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              基于AMOWOA的區域綜合能源系統運行優化調度

              韓永明 王新魯 耿志強 朱群雄 畢帥 張紅斌

              韓永明, 王新魯, 耿志強, 朱群雄, 畢帥, 張紅斌. 基于AMOWOA的區域綜合能源系統運行優化調度. 自動化學報, 2024, 50(3): 576?588 doi: 10.16383/j.aas.c211146
              引用本文: 韓永明, 王新魯, 耿志強, 朱群雄, 畢帥, 張紅斌. 基于AMOWOA的區域綜合能源系統運行優化調度. 自動化學報, 2024, 50(3): 576?588 doi: 10.16383/j.aas.c211146
              Han Yong-Ming, Wang Xin-Lu, Geng Zhi-Qiang, Zhu Qun-Xiong, Bi Shuai, Zhang Hong-Bin. Optimal scheduling for regional integrated energy system operation based on the AMOWOA. Acta Automatica Sinica, 2024, 50(3): 576?588 doi: 10.16383/j.aas.c211146
              Citation: Han Yong-Ming, Wang Xin-Lu, Geng Zhi-Qiang, Zhu Qun-Xiong, Bi Shuai, Zhang Hong-Bin. Optimal scheduling for regional integrated energy system operation based on the AMOWOA. Acta Automatica Sinica, 2024, 50(3): 576?588 doi: 10.16383/j.aas.c211146

              基于AMOWOA的區域綜合能源系統運行優化調度

              doi: 10.16383/j.aas.c211146
              基金項目: 國家自然科學基金(21978013), 中央高?;究蒲袠I務費專項資金(XK1802-4)資助
              詳細信息
                作者簡介:

                韓永明:北京化工大學信息科學與技術學院教授. 分別于2009年和2014年獲得北京化工大學學士學位和博士學位. 主要研究方向為知識圖譜分析, 神經網絡, 智能計算, 數據挖掘和分析. E-mail: hanym@mail.buct.edu.cn

                王新魯:北京化工大學碩士研究生. 2018 年獲得北京化工大學學士學位. 主要研究方向為食品安全風險預測預警, 多目標優化. E-mail: wangxinlu_9102@126.com

                耿志強:北京化工大學信息科學與技術學院教授. 1997年和2002年分別獲得鄭州大學學士學位和碩士學位. 2005年獲得北京化工大學博士學位. 主要研究方向為神經網絡, 智能計算, 數據挖掘, 知識管理與過程建模. 本文通信作者. E-mail: gengzhiqiang@mail.buct.edu.cn

                朱群雄:北京化工大學信息科學與技術學院教授. 主要研究方向為計算智能與工業應用, 過程建模與系統優化, 故障診斷與報警管理, 虛擬現實與數字孿生. E-mail: zhuqx@mail.buct.edu.cn

                畢帥:2021年獲得北京化工大學碩士學位. 主要研究方向為智能優化. E-mail: bishuai@vip.qq.com

                張紅斌:博士, 國網經濟技術研究院有限公司教授級高級工程師. 主要研究方向為智能配電網以及綜合能源規劃. E-mail: hongbin09172015@163.com

              • 中圖分類號: Y

              Optimal Scheduling for Regional Integrated Energy System Operation Based on the AMOWOA

              Funds: Supported by National Natural Science Foundation of China (21978013) and Fundamental Research Funds for the Central Universities (XK1802-4)
              More Information
                Author Bio:

                HAN Yong-Ming Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. He received his bachelor degree and Ph.D. degree from Beijing University of Chemical Technology, in 2009 and 2014, respectively. His research interest covers knowledge map analysis, neural network, intelligent computing, data mining and analysis

                WANG Xin-Lu  Master student at Beijing University of Chemical Technology. He received his bachelor degree from Beijing University of Chemical Technology in 2018. His research interest covers food safety risk prediction and early warning and multi-objective optimization

                GENG Zhi-Qiang Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. He received his bachelor degree and master degree from Zhengzhou University in 1997 and 2002, respectively. He received his Ph.D. degree from Beijing University of Chemical Technology in 2005. His research interest covers neural network, intelligent computing, data mining, knowledge management, and process modeling. Corresponding author of this paper

