IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i22p7635-d1282646.html
   My bibliography  Save this article

A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids

Author

Listed:
  • Fei Chen

    (China Southern Power Grid Guizhou Power Grid Co., Ltd., Guiyang 550003, China)

  • Zhiyang Wang

    (School of Electrical Engineering, Guizhou University, Guiyang 550025, China)

  • Yu He

    (School of Electrical Engineering, Guizhou University, Guiyang 550025, China)

Abstract

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs’ automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis.

Suggested Citation

  • Fei Chen & Zhiyang Wang & Yu He, 2023. "A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids," Energies, MDPI, vol. 16(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7635-:d:1282646
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/22/7635/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/22/7635/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Min Li & Dachuan Xu & Dongmei Zhang & Juan Zou, 2020. "The seeding algorithms for spherical k-means clustering," Journal of Global Optimization, Springer, vol. 76(4), pages 695-708, April.
    2. Lu, Jun & Liu, Tianqi & He, Chuan & Nan, Lu & Hu, Xiaotong, 2021. "Robust day-ahead coordinated scheduling of multi-energy systems with integrated heat-electricity demand response and high penetration of renewable energy," Renewable Energy, Elsevier, vol. 178(C), pages 466-482.
    3. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Kim, Min Jae & Kim, Tong Seop & Flores, Robert J. & Brouwer, Jack, 2020. "Neural-network-based optimization for economic dispatch of combined heat and power systems," Applied Energy, Elsevier, vol. 265(C).
    5. Li, Bei & Roche, Robin & Miraoui, Abdellatif, 2017. "Microgrid sizing with combined evolutionary algorithm and MILP unit commitment," Applied Energy, Elsevier, vol. 188(C), pages 547-562.
    6. Zheng, Bingle & Wu, Xiao, 2022. "Integrated capacity configuration and control optimization of off-grid multiple energy system for transient performance improvement," Applied Energy, Elsevier, vol. 311(C).
    7. Liu, Haizhou & Shen, Xinwei & Guo, Qinglai & Sun, Hongbin, 2021. "A data-driven approach towards fast economic dispatch in electricity–gas coupled systems based on artificial neural network," Applied Energy, Elsevier, vol. 286(C).
    8. Hao Hu & Renata Sotirov, 2021. "The linearization problem of a binary quadratic problem and its applications," Annals of Operations Research, Springer, vol. 307(1), pages 229-249, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qin, Yuxiao & Liu, Pei & Li, Zheng, 2022. "Multi-timescale hierarchical scheduling of an integrated energy system considering system inertia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    2. Qin, Peijia & Tan, Xianlin & Huang, Youbin & Pan, Mingming & Ouyang, Tiancheng, 2023. "Two-stage robust optimal scheduling framework applied for microgrids: Combined energy recovery and forecast," Renewable Energy, Elsevier, vol. 214(C), pages 290-306.
    3. Soheil Mohseni & Alan C. Brent, 2022. "A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
    4. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    5. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    6. Huang, Chunjun & Zong, Yi & You, Shi & Træholt, Chresten & Zheng, Yi & Wang, Jiawei & Zheng, Zixuan & Xiao, Xianyong, 2023. "Economic and resilient operation of hydrogen-based microgrids: An improved MPC-based optimal scheduling scheme considering security constraints of hydrogen facilities," Applied Energy, Elsevier, vol. 335(C).
    7. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    8. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    9. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    10. Fatemeh Marzbani & Akmal Abdelfatah, 2024. "Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review," Energies, MDPI, vol. 17(3), pages 1-31, January.
    11. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    12. Ana Rita Silva & Ana Estanqueiro, 2022. "From Wind to Hybrid: A Contribution to the Optimal Design of Utility-Scale Hybrid Power Plants," Energies, MDPI, vol. 15(7), pages 1-19, April.
    13. El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    14. Jinyuan Liu & Shouxi Wang & Nan Wei & Yi Yang & Yihao Lv & Xu Wang & Fanhua Zeng, 2023. "An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting," Energies, MDPI, vol. 16(3), pages 1-14, January.
    15. Zhang, Hong & Yuan, Tiejiang, 2022. "Optimization and economic evaluation of a PEM electrolysis system considering its degradation in variable-power operations," Applied Energy, Elsevier, vol. 324(C).
    16. Wu, Xiao & Yang, Lihua & Zheng, Bingle, 2024. "Joint capacity configuration and demand response optimization of integrated energy system considering economic and dynamic control performance," Energy, Elsevier, vol. 301(C).
    17. He, Yi & Guo, Su & Zhou, Jianxu & Wu, Feng & Huang, Jing & Pei, Huanjin, 2021. "The many-objective optimal design of renewable energy cogeneration system," Energy, Elsevier, vol. 234(C).
    18. Hongshan Zhao & Junyang Xu & Kunyu Xu & Jingjie Sun & Yufeng Wang, 2022. "Optimal Allocation Method of Source and Storage Capacity of PV-Hydrogen Zero Carbon Emission Microgrid Considering the Usage Cost of Energy Storage Equipment," Energies, MDPI, vol. 15(13), pages 1-18, July.
    19. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    20. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7635-:d:1282646. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.