Power fluctuation mitigation strategy for microgrids based on an LSTM-based power forecasting method

被引:13
|
作者
Zhao, Luo [1 ]
Zhang, Xinan [2 ]
Peng, Xiuyan [1 ]
机构
[1] Harbin Engn Univ, Coll Intelligent Syst Sci & Engn, Harbin 150001, Peoples R China
[2] Univ Western Australia, Dept Elect Elect & Comp Engn, Perth, WA 6009, Australia
关键词
Ultra -short-term power forecasting; Proactive automatic voltage control; EMD-MHPSO-LSTM; Sizing of energy storage; BIDIRECTIONAL LSTM; FREQUENCY CONTROL; NEURAL-NETWORK; SMART GRIDS; VOLTAGE; OPTIMIZATION; MANAGEMENT; ALGORITHM; SYSTEM; MODE;
D O I
10.1016/j.asoc.2022.109370
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the rapid penetration of renewable generation systems and active loads, the stability and reliability of modern power systems face several challenges owing to power fluctuations caused by renewable intermittency and load uncertainty. Power fluctuations are more significant in islanded microgrids that possess low inertia. Therefore, this study proposes a novel cost-effective proactive control strategy to mitigate power fluctuations of an islanded microgrid. The proposed strategy produces an early acting control reference for generators based on an improved ultra-short-term power fluctuation forecasting algorithm to significantly increase the fluctuation compensation capacity of the generators. Moreover, the size, workload, and cost of the energy storage system reduce. A combined LSTM neural network structure is employed to achieve accurate power fluctuation forecasting. The effectiveness of the proposed method is verified on an islanded hybrid AC/DC microgrid simulation platform.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] LSTM-Based Net Load Forecasting for Wind and Solar Power-Equipped Microgrids
    Silva-Rodriguez, Jesus
    Raffoul, Elias
    Li, Xingpeng
    2024 56TH NORTH AMERICAN POWER SYMPOSIUM, NAPS 2024, 2024,
  • [2] Investigating the Power of LSTM-Based Models in Solar Energy Forecasting
    Jailani, Nur Liyana Mohd
    Dhanasegaran, Jeeva Kumaran
    Alkawsi, Gamal
    Alkahtani, Ammar Ahmed
    Phing, Chen Chai
    Baashar, Yahia
    Capretz, Luiz Fernando
    Al-Shetwi, Ali Q.
    Tiong, Sieh Kiong
    PROCESSES, 2023, 11 (05)
  • [3] Uncertain wind power forecasting using LSTM-based prediction interval
    Banik, Abhishek
    Behera, Chinmaya
    Sarathkumar, Tirunagaru. V.
    Goswami, Arup Kumar
    IET RENEWABLE POWER GENERATION, 2020, 14 (14) : 2657 - 2667
  • [4] LSTM-based Sales Forecasting Model
    Hong, Jun-Ki
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2021, 15 (04): : 1232 - 1245
  • [5] Research on Power Load Forecasting Method Based on LSTM Model
    Cui, Can
    He, Ming
    Di, Fangchun
    Lu, Yi
    Dai, Yuhan
    Lv, Fengyi
    PROCEEDINGS OF 2020 IEEE 5TH INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC 2020), 2020, : 1657 - 1660
  • [6] LSTM-based Quick Event Detection in Power Systems
    Wang, Boyu
    Li, Yan
    Yang, Jing
    2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2020,
  • [7] Advanced LSTM-Based Time Series Forecasting for Enhanced Energy Consumption Management in Electric Power Systems
    Chandrika, V. S.
    Kumar, N. M. G.
    Kamesh, Vinjamuri Venkata
    Shobanadevi, A.
    Maheswari, V.
    Sekar, K.
    Logeswaran, T.
    Rajaram, Dr. A.
    INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2024, 14 (01): : 127 - 139
  • [8] Wind Farm Power Transfer Forecasting Method Based on CNN-LSTM
    Tang Q.
    Xiang Y.
    Dai J.
    Li Z.
    Sun W.
    Liu J.
    Gongcheng Kexue Yu Jishu/Advanced Engineering Sciences, 2024, 56 (02): : 91 - 99
  • [9] LSTM-Based Coherent Mortality Forecasting for Developing Countries
    Garrido, Jose
    Shang, Yuxiang
    Xu, Ran
    RISKS, 2024, 12 (02)
  • [10] LSTM-Based Forecasting for Urban Construction Waste Generation
    Huang, Li
    Cai, Ting
    Zhu, Ya
    Zhu, Yuliang
    Wang, Wei
    Sun, Kehua
    SUSTAINABILITY, 2020, 12 (20) : 1 - 12