Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2023 , Vol. 57 >Issue 7: 887 - 898

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.061

New Type Power System and the Integrated Energy

Calculation Method of Network Usage Charge for Market-Oriented Trading in Distributed Generation Market

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  • 1. Department of Electrical Engineering, University of Shanghai for Science and Technology,Shanghai 200093, China
    2. Quzhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Quzhou 324000, Zhejiang, China

Received date: 2022-03-09

  Revised date: 2022-04-18

  Accepted date: 2022-05-05

  Online published: 2023-01-11

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Abstract

With the gradual advancement of the market-oriented process of distributed generation, it is difficult to accurately distinguish the use degree of power grid assets by prosumers via pricing method of uniform calculation of network usage charge according to user access voltage. Therefore, this paper proposes a calculation method of network usage charge suitable for market-oriented trading of distributed generation. The characteristics of the peer-to-peer (P2P) trading model and the community-based (CB) trading model in distributed generation market are discussed from the perspective of prosumers. Meanwhile, the power trading models of the P2P model and the CB model are constructed. The optimal power flow model based on second-order cone relaxation is used to determine the distribution of power flow in distribution network, and the distribution locational marginal price is calculated with the economic significance of dual multiplier. Considering the transitivity of dual multipliers, calculation models of the network usage charge of the P2P trading model and the CB trading model are established by coupling the power trading model and the optimal power flow model. The limitations of the CB trading model are analyzed, and the Shapley value method is used to realize the fair allocation of network usage charge according to marginal contribution. By using the improved IEEE15 bus and IEEE123 bus test systems, the availability and feasibility of the proposed calculation method of network usage charge in distributed generation market are verified.

Key words： distributed generation; trading model; distribution locational marginal price (DLMP); network usage charge; Shapley value

Cite this article

WU Lei, HAN Dong, MAO Guijiang, LIU Wei, ZHOU Yangfei . Calculation Method of Network Usage Charge for Market-Oriented Trading in Distributed Generation Market[J]. Journal of Shanghai Jiaotong University, 2023 , 57(7) : 887 -898 . DOI: 10.16183/j.cnki.jsjtu.2022.061

