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Optimal Allocation of Intermittent Distributed Generation under Active Management

Author

Listed:
  • Zhong Shi

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

  • Zhijie Wang

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

  • Yue Jin

    (College of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China)

  • Nengling Tai

    (Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China)

  • Xiuchen Jiang

    (Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, Shanghai 200240, China)

  • Xiaoyu Yang

    (Economic & Technology Research Institute, State Shandong Electric Power Company, Jinan 250000, China)

Abstract

In recent years, distributed generation (DG) has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. At present, most distributed generators follow the principle of “installation is forgetting” after they are connected to a distribution network. This principle limits the popularization and benefit of distributed generation to a great extent. In order to solve these problems, this paper presents a two-tier model for optimal allocation of distributed power sources in active distribution networks (ADN). The objective of upper level planning is to minimize the annual comprehensive cost of distribution networks, and the objective of lower level planning is to minimize the active power cut-off of distributed generation through active management mode. Taking into account the time series characteristics of load and distributed power output, the improved K-means clustering method is used to cluster wind power and the photovoltaic output in different scenarios to get the daily curves in typical scenarios, and a bilevel programming model of distributed generation based on multiscenario analysis is established under active management mode. The upper level programming model is solved by Quantum genetic algorithm (QGA), and the lower level programming model is solved by the primal dual interior point method (PDIPM). The rationality of the model and the effectiveness of the algorithm are verified by simulation and analysis of a 33-bus distribution network.

Suggested Citation

  • Zhong Shi & Zhijie Wang & Yue Jin & Nengling Tai & Xiuchen Jiang & Xiaoyu Yang, 2018. "Optimal Allocation of Intermittent Distributed Generation under Active Management," Energies, MDPI, vol. 11(10), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2608-:d:172959
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    References listed on IDEAS

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    1. Mehigan, L. & Deane, J.P. & Gallachóir, B.P.Ó. & Bertsch, V., 2018. "A review of the role of distributed generation (DG) in future electricity systems," Energy, Elsevier, vol. 163(C), pages 822-836.
    2. Allan, Grant & Eromenko, Igor & Gilmartin, Michelle & Kockar, Ivana & McGregor, Peter, 2015. "The economics of distributed energy generation: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 543-556.
    3. Anaya, Karim L. & Pollitt, Michael G., 2017. "Going smarter in the connection of distributed generation," Energy Policy, Elsevier, vol. 105(C), pages 608-617.
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    Cited by:

    1. Zhang, Shenxi & Cheng, Haozhong & Li, Ke & Tai, Nengling & Wang, Dan & Li, Furong, 2018. "Multi-objective distributed generation planning in distribution network considering correlations among uncertainties," Applied Energy, Elsevier, vol. 226(C), pages 743-755.
    2. Hongwei Li & Qing Xu & Shitao Wang & Huihui Song, 2022. "Peak Shaving Methods of Distributed Generation Clusters Using Dynamic Evaluation and Self-Renewal Mechanism," Energies, MDPI, vol. 15(19), pages 1-17, September.
    3. Huy, Phung Dang & Ramachandaramurthy, Vigna K. & Yong, Jia Ying & Tan, Kang Miao & Ekanayake, Janaka B., 2020. "Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage," Energy, Elsevier, vol. 195(C).

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