Review
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Year 2024, Volume: 9 Issue: 1, 51 - 59, 30.12.2024
https://doi.org/10.55088/ijesg.1598117

Abstract

References

  • Tian, X., Zha, H., Tian, Z., Lang, G., Li, L., “Carbon emission reduction capability assessment based on synergistic optimization control of electric vehicle V2G and multiple types power supply”, Energy Reports, 11, 1191-1198, 2024.
  • Qadir, S. A., Ahmad, F., Al-Wahedi, A. M. A., Iqbal, A., Ali, A., “Navigating the complex realities of electric vehicle adoption: A comprehensive study of government strategies, policies, and incentives”, Energy Strategy Reviews, 53, 101379, 2024.
  • Çetin, M. S., Gençoğlu, M. T., Dobrzycki, A., “Investigation of Charging Technologies for Electric Vehicles”, Turkish Journal of Science and Technology, 19(1), 97-106, 2024.
  • Williams, B., Bishop, D., Hooper, G., Chase, J. G., “Driving change: Electric vehicle charging behavior and peak loading”, Renewable and Sustainable Energy Reviews, 189, 113953, 2024.
  • Zhao, X., Hu, H., Yuan, H., Chu, X., “How does adoption of electric vehicles reduce carbon emissions? Evidence from China”, Heliyon, 9(9), 2023.
  • Guo, X., Sun, Y., Ren, D., “Life cycle carbon emission and cost-effectiveness analysis of electric vehicles in China”, Energy for Sustainable Development, 72, 1-10, 2023.
  • Albrechtowicz, P., “Electric vehicle impact on the environment in terms of the electric energy source—Case study”, Energy Reports, 9, 3813-3821, 2023.
  • Rapson, D. S., Muehlegger, E., “The economics of electric vehicles”, Review of Environmental Economics and Policy, 17(2), 274-294, 2023.
  • Khalid, M. R., Khan, I. A., Hameed, S., Asghar, M. S. J., Ro, J., “A comprehensive review on structural topologies, power levels, energy storage systems, and standards for electric vehicle charging stations and their impacts on grid”, IEEE Access, 9, 128069-128094, 2021.
  • Şengör, İ., Çiçek, A., Erenoğlu, A. K., Erdinç, O., Catalão, J. P., “User-comfort oriented optimal bidding strategy of an electric vehicle aggregator in day-ahead and reserve markets”, International Journal of Electrical Power & Energy Systems, 122, 106194, 2020.
  • Dobrzycki, A., Kasprzyk, L., Çetin, M. S., Gençoğlu, M. T., “Analysis of the Influence of the Charging Process of an Electrical Vehicle on Voltage Distortions in the Electrical Installation”, Applied Sciences, 14(17), 7691, 2024.
  • Nour, M., Chaves-Ávila, J. P., Magdy, G., Sánchez-Miralles, Á., “Review of positive and negative impacts of electric vehicles charging on electric power systems”, Energies, 13(18), 4675, 2020.
  • Rahman, S., Khan, I. A., Khan, A. A., Mallik, A., Nadeem, M. F., “Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system”, Renewable and Sustainable Energy Reviews, 153, 111756, 2022.
  • Dobrzycki, A., Çetin, M. S., Gençoğlu, M. T., “Harmonics generated during the electric vehicle charging process”, 2. International Conference on Advances and Innovations in Engineering (ICAIE), Elazığ, Türkiye, pp. 173-178, 2023.
  • Tavakoli, A., Saha, S., Arif, M. T., Haque, M. E., Mendis, N., Oo, A. M., “Impacts of grid integration of solar PV and electric vehicle on grid stability, power quality and energy economics: A review”, IET Energy Systems Integration, 2(3), 243-260, 2020.
  • Wang, L., Qin, Z., Slangen, T., Bauer, P., Van Wijk, T., “Grid impact of electric vehicle fast charging stations: Trends, standards, issues and mitigation measures—An overview”, IEEE Open Journal of Power Electronics, 2, 56-74, 2021.
  • Abdullah, H. M., Gastli, A., Ben-Brahim, L., “Reinforcement learning based EV charging management systems–a review”, IEEE Access, 9, 41506-41531, 2021.
  • Zhang, X., Chan, K. W., Li, H., Wang, H., Qiu, J., Wang, G., “Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model”, IEEE Transactions on Cybernetics, 51(6), 3157-3170, 2020.
  • Dabbaghjamanesh, M., Moeini, A., Kavousi-Fard, A., “Reinforcement learning-based load forecasting of electric vehicle charging station using Q-learning technique”, IEEE Transactions on Industrial Informatics, 17(6), 4229-4237, 2020.
  • Shern, S. J., Sarker, M. T., Ramasamy, G., Thiagarajah, S. P., Al Farid, F., Suganthi, S. T., “Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia”, World Electric Vehicle Journal, 15(10), 440, 2024.
  • Shahriar, S., Al-Ali, A. R., Osman, A. H., Dhou, S., Nijim, M., “Prediction of EV charging behavior using machine learning”, IEEE Access, 9, 111576-111586, 2021.
  • Lan, T., Jermsittiparsert, K., Alrashood, S. T., Rezaei, M., Al-Ghussain, L., Mohamed, M. A., “An advanced machine learning based energy management of renewable microgrids considering hybrid electric vehicles’ charging demand”, Energies, 14(3), 569, 2021.
  • Lin, H., Zhou, Y., Li, Y., Zheng, H., “Aggregator pricing and electric vehicles charging strategy based on a two-layer deep learning model”, Electric Power Systems Research, 227, 109971, 2024.
  • Zhong, J., Liu, J., Zhang, X., “Charging navigation strategy for electric vehicles considering empty-loading ratio and dynamic electricity price”, Sustainable Energy, Grids and Networks, 34, 100987, 2023.
  • Tang, M., Zhuang, W., Li, B., Liu, H., Song, Z., Yin, G., “Energy-optimal routing for electric vehicles using deep reinforcement learning with transformer”, Applied Energy, 350, 121711, 2023.
  • Basso, R., Kulcsár, B., Sanchez-Diaz, I., Qu, X., “Dynamic stochastic electric vehicle routing with safe reinforcement learning”, Transportation Research Part E: Logistics and Transportation Review, 157, 102496, 2022.
  • Gharibi, M. A., Nafisi, H., Askarian-Abyaneh, H., Hajizadeh, A., “Deep learning framework for day-ahead optimal charging scheduling of electric vehicles in parking lot”, Applied Energy, 349, 121614, 2023.
  • Jin, J., Xu, Y., “Optimal policy characterization enhanced actor-critic approach for electric vehicle charging scheduling in a power distribution network”, IEEE Transactions on Smart Grid, 12(2), 1416-1428, 2020.
  • Ahmadian, A., Sedghisigarchi, K., Gadh, R., “Empowering dynamic active and reactive power control: A deep reinforcement learning controller for three-phase grid-connected electric vehicles”, IEEE Access, 2024.
  • Zhang, Y., Yang, Q., An, D., Li, D., Wu, Z., “Multistep multiagent reinforcement learning for optimal energy schedule strategy of charging stations in smart grid”, IEEE Transactions on Cybernetics, 53(7), 4292-4305, 2022.
  • Shibl, M., Ismail, L., Massoud, A., “Electric vehicles charging management using machine learning considering fast charging and vehicle-to-grid operation”, Energies, 14(19), 6199, 2021.

