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Identification of microturbine model for long-term dynamic analysis of distribution networks

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  • Xu, Xiandong
  • Li, Kang
  • Qi, Fengyu
  • Jia, Hongjie
  • Deng, Jing

Abstract

As one of the most successfully commercialized distributed energy resources, the long-term effects of microturbines (MTs) on the distribution network has not been fully investigated due to the complex thermo-fluid-mechanical energy conversion processes. This is further complicated by the fact that the parameter and internal data of MTs are not always available to the electric utility, due to different ownerships and confidentiality concerns. To address this issue, a general modeling approach for MTs is proposed in this paper, which allows for the long-term simulation of the distribution network with multiple MTs. First, the feasibility of deriving a simplified MT model for long-term dynamic analysis of the distribution network is discussed, based on the physical understanding of dynamic processes that occurred within MTs. Then a three-stage identification method is developed in order to obtain a piecewise MT model and predict electro-mechanical system behaviors with saturation. Next, assisted with the electric power flow calculation tool, a fast simulation methodology is proposed to evaluate the long-term impact of multiple MTs on the distribution network. Finally, the model is verified by using Capstone C30 microturbine experiments, and further applied to the dynamic simulation of a modified IEEE 37-node test feeder with promising results.

Suggested Citation

  • Xu, Xiandong & Li, Kang & Qi, Fengyu & Jia, Hongjie & Deng, Jing, 2017. "Identification of microturbine model for long-term dynamic analysis of distribution networks," Applied Energy, Elsevier, vol. 192(C), pages 305-314.
    • Handle: RePEc:eee:appene:v:192:y:2017:i:c:p:305-314
    DOI: 10.1016/j.apenergy.2016.08.149
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    3. Omar Mohamed & Ashraf Khalil, 2020. "Progress in Modeling and Control of Gas Turbine Power Generation Systems: A Survey," Energies, MDPI, vol. 13(9), pages 1-26, May.

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