A Transparent Data-Driven Method for Stability-Constrained Load Restoration Considering Multi-Phase Load Dynamics

被引:0
|
作者
Xie, Dunjian [1 ]
Xu, Yan [1 ]
Nadarajan, Sivakumar [2 ]
Viswanathan, Vaiyapuri [2 ]
Gupta, Amit Kumar [2 ]
机构
[1] Nanyang Technol Univ, Nanyang 639798, Singapore
[2] Rolls Royce Singapore Pte Ltd, Rolls Royce Elect, Singapore 797565, Singapore
关键词
Load modeling; Power system stability; Stability criteria; Power system dynamics; Numerical stability; Optimization; Transient analysis; Cold load pickup; load dynamics; stability; load restoration; data-driven;
D O I
10.1109/TPWRS.2023.3247945
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Load dynamics significantly complex the system stability behavior during the load restoration process after an outage event. It is highly challenging to model the load dynamics and solve the stability-constrained load restoration problem. In this paper, the load dynamic model considering the inrush phase and enduring phase of the cold load pickup effects are firstly modeled. Then, the load restoration optimization model is formulated with stability constraints, ramping constraints, and system operation models. A transparent data-driven method based on optimal decision trees is developed to address all the short-term stability criteria (including frequency, voltage, and transient stability) considering the inrush phase of load dynamics. A set of accurate, linear, and tractable stability constraints are extracted offline and implemented for fast online decision-making. Numerical studies based on the New-England testing system are conducted to validate the proposed method.
引用
收藏
页码:366 / 379
页数:14
相关论文
共 50 条
  • [1] Dynamic Frequency-Constrained Load Restoration Considering Multi-Phase Cold Load Pickup Behaviors
    Xie, Dunjian
    Xu, Yan
    Nadarajan, Sivakumar
    Viswanathan, Vaiyapuri
    Gupta, Amit Kumar
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2024, 39 (01) : 107 - 118
  • [2] Data-Driven Joint Voltage Stability Assessment Considering Load Uncertainty: A Variational Bayes Inference Integrated With Multi-CNNs
    Cui, Mingjian
    Li, Fangxing
    Cui, Hantao
    Bu, Siqi
    Shi, Di
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2022, 37 (03) : 1904 - 1915
  • [3] Data-driven load profiles and the dynamics of residential electricity consumption
    Mehrnaz Anvari
    Elisavet Proedrou
    Benjamin Schäfer
    Christian Beck
    Holger Kantz
    Marc Timme
    Nature Communications, 13
  • [4] Data-driven load profiles and the dynamics of residential electricity consumption
    Anvari, Mehrnaz
    Proedrou, Elisavet
    Schaefer, Benjamin
    Beck, Christian
    Kantz, Holger
    Timme, Marc
    NATURE COMMUNICATIONS, 2022, 13 (01)
  • [5] Data Driven Load Forecasting Method Considering Demand Response
    Luo, Fengzhang
    Yang, Xin
    Wei, Wei
    Lu, Hai
    Zhang, Tianyu
    Shao, Jingpeng
    2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2020,
  • [6] A data-driven load forecasting method for incentive demand response
    Wang, Haixin
    Yuan, Jiahui
    Qi, Guanqiu
    Li, Yanzhen
    Yang, Junyou
    Dong, Henan
    Ma, Yiming
    ENERGY REPORTS, 2022, 8 : 1013 - 1019
  • [7] Data-driven load identification method of structures with uncertain parameters
    Cui, Wenxu
    Jiang, Jinhui
    Sun, Huiyu
    Yang, Hongji
    Wang, Xu
    Wang, Lihui
    Li, Hongqiu
    ACTA MECHANICA SINICA, 2024, 40 (02)
  • [8] Analytical reliability evaluation of large urban dlistribution systems with multi-phase load restoration
    Carpaneto, E
    Chicco, G
    ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS, 2003, 11 (04): : 209 - 216
  • [9] On the Impact of Data-Driven Stochastic Load Models on Power System Dynamics
    Adeen, Muhammad
    Milano, Federico
    2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM, 2023,
  • [10] A novel data-driven multi-energy load forecasting model
    Yao, Yong
    Li, Shizhu
    Wu, Zhichao
    Yu, Chi
    Liu, Xinglei
    Yuan, Keyu
    Liu, JiaCheng
    Wu, Zeyang
    Liu, Jun
    Frontiers in Energy Research, 2022, 10