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绿色能源互补智能电厂云控制系统研究

夏元清 高润泽 林敏 任延明 闫策

夏元清, 高润泽, 林敏, 任延明, 闫策. 绿色能源互补智能电厂云控制系统研究. 自动化学报, 2020, 46(9): 1844−1868 doi: 10.16383/j.aas.c190581
引用本文: 夏元清, 高润泽, 林敏, 任延明, 闫策. 绿色能源互补智能电厂云控制系统研究. 自动化学报, 2020, 46(9): 1844−1868 doi: 10.16383/j.aas.c190581
Xia Yuan-Qing, Gao Run-Ze, Lin Min, Ren Yan-Ming, Yan Ce. Green energy complementary based on intelligent power plant cloud control system. Acta Automatica Sinica, 2020, 46(9): 1844−1868 doi: 10.16383/j.aas.c190581
Citation: Xia Yuan-Qing, Gao Run-Ze, Lin Min, Ren Yan-Ming, Yan Ce. Green energy complementary based on intelligent power plant cloud control system. Acta Automatica Sinica, 2020, 46(9): 1844−1868 doi: 10.16383/j.aas.c190581

绿色能源互补智能电厂云控制系统研究

doi: 10.16383/j.aas.c190581
基金项目: 国家重点研发计划(2018YFB1003700), 国家自然科学基金(61836001, 61803033), 国家自然科学基金国际合作交流项目(61720106010), 国家自然科学基金创新研究群体基金(61621063), 北京市自然科学基金(4161001, Z170039)资助
详细信息
    作者简介:

    夏元清:北京理工大学自动化学院教授. 主要研究方向为云控制系统, 云数据中心调度管理, 智能电厂, 智能交通, 模型预测控制, 自抗扰控制, 飞行器控制和空天地一体化控制. 本文通信作者.E-mail: xia_yuanqing@bit.edu.cn

    高润泽:北京理工大学自动化学院博士研究生. 主要研究方向为云控制系统, 智能电厂, 云工作流调度管理, 深度强化学习.E-mail: gaorunze0558@163.com

    林敏:北京理工大学自动化学院博士研究生. 主要研究方向为云控制系统, 移动机器人控制与协同.E-mail: brucesimpsonlm@gmail.com

    任延明:北京中水科水电科技开发有限公司工程师. 主要从事水电站和新能源计算机监控系统的项目管理和系统集成工作.E-mail: rym_bitc@163.com

    闫策:北京理工大学自动化学院博士研究生. 主要研究方向为云控制系统, 智能交通, 云工作流调度管理, 执行器饱和控制, Delta 算子, 有限频域.E-mail: yancemc@163.com

Green Energy Complementary Based on Intelligent Power Plant Cloud Control System

Funds: Supported by National Key Research and Development Program of China (2018YFB1003700), National Natural Science Foundation of China (61836001, 61803033), National Natural Science Foundation Projects of International Cooperation and Exchanges (61720106010), Foundation for Innovative Research Groups of the National Natural Science Foundation of China (61621063), and Beijing Natural Science Foundation (4161001, Z170039)
  • 摘要: 针对现代电力系统中设施庞杂、多源异构海量数据难以有效处理、“信息孤岛”长期存在以及整体优化调度管理能力不足等问题, 基于云控制系统理论, 以智能电厂为研究对象, 本文提出了智能电厂云控制系统(Intelligent power plant cloud control system, IPPCCS)解决方案. 基于智能电厂云控制系统, 针对绿色能源发电波动性强、抗扰能力差的问题, 利用机器学习算法对采集到的风电、光伏输出功率进行短时预测, 获知未来风、光机组功率输出情况. 在云端使用经济模型预测控制(Economic model predictive control, EMPC)算法, 通过实时滚动优化得到水轮机组的功率预测调度策略, 保证绿色能源互补发电的鲁棒性, 充分消纳风、光两种能源, 减少水轮机组启停和穿越振动区次数, 在为用户清洁、稳定供电的同时降低了机组寿命损耗. 最后, 一个区域云数据中心的供电算例表明了本文方法的有效性.
  • 图  1  智能电厂云控制系统云网边端架构

    Fig.  1  Cloud-network-edge-terminal architecture of intelligent power plant cloud control system (IPPCCS)

    图  2  楚雄州风水光电站平均出力曲线

    Fig.  2  Average output of wind, hydro and solar power in Chuxiong state

    图  3  智能电厂云控制系统云边端协同控制架构

    Fig.  3  Cloud-network-edge-terminal collaborative control architecture of IPPCCS

    图  4  智能电厂云控制关键技术体系

    Fig.  4  Key technologies system of IPPCCS

    图  5  智能电厂云控制系统底层边缘控制

    Fig.  5  Edge control system in IPPCCS

    图  6  智能电厂云控制数字孪生虚拟化架构

    Fig.  6  Digital-twins virtualization structure in IPPCCS

    图  7  智能电厂云端任务和资源匹配调度技术框架

    Fig.  7  Cloud tasks and resources matching scheduling framework in IPPCCS

    图  8  智能电厂云控制云网边端安全管控技术架构

    Fig.  8  Cloud-network-edge-terminal security management and control framework in IPPCCS

