IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i10p4152-d1149381.html
   My bibliography  Save this article

Research on Dynamic Modeling and Parameter Identification of the Grid-Connected PV Power Generation System

Author

Listed:
  • Kezhen Liu

    (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Yumin Mao

    (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Xueou Chen

    (Training and Evaluation Center of Yunnan Power Grid Co., Ltd., Kunming 650106, China)

  • Jiedong He

    (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Min Dong

    (Faculty of Information and Intelligent Engineering, Yunnan College of Business Management, Kunming 650304, China)

Abstract

With the increasing proportion of renewable energy in the new power system, the grid-connected capacity of photovoltaic (PV) units shows an obvious upward trend, but its dynamic behavior under different penetration rates significantly affects the transient stability of the power system, so it is crucial to establish a dynamic model that meets the actual working conditions and select a suitable parameter identification method. Therefore, in this paper, based on the electromechanical transient characteristics of the grid-connected PV power generation system, the corresponding dynamic discrete equivalent model is established, and the simulation platform of the grid-connected PV power generation system is built in MATLAB/Simulink to study the adaptability of the dynamic discrete equivalent model of the grid-connected PV power generation system from the single and multiple scenarios using the ordinary least squares (OLS) and bat algorithm (BA) while comparing the generalization ability of the parameters identified by the two methods to the model. The simulation results show that the generalization ability of the parameters identified by the OLS and BA for the model in the single scenario is better, indicating that the model has good adaptability; the generalization ability of a set of general parameters identified by the BA for the model in the multiple scenarios is better than that of the OLS, indicating that the parameters identified by the BA have better adaptability. In conclusion, the dynamic discrete equivalent model of the grid-connected PV power generation system proposed in this paper can accurately reflect the dynamic characteristics of the grid-connected PV power generation system, and the parameters identified by the BA are more generalized than the OLS.

