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基于分布式神经动态优化的综合能源系统多目标优化调度

黄博南 王勇 李玉帅 刘鑫蕊 杨超

黄博南, 王勇, 李玉帅, 刘鑫蕊, 杨超. 基于分布式神经动态优化的综合能源系统多目标优化调度. 自动化学报, 2022, 48(7): 1718−1736 doi: 10.16383/j.aas.c200168
引用本文: 黄博南, 王勇, 李玉帅, 刘鑫蕊, 杨超. 基于分布式神经动态优化的综合能源系统多目标优化调度. 自动化学报, 2022, 48(7): 1718−1736 doi: 10.16383/j.aas.c200168
Huang Bo-Nan, Wang Yong, Li Yu-Shuai, Liu Xin-Rui, Yang Chao. Multi-objective optimal scheduling of integrated energy systems based on distributed neurodynamic optimization. Acta Automatica Sinica, 2022, 48(7): 1718−1736 doi: 10.16383/j.aas.c200168
Citation: Huang Bo-Nan, Wang Yong, Li Yu-Shuai, Liu Xin-Rui, Yang Chao. Multi-objective optimal scheduling of integrated energy systems based on distributed neurodynamic optimization. Acta Automatica Sinica, 2022, 48(7): 1718−1736 doi: 10.16383/j.aas.c200168

基于分布式神经动态优化的综合能源系统多目标优化调度

doi: 10.16383/j.aas.c200168
基金项目: 国家重点研发计划(2018YFA0702200), 辽宁省兴辽英才计划 (XLYC2007181), 中央高校基础科研业务费(N2204010)资助
详细信息
    作者简介:

    黄博南:东北大学信息科学与工程学院副教授. 主要研究方向为神经动力学分析, 复杂网络, 多智能系统及其在智能电网和能源互联网中的应用. 本文通信作者. E-mail: huangbonan@ise.neu.edu.cn

    王勇:东北大学信息科学与工程学院硕士研究生. 主要研究方向为分布式优化和神经动力学方法, 以及其在微电网、智能电网和能源互联网中的应用.E-mail: 1870504@stu.neu.edu.cn

    李玉帅:美国丹佛大学电气与计算机工程系博士后研究员, 主要研究方向为分布式控制和优化, 机器学习及其在微电网、智能电网和能源互联中的应用. E-mail: yushuaili@ieee.org

    刘鑫蕊:东北大学信息科学与工程学院副教授. 主要研究方向为电网信息物理系统, 配电网故障诊断及自愈控制, 模糊控制, 网络控制, 清洁能源消纳控制. E-mail: liuxinrui@ise.neu.edu.cn

    杨超:国网辽宁省电力有限公司信息通信分公司工程师. 主要研究方向为电力物联网, 人工智能, 电网大数据挖掘. E-mail: yangchaoneu@163.com

Multi-objective Optimal Scheduling of Integrated Energy Systems Based On Distributed Neurodynamic Optimization

Funds: Supported by National Key Research and Development Program of China (2018YFA0702200), Liaoning Revitalization Talents Program (XLYC2007181), and Fundamental Research Funds for the Central Universities (N2204010)
More Information
    Author Bio:

    HUANG Bo-Nan Associate professor at the School of Information Science and Engineering, Northeastern University. His research interest covers neural dynamics analysis, complex networks, multiagents systems, and their applications in smart-grid and energy internet. Corresponding author of this paper

    WANG Yong Master student at the School of Information Science and Engineering, Northeastern University. His research interest covers distributed optimization and neurodynamic approach, and its applications in microgrids, smart grid and the energy internet

    LI Yu-Shuai Postdoctoral rese-arch scholar in the Department of Electrical and Computer Engineering, University of Denver, US. His research interest covers distributed control and optimization, machine learning with applications in microgrids, smart grid, and energy internet

    LIU Xin-Rui Associate professor at the School of Information Science and Engineering, Northeastern University. Her research interest covers grid information physics system, distribution network fault diagnosis and self-healing control, fuzzy control, network control, and clean energy absorption control

