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考虑电网线路传输安全的分布式电力市场交易模式研究

李远征 张虎 刘江平 赵勇 连义成

李远征, 张虎, 刘江平, 赵勇, 连义成. 考虑电网线路传输安全的分布式电力市场交易模式研究. 自动化学报, 2022, 48(x): 1−15 doi: 10.16383/j.aas.c211244
引用本文: 李远征, 张虎, 刘江平, 赵勇, 连义成. 考虑电网线路传输安全的分布式电力市场交易模式研究. 自动化学报, 2022, 48(x): 1−15 doi: 10.16383/j.aas.c211244
Li Yuan-Zheng, Zhang Hu, Liu Jiang-Ping, Zhao Yong, Lian Yi-Cheng. Research on distributed power market trading model considering grid transmission security. Acta Automatica Sinica, 2022, 48(x): 1−15 doi: 10.16383/j.aas.c211244
Citation: Li Yuan-Zheng, Zhang Hu, Liu Jiang-Ping, Zhao Yong, Lian Yi-Cheng. Research on distributed power market trading model considering grid transmission security. Acta Automatica Sinica, 2022, 48(x): 1−15 doi: 10.16383/j.aas.c211244

考虑电网线路传输安全的分布式电力市场交易模式研究

doi: 10.16383/j.aas.c211244
基金项目: 国家电网总部科技项目(1400-202099523A-0-0-00)资助
详细信息
    作者简介:

    李远征:华中科技大学人工智能与自动化学院副教授.主要研究方向为人工智能及其在智能电网中的应用, 深度学习, 强化学习和大数据分析. E-mail: Yuanzheng_Li@hust.edu.cn

    张虎:华中科技大学人工智能与自动化学院硕士研究生.主要研究方向为电力市场交易和电力系统优化. E-mail: dugujjiujian@gmail.com

    刘江平:湖北电力交易中心有限公司高级工程师.主要研究方向为电力市场和电力调度. E-mail: hzxjj@foxmail.com

    赵勇:华中科技大学人工智能与自动化学院教授. 主要研究方向为决策理论, 大型工程项目管理, 社会经济系统的建模与仿真和系统分析与集成. 本文通信作者. E-mail: zhiwei98530@hust.edu.cn

    连义成:华中科技大学人工智能与自动化学院博士研究生.主要研究方向为考虑新能源接入的电力系统机组组合与经济调度等. E-mail: hust2017l@163.com

Research on Distributed Power Market Trading Model Considering Grid Transmission Security

Funds: Supported by Science and Technology Project of State Grid Headquarters(1400-202099523A-0-0-00)
More Information
    Author Bio:

    LI Yuan-Zheng Associate professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers artificial intelligence and its application in smart grid, deep learning, reinforcement learning, and big data analysis

    ZHANG Hu Master student at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers power market and power system optimization

    LIU Jiang-Ping Senior Engineer of Hubei Electric Power Exchange Center Limited Company. His research interest covers power market and power dispatching

    ZHAO Yong Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers decision-making theories, large-scale engineering project management, modeling and simulation of social economic systems, and system analysis and integration. Corresponding author of this paper

    LIAN Yi-Cheng Ph.D. candidate at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers unit commitment and economic dispatch considering renewable energy uncertainty

  • 摘要: 分布式电力市场交易模式可以有效缓解传统集中模式下市场主体的隐私安全等问题, 但难以在保障市场主体收益和电力系统安全稳定运行的同时实现社会福利最大化. 因此, 考虑电网线路传输约束, 首先以社会福利最大化为目标构建了集中式交易模型, 并采用拉格朗日乘子法和对偶理论将其等价分解为各市场主体自身利益最大化的分布式交易模型. 在此基础上, 设计了两种适用于不同场景的分布式交易方法, 并构造电网安全成本影响市场主体的决策, 从而保证电网线路传输安全. 最后, 基于算例分析验证了两种方法的有效性.
  • 图  1  电力市场分布式交易示意图

    Fig.  1  Distributed trading in the power market

    图  2  发电成本和购电费用曲线

    Fig.  2  Power generation and power purchase cost

    图  3  IEEE 9节点电力系统拓扑结构

    Fig.  3  IEEE 9 bus power system topology

    图  4  两种方法求得的社会福利对比

    Fig.  4  Comparison of social welfare obtained by the two methods

    图  5  两种方法求解结果的残差

    Fig.  5  Residuals of the solution results of the two methods

    图  6  两个案例中各线路潮流的对比

    Fig.  6  Comparison of the power flow of each line in the two cases

    图  7  场景1下两种方法所求得的各发电商的出力

    Fig.  7  Power generation of each generator obtained by the two methods in Scenario 1

    图  8  场景2下两种方法所求得的各发电商的出力

    Fig.  8  Power generation of each generator obtained by the two methods in Scenario 2

    图  9  IEEE 33节点配电网系统拓扑结构

    Fig.  9  IEEE 33 bus distribution network topology

    图  10  集中式和分布式交易模式求解结果的残差

    Fig.  10  Residuals of the solution results of centralized and distributed trading models

    图  11  场景1下两种方法所求得的各发电商的出力

    Fig.  11  Power generation of each generator obtained by the two methods in Scenario 1

    图  12  场景2下两种方法所求得的各发电商的出力

    Fig.  12  Power generation of each generator obtained by the two methods in Scenario 2

    图  13  场景1下三种方法的迭代求解结果

    Fig.  13  The iterative solution results of the three methods in Scenario 1

    图  14  场景2下三种方法的迭代求解结果

    Fig.  14  The iterative solution results of the three methods in Scenario 2

    表  1  两种分布式交易场景下IEEE 9节点电力系统的发电商出力上下限(MW)

