Stochastic Unit Commitment with High-penetration Offshore Wind Power Generation in Typhoon Scenarios

被引:0
|
作者
Yanqi Liu [1 ]
Dundun Liu [2 ,3 ]
Hongcai Zhang [2 ,1 ]
机构
[1] the State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering, University of Macau
[2] IEEE
[3] the University of Macau Zhuhai UM Science and Technology Research
关键词
D O I
暂无
中图分类号
TM614 [风能发电];
学科分类号
0807 ;
摘要
To tackle the energy crisis and climate change, wind farms are being heavily invested in across the world. In China's coastal areas, there are abundant wind resources and numerous offshore wind farms are being constructed. The secure operation of these wind farms may suffer from typhoons, and researchers have studied power system operation and resilience enhancement in typhoon scenarios. However, the intricate movement of a typhoon makes it challenging to evaluate its spatial-temporal impacts. Most published papers only consider predefined typhoon trajectories neglecting uncertainties. To address this challenge, this study proposes a stochastic unit commitment model that incorporates high-penetration offshore wind power generation in typhoon scenarios. It adopts a datadriven method to describe the uncertainties of typhoon trajectories and considers the realistic anti-typhoon mode in offshore wind farms. A two-stage stochastic unit commitment model is designed to enhance power system resilience in typhoon scenarios. We formulate the model into a mixed-integer linear programming problem and then solve it based on the computationally-efficient progressive hedging algorithm(PHA). Finally, numerical experiments validate the effectiveness of the proposed method.
引用
收藏
页码:535 / 546
页数:12
相关论文
共 50 条
  • [1] Stochastic Unit Commitment with High-Penetration Offshore Wind Power Generation in Typhoon Scenarios
    Liu, Yanqi
    Liu, Dundun
    Zhang, Hongcai
    JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2024, 12 (02) : 535 - 546
  • [2] Stochastic Unit Commitment with Wind Generation Penetration
    Ahmed, Mohamed Hassan
    Bhattacharya, Kankar
    Salama, M. M. A.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2012, 40 (12) : 1405 - 1422
  • [3] Distributed Stochastic Market Clearing With High-Penetration Wind Power
    Zhang, Yu
    Giannakis, Georgios B.
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2016, 31 (02) : 895 - 906
  • [4] Unit Commitment for Systems with High Penetration of Wind Power
    Zhang Na
    Song Zhuoran
    He Xin
    Zhang Mingli
    Gao Jing
    Li Weidong
    2017 FIRST IEEE INTERNATIONAL CONFERENCE ON ENERGY INTERNET (ICEI 2017), 2017, : 101 - 106
  • [5] Statistical reliability of wind power scenarios and stochastic unit commitment cost
    Sari D.
    Ryan S.M.
    Energy Systems, 2018, 9 (04) : 873 - 898
  • [6] Hybrid Stochastic/Deterministic Unit Commitment with Wind Power Generation
    Tan, Wen-Shan
    Shaaban, Mohamed
    2015 IEEE EINDHOVEN POWERTECH, 2015,
  • [7] A stochastic security constrained unit commitment model for reconfigurable networks with high wind power penetration
    Nasrolahpour, Ehsan
    Ghasemi, Hassan
    ELECTRIC POWER SYSTEMS RESEARCH, 2015, 121 : 341 - 350
  • [8] Unit commitment in a high wind-power penetration system
    Wu, Yuan-Kang
    Chang, Shih-Ming
    Chang, Li-Tzo
    Dinh Thanh Viet
    5TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY SYSTEMS ENGINEERING (CPESE 2018), 2019, 156 : 18 - 22
  • [9] Stochastic Security Constrained Unit Commitment with High Penetration of Wind Farms
    Kia, Mohsen
    Hosseini, Seyed Hamid
    Heidari, Alireza
    Lotfi, Mohamed
    Catalao, Joao P. S.
    Shafie-khah, Miadreza
    Osorio, Gerardo
    Santos, Sergio F.
    2019 IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2019 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE), 2019,
  • [10] Stochastic Unit Commitment Performance Considering Monte Carlo Wind Power Scenarios
    Rachunok, Benjamin
    Staid, Andrea
    Watson, Jean-Paul
    Woodruff, David L.
    Yang, Dominic
    2018 IEEE INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS), 2018,