    YANG Chao Engineer at the Infor- mation and Communication Branch of State Grid Liaoning Electric Power Co., Ltd.. His research interest covers power internet of things, artificial intelligence, and grid big data mining

  • 摘要: 研究了基于神经动态优化的综合能源系统(Integrated energy systems, IES)分布式多目标优化调度问题. 首先, 将IES元件单元(包含负荷)作为独立的决策主体, 联合考量其运行成本和排放成本, 并计及多能源设备间的传输损耗, 提出了IES多目标优化调度模型, 该模型可描述为一类非凸多目标优化问题. 其次, 针对此类问题的求解, 提出了一种基于神经动力学系统的分布式多目标优化算法, 该算法基于动态权重的神经网络模型, 可以解决不可分离的不等式约束问题. 该算法计算负担小, 收敛速度快, 并且易于硬件实现. 仿真结果表明, 所提算法能同时协调综合能源系统的经济性和环境性这两个冲突的目标, 且获得了整个帕累托前沿, 有效降低了综合能源系统的污染物排放量和综合运行成本.
  • 图  1  本文所考虑的综合能源系统结构图

    Fig.  1  Architecture of integrated energy system considered in this paper

    图  2  用于分布式优化的$RN{N_i}$框图

    Fig.  2  Block diagram of $RN{N_i}$ for distributed optimization

    图  4  电热气系统的信息拓扑图

    Fig.  4  Information topology of the integrated electro-heating-gas system

    图  3  所提出的分布式方法在IES的实现过程

    Fig.  3  Implementation diagram of the proposed distributed approach for IES

    图  5  电热气系统的物理拓扑图

    Fig.  5  Physical topology of the integrated electro-heating-gas system

    图  6  综合能源系统多目标优化调度问题的帕累托前沿

    Fig.  6  The Pareto front of multi-objective optimized scheduling in the integrated energy systems

    图  7  常规负荷下综合能源系统各元件的最优出力

    Fig.  7  Optimal outputs of components the integrated energy systems under conventional load

    图  8  24小时负荷下综合能源系统各元件的最优出力

    Fig.  8  Optimal outputs of components the integrated energy systems under 24 hour load

    图  9  即插即用下综合能源系统各元件的最优出力

    Fig.  9  Optimal outputs of components the integrated energy systems under the plug and play property

    C1  各设备运行成本函数参数及出力上下限参数

    C1  The operation cost function parameters and output limit parameters of equipment

    设备名称参数名称及参数值
    CG${\alpha _i}$${\beta _i}$${\gamma _i}$$P_{{\rm{CGi}}}^{\min }$$P_{{\rm{CGi}}}^{\max }$$P_{{\rm{CGi}}}^{{\rm{ramp}}}$
    0.13251020045
    DRG${b_i}$${\varepsilon _i}$${\gamma _i}$$P_{{\rm{DRGi}}}^{\min }$$P_{{\rm{DRGi}}}^{\max }$
    0.11300−1.184.3103.5
    DRHD${b_i}$${\varepsilon _i}$${\gamma _i}$$H_{{\rm{DRHDi}}}^{\min }$$H_{{\rm{DRHDi}}}^{\max }$
    0.12534−1.3133.2148.2
    FG${a_i}$${b_i}$${c_i}$${\varepsilon _i}$${\eta _i}$$P_{{\rm{FGi}}}^{\min }$$P_{{\rm{FGi}}}^{\min }$$P_{ {\rm{FGi} } }^{{\rm{ramp}}}$
    0.0459950.013015045
    FHD${a_i}$${b_i}$${c_i}$${\varepsilon _i}$${\eta _i}$$H_{{\rm{FHDi}}}^{\min }$$H_{{\rm{FHDi}}}^{\max }$
    0.02756050.00850150
    CHP${a_i}$${b_i}$${\alpha _i}$${\beta _i}$${\sigma _i}$${c_i}$$P_{ {\rm{CHPi} } }^{{\rm{ramp}}}$
    0.034514.50.034.20.03123045
    DPSD${a_i}$${b_i}$$P_{{\rm{sti}}}^{ds,\max }$$P_{{\rm{sti}}}^{ch,\max }$$S_{{\rm{sti}}}^{\min }$$S_{{\rm{sti}}}^{\max }$$S_{{\rm{sti}}}^{\rm{0}}$
    0.02853522022035350240
    DHSD${a_i}$${b_i}$$P_{{\rm{sti}}}^{ds,\max }$$P_{{\rm{sti}}}^{ch,\max }$$S_{{\rm{sti}}}^{\min }$$S_{{\rm{sti}}}^{\max }$$S_{{\rm{sti}}}^{\rm{0}}$
    0.01396140040062620560
    GP${a_i}$${b_i}$${c_i}$${d_i}$$g_{_{{\rm{GPi}}}}^{\min }$$g_{_{{\rm{GPi}}}}^{\max }$
    $2\times{10}^{-6}$0.0065041001500
    EL$a_i^p$$b_i^p$$a_i^h$$b_i^h$$a_i^g$$b_i^g$$P_{{\rm{fli}}}^{\max }$$H_{{\rm{fli}}}^{\max }$$g_{{\rm{fli}}}^{\max }$
    0.01642.50.0125.50.0167221000800500
    下载: 导出CSV

