JCSIS
019928311823 info@jcsis.org
JCSIS

Federated Machine Learning Framework for Distributed Energy Resource Management in Microgrids.

Najaad OubeBlika
Volume 5

Abstract

Distributed Energy Resource Management is a central challenge in modern microgrids, particularly as photovoltaic systems, wind turbines, battery energy storage systems, electric vehicles, controllable loads, and demand response programs become increasingly integrated into decentralized power networks. Effective management of these resources requires accurate forecasting, adaptive control, optimal scheduling, and real-time coordination under uncertain renewable generation, variable load demand, changing electricity prices, and dynamic grid conditions. Traditional centralized machine learning approaches often require collecting operational data from multiple microgrids or distributed assets into a single server, which may raise concerns related to data privacy, cybersecurity, communication burden, ownership of energy data, and regulatory constraints. To address these limitations, this paper proposes a Federated Machine Learning framework for privacy-preserving and collaborative Distributed Energy Resource Management in microgrids.

The proposed framework enables multiple microgrids, prosumers, smart buildings, and distributed energy resource controllers to collaboratively train predictive and decision-support models without transferring raw local data. Each participating client trains a local machine learning model using its own energy consumption records, renewable generation profiles, battery state-of-charge measurements, weather data, electricity tariff information, and operational constraints. Instead of sharing sensitive operational data, only model updates or encrypted parameters are transmitted to a central or hierarchical aggregation server, where a global model is constructed using federated aggregation strategies such as FedAvg, FedProx, and adaptive federated optimization. This collaborative learning paradigm allows the global model to benefit from diverse operating conditions across multiple microgrid environments while preserving local data confidentiality.

The framework supports several core microgrid management tasks, including short-term load forecasting, renewable energy generation prediction, battery energy storage scheduling, electric vehicle charging coordination, demand response optimization, fault-aware resource dispatch, and grid-connected or islanded mode operation planning. A comprehensive preprocessing pipeline is applied locally at each client, including missing value imputation, outlier removal, normalization, time-series alignment, feature encoding, and privacy-aware data quality assessment. To address the non-independent and identically distributed nature of microgrid data, the proposed system incorporates personalization layers, client clustering, weighted aggregation, and domain-adaptive learning mechanisms. These components improve model robustness when microgrids differ in load profiles, renewable penetration levels, weather patterns, storage capacities, and user behavior.

For optimization and control, the federated learning model can be integrated with model predictive control, reinforcement learning, or multi-objective optimization modules to generate operational decisions that minimize energy cost, reduce carbon emissions, improve renewable energy utilization, maintain voltage and frequency stability, and extend battery lifetime. Privacy and security are further strengthened through secure aggregation, differential privacy, encrypted communication, anomaly detection for malicious client updates, and resilience mechanisms against data poisoning or model inversion attacks. These safeguards are essential for practical deployment in real-world energy networks where distributed assets may belong to different owners or utility operators.

The proposed framework is evaluated using simulated and real-world microgrid datasets under heterogeneous operating scenarios, including grid-connected operation, islanded operation, high renewable penetration, peak demand periods, and communication-limited environments. Performance is assessed using forecasting accuracy, operational cost reduction, renewable energy utilization rate, battery degradation cost, peak-to-average ratio reduction, loss of load probability, communication overhead, convergence speed, privacy preservation level, and robustness against non-IID data distributions. Comparative experiments are conducted against centralized learning, standalone local learning, conventional optimization-based energy management, and non-federated machine learning baselines. Experimental evaluation is expected to demonstrate that the proposed federated framework achieves near-centralized performance while significantly improving privacy preservation, scalability, and adaptability across distributed microgrid systems.

The proposed Federated Machine Learning framework offers a scalable, secure, and intelligent solution for next-generation distributed energy resource management. By enabling collaborative learning without centralized data collection, the system supports privacy-aware energy forecasting, adaptive scheduling, and coordinated control across multiple microgrids. Its integration into smart grid platforms can enhance energy efficiency, increase renewable energy hosting capacity, reduce operational uncertainty, improve cyber-resilience, and support the development of decentralized, sustainable, and data-driven energy communities.