                ZHU Qun-Xiong Professor at the College of Information Science and Technology, Beijing University of Chemical Technology. His research interest covers computational intelligence and industrial applications, process modeling and system optimization, fault diagnosis and alarm management, virtual reality and digital twinning

                BI Shuai Received his master degree from Beijing University of Chemical Technology in 2021. His main research interest is intelligent optimization

                ZHANG Hong-Bin Ph.D., Professor-level senior engineer at the State Grid Economic and Technological Research Institute Co., Ltd.. His research interest covers intelligent distribution network and integrated energy planning

              • 摘要: 目前, 智能優化算法已廣泛應用于工程優化中, 在當前多能耦合與互補的能源發展趨勢下, 僅考慮系統經濟指標的單目標優化模式已經不再適用于目前區域綜合能源系統(Integrated energy system, IES)的運行優化調度, 需要研究一種多目標運行策略來解決區域綜合能源系統的運行優化調度問題. 首先綜合考慮經濟與能源利用兩個指標并結合商業住宅區域的特性, 以系統日運行收益和一次能源利用率為優化目標構建商業住宅區域綜合能源系統多目標運行優化調度模型. 其次由于傳統多目標智能優化算法缺乏一種最優解綜合評價方法, 基于非支配排序以及擁擠度計算的多目標算法框架, 提出一種利用模糊一致矩陣選取全局最優解的多目標鯨魚優化算法(A multi-objective whale optimization algorithm, AMOWOA), 并將提出算法對商住區域綜合能源系統多目標運行優化調度模型進行求解. 最后以華東某商業住宅區域綜合能源系統為例進行仿真, 驗證了該方法的有效性和可行性.
              • 圖  1  商業住宅區域綜合能源系統架構

                Fig.  1  Integrated energy system architecture for commercial and residential area

                圖  2  ZDT1優化結果

                Fig.  2  ZDT1 optimization results

                圖  3  ZDT2優化結果

                Fig.  3  ZDT2 optimization results

                圖  4  ZDT3優化結果

                Fig.  4  ZDT3 optimization results

                圖  5  日均冷負荷與光伏預測功率曲線

                Fig.  5  Average daily cooling load and photovoltaic predicted power curves

                圖  6  日均電負荷與日均熱負荷曲線

                Fig.  6  Daily average electric load and daily average heat load curve

                圖  7  Pareto分布對比

                Fig.  7  Pareto distribution of contrast

                圖  8  結果對比

                Fig.  8  Comparison of results

                圖  9  優化前后內燃機出力對比

                Fig.  9  Comparison of internal combustion engines before and after optimize output

                圖  10  儲能設備負荷對比

                Fig.  10  Load comparison of energy storage equipment

                表  1  收斂度對比

                Table  1  Convergence contrast

                算法 指標 ZDT1 ZDT2 ZDT3
                AMOWOA M 9.41${\times{10^{-4}}}$ 9.59${\times{10^{-4}}}$ 9.68${\times{10^{-4}}}$
                V 2.26${\times{10^{-5}}}$ 3.41${\times{10^{-5}}}$ 2.16${\times{10^{-5}}}$
                NSGA-II M 9.79${\times{10^{-4}}}$ 9.68${\times{10^{-4}}}$ 9.84${\times{10^{-4}}}$
                V 4.88${\times{10^{-5}}}$ 5.84${\times{10^{-5}}}$ 3.63${\times{10^{-5}}}$
                MOPSO M 9.46${\times{10^{-4}}}$ 1.42${\times{10^{-3}}}$ 9.73${\times{10^{-4}}}$
                V 3.42${\times{10^{-5}}}$ 8.26${\times{10^{-5}}}$ 3.79${\times{10^{-5}}}$
                PESA-II M 1.05${\times{10^{-3}}}$ 7.40${\times{10^{-4}}}$ 7.89${\times{10^{-3}}}$
                V 0.00 0.00 1.10${\times{10^{-4}}}$
                NSPSO M 6.42${\times{10^{-3}}}$ 9.51${\times{10^{-3}}}$ 4.91${\times{10^{-3}}}$
                V 0.00 0.00 0.00
                下載: 導出CSV

