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Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

Author

Listed:
  • Rongxiang Yuan

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Timing Li

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Xiangtian Deng

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Jun Ye

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

Abstract

In the traditional paradigm, large power plants provide active and reactive power required for the transmission system and the distribution network purchases grid power from it. However, with more and more distributed energy resources (DERs) connected at distribution levels, it is necessary to schedule DERs to meet their demand and participate in the electricity markets at the distribution level in the near future. This paper proposes a comprehensive operational scheduling model to be used in the distribution management system (DMS). The model aims to determine optimal decisions on active elements of the network, distributed generations (DGs), and responsive loads (RLs), seeking to minimize the day-ahead composite economic cost of the distribution network. For more detailed simulation, the composite cost includes the aspects of the operation cost, emission cost, and transmission loss cost of the network. Additionally, the DMS effectively utilizes the reactive power support capabilities of wind and solar power integrated in the distribution, which is usually neglected in previous works. The optimization procedure is formulated as a nonlinear combinatorial problem and solved with a modified differential evolution algorithm. A modified 33-bus distribution network is employed to validate the satisfactory performance of the proposed methodology.

Suggested Citation

  • Rongxiang Yuan & Timing Li & Xiangtian Deng & Jun Ye, 2016. "Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation," Energies, MDPI, vol. 9(5), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:5:p:311-:d:68858
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    References listed on IDEAS

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    1. Sousa, Tiago & Morais, Hugo & Vale, Zita & Castro, Rui, 2015. "A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context," Energy, Elsevier, vol. 85(C), pages 236-250.
    2. Vahidinasab, V. & Jadid, S., 2010. "Joint economic and emission dispatch in energy markets: A multiobjective mathematical programming approach," Energy, Elsevier, vol. 35(3), pages 1497-1504.
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    Cited by:

    1. Zhigang Duan & Yamin Yan & Xiaohan Yan & Qi Liao & Wan Zhang & Yongtu Liang & Tianqi Xia, 2017. "An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand," Energies, MDPI, vol. 11(1), pages 1-23, December.
    2. Shan Deng & Qinghua Wu & Zhaoxia Jing & Lilan Wu & Feng Wei & Xiaoxin Zhou, 2017. "Optimal Capacity Configuration for Energy Hubs Considering Part-Load Characteristics of Generation Units," Energies, MDPI, vol. 10(12), pages 1-19, November.
    3. Aqsa Naeem & Naveed Ul Hassan & Chau Yuen & S. M. Muyeen, 2019. "Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity," Energies, MDPI, vol. 12(3), pages 1-22, January.

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