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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
  • [1] 孙秋野, 滕菲, 张化光. 能源互联网及其关键控制问题. 自动化学报, 2017, 43(2): 176-194 doi: 10.16383/j.aas.2017.c160390

    Sun Qiu-Ye, Teng Fei, Zhang Hua-Guang. Energy internet and its key control issues. Acta Automatica Sinica, 2017, 43(2): 176-194 doi: 10.16383/j.aas.2017.c160390
    [2] 平作为, 何维, 李俊林, 杨涛. 基于稀疏学习的微电网负载建模. 自动化学报, 2020, 46(9): 1798-1808 doi: 10.16383/j.aas.c200154

    Ping Zuo-Wei, He Wei, Li Jun-Lin, Yang Tao. Sparse learning for load modeling in microgrids. Acta Automatica Sinica, 2020, 46(9): 1798-1808 doi: 10.16383/j.aas.c200154
    [3] 李玉帅, 李天义, 高炜, 高文忠. 基于异步动态事件触发通信策略的综合能源系统分布式协同优化运行方法. 自动化学报, 2020, 46(9): 1831-1843

    Li Yu-Shuai, Li Tian-Yi, Gao Wei, Gao Wen-Zhong. Distributed collaborative optimization operation approach for integrated energy system based on asynchronous and dynamic event-triggering communication strategy. Acta Automatica Sinica, 2020, 46(9): 1831-1843
    [4] Valero S, Ortiz M, Senabre C, Alvarez C, Franco F, Gabaldón A. Methods for customer and demand response policies selection in new electricity markets. IET Generation, Transmission & Distribution, 2007, 1(1): 104-110
    [5] 张晓萱, 薛松, 杨素, 屠俊明, 魏哲, 马莉. 售电侧市场放开国际经验及其启示. 电力系统自动化, 2016, 40(9): 1-8 doi: 10.7500/AEPS20151128001

    Zhang Xiao-Xuan, Xue Song, Yang Su, Tu Jun-Ming, Wei Zhe, Ma Li. International experience and lessons in power sales side market liberalization. Automation of Electric Power Systems, 2016, 40(9): 1-8 doi: 10.7500/AEPS20151128001
    [6] 单俊嘉, 胡俊杰, 吴界辰. 面向虚拟电厂能量管理的点对点市场交易机制与模型. 电网技术, 2020, 44(9): 3401-3408

    Shan Jun-Jia, Hu Jun-Jie, Wu Jie-Chen. Peer-to-peer market trading mechanism and model for virtual power plant energy management. Power System Technology, 2020, 44(9): 3401-3408
    [7] Iweh C D, Gyamfi S, Tanyi E, Effah-Donyina E. Distributed generation and renewable energy integration into the grid: prerequisites, push factors, practical options, issues and merits. Energies, 2021, 14(17): 1-34
    [8] Bahrami S, Amini M. A decentralized trading algorithm for an electricity market with generation uncertainty. Applied Energy, 2018, 218: 520-532 doi: 10.1016/j.apenergy.2018.02.157
    [9] 田硕. 电动汽车聚合商参与日前市场的运营优化与竞标策略研究. 华北电力大学, 中国, 2018

    Tian Shuo. Operation Optimization and Bidding Strategy of the Electric Vehicle Aggregator Participating in Day-ahead Electricity Markets[Master dissertation], North China Electric Power University, China, 2018
    [10] 袁晓冬, 费骏韬, 胡波, 张友旺, 葛乐. 资源聚合商模式下的分布式电源、储能与柔性负荷联合调度模型. 电力系统保护与控制, 2019, 47(22): 17-26

    Yuan Xiao-Dong, Fei Jun-Tao, Hu Bo, Zhang You-Wang, Ge Le. Joint scheduling model of distributed generation, energy storage and flexible load under resource aggregator mode. Power System Protection and Control, 2019, 47(22): 17-26
    [11] 林俐, 许冰倩, 王皓怀. 典型分布式发电市场化交易机制分析与建议. 电力系统自动化, 2019, 43(4): 1-8 doi: 10.7500/AEPS20180829001