                表  2  多樣度對比

                Table  2  Diversity contrast

                算法 指標 ZDT1 ZDT2 ZDT3
                AMOWOA M 0.65560 0.74680 0.79080
                V 0.02109 0.03116 0.02679
                NSGA-II M 0.74470 0.87290 0.78760
                V 0.02901 0.05793 0.06771
                MOPSO M 0.75250 0.93860 0.95170
                V 0.03574 0.06475 0.01563
                PESA-II M 0.84810 0.89290 1.22730
                V 0.00287 0.05740 0.02930
                NSPSO M 0.90700 0.92200 0.06210
                V 0.00 1.20${\times{10^{-4}}}$ 6.90${\times{10^{-4}}}$
                下載: 導出CSV

                表  3  設備規格

                Table  3  Specification of equipment

                設備 配置容量 能效系數 (COP)
                內燃機 10 000 kW
                光伏 7 100 kW
                電制冷機 2 000 kW 3.1
                熱泵 5 000 kW 4.4 (熱)/5 (冷)
                溴化鋰余 8 000 kW 1.0
                熱機組
                蓄電池 6 000 kWh 0.9 (充/放)
                儲熱設備 5 000 kWh 0.9 (充/放)
                儲冷設備 2 000 kWh 0.9 (充/放)
                下載: 導出CSV

                表  4  模型參數

                Table  4  Model parameter

                參數 數值
                內燃機電效率 41.33%
                內燃機熱效率 40.54%
                內燃機燃料熱耗率 7 962.726 kJ/kWh
                電網輸電效率 92%
                發電廠發電效率 37%
                下載: 導出CSV

                表  5  初始運行條件

                Table  5  Initial operating conditions

                時段 內燃機出力 (kW)
                1 (0:00?4:00) 4 000
                2 (4:00?8:00) 4 000
                3 (8:00?12:00) 8 000
                4 (12:00?16:00) 8 000
                5 (16:00?20:00) 8 000
                6 (20:00?24:00) 4 000
                下載: 導出CSV

                A1  多目標優化標準測試函數表達式

                A1  Multi-objective optimization standard test functions expression

                測試函數表達式
                ZDT1$\left\{\begin{aligned} &\min{f}_{1}\left({x}_{1}\right)={x}_{1}\\& \mathrm{min}{f}_{2}\left(x\right)=g\left(1-\sqrt{\frac{ {f}_{1} }{g}}\right)\\ &g\left(x\right)=1 +\frac{9\sum\limits _{i=2}^{m}{x}_{i} }{m-1}\end{aligned}\right.$
                ZDT2$\left\{\begin{aligned} &\min{f}_{1}\left({x}_{1}\right)={x}_{1}\\& \mathrm{min}{f}_{2}\left(x\right)=g\left(1-{\left(\frac{ {f}_{1} }{g}\right)}^{2}\right)\\& g\left(x\right)=1 +\frac{9\sum\limits _{i=2}^{m}{x}_{i} }{m-1}\end{aligned}\right.$
                ZDT3$\left\{\begin{aligned} &\min{f}_{1}\left({x}_{1}\right)={x}_{1}\\& \mathrm{min}{f}_{2}\left(x\right)=g\left(1-\sqrt{ \frac{ {f}_{1} }{g} }-\left(\frac{ {f}_{1} }{g}\right)\mathrm{sin}\left(10\pi {f}_{1}\right)\right)\\& g\left(x\right)=1 +\frac{9\sum\limits _{i=2}^{m}{x}_{i} }{m-1}\end{aligned}\right.$
                下載: 導出CSV
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                        出版歷程
                        • 收稿日期:  2021-12-03
                        • 錄用日期:  2022-03-01
                        • 網絡出版日期:  2022-09-29
                        • 刊出日期:  2024-03-29

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