Self-Adaptive Federated Meta-Learning Framework for Hybrid Renewable Microgrid Orchestration under High Renewable Penetration | Zenodo Skip to main Communities My dashboard Log in Sign up COMPUTER DOMAIN Published April 21, 2026 | Version v1 Journal article Open Self-Adaptive Federated Meta-Learning Framework for Hybrid Renewable Microgrid Orchestration under High Renewable Penetration Authors/Creators Rahul Shankar Pandey Description The rapid expansion of renewable energy resources has fundamentally transformed the operational dynamics of distributed power systems. Hybrid microgrids integrating photo- voltaic arrays, wind turbines, and battery storage offer localized resilience and improved energy efficiency. However, renewable intermittency introduces significant uncertainty in forecasting and dispatch planning, particularly under high penetration sce- narios. Conventional energy management systems rely on static weighting strategies and centralized machine learning models, limiting adaptability and scalability in heterogeneous microgrid environments. This paper proposes a unified self-adaptive renewable energy orchestration framework that integrates meta-learning, federated gradient aggregation, and convex hybrid dispatch optimization within a hierarchical edge–fog–cloud architecture. An adaptive renewable weighting mechanism is mathematically formulated using softmax-based meta-parameterization, enabling dynamic regulation of solar, wind, and storage contributions. A distributed convex optimization problem is constructed for each microgrid, ensuring bounded state-of-charge dynamics under operational constraints. Federated learning enables cross-microgrid parame- ter adaptation without raw data exchange, preserving scalability and data locality. Formal stability guarantees are established under bounded learning rates and convex feasibility conditions. Semi-realistic dataset-driven simulations involving multiple hybrid microgrids demonstrate statistically significant improvements in forecasting accuracy, renewable utilization, and operational cost compared to static and centralized baselines. The proposed framework provides a scalable and resilient foundation for intelligent de- centralized renewable energy management systems . Files Self-Adaptive Federated Meta-Learning Framework.pdf Files (1.1 MB) Name Size Download all Self-Adaptive Federated Meta-Learning Framework.pdf md5:d430d759fa462a812e65f7eb1598226c 1.1 MB Preview Download Additional details References 1. Z. Y. Dong, D. Wang, and F. Luo, "Wind power forecasting using long short-term memory network," IEEE Transactions on Sustainable Energy, vol. 9, no. 4, pp. 1667–1675, 2018. 2. J. Wang, J. Yan, and K. 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Wang, Y. Han, and C. Wang, "Edge computing in smart grid: A survey," IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4856–4869, 2019. 9. Y. Liu, P. Wang, and L. Xiao, "Edge-based distributed control for microgrids," IEEE Transactions on Industrial Informatics, vol. 16, no. 8, pp. 5387–5397, 2020. 10. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Arcas, "Communication-efficient learning of deep networks from decentralized data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273–1282. 30 Views 51 Downloads Show more details All versions This version Views Total views 30 30 Downloads Total downloads 51 51 Data volume Total data volume 77.1 MB 77.1 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords Smart Grid, Federated Learning, Meta- Learning, Hybrid Microgrids, Renewable Forecasting, Dis- tributed Optimization. Details DOI DOI Badge DOI 10.5281/zenodo.19676442 Markdown [](https://doi.org/10.5281/zenodo.19676442) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19676442.svg :target: https://doi.org/10.5281/zenodo.19676442 HTML <a href="https://doi.org/10.5281/zenodo.19676442"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19676442.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19676442.svg Target URL https://doi.org/10.5281/zenodo.19676442 Resource type Journal article Publisher Zenodo Published in Research and Reviews: Advancement in Robotics, 9(3), 16-27, ISSN: 3048-6416, 2026. Languages English Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. 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