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Assessment of Credible Capacity for Intermittent Distributed Energy Resources in Active Distribution Network

Author

Listed:
  • Chen Sun

    (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Dong Liu

    (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yun Wang

    (Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yi You

    (Electric Power Research Institute of Guangdong Power Grid Corporation, Guangzhou 510080, China)

Abstract

The irregularity and randomness of distributed energy sources’ (DERs) output power characteristic usually brings difficulties for grid analysis. In order to reliably and deterministically evaluate intermittent distributed generation’s active power output, a credible capacity index for active distribution network (ADN) is proposed. According to the definition, it is a certain interval that the stochastic active power output of DERs may fall in with larger probability in all kinds of possible dynamic and time varying operation scenarios. Based on the description and analysis on the time varying scenarios, multiple scenarios considered dynamic power flow method for and are proposed. The method to calculate and evaluate credible capacity based on dynamic power flow (DPF) result is illustrated. A study case of an active distribution network with DERs integrated and containing 32 nodes is selected; multiple operation scenarios with various fractal dimension are established and used. Results of calculated credible capacity based on several groups of scenarios have been analyzed, giving the variance analysis of groups of credible capacity values. A deterministic value with the maximum occurrence probability representing credible capacity is given. Based on the same network case, an application of credible capacity to grid extension planning is given, which contributes to expenditure and cost reduction. The effectiveness and significance of the proposed credible capacity and solution method have been demonstrated and verified.

Suggested Citation

  • Chen Sun & Dong Liu & Yun Wang & Yi You, 2017. "Assessment of Credible Capacity for Intermittent Distributed Energy Resources in Active Distribution Network," Energies, MDPI, vol. 10(8), pages 1-24, July.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:8:p:1104-:d:106131
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    References listed on IDEAS

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    1. Mokryani, Geev & Hu, Yim Fun & Pillai, Prashant & Rajamani, Haile-Selassie, 2017. "Active distribution networks planning with high penetration of wind power," Renewable Energy, Elsevier, vol. 104(C), pages 40-49.
    2. Fei Chen & Dong Liu & Xiaofang Xiong, 2017. "Research on Stochastic Optimal Operation Strategy of Active Distribution Network Considering Intermittent Energy," Energies, MDPI, vol. 10(4), pages 1-23, April.
    3. Katinas, Vladislovas & Sankauskas, Donatas & Markevičius, Antanas & Perednis, Eugenijus, 2014. "Investigation of the wind energy characteristics and power generation in Lithuania," Renewable Energy, Elsevier, vol. 66(C), pages 299-304.
    4. Yun Wang & Dong Liu & Chen Sun, 2017. "A Cyber Physical Model Based on a Hybrid System for Flexible Load Control in an Active Distribution Network," Energies, MDPI, vol. 10(3), pages 1-20, February.
    5. Wenpeng Yu & Dong Liu & Yuhui Huang, 2013. "Operation Optimization Based on the Power Supply and Storage Capacity of an Active Distribution Network," Energies, MDPI, vol. 6(12), pages 1-16, December.
    6. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
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