IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i16p12286-d1215491.html
   My bibliography  Save this article

Combining Data-Driven and Model-Driven Approaches for Optimal Distributed Control of Standalone Microgrid

Author

Listed:
  • Parvaiz Ahmad Ahangar

    (Department of Electrical Engineering, National Institute of Technology, Srinagar 190006, Jammu and Kashmir, India)

  • Shameem Ahmad Lone

    (Department of Electrical Engineering, National Institute of Technology, Srinagar 190006, Jammu and Kashmir, India)

  • Neeraj Gupta

    (Department of Electrical Engineering, National Institute of Technology, Srinagar 190006, Jammu and Kashmir, India)

Abstract

This paper focuses on the comprehensive restoration of both voltage and frequency in a standalone microgrid (SAMG). In a SAMG, the power balance is achieved through traditional methods such as droop control for power sharing among distributed generators (DGs). However, when such microgrids (MGs) are subjected to perturbations coming from stochastic renewables, the frequency and voltage parameters deviate from their specified values. In this paper, a novel hybrid-type consensus-based distributed controller is proposed for voltage and frequency restoration. Data-based communication is ensured among the DGs for controlling voltage and frequency parameters. Different parameters such as voltage, frequency, and active and reactive power converge successfully to their nominal values using the proposed algorithms, thereby ensuring smooth operation of inverter-dominated DGs. Additionally, the machine-learning-based long short-term memory (LSTM) algorithm is implemented for renewable power forecasting using historical data from the proposed location for visualising the insolation profile. The effectiveness of our approach is demonstrated through a SAMG, which consists of four inverters, showing that the proposed approach can improve system stability, increase efficiency and reliability, and reduce costs compared to traditional methods. The complete study is performed in Python and MATLAB environments. Our results highlight the potential of data-driven approaches to revolutionise power system operation and control.

