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                    綠色能源互補智能電廠云控制系統研究

                    夏元清 高潤澤 林敏 任延明 閆策

                    夏元清, 高潤澤, 林敏, 任延明, 閆策. 綠色能源互補智能電廠云控制系統研究. 自動化學報, 2020, 46(9): 1844?1868 doi: 10.16383/j.aas.c190581
                    引用本文: 夏元清, 高潤澤, 林敏, 任延明, 閆策. 綠色能源互補智能電廠云控制系統研究. 自動化學報, 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

                    綠色能源互補智能電廠云控制系統研究

                    doi: 10.16383/j.aas.c190581
                    基金項目: 國家重點研發計劃(2018YFB1003700), 國家自然科學基金(61836001, 61803033), 國家自然科學基金國際合作交流項目(61720106010), 國家自然科學基金創新研究群體基金(61621063), 北京市自然科學基金(4161001, Z170039)資助
                    詳細信息
                      作者簡介:

                      夏元清:北京理工大學自動化學院教授. 主要研究方向為云控制系統, 云數據中心調度管理, 智能電廠, 智能交通, 模型預測控制, 自抗擾控制, 飛行器控制和空天地一體化控制. 本文通信作者.E-mail: xia_yuanqing@bit.edu.cn

                      高潤澤:北京理工大學自動化學院博士研究生. 主要研究方向為云控制系統, 智能電廠, 云工作流調度管理, 深度強化學習.E-mail: gaorunze0558@163.com

                      林敏:北京理工大學自動化學院博士研究生. 主要研究方向為云控制系統, 移動機器人控制與協同.E-mail: brucesimpsonlm@gmail.com

                      任延明:北京中水科水電科技開發有限公司工程師. 主要從事水電站和新能源計算機監控系統的項目管理和系統集成工作.E-mail: rym_bitc@163.com

                      閆策:北京理工大學自動化學院博士研究生. 主要研究方向為云控制系統, 智能交通, 云工作流調度管理, 執行器飽和控制, Delta 算子, 有限頻域.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: 針對現代電力系統中設施龐雜、多源異構海量數據難以有效處理、“信息孤島”長期存在以及整體優化調度管理能力不足等問題, 基于云控制系統理論, 以智能電廠為研究對象, 本文提出了智能電廠云控制系統(Intelligent power plant cloud control system, IPPCCS)解決方案. 基于智能電廠云控制系統, 針對綠色能源發電波動性強、抗擾能力差的問題, 利用機器學習算法對采集到的風電、光伏輸出功率進行短時預測, 獲知未來風、光機組功率輸出情況. 在云端使用經濟模型預測控制(Economic model predictive control, EMPC)算法, 通過實時滾動優化得到水輪機組的功率預測調度策略, 保證綠色能源互補發電的魯棒性, 充分消納風、光兩種能源, 減少水輪機組啟停和穿越振動區次數, 在為用戶清潔、穩定供電的同時降低了機組壽命損耗. 最后, 一個區域云數據中心的供電算例表明了本文方法的有效性.
                    • 圖  1  智能電廠云控制系統云網邊端架構

