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Research on Modeling of Microgrid Based on Data Testing and Parameter Identification

Author

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  • Junjun Zhang

    (College of Information Science and Technology, Fudan University, Shanghai 200433, China
    State Key Laboratory of Operation and Control of Renewable Energy & Storage, China EPRI, Nanjing 210003, China)

  • Yaojie Sun

    (College of Information Science and Technology, Fudan University, Shanghai 200433, China)

  • Meiyin Liu

    (State Key Laboratory of Operation and Control of Renewable Energy & Storage, China EPRI, Nanjing 210003, China)

  • Wei Dong

    (State Key Laboratory of Operation and Control of Renewable Energy & Storage, China EPRI, Nanjing 210003, China)

  • Pingping Han

    (College of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China)

Abstract

The model parameter identification based on real operation data is a means to accurately determine the simulation parameters of the microgrid, but the real operation data cannot guarantee the exact agreement with the required data for parameter identification, which has become an important restriction factor in the accurate simulation and analysis of the dynamics of the microgrid. This paper provides a method of modeling of microgrid based on data testing and parameter identification. In this paper, the method of parameter trajectory sensitivity is first introduced. Then, the data testing scheme for parameter identification is presented, and the parameter identification flow chart is given. Thirdly, a microgrid demonstration system in China is taken as an example, the important parameters of the distributed photovoltaic, direct-drive wind turbine and energy storage unit in the system are obtained by data testing and parameter identification, and in the end, the accuracy of the model is verified through the comparison of the simulation data and the test data of the microgrid during grid-connection/island switching process. The obtained microgrid model provides a base model for the analysis of the overall characteristics, such as the transient stability, as well as power quality of the microgrid.

Suggested Citation

  • Junjun Zhang & Yaojie Sun & Meiyin Liu & Wei Dong & Pingping Han, 2018. "Research on Modeling of Microgrid Based on Data Testing and Parameter Identification," Energies, MDPI, vol. 11(10), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2525-:d:171405
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    References listed on IDEAS

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    1. Weiqiang Dong & Yanjun Li & Ji Xiang, 2016. "Optimal Sizing of a Stand-Alone Hybrid Power System Based on Battery/Hydrogen with an Improved Ant Colony Optimization," Energies, MDPI, vol. 9(10), pages 1-17, September.
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    3. Pingping Han & Zihao Lin & Lei Wang & Guijun Fan & Xiaoan Zhang, 2018. "A Survey on Equivalence Modeling for Large-Scale Photovoltaic Power Plants," Energies, MDPI, vol. 11(6), pages 1-14, June.
    4. Sehrish Malik & DoHyeun Kim, 2018. "Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks," Energies, MDPI, vol. 11(5), pages 1-21, May.
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    Cited by:

    1. Li Wang & Teng Qiao & Bin Zhao & Xiangjun Zeng & Qing Yuan, 2020. "Modeling and Parameter Optimization of Grid-Connected Photovoltaic Systems Considering the Low Voltage Ride-through Control," Energies, MDPI, vol. 13(15), pages 1-23, August.
    2. Eros D. Escobar & Tatiana Manrique & Idi A. Isaac, 2022. "Campus Microgrid Data-Driven Model Identification and Secondary Voltage Control," Energies, MDPI, vol. 15(21), pages 1-19, October.

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