A REVIEW OF ELECTRIC VEHICLES: THEIR IMPACT ON THE ELECTRICITY GRID AND ARTIFICIAL INTELLIGENCE-BASED APPROACHES FOR CHARGING LOAD MANAGEMENT

Year 2024, Volume: 9 Issue: 1, 51 - 59, 30.12.2024
https://doi.org/10.55088/ijesg.1598117

Abstract

The widespread use of electric vehicles contributes significantly to environmental sustainability by reducing the use of fossil fuels. However, the increasing number of electric vehicles and the intense charging demand may cause negative impacts such as overloading, voltage fluctuations and energy supply-demand imbalances in electricity grids. In this paper, artificial intelligence-based methods applied for the management of the negative impacts of electric vehicles on the grid are discussed comprehensively and artificial intelligence approaches in the literature used to manage electric vehicle charging load are analysed. Among these approaches, energy management strategies based on charging demand forecasting, dynamic pricing, routing, charging scheduling and smart grid integration are analysed in detail. This paper summarises the latest innovative AI-based solutions developed to manage the charging load of electric vehicles, improve grid stability, increase charging service price prediction accuracies, maximise grid and user satisfaction, ensure load balance, reduce charging and operating costs, reduce energy consumption and optimise power flow. This paper contains comprehensive and qualified information about the bilateral (grid and user perspective) management algorithms of the charging load of electric vehicles.

