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基于AMOWOA的区域综合能源系统运行优化调度

韩永明 王新鲁 耿志强 朱群雄 毕帅 张红斌

韩永明, 王新鲁, 耿志强, 朱群雄, 毕帅, 张红斌. 基于AMOWOA的区域综合能源系统运行优化调度. 自动化学报, 2024, 50(3): 576−588 doi: 10.16383/j.aas.c211146
引用本文: 韩永明, 王新鲁, 耿志强, 朱群雄, 毕帅, 张红斌. 基于AMOWOA的区域综合能源系统运行优化调度. 自动化学报, 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的区域综合能源系统运行优化调度

doi: 10.16383/j.aas.c211146
基金项目: 国家自然科学基金(21978013), 中央高校基本科研业务费专项资金(XK1802-4)资助
详细信息
    作者简介:

    韩永明:北京化工大学信息科学与技术学院教授. 分别于2009年和2014年获得北京化工大学学士学位和博士学位. 主要研究方向为知识图谱分析, 神经网络, 智能计算, 数据挖掘和分析. E-mail: hanym@mail.buct.edu.cn

    王新鲁:北京化工大学硕士研究生. 2018 年获得北京化工大学学士学位. 主要研究方向为食品安全风险预测预警, 多目标优化. E-mail: wangxinlu_9102@126.com

    耿志强:北京化工大学信息科学与技术学院教授. 1997年和2002年分别获得郑州大学学士学位和硕士学位. 2005年获得北京化工大学博士学位. 主要研究方向为神经网络, 智能计算, 数据挖掘, 知识管理与过程建模. 本文通信作者. E-mail: gengzhiqiang@mail.buct.edu.cn

    朱群雄:北京化工大学信息科学与技术学院教授. 主要研究方向为计算智能与工业应用, 过程建模与系统优化, 故障诊断与报警管理, 虚拟现实与数字孪生. E-mail: zhuqx@mail.buct.edu.cn

    毕帅:2021年获得北京化工大学硕士学位. 主要研究方向为智能优化. E-mail: bishuai@vip.qq.com

    张红斌:博士, 国网经济技术研究院有限公司教授级高级工程师. 主要研究方向为智能配电网以及综合能源规划. E-mail: hongbin09172015@163.com

  • 中图分类号: 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, 智能优化算法已广泛应用于工程优化中, 在当前多能耦合与互补的能源发展趋势下, 仅考虑系统经济指标的单目标优化模式已经不再适用于目前区域综合能源系统(Integrated energy system, IES)的运行优化调度, 需要研究一种多目标运行策略来解决区域综合能源系统的运行优化调度问题. 首先综合考虑经济与能源利用两个指标并结合商业住宅区域的特性, 以系统日运行收益和一次能源利用率为优化目标构建商业住宅区域综合能源系统多目标运行优化调度模型. 其次由于传统多目标智能优化算法缺乏一种最优解综合评价方法, 基于非支配排序以及拥挤度计算的多目标算法框架, 提出一种利用模糊一致矩阵选取全局最优解的多目标鲸鱼优化算法(A multi-objective whale optimization algorithm, AMOWOA), 并将提出算法对商住区域综合能源系统多目标运行优化调度模型进行求解. 最后以华东某商业住宅区域综合能源系统为例进行仿真, 验证了该方法的有效性和可行性.
  • 图  1  商业住宅区域综合能源系统架构

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

    图  2  ZDT1优化结果

    Fig.  2  ZDT1 optimization results

    图  3  ZDT2优化结果

    Fig.  3  ZDT2 optimization results

    图  4  ZDT3优化结果

    Fig.  4  ZDT3 optimization results

    图  5  日均冷负荷与光伏预测功率曲线

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

    图  6  日均电负荷与日均热负荷曲线

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

    图  7  Pareto分布对比

    Fig.  7  Pareto distribution of contrast

    图  8  结果对比

    Fig.  8  Comparison of results

    图  9  优化前后内燃机出力对比

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

    图  10  储能设备负荷对比

    Fig.  10  Load comparison of energy storage equipment

    表  1  收敛度对比

    Table  1  Convergence contrast

    算法 指标 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
    下载: 导出CSV

    表  2  多样度对比

    Table  2  Diversity contrast

    算法 指标 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${\times{10^{-4}}}$
    下载: 导出CSV

    表  3  设备规格

    Table  3  Specification of equipment

    设备 配置容量 能效系数 (COP)
    内燃机 10 000 kW
    光伏 7 100 kW
    电制冷机 2 000 kW 3.1
    热泵 5 000 kW 4.4 (热)/5 (冷)
    溴化锂余 8 000 kW 1.0
    热机组
    蓄电池 6 000 kWh 0.9 (充/放)
    储热设备 5 000 kWh 0.9 (充/放)
    储冷设备 2 000 kWh 0.9 (充/放)
    下载: 导出CSV

    表  4  模型参数

    Table  4  Model parameter

    参数 数值
    内燃机电效率 41.33%
    内燃机热效率 40.54%
    内燃机燃料热耗率 7 962.726 kJ/kWh
    电网输电效率 92%
    发电厂发电效率 37%
    下载: 导出CSV

    表  5  初始运行条件

    Table  5  Initial operating conditions

    时段 内燃机出力 (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
    下载: 导出CSV

    A1  多目标优化标准测试函数表达式

    A1  Multi-objective optimization standard test functions expression

    测试函数表达式
    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$\left\{\begin{aligned} &\min{f}_{1}\left({x}_{1}\right)={x}_{1}\\& \mathrm{min}{f}_{2}\left(x\right)=g\left(1-\sqrt{ \frac{ {f}_{1} }{g} }-\left(\frac{ {f}_{1} }{g}\right)\mathrm{sin}\left(10\pi {f}_{1}\right)\right)\\& g\left(x\right)=1 +\frac{9\sum\limits _{i=2}^{m}{x}_{i} }{m-1}\end{aligned}\right.$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-03
  • 录用日期:  2022-03-01
  • 网络出版日期:  2022-09-29
  • 刊出日期:  2024-03-29

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