Reducing Residential Grid Congestion from Electric Vehicles Using Privacy-Preserving Federated Smart Charging
Downloads
Residential electric vehicle charging can create short feeder peaks during already crowded evening hours. Utilities need coordinated charging, yet household charging records reveal daily routines and travel habits. This paper develops a privacy-preserving federated smart charging method for residential feeders. Each client trains an energy predictor without sending raw sessions to a server. Clipped updates receive distributed Gaussian noise before pairwise-mask secure aggregation. A feeder-aware scheduler then places predicted energy within each vehicle connection window. The experiment uses 6,163 measured charging sessions from 74 apartment users. It also uses measured household imports from Open Power System Data. The corrected workflow converts cumulative meter readings into hourly demand increments. Testing covers 958 unseen sessions on a modeled feeder serving 100 homes. The proposed private model uses a noise multiplier of 1.0. Its privacy budget is 14.91 at a delta value of 0.00001. Private smart charging lowers the combined peak from 160.07 kW to 100.55 kW. This reduction equals 37.18 percent and removes ten modeled overload hours. However, the schedule leaves 10.57 percent of requested energy unmet. Its mean charging delay reaches 8.37 hours. The private predictor records 12.80 kWh test RMSE. The centralized model performs better, with 8.60 kWh RMSE. Results show that privacy noise adds little error beyond ordinary federated training. Client data differences remain the main accuracy barrier. The method protects data location and bounds shared updates. It exposes the service cost behind congestion relief. These findings support stronger local models and service constraints before deployment.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–318. https://doi.org/10.1145/2976749.2978318
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 1175–1191. https://doi.org/10.1145/3133956.3133982
Brinkel, N. B. G., Schram, W. L., AlSkaif, T. A., Lampropoulos, I., & van Sark, W. G. J. H. M. (2020). Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits. Applied Energy, 276, 115285. https://doi.org/10.1016/j.apenergy.2020.115285
Clement-Nyns, K., Haesen, E., & Driesen, J. (2010). The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Transactions on Power Systems, 25(1), 371–380. https://doi.org/10.1109/TPWRS.2009.2036481
Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407. https://doi.org/10.1561/0400000042
Gan, L., Topcu, U., & Low, S. H. (2013). Optimal decentralized protocol for electric vehicle charging. IEEE Transactions on Power Systems, 28(2), 940–951. https://doi.org/10.1109/TPWRS.2012.2210288
Hilshey, A. D., Hines, P. D. H., Rezaei, P., & Dowds, J. R. (2013). Estimating the impact of electric vehicle smart charging on distribution transformer aging. IEEE Transactions on Smart Grid, 4(2), 905–913. https://doi.org/10.1109/TSG.2012.2217385
Abdussalam Ali Ahmed (2025). From Transition to Transformation: A Comparative Engineering Study of Hybrid and Electric Vehicles. Libyan Open University Journal of Applied Sciences (LOUJAS), 1(1), 11-19. https://doi.org/10.65422/loujas.v1i1.48
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., D'Oliveira, R. G. L., Eichner, H., El Rouayheb, S., Evans, D., Gardner, J., Garrett, Z., Gascón, A., Ghazi, B., Gibbons, P. B., ... Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210. https://doi.org/10.1561/2200000083
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 2, 429–450. https://proceedings.mlsys.org/paper/2020/hash/1f5fe83998a09396ebe6477d9475ba0c-Abstract.html
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Agüera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, 1273–1282. https://proceedings.mlr.press/v54/mcmahan17a.html
Mironov, I. (2017). Rényi differential privacy. 2017 IEEE 30th Computer Security Foundations Symposium, 263–275. https://doi.org/10.1109/CSF.2017.11
Muratori, M. (2018). Impact of uncoordinated plug-in electric vehicle charging on residential power demand. Nature Energy, 3, 193–201. https://doi.org/10.1038/s41560-017-0074-z
Richardson, D. B. (2013). Electric vehicles and the electric grid. Renewable and Sustainable Energy Reviews, 19, 247–254. https://doi.org/10.1016/j.rser.2012.11.042
Azar Ali Matoug, & Abdussalam Ali Ahmed. (2026). Simulation-Based Performance Analysis of Phosphoric Acid Fuel Cells: Polarization Behavior, Power Density, and Sensitivity to Operating Conditions. African Union Journal of Academic and Research Studies, 1(2), 1-8.
Sadeghian, O., Oshnoei, A., Mohammadi-Ivatloo, B., Vahidinasab, V., & Anvari-Moghaddam, A. (2022). A comprehensive review on electric vehicles smart charging. Journal of Energy Storage, 54, 105241. https://doi.org/10.1016/j.est.2022.105241
Sortomme, E., & El-Sharkawi, M. A. (2011). Optimal charging strategies for unidirectional vehicle-to-grid. IEEE Transactions on Smart Grid, 2(1), 131–138. https://doi.org/10.1109/TSG.2010.2090910
Sørensen, Å. L., Lindberg, K. B., Sartori, I., & Andresen, I. (2021a). Residential electric vehicle charging datasets from apartment buildings. Data in Brief, 36, 107105. https://doi.org/10.1016/j.dib.2021.107105
Sørensen, Å. L., Lindberg, K. B., Sartori, I., & Andresen, I. (2021b). Analysis of residential electric vehicle energy flexibility potential based on real-world charging reports and smart meter data. Energy and Buildings, 241, 110923. https://doi.org/10.1016/j.enbuild.2021.110923
Wiese, F., Schlecht, I., Bunke, W.-D., Gerbaulet, C., Hirth, L., Jahn, M., Kunz, F., Lorenz, C., Mühlenpfordt, J., Reimann, J., & Scharf, M. (2019). Open Power System Data: Frictionless data for electricity system modelling. Applied Energy, 236, 401–409. https://doi.org/10.1016/j.apenergy.2018.11.097
Abdulgader Alsharif, Abdussalam Ali Ahmed, Omar Ahmed Mohamed, & Taha Muftah Abuali. (2026). Hybrid Machine Learning Approaches for Accurate Solar Energy Forecasting from Real-World Weather Data. Libyan Journal of Health, Science, and Development (LJHSD), 2(1), 09-17