    图  9  集控中心层网络安全分区业务分布图

    Fig.  9  Services distribution in centralized control center for network security

    图  10  含安全防护机制的云端集控中心与场站现地通信和规约方式

    Fig.  10  Cloud-local communication and protocol mode with security protection mechanism

    图  11  智能电厂云控制系统工作拓扑图

    Fig.  11  Work topology of IPPCCS

    图  12  LSTM神经网络细胞结构

    Fig.  12  Cell structure of LSTM neural network

    图  13  LSTM-EMPC算法框架及流程图

    Fig.  13  Framework and flow chart of LSTM-EMPC

    图  14  云端−边缘预测控制算法流程图

    Fig.  14  Flow chart of cloud-edge predictive control algorithm

    图  15  区域新能源电厂和绿色数据中心联合运行示意图

    Fig.  15  Schematic diagram of joint operation of regional new energy power plant and green data center

    图  16  基于LSTM网络的机组输出功率预测效果

    Fig.  16  Prediction results and error rates of generators output power based on LSTM network

    图  17  联合运行区域负载功率变化曲线

    Fig.  17  Load power change curve of joint operation area

    图  18  场景1的水电输出功率补偿效果

    Fig.  18  Compensation effect of hydro power in Scenario 1

    图  19  场景1的各水电机组输出功率调度方案

    Fig.  19  Scheduling plan of hydro generators in Scenario 1

    图  20  场景2的水电输出功率补偿效果

    Fig.  20  Compensation effect of hydro power in Scenario 2

    图  21  场景2的各水电机组输出功率调度方案

    Fig.  21  Scheduling plan of hydro generators in Scenario 2

    图  22  场景3的水电输出功率补偿效果

    Fig.  22  Compensation effect of hydro power in Scenario 3

    图  23  场景3的各水电机组输出功率调度方案

    Fig.  23  Scheduling plan of hydro generators in Scenario 3

    表  1  1号风机与1号光机未来时段预测结果

    Table  1  Prediction results of No.1 wind generator and No.1 solar generator

    预测时段 1号风机 1号光机
    时段1 时段2 时段3 时段1 时段2 时段3
    RMSE 17.383 25.569 32.469 10.703 12.787 13.645
    平均误差 12.2974 19.3473 26.2758 6.2836 9.2977 11.2038
    平均误差率 0.0416 0.0649 0.0878 0.0197 0.0292 0.0354
    下载: 导出CSV

    表  2  2 ~ 5 号风机未来时段预测结果

    Table  2  Prediction results of No. 2 ~ 5 wind generators

    预测时段 2号风机 3号风机
    时段1 时段2 时段3 时段1 时段2 时段3
    RMSE 22.869 30.357 34.298 22.842 31.128 34.999
    平均误差 16.4035 23.7910 27.1607 16.4035 23.7910 27.1607
    平均误差率 0.0870 0.1290 0.1489 0.0813 0.1209 0.1291
    预测时段 4号风机 5号风机
    时段1 时段2 时段3 时段1 时段2 时段3
    RMSE 25.314 37.057 41.635 28.273 37.187 44.354
    平均误差 22.0610 27.7490 33.7304 20.1751 28.2186 33.6929
    平均误差率 0.0770 0.0954 0.1169 0.0696 0.0974 0.1138
    下载: 导出CSV

    表  3  2 ~ 5 号光机未来时段预测结果

    Table  3  Prediction results of No. 2 ~ 5 solar generators

    预测时段 2号光机 3号光机
    时段1 时段2 时段3 时段1 时段2 时段3
    RMSE 6.778 14.388 19.350 9.624 11.194 14.049
    平均误差 5.5040 13.3298 16.5947 10.3386 11.2576 13.0231
    平均误差率 0.0187 0.0454 0.0566 0.0333 0.0365 0.0424
    预测时段 4号光机 5号光机
    时段1 时段2 时段3 时段1 时段2 时段3
    RMSE 9.467 9.549 14.924 7.149 8.264 17.235
    平均误差 7.6231 12.4947 15.6101 8.6143 7.6891 9.6818
    平均误差率 0.0242 0.0398 0.0500 0.0301 0.0272 0.0344
    下载: 导出CSV

    表  4  风机与光机未来时段预测平均结果

    Table  4  Average results of wind and solar generators

    预测时段 $1\sim 5 $号风机 $1\sim 5 $号光机
    时段1 时段2 时段3 时段1 时段2 时段3
    平均RMSE 23.336 32.260 37.551 8.744 11.236 15.841
    平均误差 17.5651 24.8910 29.6411 7.6727 10.8138 13.2227
    平均误差率 0.0713 0.1015 0.1193 0.0252 0.0356 0.0438
    下载: 导出CSV

    表  5  开停机和穿越振动区次数对比

    Table  5  Comparison of times of on/off and crossing vibration areas

    调度方式 开停机次数 穿越振动区次数
    平均分配调度 6 30
    AGC模拟调度 3 4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-08-13
  • 录用日期:  2020-02-23
  • 网络出版日期:  2020-09-28
  • 刊出日期:  2020-09-28

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