References

  • Tian, X., Zha, H., Tian, Z., Lang, G., Li, L., “Carbon emission reduction capability assessment based on synergistic optimization control of electric vehicle V2G and multiple types power supply”, Energy Reports, 11, 1191-1198, 2024.
  • Qadir, S. A., Ahmad, F., Al-Wahedi, A. M. A., Iqbal, A., Ali, A., “Navigating the complex realities of electric vehicle adoption: A comprehensive study of government strategies, policies, and incentives”, Energy Strategy Reviews, 53, 101379, 2024.
  • Çetin, M. S., Gençoğlu, M. T., Dobrzycki, A., “Investigation of Charging Technologies for Electric Vehicles”, Turkish Journal of Science and Technology, 19(1), 97-106, 2024.
  • Williams, B., Bishop, D., Hooper, G., Chase, J. G., “Driving change: Electric vehicle charging behavior and peak loading”, Renewable and Sustainable Energy Reviews, 189, 113953, 2024.
  • Zhao, X., Hu, H., Yuan, H., Chu, X., “How does adoption of electric vehicles reduce carbon emissions? Evidence from China”, Heliyon, 9(9), 2023.
  • Guo, X., Sun, Y., Ren, D., “Life cycle carbon emission and cost-effectiveness analysis of electric vehicles in China”, Energy for Sustainable Development, 72, 1-10, 2023.
  • Albrechtowicz, P., “Electric vehicle impact on the environment in terms of the electric energy source—Case study”, Energy Reports, 9, 3813-3821, 2023.
  • Rapson, D. S., Muehlegger, E., “The economics of electric vehicles”, Review of Environmental Economics and Policy, 17(2), 274-294, 2023.
  • Khalid, M. R., Khan, I. A., Hameed, S., Asghar, M. S. J., Ro, J., “A comprehensive review on structural topologies, power levels, energy storage systems, and standards for electric vehicle charging stations and their impacts on grid”, IEEE Access, 9, 128069-128094, 2021.
  • Şengör, İ., Çiçek, A., Erenoğlu, A. K., Erdinç, O., Catalão, J. P., “User-comfort oriented optimal bidding strategy of an electric vehicle aggregator in day-ahead and reserve markets”, International Journal of Electrical Power & Energy Systems, 122, 106194, 2020.
  • Dobrzycki, A., Kasprzyk, L., Çetin, M. S., Gençoğlu, M. T., “Analysis of the Influence of the Charging Process of an Electrical Vehicle on Voltage Distortions in the Electrical Installation”, Applied Sciences, 14(17), 7691, 2024.
  • Nour, M., Chaves-Ávila, J. P., Magdy, G., Sánchez-Miralles, Á., “Review of positive and negative impacts of electric vehicles charging on electric power systems”, Energies, 13(18), 4675, 2020.
  • Rahman, S., Khan, I. A., Khan, A. A., Mallik, A., Nadeem, M. F., “Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system”, Renewable and Sustainable Energy Reviews, 153, 111756, 2022.
  • Dobrzycki, A., Çetin, M. S., Gençoğlu, M. T., “Harmonics generated during the electric vehicle charging process”, 2. International Conference on Advances and Innovations in Engineering (ICAIE), Elazığ, Türkiye, pp. 173-178, 2023.
  • Tavakoli, A., Saha, S., Arif, M. T., Haque, M. E., Mendis, N., Oo, A. M., “Impacts of grid integration of solar PV and electric vehicle on grid stability, power quality and energy economics: A review”, IET Energy Systems Integration, 2(3), 243-260, 2020.
  • Wang, L., Qin, Z., Slangen, T., Bauer, P., Van Wijk, T., “Grid impact of electric vehicle fast charging stations: Trends, standards, issues and mitigation measures—An overview”, IEEE Open Journal of Power Electronics, 2, 56-74, 2021.
  • Abdullah, H. M., Gastli, A., Ben-Brahim, L., “Reinforcement learning based EV charging management systems–a review”, IEEE Access, 9, 41506-41531, 2021.
