Green Energy Complementary Based on Intelligent Power Plant Cloud Control System
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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.Abstract: Based on the theory of cloud control system, an intelligent power plant cloud control system (IPPCCS) is designed to overcome problems of complex objects, multi-sources heterogenous data, “information island” and the poor ability of overall optimization scheduling in modern electric power enterprise. To solve problems of strong fluctuation and poor disturbance resistance of green power generation, a machine learning method is used to obtain the short-term prediction value of wind and solar power based on their history data. Then in the cloud, the economic model predictive control (EMPC) algorithm is applied to provide the power predictive scheduling strategy of water turbines by real-time rolling optimization, to ensure the robustness of green energy complementary power generation, consume wind and solar power fully and reduce the frequency of starting/stopping and crossing the vibration zones of the turbines, which both provides clear and stable energy support for the users and protects the devices. The simulations show the effectiveness of the proposed method in an example of regional cloud data center.
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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 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 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
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 
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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
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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
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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
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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
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