|
1. Agah, S. M., & Abbasi, A. (2012). The impact of charging plug-in hybrid electric vehicles on residential distribution transformers. Iranian Conference on Smart Grids, 2. Daliri, H. S., Dejagah, M., Baghaee, H. R., Abyaneh, H. A., & Bakhshai, A. (2025). HMM-based feature extraction and machine learning methods for event detection and classification in microgrids. IEEE Transactions on Smart grid. [ DOI:10.1109/TSG.2025.3590518] 3. Daliri, H. S., Dejagah, M., Khodabakhshi-Javinani, N., Baghaee, H. R., Nafisi, H., & Abyaneh, H. A. (2024). Dwtgb-based power quality disturbance detection in electric vehicle charging station. 2024 14th Smart Grid Conference (SGC), [ DOI:10.1109/SGC64640.2024.10983685] 4. He, Y., Venkatesh, B., & Guan, L. (2012). Optimal scheduling for charging and discharging of electric vehicles. IEEE Transactions on Smart grid, 3(3), 1095-1105. [ DOI:10.1109/TSG.2011.2173507] 5. Jang, M.-J., & Oh, E. (2024). Deep-reinforcement-learning-based vehicle-to-grid operation strategies for managing solar power generation forecast errors. Sustainability, 16(9), 3851. [ DOI:10.3390/su16093851] 6. Li, Y., Xiang, Y., Zheng, T., Wen, C., Wei, W., Tong, J., Hu, H., Sun, Z., Chen, T., & Zhang, Q. (2026). Deep Q-Network Based Optimal Charging Coordination of Electric Vehicles Considering Vehicle-to-Grid Technology. Electricity, 7(2), 44. [ DOI:10.3390/electricity7020044] 7. Liu, D., Zeng, P., Cui, S., & Song, C. (2023). Deep reinforcement learning for charging scheduling of electric vehicles considering distribution network voltage stability. Sensors, 23(3), 1618. [ DOI:10.3390/s23031618] 8. Qian, J., Jiang, Y., Liu, X., Wang, Q., Wang, T., Shi, Y., & Chen, W. (2023). Federated reinforcement learning for electric vehicles charging control on distribution networks. IEEE Internet of Things Journal, 11(3), 5511-5525. [ DOI:10.1109/JIOT.2023.3306826] 9. Rashid, T. S., Refat, K. A., Islam, M. N., Khan, R., Mostofa, E. R., Hossain, M. A., Kabir, H., & Ali, M. F. (2025). Machine learning-driven PV forecasting and coordinated PV-BESS dispatch for optimizing distribution grid performance under high EV penetration. Energy Conversion and Management: X, 101418. [ DOI:10.1016/j.ecmx.2025.101418] 10. Sharif, M., Seker, H., & Javed, Y. (2026). Context-Aware Multi-Agent Coordination Framework for Intelligent Electric Vehicle Charging Optimization. IEEE Access. [ DOI:10.1109/ACCESS.2026.3687146] 11. Sundstrom, O., & Binding, C. (2011). Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Transactions on Smart grid, 3(1), 26-37. [ DOI:10.1109/TSG.2011.2168431] 12. Wan, Z., Li, H., He, H., & Prokhorov, D. (2018). Model-free real-time EV charging scheduling based on deep reinforcement learning. IEEE Transactions on Smart grid, 10(5), 5246-5257. [ DOI:10.1109/TSG.2018.2879572] 13. Xiao, Q., Zhang, R., Wang, Y., Shi, P., Wang, X., Chen, B., Fan, C., & Chen, G. (2024). A deep reinforcement learning based charging and discharging scheduling strategy for electric vehicles. Energy Reports, 12, 4854-4863. [ DOI:10.1016/j.egyr.2024.10.056] 14. Yin, W., Qin, X., & Huang, Z. (2022). Optimal dispatching of large-scale electric vehicles into grid based on improved second-order cone. Energy, 254, 124346. [ DOI:10.1016/j.energy.2022.124346] |