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

                      圖  2  楚雄州風水光電站平均出力曲線

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

                      圖  3  智能電廠云控制系統云邊端協同控制架構

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

                      圖  4  智能電廠云控制關鍵技術體系

                      Fig.  4  Key technologies system of IPPCCS

                      圖  5  智能電廠云控制系統底層邊緣控制

                      Fig.  5  Edge control system in IPPCCS

                      圖  6  智能電廠云控制數字孿生虛擬化架構

                      Fig.  6  Digital-twins virtualization structure in IPPCCS

                      圖  7  智能電廠云端任務和資源匹配調度技術框架

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

                      圖  8  智能電廠云控制云網邊端安全管控技術架構

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

                      圖  9  集控中心層網絡安全分區業務分布圖

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

                      圖  10  含安全防護機制的云端集控中心與場站現地通信和規約方式

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

                      圖  11  智能電廠云控制系統工作拓撲圖

                      Fig.  11  Work topology of IPPCCS

                      圖  12  LSTM神經網絡細胞結構

                      Fig.  12  Cell structure of LSTM neural network

                      圖  13  LSTM-EMPC算法框架及流程圖

                      Fig.  13  Framework and flow chart of LSTM-EMPC

                      圖  14  云端?邊緣預測控制算法流程圖

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

                      圖  15  區域新能源電廠和綠色數據中心聯合運行示意圖

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

                      圖  16  基于LSTM網絡的機組輸出功率預測效果

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

                      圖  17  聯合運行區域負載功率變化曲線

                      Fig.  17  Load power change curve of joint operation area

                      圖  18  場景1的水電輸出功率補償效果

                      Fig.  18  Compensation effect of hydro power in Scenario 1

                      圖  19  場景1的各水電機組輸出功率調度方案

                      Fig.  19  Scheduling plan of hydro generators in Scenario 1

                      圖  20  場景2的水電輸出功率補償效果

                      Fig.  20  Compensation effect of hydro power in Scenario 2

                      圖  21  場景2的各水電機組輸出功率調度方案

                      Fig.  21  Scheduling plan of hydro generators in Scenario 2

                      圖  22  場景3的水電輸出功率補償效果

                      Fig.  22  Compensation effect of hydro power in Scenario 3

                      圖  23  場景3的各水電機組輸出功率調度方案

                      Fig.  23  Scheduling plan of hydro generators in Scenario 3

                      表  1  1號風機與1號光機未來時段預測結果

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

                      預測時段 1號風機 1號光機
                      時段1 時段2 時段3 時段1 時段2 時段3
                      RMSE 17.383 25.569 32.469 10.703 12.787 13.645
                      平均誤差 12.2974 19.3473 26.2758 6.2836 9.2977 11.2038
                      平均誤差率 0.0416 0.0649 0.0878 0.0197 0.0292 0.0354
                      下載: 導出CSV

                      表  2  2 ~ 5 號風機未來時段預測結果

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

                      預測時段 2號風機 3號風機
                      時段1 時段2 時段3 時段1 時段2 時段3
                      RMSE 22.869 30.357 34.298 22.842 31.128 34.999
                      平均誤差 16.4035 23.7910 27.1607 16.4035 23.7910 27.1607
                      平均誤差率 0.0870 0.1290 0.1489 0.0813 0.1209 0.1291
                      預測時段 4號風機 5號風機
                      時段1 時段2 時段3 時段1 時段2 時段3
                      RMSE 25.314 37.057 41.635 28.273 37.187 44.354
                      平均誤差 22.0610 27.7490 33.7304 20.1751 28.2186 33.6929
                      平均誤差率 0.0770 0.0954 0.1169 0.0696 0.0974 0.1138
                      下載: 導出CSV

                      表  3  2 ~ 5 號光機未來時段預測結果

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

                      預測時段 2號光機 3號光機
                      時段1 時段2 時段3 時段1 時段2 時段3
                      RMSE 6.778 14.388 19.350 9.624 11.194 14.049
                      平均誤差 5.5040 13.3298 16.5947 10.3386 11.2576 13.0231
                      平均誤差率 0.0187 0.0454 0.0566 0.0333 0.0365 0.0424
                      預測時段 4號光機 5號光機
                      時段1 時段2 時段3 時段1 時段2 時段3
                      RMSE 9.467 9.549 14.924 7.149 8.264 17.235
                      平均誤差 7.6231 12.4947 15.6101 8.6143 7.6891 9.6818
                      平均誤差率 0.0242 0.0398 0.0500 0.0301 0.0272 0.0344
                      下載: 導出CSV

                      表  4  風機與光機未來時段預測平均結果

                      Table  4  Average results of wind and solar generators

                      預測時段 $1\sim 5 $號風機 $1\sim 5 $號光機
                      時段1 時段2 時段3 時段1 時段2 時段3
                      平均RMSE 23.336 32.260 37.551 8.744 11.236 15.841
                      平均誤差 17.5651 24.8910 29.6411 7.6727 10.8138 13.2227
                      平均誤差率 0.0713 0.1015 0.1193 0.0252 0.0356 0.0438
                      下載: 導出CSV

                      表  5  開停機和穿越振動區次數對比

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

                      調度方式 開停機次數 穿越振動區次數
                      平均分配調度 6 30
                      AGC模擬調度 3 4
                      下載: 導出CSV
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                    • 收稿日期:  2019-08-13
                    • 錄用日期:  2020-02-23
                    • 網絡出版日期:  2020-09-28
                    • 刊出日期:  2020-09-28

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