IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2018i1p109-d193943.html
   My bibliography  Save this article

Data-Driven Kernel Extreme Learning Machine Method for the Location and Capacity Planning of Distributed Generation

Author

Listed:
  • Jingjing Tu

    (School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Yonghai Xu

    (School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Zhongdong Yin

    (School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

For the integration of distributed generations such as large-scale wind and photovoltaic power generation, the characteristics of the distribution network are fundamentally changed. The intermittence, variability, and uncertainty of wind and photovoltaic power generation make the adjustment of the network peak load and the smooth control of power become the key issues of the distribution network to accept various types of distributed power. This paper uses data-driven thinking to describe the uncertainty of scenery output, and introduces it into the power flow calculation of distribution network with multi-class DG, improving the processing ability of data, so as to better predict DG output. For the problem of network stability and operational control complexity caused by DG access, using KELM algorithm to simplify the complexity of the model and improve the speed and accuracy. By training and testing the KELM model, various DG configuration schemes that satisfy the minimum network loss and constraints are given, and the voltage stability evaluation index is introduced to evaluate the results. The general recommendation for DG configuration is obtained. That is, DG is more suitable for accessing the lower point of the network voltage or the end of the network. By configuring the appropriate capacity, it can reduce the network loss, improve the network voltage stability, and the quality of the power supply. Finally, the IEEE33&69-bus radial distribution system is used to simulate, and the results are compared with the existing particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM). The feasibility and effectiveness of the proposed model and method are verified.

