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Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration

Author

Listed:
  • Kyungsung An

    (School of Electrical & Electronic Engineering, Yonsei University, Seoul 03722, Korea)

  • Kyung-Bin Song

    (Department of Electrical Engineering, Soongsil University, Seoul 06978, Korea)

  • Kyeon Hur

    (School of Electrical & Electronic Engineering, Yonsei University, Seoul 03722, Korea)

Abstract

This research aims to improve the operational efficiency and security of electric power systems at high renewable penetration by exploiting the envisioned controllability or flexibility of electric vehicles (EVs); EVs interact with the grid through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services to ensure reliable and cost-effective grid operation. This research provides a computational framework for this decision-making process. Charging and discharging strategies of EV aggregators are incorporated into a security-constrained optimal power flow (SCOPF) problem such that overall energy cost is minimized and operation within acceptable reliability criteria is ensured. Particularly, this SCOPF problem has been formulated for Jeju Island in South Korea, in order to lower carbon emissions toward a zero-carbon island by, for example, integrating large-scale renewable energy and EVs. On top of conventional constraints on the generators and line flows, a unique constraint on the system inertia constant, interpreted as the minimum synchronous generation, is considered to ensure grid security at high renewable penetration. The available energy constraint of the participating EV associated with the state-of-charge (SOC) of the battery and market price-responsive behavior of the EV aggregators are also explored. Case studies for the Jeju electric power system in 2030 under various operational scenarios demonstrate the effectiveness of the proposed method and improved operational flexibility via controllable EVs.

Suggested Citation

  • Kyungsung An & Kyung-Bin Song & Kyeon Hur, 2017. "Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration," Energies, MDPI, vol. 10(5), pages 1-15, May.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:729-:d:99278
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    References listed on IDEAS

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    1. Minhan Yoon & Yong-Tae Yoon & Gilsoo Jang, 2015. "A Study on Maximum Wind Power Penetration Limit in Island Power System Considering High-Voltage Direct Current Interconnections," Energies, MDPI, vol. 8(12), pages 1-16, December.
    2. Richardson, David B., 2013. "Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 247-254.
    3. Lund, Henrik & Kempton, Willett, 2008. "Integration of renewable energy into the transport and electricity sectors through V2G," Energy Policy, Elsevier, vol. 36(9), pages 3578-3587, September.
    4. Papathanassiou, Stavros A. & Boulaxis, Nikos G., 2006. "Power limitations and energy yield evaluation for wind farms operating in island systems," Renewable Energy, Elsevier, vol. 31(4), pages 457-479.
    5. Carrión, Miguel & Zárate-Miñano, Rafael, 2015. "Operation of renewable-dominated power systems with a significant penetration of plug-in electric vehicles," Energy, Elsevier, vol. 90(P1), pages 827-835.
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    Cited by:

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    2. Velaz-Acera, Néstor & Álvarez-García, Javier & Borge-Diez, David, 2023. "Economic and emission reduction benefits of the implementation of eVTOL aircraft with bi-directional flow as storage systems in islands and case study for Canary Islands," Applied Energy, Elsevier, vol. 331(C).
    3. Zain Anwer Memon & Riccardo Trinchero & Paolo Manfredi & Flavio Canavero & Igor S. Stievano, 2020. "Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks," Energies, MDPI, vol. 13(18), pages 1-18, September.
    4. Akhtar Hussain & Hak-Man Kim, 2020. "Goal-Programming-Based Multi-Objective Optimization in Off-Grid Microgrids," Sustainability, MDPI, vol. 12(19), pages 1-18, October.
    5. Ruifeng Shi & Shaopeng Li & Changhao Sun & Kwang Y. Lee, 2018. "Adjustable Robust Optimization Algorithm for Residential Microgrid Multi-Dispatch Strategy with Consideration of Wind Power and Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-22, August.
    6. Umar Fitra Ramadhan & Jaewan Suh & Sungchul Hwang & Jaehyeong Lee & Minhan Yoon, 2022. "A Comprehensive Study of HVDC Link with Reserve Operation Control in a Multi-Infeed Direct Current Power System," Sustainability, MDPI, vol. 14(10), pages 1-27, May.
    7. Xi Wu & Zhengyu Zhou & Gang Liu & Wanchun Qi & Zhenjian Xie, 2017. "Preventive Security-Constrained Optimal Power Flow Considering UPFC Control Modes," Energies, MDPI, vol. 10(8), pages 1-15, August.
    8. Neofytos Neofytou & Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2019. "Modeling Vehicles to Grid as a Source of Distributed Frequency Regulation in Isolated Grids with Significant RES Penetration," Energies, MDPI, vol. 12(4), pages 1-23, February.
    9. Yusuf A. Sha’aban & Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie, 2017. "Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control," Energies, MDPI, vol. 10(10), pages 1-20, September.
    10. Victor H. Hinojosa, 2020. "Comparing Corrective and Preventive Security-Constrained DCOPF Problems Using Linear Shift-Factors," Energies, MDPI, vol. 13(3), pages 1-16, January.
    11. Jun Bi & Yongxing Wang & Shuai Sun & Wei Guan, 2018. "Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing," Energies, MDPI, vol. 11(5), pages 1-18, April.
    12. Ruifeng Shi & Jie Zhang & Hao Su & Zihang Liang & Kwang Y. Lee, 2020. "An Economic Penalty Scheme for Optimal Parking Lot Utilization with EV Charging Requirements," Energies, MDPI, vol. 13(22), pages 1-21, November.
    13. Jean-Michel Clairand & Javier Rodríguez-García & Carlos Álvarez-Bel, 2018. "Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation," Energies, MDPI, vol. 11(11), pages 1-21, November.
    14. Gracita Batista Rosas & Elizete Maria Lourenço & Djalma Mosqueira Falcão & Thelma Solange Piazza Fernandes, 2019. "An Expeditious Methodology to Assess the Effects of Intermittent Generation on Power Systems," Energies, MDPI, vol. 12(6), pages 1-18, March.

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