies, computational experiments, and parallel systems:a discussion on computational theory of complex social-economic systems. Complex Systems and Complexity Science, 2004, 1(4):25-35 doi: 10.3969/j.issn.1672-3813.2004.04.002
    [16] Wang F Y. Parallel control and management for intelligent transportation systems:concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3):630-638 doi: 10.1109/TITS.2010.2060218
    [17] 白天翔, 王帅, 沈震, 曹东璞, 郑南宁, 王飞跃.平行机器人与平行无人系统:框架、结构、过程、平台及其应用.自动化学报, 2017, 43(2):161-175 http://www.aas.net.cn/CN/abstract/abstract18998.shtml

    Bai Tian-Xiang, Wang Shuai, Shen Zhen, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel robotics and parallel unmanned systems:framework, structure, process, platform and applications. Acta Automatica Sinica, 2017, 43(2):161-175 http://www.aas.net.cn/CN/abstract/abstract18998.shtml
    [18] 袁勇, 王飞跃.平行区块链:概念、方法与内涵解析.自动化学报, 2017, 43(10):1703-1712 http://www.aas.net.cn/CN/Y2017/V43/I10/1703

    Yuan Yong, Wang Fei-Yue. Parallel blockchain:concept, methods and issues. Acta Automatica Sinica, 2017, 43(10):1703-1712 http://www.aas.net.cn/CN/Y2017/V43/I10/1703
    [19] 李力, 林懿伦, 曹东璞, 郑南宁, 王飞跃.平行学习—机器学习的一个新型理论框架.自动化学报, 2017, 43(1):1-8 doi: 10.3969/j.issn.1003-8930.2017.01.001

    Li Li, Lin Yi-Lun, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel learning—a new framework for machine learning. Acta Automatica Sinica, 2017, 43(1):1-8 doi: 10.3969/j.issn.1003-8930.2017.01.001
    [20] 熊刚, 王飞跃, 侯家琛, 董西松, 张家麟, 付满昌.提高核电站安全可靠性的平行系统方法.系统工程理论与实践, 2012, 32(5):1018-1026 doi: 10.3969/j.issn.1000-6788.2012.05.014

    Xiong Gang, Wang Fei-Yue, Hou Jia-Chen, Dong Xi-Song, Zhang Jia-Lin, Fu Man-Chang. To improve safety and reliability of nuclear power plant with parallel system method. Systems Engineering-Theory and Practice, 2012, 32(5):1018-1026 doi: 10.3969/j.issn.1000-6788.2012.05.014
    [21] Wang F Y. The emergence of intelligent enterprises:from CPS to CPSS. IEEE Intelligent Systems, 2010, 25(4):85-88 doi: 10.1109/MIS.2010.104
    [22] 邓建玲, 王飞跃, 陈耀斌, 赵向阳.从工业4.0到能源5.0:智能能源系统的概念、内涵及体系框架.自动化学报, 2015, 41(12):2003-2016 http://www.aas.net.cn/CN/abstract/abstract18774.shtml

    Deng Jian-Ling, Wang Fei-Yue, Chen Yao-Bin, Zhao Xiang-Yang. From industries 4.0 to energy 5.0:concept and framework of intelligent energy systems. Acta Automatica Sinica, 2015, 41(12):2003-2016 http://www.aas.net.cn/CN/abstract/abstract18774.shtml
    [23] 王进, 周宇轩, 戴伟, 李亚峰, 宋翼颉. NSGA-Ⅱ算法的改进及其在风火机组多目标动态组合优化中的应用.电力系统及其自动化学报, 2017, 29(2):107-111 doi: 10.3969/j.issn.1003-8930.2017.02.018

    Wang Jin, Zhou Yu-Xuan, Dai Wei, Li Ya-Feng, Song Yi-Jie. Improvement of NSGA-Ⅱ algorithm and its application to multi-objective dynamic unit commitment optimization with wind-thermal power. Proceedings of the CSU-EPSA, 2017, 29(2):107-111 doi: 10.3969/j.issn.1003-8930.2017.02.018
    [24] 刘洪, 陈星屹, 李吉峰, 徐科.基于改进CPSO算法的区域电热综合能源系统经济调度.电力自动化设备, 2017, 37(6):193-200 http://d.old.wanfangdata.com.cn/Periodical/dlzdhsb201706027

    Liu Hong, Chen Xing-Yi, Li Ji-Feng, Xu Ke. Economic dispatch based on improved CPSO algorithm for regional power-heat integrated energy system. Electric Power Automation Equipment, 2017, 37(6):193-200 http://d.old.wanfangdata.com.cn/Periodical/dlzdhsb201706027
    [25] 李正茂, 张峰, 梁军, 贠志皓, 张旭.计及附加机会收益的冷热电联供型微电网动态调度.电力系统自动化, 2015, 39(14):8-15 doi: 10.7500/AEPS20141109002