Suggested Citation

  • Jingjing Tu & Yonghai Xu & Zhongdong Yin, 2018. "Data-Driven Kernel Extreme Learning Machine Method for the Location and Capacity Planning of Distributed Generation," Energies, MDPI, vol. 12(1), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:109-:d:193943
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/1/109/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/1/109/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vasiliki Vita, 2017. "Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks," Energies, MDPI, vol. 10(9), pages 1-13, September.
    2. Mohammad Reza Baghayipour & Amin Hajizadeh & Amir Shahirinia & Zhe Chen, 2018. "Dynamic Placement Analysis of Wind Power Generation Units in Distribution Power Systems," Energies, MDPI, vol. 11(9), pages 1-16, September.
    3. Jingjing Tu & Zhongdong Yin & Yonghai Xu, 2018. "Study on the Evaluation Index System and Evaluation Method of Voltage Stability of Distribution Network with High DG Penetration," Energies, MDPI, vol. 11(1), pages 1-15, January.
    4. Aman, M.M. & Jasmon, G.B. & Bakar, A.H.A. & Mokhlis, H., 2014. "A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm," Energy, Elsevier, vol. 66(C), pages 202-215.
    5. Ehsan, Ali & Yang, Qiang, 2018. "Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques," Applied Energy, Elsevier, vol. 210(C), pages 44-59.
    6. Pesaran H.A, Mahmoud & Huy, Phung Dang & Ramachandaramurthy, Vigna K., 2017. "A review of the optimal allocation of distributed generation: Objectives, constraints, methods, and algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 293-312.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gang Liang & Bing Sun & Yuan Zeng & Leijiao Ge & Yunfei Li & Yu Wang, 2022. "An Optimal Allocation Method of Distributed PV and Energy Storage Considering Moderate Curtailment Measure," Energies, MDPI, vol. 15(20), pages 1-19, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eshan Karunarathne & Jagadeesh Pasupuleti & Janaka Ekanayake & Dilini Almeida, 2020. "Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm," Energies, MDPI, vol. 13(23), pages 1-25, November.
    2. José Adriano da Costa & David Alves Castelo Branco & Max Chianca Pimentel Filho & Manoel Firmino de Medeiros Júnior & Neilton Fidelis da Silva, 2019. "Optimal Sizing of Photovoltaic Generation in Radial Distribution Systems Using Lagrange Multipliers," Energies, MDPI, vol. 12(9), pages 1-19, May.
    3. Habib Ur Rehman & Arif Hussain & Waseem Haider & Sayyed Ahmad Ali & Syed Ali Abbas Kazmi & Muhammad Huzaifa, 2023. "Optimal Planning of Solar Photovoltaic (PV) and Wind-Based DGs for Achieving Techno-Economic Objectives across Various Load Models," Energies, MDPI, vol. 16(5), pages 1-38, March.
    4. Mahesh Kumar & Amir Mahmood Soomro & Waqar Uddin & Laveet Kumar, 2022. "Optimal Multi-Objective Placement and Sizing of Distributed Generation in Distribution System: A Comprehensive Review," Energies, MDPI, vol. 15(21), pages 1-48, October.
    5. 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.
    6. Teketay Mulu Beza & Yen-Chih Huang & Cheng-Chien Kuo, 2020. "A Hybrid Optimization Approach for Power Loss Reduction and DG Penetration Level Increment in Electrical Distribution Network," Energies, MDPI, vol. 13(22), pages 1-17, November.
    7. Ali Selim & Salah Kamel & Amal A. Mohamed & Ehab E. Elattar, 2021. "Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique," Sustainability, MDPI, vol. 13(12), pages 1-31, June.
    8. Mohammadhafez Bazrafshan & Likhitha Yalamanchili & Nikolaos Gatsis & Juan Gomez, 2019. "Stochastic Planning of Distributed PV Generation," Energies, MDPI, vol. 12(3), pages 1-20, January.
    9. Jing, Rui & Wang, Meng & Liang, Hao & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2018. "Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints," Applied Energy, Elsevier, vol. 231(C), pages 534-548.
    10. Xie, Shiwei & Hu, Zhijian & Zhou, Daming & Li, Yan & Kong, Shunfei & Lin, Weiwei & Zheng, Yunfei, 2018. "Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network," Applied Energy, Elsevier, vol. 219(C), pages 207-225.
    11. Kadir Doğanşahin & Bedri Kekezoğlu & Recep Yumurtacı & Ozan Erdinç & João P. S. Catalão, 2018. "Maximum Permissible Integration Capacity of Renewable DG Units Based on System Loads," Energies, MDPI, vol. 11(1), pages 1-16, January.
    12. Gianpiero Colangelo & Gianluigi Spirto & Marco Milanese & Arturo de Risi, 2021. "Progresses in Analytical Design of Distribution Grids and Energy Storage," Energies, MDPI, vol. 14(14), pages 1-43, July.
    13. Stavros Lazarou & Vasiliki Vita & Lambros Ekonomou, 2018. "Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles," Energies, MDPI, vol. 11(11), pages 1-17, November.
    14. Syed Ali Abbas Kazmi & Abdul Kashif Janjua & Dong Ryeol Shin, 2018. "Enhanced Voltage Stability Assessment Index Based Planning Approach for Mesh Distribution Systems," Energies, MDPI, vol. 11(5), pages 1-36, May.
    15. Minh Quan Duong & Thai Dinh Pham & Thang Trung Nguyen & Anh Tuan Doan & Hai Van Tran, 2019. "Determination of Optimal Location and Sizing of Solar Photovoltaic Distribution Generation Units in Radial Distribution Systems," Energies, MDPI, vol. 12(1), pages 1-24, January.
    16. Jingjing Tu & Zhongdong Yin & Yonghai Xu, 2018. "Study on the Evaluation Index System and Evaluation Method of Voltage Stability of Distribution Network with High DG Penetration," Energies, MDPI, vol. 11(1), pages 1-15, January.
    17. Xuejun Zheng & Shaorong Wang & Zia Ullah & Mengmeng Xiao & Chang Ye & Zhangping Lei, 2021. "A Novel Optimization Method for a Multi-Year Planning Scheme of an Active Distribution Network in a Large Planning Zone," Energies, MDPI, vol. 14(12), pages 1-16, June.
    18. Mohseni, Soheil & Brent, Alan C. & Burmester, Daniel, 2020. "A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid," Applied Energy, Elsevier, vol. 259(C).
    19. Sultana, U. & Khairuddin, Azhar B. & Aman, M.M. & Mokhtar, A.S. & Zareen, N., 2016. "A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 363-378.
    20. Das, Sangeeta & Das, Debapriya & Patra, Amit, 2019. "Operation of distribution network with optimal placement and sizing of dispatchable DGs and shunt capacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:109-:d:193943. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.