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Adaptive robust co-optimization of wind energy generation, electric vehicle batteries and flexible AC transmission system devices

Author

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  • Nikoobakht, Ahmad
  • Aghaei, Jamshid
  • Mokarram, Mohammad Jafar
  • Shafie-khah, Miadreza
  • Catalão, João P.S.

Abstract

The ever-increasing penetration of wind energy generation (WEG) and electric vehicle (EV) batteries in power systems could bring two significant challenges to day-ahead energy balancing markets. First challenge, the uncertainty of WEG and random behavior of the EV batteries can raise energy imbalance in energy markets. Second challenge, the intermittent nature of WEG and uncontrolled EV batteries charging, bring high congestion costs and new congestion patterns in transmission system. In this condition, there is no guarantee that the WEG is deliverable throughout the power system. The hourly coordination of WEG units with EV batteries can play important roles in addressing the first challenge. Similarly, to addressing the second challenge, a transmission impedance adjustment (TIA) device is taken as a promising way to reduce transmission congestion and promote the integration of large-scale WEG and EV batteries through controlling the power flows. However, utilization of TIA device is limited nowadays due to the complexity of this device in the optimization problem of day-ahead energy market clearing with the DC approximation of the power flow network. Consequently, a computationally efficient methodology, which is compatible with existing customary solvers, is proposed to adjust the TIA device set point with minimal modification efforts. An adaptive robust optimization formulation is adapted in the proposed problem to handle the WEG uncertainty. Finally, the simulation results on six and IEEE 118 bus test systems suggest that: 1) substantial economic savings can be achieved through utilization of WEG and storage capability of EV batteries, beyond the independent capabilities of TIA technology; 2) the TIA device plays a critical role in removing transmission congestion; and 3) the storage capability of EV batteries could relieve the uncertainty of WEG and increase its dispatchability.

Suggested Citation

  • Nikoobakht, Ahmad & Aghaei, Jamshid & Mokarram, Mohammad Jafar & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Adaptive robust co-optimization of wind energy generation, electric vehicle batteries and flexible AC transmission system devices," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s036054422101029x
    DOI: 10.1016/j.energy.2021.120781
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    Cited by:

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    2. Akhlaghi, M. & Moravej, Z. & Bagheri, A., 2022. "Maximizing wind energy utilization in smart power systems using a flexible network-constrained unit commitment through dynamic lines and transformers rating," Energy, Elsevier, vol. 261(PA).
    3. Tostado-Véliz, Marcos & Liang, Yingqi & Hasanien, Hany M. & Turky, Rania A. & Martínez-Moreno, Juan & Jurado, Francisco, 2023. "Robust optimal coordination of active distribution networks and energy communities with high penetration of renewables," Renewable Energy, Elsevier, vol. 218(C).
    4. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    5. Qiu, Haifeng & Gu, Wei & Liu, Pengxiang & Sun, Qirun & Wu, Zhi & Lu, Xi, 2022. "Application of two-stage robust optimization theory in power system scheduling under uncertainties: A review and perspective," Energy, Elsevier, vol. 251(C).
    6. Powell, Siobhan & Vianna Cezar, Gustavo & Apostolaki-Iosifidou, Elpiniki & Rajagopal, Ram, 2022. "Large-scale scenarios of electric vehicle charging with a data-driven model of control," Energy, Elsevier, vol. 248(C).
    7. Mohammad Kamrul Hasan & AKM Ahasan Habib & Shayla Islam & Mohammed Balfaqih & Khaled M. Alfawaz & Dalbir Singh, 2023. "Smart Grid Communication Networks for Electric Vehicles Empowering Distributed Energy Generation: Constraints, Challenges, and Recommendations," Energies, MDPI, vol. 16(3), pages 1-20, January.

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