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A Framework for Real-Time Optimal Power Flow under Wind Energy Penetration

Author

Listed:
  • Erfan Mohagheghi

    (Department of Simulation and Optimal Processes, Institute of Automation and Systems Engineering, Ilmenau University of Technology, Ilmenau 98693, Germany)

  • Aouss Gabash

    (Department of Simulation and Optimal Processes, Institute of Automation and Systems Engineering, Ilmenau University of Technology, Ilmenau 98693, Germany)

  • Pu Li

    (Department of Simulation and Optimal Processes, Institute of Automation and Systems Engineering, Ilmenau University of Technology, Ilmenau 98693, Germany)

Abstract

Developing a suitable framework for real-time optimal power flow (RT-OPF) is of utmost importance for ensuring both optimality and feasibility in the operation of energy distribution networks (DNs) under intermittent wind energy penetration. The most challenging issue thereby is that a large-scale complex optimization problem has to be solved in real-time. Online simultaneous optimization of the wind power curtailments of wind stations and the discrete reference values of the slack bus voltage which leads to a mixed-integer nonlinear programming (MINLP) problem, in addition to considering variable reverse power flow, make the optimization problem even much more complicated. To address these difficulties, a two-phase solution approach to RT-OPF is proposed in this paper. In the prediction phase, a number of MINLP OPF problems corresponding to the most probable scenarios of the wind energy penetration in the prediction horizon, by taking its forecasted value and stochastic distribution into account, are solved in parallel. The solution provides a lookup table for optional control strategies for the current prediction horizon which is further divided into a certain number of short time intervals. In the realization phase, one of the control strategies is selected from the lookup table based on the actual wind power and realized to the grid in the current time interval, which will proceed from one interval to the next, till the end of the current prediction horizon. Then, the prediction phase for the next prediction horizon will be activated. A 41-bus medium-voltage DN is taken as a case study to demonstrate the proposed RT-OPF approach.

Suggested Citation

  • Erfan Mohagheghi & Aouss Gabash & Pu Li, 2017. "A Framework for Real-Time Optimal Power Flow under Wind Energy Penetration," Energies, MDPI, vol. 10(4), pages 1-28, April.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:535-:d:95819
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    References listed on IDEAS

    as
    1. Aouss Gabash & Pu Li, 2016. "On Variable Reverse Power Flow-Part I: Active-Reactive Optimal Power Flow with Reactive Power of Wind Stations," Energies, MDPI, vol. 9(3), pages 1-12, February.
    2. Li, Zhigang & Qiu, Feng & Wang, Jianhui, 2016. "Data-driven real-time power dispatch for maximizing variable renewable generation," Applied Energy, Elsevier, vol. 170(C), pages 304-313.
    3. Taylor, James W., 2008. "An evaluation of methods for very short-term load forecasting using minute-by-minute British data," International Journal of Forecasting, Elsevier, vol. 24(4), pages 645-658.
    4. Reddy, S. Surender & Bijwe, P.R., 2015. "Real time economic dispatch considering renewable energy resources," Renewable Energy, Elsevier, vol. 83(C), pages 1215-1226.
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    Cited by:

    1. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    2. Li, Zhengmao & Xu, Yan, 2019. "Temporally-coordinated optimal operation of a multi-energy microgrid under diverse uncertainties," Applied Energy, Elsevier, vol. 240(C), pages 719-729.
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    5. Zhang, Wenyu & Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong, 2020. "Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting," Applied Energy, Elsevier, vol. 277(C).
    6. Kumar, Abhishek & Meena, Nand K. & Singh, Arvind R. & Deng, Yan & He, Xiangning & Bansal, R.C. & Kumar, Praveen, 2019. "Strategic integration of battery energy storage systems with the provision of distributed ancillary services in active distribution systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Pu Li, 2018. "A Survey of Real-Time Optimal Power Flow," Energies, MDPI, vol. 11(11), pages 1-20, November.
    8. Erfan Mohagheghi & Mansour Alramlawi & Aouss Gabash & Frede Blaabjerg & Pu Li, 2020. "Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems," Energies, MDPI, vol. 13(7), pages 1-17, April.
    9. Jaka Marguč & Mitja Truntič & Miran Rodič & Miro Milanovič, 2019. "FPGA Based Real-Time Emulation System for Power Electronics Converters," Energies, MDPI, vol. 12(6), pages 1-23, March.

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