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Self-balancing robust scheduling with flexible batch loads for energy intensive corporate microgrid

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  • Liu, Kun
  • Guan, Xiaohong
  • Gao, Feng
  • Zhai, Qiaozhu
  • Wu, Jiang

Abstract

With the development of microgrid technology, for an energy intensive corporate such as an iron and steel plant energy consumptions and costs can be saved significantly by achieving the balance between self-generation and consumption. In this paper, we present a self-balancing and robust scheduling model with flexible batch loads for an energy intensive corporate. The model is a multi-level optimization model with the objective to minimize the unbalance cost in the worst case. Load following properties are given to determine whether the uncertain loads can be followed or not, and the self-balancing capability of an energy intensive corporate is analyzed. The problem is equivalently converted from the multi-level model to a single-level optimization model with a set of constraints. In this way, the iteration between the outer and inner level can be avoided. Case study based on an energy intensive corporate microgrid is tested and the results show that the unbalance cost can be significantly reduced by using the robust self-balancing model. In addition, compared the approach method with iterative solving method, computational efficiency can be improved and local optimum can be avoided.

Suggested Citation

  • Liu, Kun & Guan, Xiaohong & Gao, Feng & Zhai, Qiaozhu & Wu, Jiang, 2015. "Self-balancing robust scheduling with flexible batch loads for energy intensive corporate microgrid," Applied Energy, Elsevier, vol. 159(C), pages 391-400.
    • Handle: RePEc:eee:appene:v:159:y:2015:i:c:p:391-400
    DOI: 10.1016/j.apenergy.2015.09.014
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    Cited by:

    1. Weiwei Cui & Lin Li & Zhiqiang Lu, 2019. "Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 154-173, March.
    2. Sreedharan, P. & Farbes, J. & Cutter, E. & Woo, C.K. & Wang, J., 2016. "Microgrid and renewable generation integration: University of California, San Diego," Applied Energy, Elsevier, vol. 169(C), pages 709-720.
    3. Chongxin Huang & Dong Yue & Song Deng & Jun Xie, 2017. "Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization," Energies, MDPI, vol. 10(3), pages 1-23, March.

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