    C2  各设备环境成本参数

    C2  The environmental cost function parameters of equipment

    设备名称参数名称及参数值
    CG${\omega _i}$${\mu _i}$${\kappa _i}$${\zeta _i}$${\pi _i}$${\tau _i}$
    0.0409−2.70.64920.028570.64
    FG${\omega _i}$${\mu _i}$${\kappa _i}$${\zeta _i}$${\pi _i}$${\tau _i}$
    0.0254−3.0250.0563850.03330.52
    CHP${\omega _i}$${\mu _i}$${\tau _i}$
    $ 1.5{10}^{-6} $$ 1.5{10}^{-5} $0.2
    FHD${\omega _i}$${\mu _i}$${\tau _i}$
    $ 8{10}^{-6} $$ 1{10}^{-5} $0.8
    GP${\omega _i}$${\mu _i}$${\tau _i}$
    $ 8{10}^{-6} $$ 1{10}^{-5} $0.6
    下载: 导出CSV

    C3  电力网络传输线路参数 (MW)

    C3  The parameters of power network transmission pipelines (MW)

    线路$P_e^{\min }$$P_e^{\max }$线路$P_e^{\min }$$P_e^{\max }$
    1 ~ 1302004 ~ 130200
    2 ~ 1302005 ~ 130200
    3 ~ 1302006 ~ 130200
    下载: 导出CSV

    C4  热力网络传输管道参数 (MW)

    C4  parameters of heating network transmission pipelines (MW)

    管道${l_g}$$m_g^{\min }$$m_g^{\max }$${R_h}$节点$t_{s,f}^{\min }$$t_{s,f}^{\min }$
    4 ~ 142.80270020480100
    7 ~ 142.50270020780100
    8 ~ 143.00270020880100
    下载: 导出CSV

    C5  气网网络传输管道参数 (MW)

    C5  The parameters of gas network transmission pipelines (MW)

    管道${C_{ij}}$节点$\pi _i^{\min }$$\pi _i^{\max }$节点$\pi _i^{\min }$$\pi _i^{\max }$
    11 ~ 3103100040001210004000
    11 ~ 410410004000
    11 ~ 710710004000
    11 ~ 12101210004000
    下载: 导出CSV

    C6  各设备在不同运行方式下的功率 (MW)

    C6  Power of each device under different operating modes (MW)

    CGDRGDRHDFGFHDCHPDPSDDHSDGPPLHLGL
    以电定热10100135305060.5/14.7−100−100375.59095190
    本文12.6103.5133.2305055/44−100−100405.99095190
    下载: 导出CSV
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  • 收稿日期:  2020-03-31
  • 录用日期:  2020-08-14
  • 网络出版日期:  2022-06-07
  • 刊出日期:  2022-07-01

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