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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
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