    Table  1  Upper and lower limits on generator output for IEEE 9 bus power systems in two distributed trading scenarios(MW)

    发电商 $G_1$ $G_2$ $G_3$
    场景1 $p_{G,i}^{\max }$ 350 290 400
    $p_{G,i}^{\min }$ 10 20 15
    场景2 $p_{G,i}^{\max }$ 120 100 140
    $p_{G,i}^{\min }$ 10 20 15
    下载: 导出CSV

    表  2  两种分布式交易场景下IEEE 9节点电力系统的负荷商需求上下限(MW)

    Table  2  Upper and lower limits on loaders' demand for IEEE 9 bus power systems in two distributed trading scenarios(MW)

    柔性负荷商 ${{D}_{4}}$ ${{D}_{5}}$ ${{D}_{6}}$ ${{D}_{7}}$ ${{D}_{8}}$ ${{D}_{9}}$
    场景1 $p_{D,j}^{\max }$ 150 100 145 140 150 170
    $p_{D,j}^{\min }$ 60 50 90 60 50 70
    场景2 $p_{D,j}^{\max }$ 150 90 100 140 150 150
    $p_{D,j}^{\min }$ 20 15 30 30 15 20
    下载: 导出CSV

    表  3  IEEE 9节点电力系统线路潮流上限(MW)

    Table  3  Upper limit of line power flow for IEEE 9 bus power system(MW)

    线路 1-4 4-6 6-9 3-9 9-8 8-7 7-2 7-5 5-4
    $P_{l}^{PF\max}$ 160 100 100 150 100 100 120 100 100
    下载: 导出CSV

    表  4  两种方法下市场主体的交易量对比 (MW)

    Table  4  Comparison of the trading volume of market entities obtained by the two methods (MW)

    交易量 集中式 分布式
    ${{G}_{1}}$ 155.374 155.376
    ${{G}_{2}}$ 97.747 97.747
    ${{G}_{3}}$ 126.912 126.918
    ${{D}_{4}}$ 59.998 60.002
    ${{D}_{5}}$ 50.01 50.007
    ${{D}_{6}}$ 90.007 90.007
    ${{D}_{7}}$ 60.006 60.006
    ${{D}_{8}}$ 50.009 50.006
    ${{D}_{9}}$ 70.012 70.006
    下载: 导出CSV

    表  5  IEEE 9节点系统下两种方法迭代次数和计算时间对比

    Table  5  Comparison of iterations and computation time of the two methods in IEEE 9 bus system

    对比结果 本文方法1 本文方法2
    场景1 248次, 71.5s 265次, 79.8s
    场景2 216次, 60.2s 52次, 15.7s
    下载: 导出CSV

    表  6  IEEE 33节点典型中压配电网系统下两个案例的潮流对比(MW)

    Table  6  Comparison of the power flow of each line in the two cases of the IEEE 33 bus system (MW)

    线路 $P_{l}^{PF,Case1}$ $P_{l}^{PF,Case2}$ $P_{l}^{PF\max}$
    1-2 189.22 190.36 250
    2-3 145.40 78.52 250
    3-4 136.56 98.60 150
    4-5 58.56 56.30 250
    5-6 258.55 168.56 200
    6-7 59.87 43.69 250
    7-8 25.21 8.96 250
    8-9 62.17 56.18 250
    9-10 32.74 32.80 250
    10-11 62.15 57.71 150
    11-12 16.12 15.23 150
    12-13 30.06 34.89 200
    13-14 51.10 40.55 250
    14-15 218.53 188.37 200
    15-16 39.89 22.97 150
    16-17 101.01 59.95 150
    17-18 165.36 137.69 150
    2-19 74.80 60.84 150
    19-20 95.37 87.90 250
    20-21 212.80 183.99 200
    21-22 61.00 68.73 150
    3-23 58.97 60.04 150
    23-24 45.51 37.73 200
    24-25 75.72 43.37 250
    6-26 145.70 155.01 250
    26-27 169.74 125.79 150
    27-28 243.25 188.26 200
    28-29 135.98 97.89 150
    29-30 14.31 32.59 150
    30-31 34.64 44.72 250
    31-32 43.94 44.88 150
    32-33 140.00 122.20 150
    21-8 122.87 99.63 150
    9-15 87.32 65.97 150
    12-22 120.66 156.98 200
    18-33 35.62 40.33 200
    25-29 158.77 142.65 200
    下载: 导出CSV

    表  7  IEEE 33节点系统下两种方法迭代次数和计算时间对比

    Table  7  Comparison of iterations and computation time of the two methods in IEEE 33 bus system

    对比结果 本文方法1 本文方法2
    场景1 402次, 158.3s 433次, 165.9s
    场景2 374次, 143.3s 86次, 32.6s
    下载: 导出CSV

    表  8  场景1下三种方法的迭代次数和计算时间对比

    Table  8  Comparison of iterations and computation time of the three methods in Scenario 1

    方法 原始对偶方法 F-ADMM方法 本文方法1
    迭代次数 458 262 402
    计算时间 162.2s 96.5s 158.3s
    下载: 导出CSV

    表  9  场景2下三种方法的迭代次数和计算时间对比

    Table  9  Comparison of iterations and computation time of the three methods in Scenario 2

    方法 原始对偶方法 F-ADMM方法 本文方法2
    迭代次数 395 218 86
    计算时间 149.8s 80.4s 32.6s
    下载: 导出CSV
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  • 收稿日期:  2021-12-28
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