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Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems

Author

Listed:
  • Erfan Mohagheghi

    (Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany
    MicroFuzzy GmbH, Taunusstraße 38, 80807 Munich, Germany)

  • Mansour Alramlawi

    (Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany)

  • Aouss Gabash

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

  • Frede Blaabjerg

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Pu Li

    (Department of Process Optimization, Institute of Automation and Systems Engineering, Ilmenau University of Technology, 98693 Ilmenau, Germany)

Abstract

In this paper, a multi-phase multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework is developed to optimally deal with spontaneous changes in wind power in distribution networks (DNs) with battery storage systems (BSSs). The most challenging issue hereby is that a large-scale ‘dynamic’ (i.e., with differential/difference equations rather than only algebraic equations) mixed-integer nonlinear programming (MINLP) problem has to be solved in real time. Moreover, considering the active-reactive power capabilities of BSSs with flexible operation strategies, as well as minimizing the expended life costs of BSSs further increases the complexity of the problem. To solve this problem, in the first phase, we implement simultaneous optimization of a huge number of mixed-integer decision variables to compute optimal operations of BSSs on a day-to-day basis. In the second phase, based on the forecasted wind power values for short prediction horizons, wind power scenarios are generated to describe uncertain wind power with non-Gaussian distribution. Then, MINLP AR-OPF problems corresponding to the scenarios are solved and reconciled in advance of each prediction horizon. In the third phase, based on the measured actual values of wind power, one of the solutions is selected, modified, and realized to the network for very short intervals. The applicability of the proposed RT-DAR-OPF is demonstrated using a medium-voltage DN.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1697-:d:341093
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    References listed on IDEAS

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    1. Ahmadian, Ali & Sedghi, Mahdi & Aliakbar-Golkar, Masoud & Elkamel, Ali & Fowler, Michael, 2016. "Optimal probabilistic based storage planning in tap-changer equipped distribution network including PEVs, capacitor banks and WDGs: A case study for Iran," Energy, Elsevier, vol. 112(C), pages 984-997.
    2. Anthony Papavasiliou & Yuting Mou & Léopold Cambier & Damien Scieur, 2018. "Application of stochastic dual dynamic programming to the real-time dispatch of storage under renewable supply uncertainty," LIDAM Reprints CORE 2943, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. 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.
    4. Gandhi, Oktoviano & Rodríguez-Gallegos, Carlos D. & Zhang, Wenjie & Srinivasan, Dipti & Reindl, Thomas, 2018. "Economic and technical analysis of reactive power provision from distributed energy resources in microgrids," Applied Energy, Elsevier, vol. 210(C), pages 827-841.
    5. Anthony Papavasiliou & Yuting Mou & Léopold Cambier & Damien Scieur, 2018. "Application of stochastic dual dynamic programming to the real-time dispatch of storage under renewable supply uncertainty," LIDAM Reprints CORE 3044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. 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.
    7. Iver Bakken Sperstad & Magnus Korpås, 2019. "Energy Storage Scheduling in Distribution Systems Considering Wind and Photovoltaic Generation Uncertainties," Energies, MDPI, vol. 12(7), pages 1-24, March.
    8. 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.
    9. 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.
    10. 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.
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    2. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Xue, Xizhen & Lin, Zhongwei & Fang, Fang, 2021. "Real-time optimal operation of integrated electricity and heat system considering reserve provision of large-scale heat pumps," Energy, Elsevier, vol. 237(C).
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