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Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation

Author

Listed:
  • Yi Hao

    (State Grid Tianjin Electric Power Company, Tianjin 300010, China)

  • Zhigang Huang

    (State Grid Tianjin Electric Power Company, Tianjin 300010, China)

  • Shiqian Ma

    (State Grid Tianjin Electric Power Research Institute, Tianjin 300010, China)

  • Jiakai Huang

    (State Grid Tianjin Electric Power Research Institute, Tianjin 300010, China)

  • Jiahao Chen

    (School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Bing Sun

    (School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

More and more distributed generation (DG) and energy storage (ES) devices are being connected to the distribution network (DN). They have the potential of maintaining a stable supply load during failure periods when using islanding operations. Therefore, DG and ES have capacity value, i.e., improving the power supply capability of the system. However, there are strong fluctuations in DG outputs, and the operations of ES devices have sequential characteristics. The same capacity of DG has different load-bearing capabilities compared to conventional thermal or hydroelectric units. This paper proposes a method for evaluation of power supply capability improvement in DNs. First, the temporal fluctuation in both power source and load demand during fault periods is considered. A DN island partition model considering the secondary power outage constraint is established. Then, a modified genetic algorithm is designed. The complex island partition model is solved to achieve accurate power supply reliability evaluation. And the incremental power supply capability associated to DG and ES devices is calculated. Finally, a case study is conducted on the PG&E 69-bus system to verify the effectiveness of the proposed method. It is found that with a 20% configuration ratio of ES devices, the power supply capability improvement brought about by 6 MW DG can reach about 773 kW.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6062-:d:1220364
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    References listed on IDEAS

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    1. Xu, Keyi & Yan, Jie & Zhang, Hao & Zhang, Haoran & Han, Shuang & Liu, Yongqian, 2021. "Quantile based probabilistic wind turbine power curve model," Applied Energy, Elsevier, vol. 296(C).
    2. 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.
    3. Zhao, Jinli & Zhang, Mengzhen & Yu, Hao & Ji, Haoran & Song, Guanyu & Li, Peng & Wang, Chengshan & Wu, Jianzhong, 2019. "An islanding partition method of active distribution networks based on chance-constrained programming," Applied Energy, Elsevier, vol. 242(C), pages 78-91.
    4. Zheng, Boshen & Wei, Wei & Chen, Yue & Wu, Qiuwei & Mei, Shengwei, 2022. "A peer-to-peer energy trading market embedded with residential shared energy storage units," Applied Energy, Elsevier, vol. 308(C).
    5. Paik, Chunhyun & Chung, Yongjoo & Kim, Young Jin, 2021. "ELCC-based capacity credit estimation accounting for uncertainties in capacity factors and its application to solar power in Korea," Renewable Energy, Elsevier, vol. 164(C), pages 833-841.
    6. Sun, Bing & Yu, Yixin & Qin, Chao, 2017. "Should China focus on the distributed development of wind and solar photovoltaic power generation? A comparative study," Applied Energy, Elsevier, vol. 185(P1), pages 421-439.
    7. 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.
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