Keywords

References

  • [1] Hassib, E. M., El -Desouky, A. I., Labib, L. M., & El -Kenawy, E. S. M. (2020). WOA+ BRNN: An imbalanced big data classification
  • [2] Eid, M. M., El -kenawy, E. S. M., & Ibrahim, A. (2021, March). A binary sine cosine -modified whale optimization algorithm for feature
  • [3] El-Sayed Towfek, M. (2018). El -kenawy. Trust Model for Dependable File Exchange in Cloud Computing. International Journal of
  • [4] Abdelhamid, A. A., Towfek, S. K., Khodadadi, N., Alhussan, A. A., Khafaga, D. S., Eid, M. M., & Ibrahim, A. (2023). Waterwhee l plant
  • [5] El-Kenawy, E. S. M., Abdelhamid, A. A., Ibrahim, A., Mirjalili, S., Khodadad, N., Alduailij, M. A., ... & Khafaga, D. S. (2023). Al-
  • [6] Alhussan, A. A., Abdelhamid, A. A., El -Kenawy, E. S. M., Ibrahim, A., Eid, M. M., Khafaga, D. S., & Ahmed, A. E. (2023). A binary
  • [7] Alhussan, A. A., Khafaga, D. S., Abotaleb, M., Mishra, P., & El -Kenawy, E. S. M. (2024). Global Potato Production Forecasting Based
  • [8] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
  • [9] Mishra, P., Alhussan, A. A., Khafaga, D. S., Lal, P., Ray, S., Abotaleb, M., ... & El -Kenawy, E. S. M. (2024). Forecasting production of
  • [10] El-Kenawy, E. S. M., Mirjalili, S., Abdelhamid, A. A., Ibrahim, A., Khodadadi, N., & Eid, M. M. (2022). Meta -heuristic optimization
  • [11] Abdelhamid, A. A., El -Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Khafaga, D. S., Alharbi, A. H., ... & Saber, M. (2022).
  • [12] Alharbi, A. H., Towfek, S. K., Abdelhamid, A. A., Ibrahim, A., Eid, M. M., & Khafaga, D. S. & Saber, M.(2023). Diagnosis of
  • [13] Alhussan, A. A., & Towfek, S. K. (2024). 5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algori thm.
  • [14] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
  • [15] Abdelhamid, A. A., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Eed, M. (2024). Potato harvesting pred iction
  • [16] Eed, M., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Arnous, R. (2024). Potato Consumption Forecastin g
  • [17] Mahmood, S., Sun, H., Iqbal, A., Alhussan, A. A., & El -kenawy, E. S. M. (2024). Green finance, sustainable infrastructure, and green
  • [18] El-Kenawy, E. S. M., Alhussan, A. A., Khodadadi, N., Mirjalili, S., & Eid, M. M. (2024). Predicting potato crop yield with machi ne
  • [19] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [20] Ghasemi, M., Khodadadi, N., Trojovský, P., Li, L., Mansor, Z., Abualigah, L., ... & El -Kenawy, E. S. M. (2025). Kirchhoff’s law
  • [21] Yassen, M. A., El -Kenawy, E. S. M., Abdel -Fattah, M. G., Ismail, I., & Mostafa, H. E. D. S. (2025). Explainable artificial intelligence
  • [22] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [23] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [24] El-kenawy, E. S. M., Alhussan, A. A., Mattar, E. A., & Radwan, M. (2026). Feature selection and hyperparameter tuning in transfo rmer -
  • [25] Chen, L., Xu, C., Lim, W. H., Sharma, A., Tiang, S. S., Chong, K. S., ... & Khafaga, D. S. (2025). Transparent and reliable c onstruction
  • [26] Khodadadi, N., Towfek, S. K., Zaki, A. M., Alharbi, A. H., Khodadadi, E., Khafaga, D. S., ... & Eid, M. M. (2025). Predicting
  • [27] Alhussan, A. A., El -Kenawy, E. S. M., Khafaga, D. S., Alharbi, A. H., & Eid, M. M. (2025). Groundwater resource prediction and
  • [28] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [29] Lopes, J. M., Pinho, C. S., Martins, R. C., & Bandeira, T. (2026). Sustainability Gets Smarter: Competitive Pressure and AI L eading the
  • [30] Radwan, M., Ibrahim, A., Abdelsalam, M. M., Alhussan, A. A., Mattar, E. A., & El -Kenawy, E. S. M. (2026). Optimizing solar and wind
  • [31] Alhussan, A. A., El -Kenawy, E. S. M., Eid, M. M., & Khodadadi, N. (2026). Hybrid Al -Biruni and Puma Optimization (BERPO) for
  • [32] El-Kenawy, E. S. M., Ibrahim, A., Alhussan, A. A., Khafaga, D. S., Ahmed, A. E., & Eid, M. M. (2026). Smart city electricity loa d
  • [33] El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Zaki, A. M., Ibrahim, A., Alhussan, A. A., ... & Eid, M. M. (2026). Glider sn ake
  • [34] Mekaret, F., Rabehi, A., Zebentout, B., Tizi, S., Douara, A., Bellucci, S., ... & Alhussan, A. A. (2024). A comparative study of Schottky
  • [35] KOUADRI, Ali, RABEHI, Abdelhalim, BENZIANE, Ali, et al. A Robust Multi -Transform Watermarking Scheme for Medical Images
  • [36] Tibermacine, I. E., Russo, S., Scarano, G., Tedesco, G., Rabehi, A., Alhussan, A. A., ... & Napoli, C. (2025). Conditional VA E for
  • [37] Kouadri, A., Benziane, A., Rabehi, A., Rabehi, A., Alhussan, A. A., Khafaga, D. S., & El -Kenawy, E. S. M. (2025). A novel hybrid
  • [38] Russo, S., Tibermacine, I. E., Randieri, C., Rabehi, A., Alharbi, A. H., El -Kenawy, E. S. M., & Napoli, C. (2025). Exploiting facial
  • [39] Bentegri, H., Rabehi, M., Kherfane, S., Nahool, T. A., Rabehi, A., Guermoui, M., ... & El -Kenawy, E. S. M. (2025). Assessment of
  • [40] Tibermacine, I. E., Russo, S., Citeroni, F., Mancini, G., Rabehi, A., Alharbi, A. H., ... & Napoli, C. (2025). Adversarial de noising of EEG
  • [41] Ouahabi, M. S., Benyounes, A., Barkat, S., Ihammouchen, S., Rekioua, T., Rabehi, A., ... & Alharbi, A. H. (2025). Real -time sensor fault
  • [42] Belaid, A., Guermoui, M., Khelifi, R., Arrif, T., Chekifi, T., Rabehi, A., ... & Alhussan, A. A. (2024). Assessing suitable a reas for PV
  • [43] Mehallou, A., M’hamdi, B., Amari, A., Teguar, M., Rabehi, A., Guermoui, M., ... & Khafaga, D. S. (2025). Optimal multiobjecti ve
  • [44] Rabehi, A., El -Hadi, M., Benmahmoud, S., Rabehi, A., Alharbi, A. H., & El -Kenawy, E. S. M. (2025). SOCA -CFAR Processor in A
  • [45] Bakria, D., Beladel, A., Korich, B., Teta, A., Mohammedi, R. D., Laouid, A. A., ... & El -kenawy, E. S. (2025). A novel enhanced Grey
Download