References

[1] 肖云鹏, 王锡凡, 王秀丽, 等. 面向高比例可再生能源的电力市场研究综述[J]. 中国电机工程学报, 2018, 38(3): 663-674.
[1] XIAO Yunpeng, WANG Xifan, WANG Xiuli, et al. Review on electricity market towards high proportion of renewable energy[J]. Proceedings of the CSEE, 2018, 38(3): 663-674.
[2] 中华人民共和国国家发展和改革委员会, 国家能源局. 关于开展分布式发电市场化交易试点的通知[EB/OL]. (2017-10-31)[2022-01-01]. http://zfxxgk.nea.gov.cn/auto87/201711/t20171113_3055.htm.
[2] National Development and Reform Commission, National Energy Administration. Notice on piloting market-based distributed generation trading[EB/OL]. (2017-10-31)[2022-01-01]. http://zfxxgk.nea.gov.cn/auto87/201711/t20171113_3055.htm.
[3] ILI? M D. Toward regional transmission provision and its pricing in New England[J]. Utilities Policy, 1997, 6(3): 245-256.
[4] ZOLEZZI J, RUDNICK H, DANITZ F, et al. Review of usage-based transmission cost allocation methods under open access[J]. IEEE Transactions on Power Systems, 2001, 16(4): 933-934.
[5] HAPP H H. Cost of wheeling methodologies[J]. IEEE Transactions on Power Systems, 1994, 9(1): 147-156.
[6] KIRSCHEN D, ALLAN R, STRBAC G. Contributions of individual generators to loads and flows[J]. IEEE Transactions on Power Systems, 1997, 12(1): 52-60.
[7] 王冰钰, 颜拥, 文福拴, 等. 基于区块链的分布式电力交易机制[J]. 电力建设, 2019, 40(12): 3-10.
[7] WANG Bingyu, YAN Yong, WEN Fushuan, et al. A blockchain based distributed power trading mechanism[J]. Electric Power Construction, 2019, 40(12): 3-10.
[8] ULLAH M H, PARK J D. Peer-to-peer energy trading in transactive markets considering physical network constraints[J]. IEEE Transactions on Smart Grid, 2021, 12(4): 3390-3403.
[9] PAUDEL A, SAMPATH L P M I, YANG J W, et al. Peer-to-peer energy trading in smart grid considering power losses and network fees[J]. IEEE Transactions on Smart Grid, 2020, 11(6): 4727-4737.
[10] 石方迪, 刘敦楠, 余涛, 等. 适应光伏学习曲线的分布式交易过网费机制[J]. 智慧电力, 2020, 48(3): 96-103.
[10] SHI Fangdi, LIU Dunnan, YU Tao, et al. Probe into mechanism of use of system charge in distributed trading adapted to photovoltaic learning curve[J]. Smart Power, 2020, 48(3): 96-103.
[11] ABDELMOTTELEB I, GóMEZ T, CHAVES V, et al. Designing efficient distribution network charges in the context of active customers[J]. Applied Energy, 2018, 210: 815-826.
[12] 芮涛, 李国丽, 王群京, 等. 配电侧多微电网日前电能交易纳什议价方法[J]. 电网技术, 2019, 43(7): 2576-2585.
[12] RUI Tao, LI Guoli, WANG Qunjing, et al. Nash bargaining method for multi-microgrid energy trading in distribution network[J]. Power System Technology, 2019, 43(7): 2576-2585.
[13] 吴治国, 刘继春, 张帅, 等. 多售电主体点对点交易模式及其动态过网费机制[J]. 电力系统自动化, 2021, 45(19): 100-108.
[13] WU Zhiguo, LIU Jichun, ZHANG Shuai, et al. Peer-to-peer transaction model of multiple power sellers and its dynamic network fee mechanism[J]. Automation of Electric Power Systems, 2021, 45(19): 100-108.
[14] 姚昊天, 向月, 刘俊勇. 基于过网费动态更新的分布式电源多主体协同规划方法[J]. 电力系统自动化, 2021, 45(17): 70-78.
[14] YAO Haotian, XIANG Yue, LIU Junyong. Multi-agent collaborative planning method for distributed generators based on dynamic updating of network tariffs[J]. Automation of Electric Power Systems, 2021, 45(17): 70-78.
[15] MA L, WANG L F, LIU Z X. Multi-level trading community formation and hybrid trading network construction in local energy market[J]. Applied Energy, 2021, 285: 116399.
[16] PARAG Y, SOVACOOL B K. Electricity market design for the prosumer era[J]. Nature Energy, 2016, 1: 16032.
[17] KHORASANY M, MISHRA Y, LEDWICH G. Distributed market clearing approach for local energy trading in transactive market[C]// 2018 IEEE Power & Energy Society General Meeting. Portland, USA: IEEE, 2018: 1-5.
[18] MORSTYN T, TEYTELBOYM A, MCCULLOCH M D. Designing decentralized markets for distribution system flexibility[J]. IEEE Transactions on Power Systems, 2019, 34(3): 2128-2139.
[19] MORET F, PINSON P. Energy collectives: A community and fairness based approach to future electricity markets[J]. IEEE Transactions on Power Systems, 2019, 34(5): 3994-4004.
[20] PAPAVASILIOU A. Analysis of distribution locational marginal prices[J]. IEEE Transactions on Smart Grid, 2018, 9(5): 4872-4882.
[21] 常垚, 程林, 黄仁乐, 等. 基于夏普利值的中压配电网扩建费用分摊方法[J]. 电网技术, 2016, 40(11): 3322-3327.
[21] CHANG Yao, CHENG Lin, HUANG Renle, et al. Distribution system expansion cost allocation based on shapley value[J]. Power System Technology, 2016, 40(11): 3322-3327.
[22] JUNQUEIRA M, DA COSTA L C, BARROSO L A, et al. An aumann-shapley approach to allocate transmission service cost among network users in electricity markets[J]. IEEE Transactions on Power Systems, 2007, 22(4): 1532-1546.
[23] MOLINA Y P, SAAVEDRA O R, AMARíS H. Transmission network cost allocation based on circuit theory and the aumann-shapley method[J]. IEEE Transactions on Power Systems, 2013, 28(4): 4568-4577.
[24] MOLINA Y P, PRADA R B, SAAVEDRA O R. Complex losses allocation to generators and loads based on circuit theory and aumann-shapley method[J]. IEEE Transactions on Power Systems, 2010, 25(4): 1928-1936.
[25] VOROPAI N I, IVANOVA E Y. Shapley game for expansion planning of generating companies at many non-coincident criteria[J]. IEEE Transactions on Power Systems, 2006, 21(4): 1630-1637.
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