    Lin Li, Xu Bing-Qian, Wang Hao-Huai. Analysis and recommendations of typical market-based distributed generation trading mechanisms. Automation of Electric Power Systems, 2019, 43(4): 1-8 doi: 10.7500/AEPS20180829001
    [12] 蒋嗣凡. 分布式光伏P2P交易机制和交易策略研究. 浙江大学, 中国, 2020

    Jiang Si-Fan. Research on P2P Trading Mechanism and Strategy of Distributed PV Generation[Master dissertation], Zhejiang University, China, 2020
    [13] Paudel A, Chaudhari K, Chao L, Gooi H. Peer-to-peer energy trading in a prosumer based community microgrid: a game-theoretic model. IEEE Transactions on Industrial Electronics, 2019, 66(8): 6087-6097 doi: 10.1109/TIE.2018.2874578
    [14] Zhang K, Troitzsch S, Hanif S, Hamacher T. Coordinated market design for peer-to-peer energy trade and ancillary services in distribution grids. IEEE Transactions on Smart Grid, 2020, 11(4): 2929-2941 doi: 10.1109/TSG.2020.2966216
    [15] Paudel A, Sampath L, Yang J, Gooi H. Peer-to-peer energy trading in smart grid considering power losses and network fees. IEEE Transactions on Smart Grid, 2020, 11(6): 4727-4737 doi: 10.1109/TSG.2020.2997956
    [16] Tushar W, Saha T K, Yuen C, Smith D, Poor H V. Peer-to-peer trading in electricity networks: an overview. IEEE Transactions on Smart Grid, 2020, 11(4): 3185-3200 doi: 10.1109/TSG.2020.2969657
    [17] 马腾, 刘洋, 刘俊, 蒋拯, 许立雄. 智能合约技术下微电网群电能分布式交易模型. 电力建设, 2021, 42(1): 41-48 doi: 10.12204/j.issn.1000-7229.2021.01.005

    Ma Teng, Liu Yang, Liu Jun, Jiang Zheng, Xu Li-Xiong. Distributed transaction model of electricity in multi-microgrid applying smart contract technology. Electric Power Construction, 2021, 42(1): 41-48 doi: 10.12204/j.issn.1000-7229.2021.01.005
    [18] Guerrero J, Chapman A, Verbi G. Decentralized P2P energy trading under network constraints in a low-voltage network. IEEE Transactions on Smart Grid, 2019, 10(5): 5163-5173 doi: 10.1109/TSG.2018.2878445
    [19] Khorasany M, Mishra Y, Ledwich G. A decentralized bilateral energy trading system for peer-to-peer electricity markets. IEEE Transactions on Industrial Electronics, 2020, 67(6): 4646-4657 doi: 10.1109/TIE.2019.2931229
    [20] Morstyn T, Teytelboym A, Hepburn C, McCulloch M D. Integrating P2P energy trading with probabilistic distribution locational marginal pricing. IEEE Transactions on Smart Grid, 2020, 11(4): 3095-3106 doi: 10.1109/TSG.2019.2963238
    [21] Coffrin C, Knueven B, Holzer J, Vuffary M. The impacts of convex piecewise linear cost formulations on AC optimal power flow. Electric Power Systems Research, 2021, 199(1): 1-11
    [22] 王怡, 杨知方, 余娟, 赵唯嘉. 节点电价与对偶乘子的内在关联分析与扩展. 电力系统自动化, 2021, 45(6): 82-91

    Wang Yi, Yang Zhi-Fang, Yu Juan, Zhao Wei-Jia. Analysis and extension of internet relationship between locational marginal price and dual multiplier. Automation of Electric Power Systems, 2021, 45(6): 82-91
    [23] Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine learning, 2011, 3(1): 1-12
    [24] Bertsekas D. Nonlinear Programming. Belmont: Athena Scientific, 1999. 234-244, 334
    [25] Ullah M H, Park J D. Peer-to-peer energy trading in transactive markets considering physical network constraints. IEEE Transactions on Smart Grid, 2021, 12(4): 3390-3403 doi: 10.1109/TSG.2021.3063960
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  • 收稿日期:  2021-12-28
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