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Enhancing Grid Operation with Electric Vehicle Integration in Automatic Generation Control

Author

Listed:
  • Zahid Ullah

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy)

  • Kaleem Ullah

    (US-Pakistan Center for Advanced Studies in Energy, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan)

  • Cesar Diaz-Londono

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy)

  • Giambattista Gruosso

    (Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy)

  • Abdul Basit

    (Manager R&D, National Power Control Center, National Transmission and Dispatch Company, Islamabad 44000, Pakistan)

Abstract

Wind energy has been recognized as a clean energy source with significant potential for reducing carbon emissions. However, its inherent variability poses substantial challenges for power system operators due to its unpredictable nature. As a result, there is an increased dependence on conventional generation sources to uphold the power system balance, resulting in elevated operational costs and an upsurge in carbon emissions. Hence, an urgent need exists for alternative solutions that can reduce the burden on traditional generating units and optimize the utilization of reserves from non-fossil fuel technologies. Meanwhile, vehicle-to-grid (V2G) technology integration has emerged as a remedial approach to rectify power capacity shortages during grid operations, enhancing stability and reliability. This research focuses on harnessing electric vehicle (EV) storage capacity to compensate for power deficiencies caused by forecasting errors in large-scale wind energy-based power systems. A real-time dynamic power dispatch strategy is developed for the automatic generation control (AGC) system to integrate EVs and utilize their reserves optimally to reduce reliance on conventional power plants and increase system security. The results obtained from this study emphasize the significant prospects associated with the fusion of EVs and traditional power plants, offering a highly effective solution for mitigating real-time power imbalances in large-scale wind energy-based power systems.

Suggested Citation

  • Zahid Ullah & Kaleem Ullah & Cesar Diaz-Londono & Giambattista Gruosso & Abdul Basit, 2023. "Enhancing Grid Operation with Electric Vehicle Integration in Automatic Generation Control," Energies, MDPI, vol. 16(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7118-:d:1261284
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    References listed on IDEAS

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    1. Guille, Christophe & Gross, George, 2009. "A conceptual framework for the vehicle-to-grid (V2G) implementation," Energy Policy, Elsevier, vol. 37(11), pages 4379-4390, November.
    2. Rahmat Khezri & Arman Oshnoei & Mehrdad Tarafdar Hagh & SM Muyeen, 2018. "Coordination of Heat Pumps, Electric Vehicles and AGC for Efficient LFC in a Smart Hybrid Power System via SCA-Based Optimized FOPID Controllers," Energies, MDPI, vol. 11(2), pages 1-21, February.
    3. Cesar Diaz-Londono & José Vuelvas & Giambattista Gruosso & Carlos Adrian Correa-Florez, 2022. "Remuneration Sensitivity Analysis in Prosumer and Aggregator Strategies by Controlling Electric Vehicle Chargers," Energies, MDPI, vol. 15(19), pages 1-24, September.
    4. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    5. Rafiq Asghar & Zahid Ullah & Babar Azeem & Sheraz Aslam & Muhammad Harris Hashmi & Ehtsham Rasool & Bilawal Shaker & Muhammad Junaid Anwar & Kainat Mustafa, 2022. "Wind Energy Potential in Pakistan: A Feasibility Study in Sindh Province," Energies, MDPI, vol. 15(22), pages 1-23, November.
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    Cited by:

    1. Bin Li & Haoran Li & Zhencheng Liang & Xiaoqing Bai, 2024. "Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm," Energies, MDPI, vol. 17(2), pages 1-25, January.
    2. Sulman Shahzad & Elżbieta Jasińska, 2024. "Renewable Revolution: A Review of Strategic Flexibility in Future Power Systems," Sustainability, MDPI, vol. 16(13), pages 1-24, June.

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