Suggested Citation

  • Parvaiz Ahmad Ahangar & Shameem Ahmad Lone & Neeraj Gupta, 2023. "Combining Data-Driven and Model-Driven Approaches for Optimal Distributed Control of Standalone Microgrid," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12286-:d:1215491
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/16/12286/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/16/12286/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nguyen, Hoang-Phuong & Baraldi, Piero & Zio, Enrico, 2021. "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants," Applied Energy, Elsevier, vol. 283(C).
    2. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid, 2019. "Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems," Energies, MDPI, vol. 12(9), pages 1-23, May.
    3. Hou, Qingchun & Zhang, Ning & Du, Ershun & Miao, Miao & Peng, Fei & Kang, Chongqing, 2019. "Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China," Applied Energy, Elsevier, vol. 242(C), pages 205-215.
    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. Debnath, Kumar Biswajit & Jenkins, David P. & Patidar, Sandhya & Peacock, Andrew D., 2024. "Remote work might unlock solar PV's potential of cracking the ‘Duck Curve’," Applied Energy, Elsevier, vol. 367(C).
    2. Sibtain, Muhammad & Li, Xianshan & Saleem, Snoober & Ain, Qurat-ul- & Shi, Qiang & Li, Fei & Saeed, Muhammad & Majeed, Fatima & Shah, Syed Shoaib Ahmed & Saeed, Muhammad Hammad, 2022. "Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models," Renewable Energy, Elsevier, vol. 196(C), pages 648-682.
    3. Ricardo Echeverri Mart nez & Eduardo Caicedo Bravo & Wilfredo Alfonso Morales & Juan David Garcia-Racines, 2020. "A Bi-level Multi-objective Optimization Model for the Planning, Design and Operation of Smart Grid Projects. Case Study: An Islanded Microgrid," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 325-341.
    4. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
    5. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    6. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    7. Zhe Dong & Zhonghua Cheng & Yunlong Zhu & Xiaojin Huang & Yujie Dong & Zuoyi Zhang, 2023. "Review on the Recent Progress in Nuclear Plant Dynamical Modeling and Control," Energies, MDPI, vol. 16(3), pages 1-19, February.
    8. Yi Hao & Zhigang Huang & Shiqian Ma & Jiakai Huang & Jiahao Chen & Bing Sun, 2023. "Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation," Energies, MDPI, vol. 16(16), pages 1-17, August.
    9. Kanato Tamashiro & Talal Alharbi & Alexey Mikhaylov & Ashraf M. Hemeida & Narayanan Krishnan & Mohammed Elsayed Lotfy & Tomonobu Senjyu, 2021. "Investigation of Home Energy Management with Advanced Direct Load Control and Optimal Scheduling of Controllable Loads," Energies, MDPI, vol. 14(21), pages 1-14, November.
    10. Junchao Cheng & Yongyi Huang & Hongjing He & Abdul Matin Ibrahimi & Tomonobu Senjyu, 2023. "Optimal Operation of CCHP System Combined Electric Vehicles Considering Seasons," Energies, MDPI, vol. 16(10), pages 1-21, May.
    11. Chen, Jiahao & Sun, Bing & Li, Yunfei & Jing, Ruipeng & Zeng, Yuan & Li, Minghao, 2022. "Credible capacity calculation method of distributed generation based on equal power supply reliability criterion," Renewable Energy, Elsevier, vol. 201(P1), pages 534-547.
    12. Yang, Kailing & Zhang, Xi & Luo, Haojia & Hou, Xianping & Lin, Yu & Wu, Jingyu & Yu, Liang, 2024. "Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting," Energy, Elsevier, vol. 298(C).
    13. Homeyra Akter & Harun Or Rashid Howlader & Ahmed Y. Saber & Paras Mandal & Hiroshi Takahashi & Tomonobu Senjyu, 2021. "Optimal Sizing of Hybrid Microgrid in a Remote Island Considering Advanced Direct Load Control for Demand Response and Low Carbon Emission," Energies, MDPI, vol. 14(22), pages 1-19, November.
    14. Khush Bakht & Syed Abdul Rahman Kashif & Muhammad Salman Fakhar & Irfan Ahmad Khan & Ghulam Abbas, 2023. "Accelerated Particle Swarm Optimization Algorithms Coupled with Analysis of Variance for Intelligent Charging of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 16(7), pages 1-23, April.
    15. Mingyue He & Zahra Soltani & Mojdeh Khorsand & Aaron Dock & Patrick Malaty & Masoud Esmaili, 2022. "Behavior-Aware Aggregation of Distributed Energy Resources for Risk-Aware Operational Scheduling of Distribution Systems," Energies, MDPI, vol. 15(24), pages 1-18, December.
    16. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    17. Harsh Wardhan Pandey & Ramesh Kumar & Rajib Kumar Mandal, 2023. "Ranking of mitigation strategies for duck curve in Indian active distribution network using MCDM," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1255-1275, August.
    18. Binghui Han & Younes Zahraoui & Marizan Mubin & Saad Mekhilef & Mehdi Seyedmahmoudian & Alex Stojcevski, 2023. "Optimal Strategy for Comfort-Based Home Energy Management System Considering Impact of Battery Degradation Cost Model," Mathematics, MDPI, vol. 11(6), pages 1-26, March.
    19. Zhang, Yusheng & Ma, Chao & Yang, Yang & Pang, Xiulan & Liu, Lu & Lian, Jijian, 2021. "Study on short-term optimal operation of cascade hydro-photovoltaic hybrid systems," Applied Energy, Elsevier, vol. 291(C).
    20. Pierre Cayet & Arash Farnoosh, 2022. "A robust structural electric system model with significant share of intermittent renewables under auto-correlated residual demand," EconomiX Working Papers 2022-6, University of Paris Nanterre, EconomiX.

    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:jsusta:v:15:y:2023:i:16:p:12286-:d:1215491. 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.