Improving Peak-load Pricing Method for Provincial Transmission and Distribution Network Considering Power Backflow

被引:0
|
作者
Qian Junjie [1 ]
You Dahai [1 ]
Ruan Bo [2 ]
Zou Qi [1 ]
Liu Hengwei [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Hubei, Peoples R China
[2] Hubei Elect Power Co Ltd, Res Inst Econ & Technol, Wuhan 430074, Hubei, Peoples R China
关键词
D O I
10.1088/1755-1315/267/4/042022
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In view of the power backflow phenomenon in provincial transmission and distribution network caused by the fact that more and more new energy power is connected to the power grid, a provincial network voltage level pricing method considering power backflow is proposed. For exactly describing the complex power transmission relationships among various voltage levels in consideration of power backflow, a new power transmission model is proposed. And the balance parameters of the whole transmission and distribution network are gained based on the transmission data. The problems in cost sharing caused by power backflow are analysed and an improving peak-load pricing method solving the problems is proposed. Conceptions of source point transmission and distribution cost (SPTDC) and load point transmission and distribution cost (LPTDC) are defined. Through calculating the LPTDC in each voltage level, the cost apportionment is accomplished. The case study of a certain provincial power network is carried out and the effectiveness of this pricing method is proved. The results show that this method can recover all the transmission and distribution cost of the network and can authentically reflect the influence of power backflow on the transmission and distribution price.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Peak Load Shifting Benefit Evaluation of Distribution Network With Distributed Photovoltaic Considering Uncertainty
    Chen, Boda
    Zhang, Yongjun
    2018 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON), 2018, : 1681 - 1686
  • [22] Power distribution network design considering the distributed generations and differential and dynamic pricing
    Tsao, Yu-Chung
    Beyene, Tsehaye Dedimas
    Vo-Van Thanh
    Gebeyehu, Sisay Geremew
    Kuo, Tsai-Chi
    ENERGY, 2022, 241
  • [23] Peak load forecasting method of distribution network lines based on XGBoost
    Jiang J.
    Liu H.
    Li H.
    Zhao B.
    Bao W.
    Zheng M.
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2021, 49 (16): : 119 - 127
  • [24] Synthesis Load Modeling of Power System Considering Distribution Network Structure
    Qu X.
    Li X.
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2020, 44 (12): : 117 - 123
  • [25] Reactive Power Optimization in Distribution Network Considering Seasonal Load Changing
    Mao Hang-yin
    Yu Shao-feng
    Shen Pei-qi
    Cao Song-wei
    Lin Yong-tao
    2012 CHINA INTERNATIONAL CONFERENCE ON ELECTRICITY DISTRIBUTION (CICED), 2012,
  • [26] Load Factor Based Transmission Network Pricing: An Evaluation for the Improved ICRP Method
    Li, Jiangtao
    Yuan, Chenchen
    Zheng, Zhanghua
    Li, Furong
    2013 48TH INTERNATIONAL UNIVERSITIES' POWER ENGINEERING CONFERENCE (UPEC), 2013,
  • [27] LSTM Power Load Peak Prediction Method Based on Bayesian Network
    Sun, Liyuan
    Ai, Yuan
    Zhang, Yiming
    Ren, Jianyu
    Engineering Intelligent Systems, 2024, 32 (02): : 149 - 158
  • [28] A Pricing Method for Distribution System Aggregators Considering Differentiated Load Types and Price Uncertainty
    Liang, Bomiao
    Yang, Jiajia
    Hou, Beiping
    He, Zhiyuan
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2021, 36 (03) : 1973 - 1983
  • [29] Coordinated restoration method of transmission and distribution network considering distribution network fault repair scenario
    Bai, Yansong
    Gu, Xueping
    Li, Shaoyan
    Liu, Ke
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2023, 151
  • [30] A Coordination Optimization Method for Load Shedding Considering Distribution Network Reconfiguration
    Wang, Kai
    Kang, Lixia
    Yang, Songhao
    ENERGIES, 2022, 15 (21)