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Optimal operation of stationary and mobile batteries in distribution grids

Author

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  • Wang, Yubo
  • Shi, Wenbo
  • Wang, Bin
  • Chu, Chi-Cheng
  • Gadh, Rajit

Abstract

The trending integrations of Battery Energy Storage System (BESS, stationary battery) and Electric Vehicles (EV, mobile battery) to distribution grids call for advanced Demand Side Management (DSM) technique that addresses the scalability concerns of the system and stochastic availabilities of EVs. Towards this goal, a stochastic DSM is proposed to capture the uncertainties in EVs. Numerical approximation is then used to make the problem tractable. To accelerate the computational speed, the proposed DSM is tightly relaxed to a convex form using second-order cone programming. Furthermore, in light of the continuous increasing problem size, a distributed method with a guaranteed convergence is applied to shift the centralized computational burden to distributed controllers. To verify the proposed DSM, real-life EV data collected on UCLA campus is used to test the proposed DSM in an IEEE benchmark test system. Numerical results demonstrate the correctness and merits of the proposed approach.

Suggested Citation

  • Wang, Yubo & Shi, Wenbo & Wang, Bin & Chu, Chi-Cheng & Gadh, Rajit, 2017. "Optimal operation of stationary and mobile batteries in distribution grids," Applied Energy, Elsevier, vol. 190(C), pages 1289-1301.
    • Handle: RePEc:eee:appene:v:190:y:2017:i:c:p:1289-1301
    DOI: 10.1016/j.apenergy.2016.12.139
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    References listed on IDEAS

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    Cited by:

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    3. Morris Brenna & Michela Longo & Wahiba Yaïci, 2017. "Modelling and Simulation of Electric Vehicle Fast Charging Stations Driven by High Speed Railway Systems," Energies, MDPI, vol. 10(9), pages 1-23, August.
    4. Yunfeng Jiang & Louis J. Shrinkle & Raymond A. de Callafon, 2019. "Autonomous Demand-Side Current Scheduling of Parallel Buck Regulated Battery Modules," Energies, MDPI, vol. 12(11), pages 1-20, May.
    5. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
    6. Wang, Yubo & Song, Zhen & De Angelis, Valerio & Srivastava, Sanjeev, 2018. "Battery life-cycle optimization and runtime control for commercial buildings demand side management: A New York City case study," Energy, Elsevier, vol. 165(PA), pages 782-791.
    7. Anna Auza & Ehsan Asadi & Behrang Chenari & Manuel Gameiro da Silva, 2023. "A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles," Energies, MDPI, vol. 16(13), pages 1-25, June.
    8. Salvatore Favuzza & Mariano Giuseppe Ippolito & Fabio Massaro & Rossano Musca & Eleonora Riva Sanseverino & Giuseppe Schillaci & Gaetano Zizzo, 2018. "Building Automation and Control Systems and Electrical Distribution Grids: A Study on the Effects of Loads Control Logics on Power Losses and Peaks," Energies, MDPI, vol. 11(3), pages 1-15, March.
    9. Trancho, E. & Ibarra, E. & Arias, A. & Kortabarria, I. & Prieto, P. & Martínez de Alegría, I. & Andreu, J. & López, I., 2018. "Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles," Applied Energy, Elsevier, vol. 224(C), pages 647-658.

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