  • Zhang, X., Chan, K. W., Li, H., Wang, H., Qiu, J., Wang, G., “Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model”, IEEE Transactions on Cybernetics, 51(6), 3157-3170, 2020.
  • Dabbaghjamanesh, M., Moeini, A., Kavousi-Fard, A., “Reinforcement learning-based load forecasting of electric vehicle charging station using Q-learning technique”, IEEE Transactions on Industrial Informatics, 17(6), 4229-4237, 2020.
  • Shern, S. J., Sarker, M. T., Ramasamy, G., Thiagarajah, S. P., Al Farid, F., Suganthi, S. T., “Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia”, World Electric Vehicle Journal, 15(10), 440, 2024.
  • Shahriar, S., Al-Ali, A. R., Osman, A. H., Dhou, S., Nijim, M., “Prediction of EV charging behavior using machine learning”, IEEE Access, 9, 111576-111586, 2021.
  • Lan, T., Jermsittiparsert, K., Alrashood, S. T., Rezaei, M., Al-Ghussain, L., Mohamed, M. A., “An advanced machine learning based energy management of renewable microgrids considering hybrid electric vehicles’ charging demand”, Energies, 14(3), 569, 2021.
  • Lin, H., Zhou, Y., Li, Y., Zheng, H., “Aggregator pricing and electric vehicles charging strategy based on a two-layer deep learning model”, Electric Power Systems Research, 227, 109971, 2024.
  • Zhong, J., Liu, J., Zhang, X., “Charging navigation strategy for electric vehicles considering empty-loading ratio and dynamic electricity price”, Sustainable Energy, Grids and Networks, 34, 100987, 2023.
  • Tang, M., Zhuang, W., Li, B., Liu, H., Song, Z., Yin, G., “Energy-optimal routing for electric vehicles using deep reinforcement learning with transformer”, Applied Energy, 350, 121711, 2023.
  • Basso, R., Kulcsár, B., Sanchez-Diaz, I., Qu, X., “Dynamic stochastic electric vehicle routing with safe reinforcement learning”, Transportation Research Part E: Logistics and Transportation Review, 157, 102496, 2022.
  • Gharibi, M. A., Nafisi, H., Askarian-Abyaneh, H., Hajizadeh, A., “Deep learning framework for day-ahead optimal charging scheduling of electric vehicles in parking lot”, Applied Energy, 349, 121614, 2023.
  • Jin, J., Xu, Y., “Optimal policy characterization enhanced actor-critic approach for electric vehicle charging scheduling in a power distribution network”, IEEE Transactions on Smart Grid, 12(2), 1416-1428, 2020.
  • Ahmadian, A., Sedghisigarchi, K., Gadh, R., “Empowering dynamic active and reactive power control: A deep reinforcement learning controller for three-phase grid-connected electric vehicles”, IEEE Access, 2024.
  • Zhang, Y., Yang, Q., An, D., Li, D., Wu, Z., “Multistep multiagent reinforcement learning for optimal energy schedule strategy of charging stations in smart grid”, IEEE Transactions on Cybernetics, 53(7), 4292-4305, 2022.
  • Shibl, M., Ismail, L., Massoud, A., “Electric vehicles charging management using machine learning considering fast charging and vehicle-to-grid operation”, Energies, 14(19), 6199, 2021.
There are 31 citations in total.

Details

Primary Language English
Subjects Electrical Engineering (Other)
Journal Section Review
Authors

Muhammed Sefa Çetin 0000-0001-5587-0001

Muhsin Tunay Gençoğlu 0000-0002-1774-1986

Habip Şahin 0000-0002-0907-2022

Publication Date December 30, 2024
Submission Date December 8, 2024
Acceptance Date December 25, 2024
Published in Issue Year 2024 Volume: 9 Issue: 1

Cite

IEEE M. S. Çetin, M. T. Gençoğlu, and H. Şahin, “A REVIEW OF ELECTRIC VEHICLES: THEIR IMPACT ON THE ELECTRICITY GRID AND ARTIFICIAL INTELLIGENCE-BASED APPROACHES FOR CHARGING LOAD MANAGEMENT”, IJESG, vol. 9, no. 1, pp. 51–59, 2024, doi: 10.55088/ijesg.1598117.

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