A Bi-level Approach to Transmission Network Planning Considering Operational Flexibility

被引:0
|
作者
Zhong, Wentao [1 ]
Cao, Jinjing [1 ]
Gai, Pengyu [1 ]
Liu, Jianan [2 ]
Wang, Mingqiang [2 ]
机构
[1] Binzhou Elect Co, Binzhou, Shandong, Peoples R China
[2] Shandong Univ, Minist Educ, Key Lab Power Syst Intelligent Dispatch & Control, Jinan, Peoples R China
来源
2022 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (I&CPS ASIA 2022) | 2022年
关键词
operational flexibility; bi-level model; transmission network planning; renewable energy;
D O I
10.1109/ICPSAsia55496.2022.9949795
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
With a large scale integration of fluctuating wind power, it is imperative to improve the operational flexibility in transmission network planning on the basis of considering system security and economy to achieve the goal of safe wind power consumption and reliable power supply. In this paper, a bi-level transmission network planning model considering operational flexibility is proposed to optimize the solution of transmission lines capacity and new thermal generator to be installed. The upper-level model minimizes the investment cost and the operation cost. The lower-level model calculates the lack of flexibility and loss of load of the system based on the line capacity and installed units from upper layer. Both upper and lower models can be transformed into mixed integer linear programming (MILP) problems. The effectiveness and validity of the proposed method are illustrated on the IEEE-RTS system.
引用
收藏
页码:1368 / 1374
页数:7
相关论文
共 50 条
  • [21] A bi-level model for logistics network design problem based on SPE
    Sun, HJ
    Gao, ZY
    TRAFFIC AND TRANSPORTATION STUDIES, PROCEEDINGS, 2004, : 555 - 564
  • [22] Designing electricity tariffs in the retail market: A stochastic bi-level approach
    Beraldi, Patrizia
    Khodaparasti, Sara
    INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS, 2023, 257
  • [23] Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties
    Li, Guoliang
    Lin, Xia
    Kong, Lingyuan
    Xia, Wenhua
    Yan, Shuang
    FRONTIERS IN ENERGY RESEARCH, 2024, 12
  • [24] A bi-level reinforcement learning model for optimal scheduling and planning of battery energy storage considering uncertainty in the energy-sharing community
    Kang, Hyuna
    Jung, Seunghoon
    Jeoung, Jaewon
    Hong, Juwon
    Hong, Taehoon
    SUSTAINABLE CITIES AND SOCIETY, 2023, 94
  • [25] Bi-level model for operational scheduling of a distribution company that supplies electric vehicle parking lots
    Sadati, S. Muhammad Bagher
    Moshtagh, Jamal
    Shafie-khah, Miadreza
    Rastgou, Abdollah
    Catalao, Joao P. S.
    ELECTRIC POWER SYSTEMS RESEARCH, 2019, 174
  • [26] Bi-Level Optimal Allocation of Flexible Resources for Distribution Network Considering Different Energy Storage Operation Strategies in Electricity Market
    Ren, Zhijun
    Guo, Hongxia
    Yang, Ping
    Zuo, Guanlin
    Zhao, Zhuoli
    IEEE ACCESS, 2020, 8 : 58497 - 58508
  • [27] Bi-Level Expansion Planning of Multiple Energy Systems under Carbon Emission Constraints
    Cheng, Yaohua
    Zhang, Ning
    Kang, Chongqing
    2018 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2018,
  • [28] Bi-level optimization model of an active distribution network based on demand response
    Chen Q.
    Wang W.
    Wang H.
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2022, 50 (16): : 1 - 13
  • [29] Study on the bi-level optimal clearing model of electricity market considering the weight of consumption responsibility
    Wang, Chong
    Li, Jialun
    Li, Wenjie
    Zhang, Zheng
    Shi, Yan
    Li, Wei
    ENERGY REPORTS, 2021, 7 : 5527 - 5535
  • [30] Polytope-Based Aggregation of Electric Vehicles for Day-Ahead Bi-Level Scheduling of Active Distribution Network Considering Uncertainties
    Lu, Jiangang
    Zhu, Jie
    Zhao, Ruifeng
    Guo, Wenxin
    Xu, Yinliang
    Lin, Chenhui
    2024 THE 7TH INTERNATIONAL CONFERENCE ON ENERGY, ELECTRICAL AND POWER ENGINEERING, CEEPE 2024, 2024, : 808 - 812