Suggested Citation

  • Kezhen Liu & Yumin Mao & Xueou Chen & Jiedong He & Min Dong, 2023. "Research on Dynamic Modeling and Parameter Identification of the Grid-Connected PV Power Generation System," Energies, MDPI, vol. 16(10), pages 1-17, May.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4152-:d:1149381
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/10/4152/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/10/4152/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaabane Bouali & Horst Schulte & Abdelkader Mami, 2019. "A High Performance Optimizing Method for Modeling Photovoltaic Cells and Modules Array Based on Discrete Symbiosis Organism Search," Energies, MDPI, vol. 12(12), pages 1-32, June.
    2. Nouha Mansouri & Abderezak Lashab & Dezso Sera & Josep M. Guerrero & Adnen Cherif, 2019. "Large Photovoltaic Power Plants Integration: A Review of Challenges and Solutions," Energies, MDPI, vol. 12(19), pages 1-16, October.
    3. Fiedler, T., 2019. "Simulation of a power system with large renewable penetration," Renewable Energy, Elsevier, vol. 130(C), pages 319-328.
    4. Long, Wen & Jiao, Jianjun & Liang, Ximing & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2022. "Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm," Energy, Elsevier, vol. 249(C).
    5. Aryuanto Soetedjo & Irrine Budi Sulistiawati, 2020. "Implementing Discrete Model of Photovoltaic System on the Embedded Platform for Real-Time Simulation," Energies, MDPI, vol. 13(17), pages 1-22, August.
    6. Lee, Yu-Wei & Kuo, Chung-Feng Jeffrey & Weng, Wei-Han & Huang, Chao-Yang & Peng, Cheng-Yu, 2017. "Dynamic modeling and entity validation of a photovoltaic system," Applied Energy, Elsevier, vol. 200(C), pages 370-382.
    7. Shair, Jan & Li, Haozhi & Hu, Jiabing & Xie, Xiaorong, 2021. "Power system stability issues, classifications and research prospects in the context of high-penetration of renewables and power electronics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    8. Gao, Yuan & Dong, Jianfei & Isabella, Olindo & Santbergen, Rudi & Tan, Hairen & Zeman, Miro & Zhang, Guoqi, 2019. "Modeling and analyses of energy performances of photovoltaic greenhouses with sun-tracking functionality," Applied Energy, Elsevier, vol. 233, pages 424-442.
    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. Oluwafemi Emmanuel Oni & Omowunmi Mary Longe, 2023. "Analysis of Secondary Controller on MTDC Link with Solar PV Integration for Inter-Area Power Oscillation Damping," Energies, MDPI, vol. 16(17), pages 1-18, August.
    2. Shen, Boyang & Chen, Yu & Li, Chuanyue & Wang, Sheng & Chen, Xiaoyuan, 2021. "Superconducting fault current limiter (SFCL): Experiment and the simulation from finite-element method (FEM) to power/energy system software," Energy, Elsevier, vol. 234(C).
    3. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    4. Shan, Chuan & Sun, Kangwen & Ji, Xinzhe & Cheng, Dongji, 2023. "A reconfiguration method for photovoltaic array of stratospheric airship based on multilevel optimization algorithm," Applied Energy, Elsevier, vol. 352(C).
    5. Dhanuja Lekshmi J & Zakir Hussain Rather & Bikash C Pal, 2021. "A New Tool to Assess Maximum Permissible Solar PV Penetration in a Power System," Energies, MDPI, vol. 14(24), pages 1-21, December.
    6. Imed Khabbouchi & Dhaou Said & Aziz Oukaira & Idir Mellal & Lyes Khoukhi, 2023. "Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)," Energies, MDPI, vol. 16(5), pages 1-15, February.
    7. Nawal Rai & Amel Abbadi & Fethia Hamidia & Nadia Douifi & Bdereddin Abdul Samad & Khalid Yahya, 2023. "Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module," Mathematics, MDPI, vol. 11(8), pages 1-30, April.
    8. Giorgio M. Giannuzzi & Viktoriya Mostova & Cosimo Pisani & Salvatore Tessitore & Alfredo Vaccaro, 2022. "Enabling Technologies for Enhancing Power System Stability in the Presence of Converter-Interfaced Generators," Energies, MDPI, vol. 15(21), pages 1-13, October.
    9. Huang, Yuqing & Lan, Hai & Hong, Ying-Yi & Wen, Shuli & Yin, He, 2019. "Optimal generation scheduling for a deep-water semi-submersible drilling platform with uncertain renewable power generation and loads," Energy, Elsevier, vol. 181(C), pages 897-907.
    10. Ke Guo & Qiang Liu & Xinze Xi & Mingxuan Mao & Yihao Wan & Hao Wu, 2020. "Coordinated Control Strategy of a Combined Converter in a Photovoltaic DC Boost Collection System under Partial Shading Conditions," Energies, MDPI, vol. 13(2), pages 1-18, January.
    11. Li, Zhi & Yano, Akira & Yoshioka, Hidekazu, 2020. "Feasibility study of a blind-type photovoltaic roof-shade system designed for simultaneous production of crops and electricity in a greenhouse," Applied Energy, Elsevier, vol. 279(C).
    12. Karni Siraganyan & Amarasinghage Tharindu Dasun Perera & Jean-Louis Scartezzini & Dasaraden Mauree, 2019. "Eco-Sim: A Parametric Tool to Evaluate the Environmental and Economic Feasibility of Decentralized Energy Systems," Energies, MDPI, vol. 12(5), pages 1-22, February.
    13. Saud Alotaibi & Ahmed Darwish, 2021. "Modular Multilevel Converters for Large-Scale Grid-Connected Photovoltaic Systems: A Review," Energies, MDPI, vol. 14(19), pages 1-30, September.
    14. Zhu, Yongqiang & Liu, Jiahao & Yang, Xiaohua, 2020. "Design and performance analysis of a solar tracking system with a novel single-axis tracking structure to maximize energy collection," Applied Energy, Elsevier, vol. 264(C).
    15. Achour, Yasmine & Ouammi, Ahmed & Zejli, Driss, 2021. "Technological progresses in modern sustainable greenhouses cultivation as the path towards precision agriculture," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    16. Ji, Zhengsen & Li, Wanying & Niu, Dongxiao, 2024. "Optimal investment decision of agrivoltaic coupling energy storage project based on distributed linguistic trust and hybrid evaluation method," Applied Energy, Elsevier, vol. 353(PA).
    17. Mao, Mingxuan & Chen, Siyu & Yan, Jinyue, 2023. "Modelling pavement photovoltaic arrays with cellular automata," Applied Energy, Elsevier, vol. 330(PB).
    18. Francesco Castellani & Abdelgalil Eltayesh & Francesco Natili & Tommaso Tocci & Matteo Becchetti & Lorenzo Capponi & Davide Astolfi & Gianluca Rossi, 2021. "Wind Flow Characterisation over a PV Module through URANS Simulations and Wind Tunnel Optical Flow Methods," Energies, MDPI, vol. 14(20), pages 1-21, October.
    19. Zeno, Aldrich & Orillaza, Jordan Rel & Kolhe, Mohan Lal, 2020. "Analysing the effects of power swing on wind farms using instantaneous impedances," Renewable Energy, Elsevier, vol. 147(P1), pages 1432-1452.
    20. Uvini Perera & Amanullah Maung Than Oo & Ramon Zamora, 2022. "Sub Synchronous Oscillations under High Penetration of Renewables—A Review of Existing Monitoring and Damping Methods, Challenges, and Research Prospects," Energies, MDPI, vol. 15(22), pages 1-23, November.

    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:10:p:4152-:d:1149381. 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.