Data-Driven Dispatchable Regions With Potentially Active Boundaries for Renewable Power Generation: Concept and Construction

被引:13
|
作者
Liu, Yanqi [1 ]
Li, Zhigang [1 ]
Wei, Wei [2 ]
Zheng, J. H. [1 ]
Zhang, Hongcai [3 ,4 ]
机构
[1] South China Univ Technol, Sch Elect Power Engn, Guangzhou 510641, Peoples R China
[2] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[3] Univ Macau, State Key Lab Internet Things Smart City, Macau 999078, Peoples R China
[4] Univ Macau, Dept Elect & Comp Engn, Macau 999078, Peoples R China
基金
中国国家自然科学基金;
关键词
Renewable energy sources; Uncertainty; Power systems; Wind power generation; Mathematical models; Indexes; Optimization; Column generation; data driven; dispatchable region; mixed-integer linear program; potentially active boundary; renewable power generation; ECONOMIC-DISPATCH; SYSTEMS;
D O I
10.1109/TSTE.2021.3138125
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The dispatchable region of volatile renewable power generation (RPG) quantifies how much uncertainty the power system can handle at a given operating point. State-of-the-art dispatchable region (DR) research has studied how system operational constraints influence the DR but has seldom considered the effect of the uncertainty features of RPG outputs. The traditional DR is generally described by a large number of boundaries, and it is computationally intensive to construct. To bridge these gaps, a novel type of DR is defined, which is enclosed by potentially active boundaries (PABs) that consider the operational constraints and uncertainty features of RPG outputs. The proposed DR is easier to construct because the PABs are only a small part of the traditional DR boundaries. The procedure for constructing the proposed DR is described in terms of the progressive search for PABs, which is formulated as a mixed-integer linear program by incorporating the discrete observed data points of RPG outputs as an approximate distribution. A parallel solution paradigm is also developed to expedite the construction procedure when using a large observed dataset. Simulation tests on the IEEE 30-bus and 118-bus systems verify the effectiveness and scalability of the proposed DR and the efficiency of the proposed algorithm.
引用
收藏
页码:882 / 891
页数:10
相关论文
共 50 条
  • [11] Measurement data-driven investigation of the actual power grid resilience with increasing renewable energy feed-in
    Schaedler, Yannik
    Sorg, Michael
    Fischer, Andreas
    ENERGY SCIENCE & ENGINEERING, 2022, 10 (01) : 145 - 154
  • [12] Data-driven Based Active Power Distribution Algorithm in Wind Farm
    Liu J.
    Zhang B.
    Zhao C.
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2019, 43 (17): : 125 - 131
  • [13] Data-Driven Coordination of Distributed Energy Resources for Active Power Provision
    Xu, Hanchen
    Dominguez-Garcia, Alejandro D.
    Sauer, Peter W.
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2019, 34 (04) : 3047 - 3058
  • [14] Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics
    Ashraf, Waqar Muhammad
    Uddin, Ghulam Moeen
    Farooq, Muhammad
    Riaz, Fahid
    Ahmad, Hassan Afroze
    Kamal, Ahmad Hassan
    Anwar, Saqib
    El-Sherbeeny, Ahmed M.
    Khan, Muhammad Haider
    Hafeez, Noman
    Ali, Arman
    Samee, Abdul
    Naeem, Muhammad Ahmad
    Jamil, Ahsaan
    Hassan, Hafiz Ali
    Muneeb, Muhammad
    Chaudhary, Ijaz Ahmad
    Sosnowski, Marcin
    Krzywanski, Jaroslaw
    ENERGIES, 2021, 14 (05)
  • [15] Efficient Database Generation for Data-Driven Security Assessment of Power Systems
    Thams, Florian
    Venzke, Andreas
    Eriksson, Robert
    Chatzivasileiadis, Spyros
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2020, 35 (01) : 30 - 41
  • [16] Active Defense Research against False Data Injection Attacks of Power CPS Based on Data-Driven Algorithms
    Bo, Xiaoyong
    Qu, Zhaoyang
    Wang, Lei
    Dong, Yunchang
    Zhang, Zhenming
    Wang, Da
    ENERGIES, 2022, 15 (19)
  • [17] Power Management in Active Distribution Systems Penetrated by Photovoltaic Inverters: A Data-Driven Robust Approach
    Mancilla-David, Fernando
    Angulo, Alejandro
    Street, Alexandre
    IEEE TRANSACTIONS ON SMART GRID, 2020, 11 (03) : 2271 - 2280
  • [18] Data-Driven Fault Location of Electric Power Distribution Systems With Distributed Generation
    Jiang, Yazhou
    IEEE TRANSACTIONS ON SMART GRID, 2020, 11 (01) : 129 - 137
  • [19] DDE-GAN: Integrating a Data-Driven Design Evaluator Into Generative Adversarial Networks for Desirable and Diverse Concept Generation
    Yuan, Chenxi
    Marion, Tucker
    Moghaddam, Mohsen
    JOURNAL OF MECHANICAL DESIGN, 2023, 145 (04)
  • [20] A Gramian angular field-based data-driven approach for multiregion and multisource renewable scenario generation
    Wu, Yifei
    Wang, Bo
    Yuan, Ran
    Watada, Junzo
    INFORMATION SCIENCES, 2023, 619 : 578 - 602