    Li Zheng-Mao, Zhang Feng, Liang Jun, Yun Zhi-Hao, Zhang Xu. Dynamic scheduling of CCHP type of microgrid considering additional opportunity income. Automation of Electric Power Systems, 2015, 39(14):8-15 doi: 10.7500/AEPS20141109002
    [26] Faris H, Aljarah I, Mirjalili S. Training feedforward neural networks using multi-verse optimizer for binary classification problems. Applied Intelligence, 2016, 45(2):322-332 doi: 10.1007/s10489-016-0767-1
    [27] Yang B, Jiang L, Wang L, Yao W, Wu Q H. Nonlinear maximum power point tracking control and modal analysis of DFIG based wind turbine. International Journal of Electrical Power and Energy Systems, 2016, 74:429-436 doi: 10.1016/j.ijepes.2015.07.036
    [28] Zeng G Q, Chen J, Dai Y X, Li L M, Zheng C W, Chen M R. Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization. Neurocomputing, 2015, 160:173-184 doi: 10.1016/j.neucom.2015.02.051
    [29] Pan I, Das S. Fractional-order load-frequency control of interconnected power systems using chaotic multi-objective optimization. Applied Soft Computing, 2015, 29:328-344 doi: 10.1016/j.asoc.2014.12.032
    [30] Shabani H, Vahidi B, Ebrahimpour M. A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems. ISA Transactions, 2013, 52(1):88-95 doi: 10.1016/j.isatra.2012.09.008
    [31] Mohanty P K, Sahu B K, Pati T K, Panda S, Kar K S. Design and analysis of fuzzy PID controller with derivative filter for AGC in multi-area interconnected power system. IET Generation, Transmission and Distribution, 2016, 10(15):3764-3776 doi: 10.1049/iet-gtd.2016.0106
    [32] Dahiya P, Sharma V, Naresh R. Automatic generation control using disrupted oppositional based gravitational search algorithm optimised sliding mode controller under deregulated environment. IET Generation, Transmission and Distribution, 2016, 10(16):3995-4005 doi: 10.1049/iet-gtd.2016.0175
    [33] 姚书龙, 刘志刚, 张桂南, 向川.基于自抗扰控制的牵引网网压低频振荡抑制方法.电网技术, 2016, 40(1):207-213 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dwjs201601028

    Yao Shu-Long, Liu Zhi-Gang, Zhang Gui-Nan, Xiang Chuan. A novel approach based on ADRC to traction network voltage low frequency oscillation suppression research. Power System Technology, 2016, 40(1):207-213 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dwjs201601028
    [34] 杨平, 董国威.互联电网AGC的分数阶PID控制.电力系统及其自动化学报, 2013, 25(3):124-129 doi: 10.3969/j.issn.1003-8930.2013.03.024

    Yang Ping, Dong Guo-Wei. Fractional order PID control for AGC of interconnected power system. Proceedings of the CSU-EPSA, 2013, 25(3):124-129 doi: 10.3969/j.issn.1003-8930.2013.03.024
    [35] 张孝顺, 李清, 余涛, 陈柏熹.基于协同一致性迁移Q学习算法的虚拟发电部落AGC功率动态分配.中国电机工程学报, 2017, 37(5):1455-1466 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb201705020

    Zhang Xiao-Shun, Li Qing, Yu Tao, Chen Bai-Xi. Collaborative consensus transfer Q-learning based dynamic generation dispatch of automatic generation control with virtual generation tribe. Proceedings of the CSEE, 2017, 37(5):1455-1466 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=zgdjgcxb201705020
    [36] 余涛, 袁野.基于平均报酬模型全过程R(λ)学习的互联电网CPS最优控制.电力系统自动化, 2010, 34(21):27-33 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlxtzdh201021005

    Yu Tao, Yuan Ye. An average reward model based whole process R(λ)-learning for optimal CPS control. Automation of Electric Power Systems, 2010, 34(21):27-33 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dlxtzdh201021005
    [37] Zhang Y, Liu X J, Qu B. Distributed model predictive load frequency control of multi-area power system with DFIGs. IEEE/CAA Journal of Automatica Sinica, 2017, 4(1):125-135 doi: 10.1109/JAS.2017.7510346
  • 加载中
图(12) / 表(10)
计量
  • 文章访问数:  3071
  • HTML全文浏览量:  329
  • PDF下载量:  546
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-04-17
  • 录用日期:  2018-07-02
  • 刊出日期:  2019-04-20

目录

    /

    返回文章
    返回