Energy Storage Controls and Degradation Mitigation across Microgrid Demonstration Sites
Keywords:
Microgrid Resilience, Energy Storage Control, Battery Degradation, Reliability Testing, Decentralized EnergyAbstract
The integration of energy storage systems into microgrids is a cornerstone of modern decentralized energy architectures. As microgrids transition from supplementary power sources to primary energy providers for localized communities and industrial complexes, the reliability of their energy storage controls becomes a paramount concern. This paper presents a comprehensive reliability testing framework for energy storage controls, coupled with a detailed investigation into degradation mitigation strategies deployed across multiple microgrid demonstration sites. Through rigorous empirical testing over an extended operational period, this study evaluates the behavioral responses of advanced control algorithms under both standard and extreme grid conditions. The research focuses on balancing real time grid support functions, such as frequency regulation and peak shaving, with long term battery health preservation. By utilizing a decentralized control architecture, the proposed mitigation strategies dynamically adjust operational parameters based on predictive models of battery cell degradation. The findings demonstrate a significant reduction in capacity fade while maintaining high fidelity in grid support responses. The data collected from the demonstration sites provide a robust empirical foundation for validating these control mechanisms in real world environments. This study concludes that proactive, health aware control systems are essential for maximizing the socioeconomic returns of energy storage investments in microgrid infrastructures, thereby ensuring sustainable and resilient decentralized power networks.References
1. Yue, Y.; Cao, L.; Chen, C.; Chen, Y.; Chen, B. Snake Optimization Algorithm Augmented by Adaptive t-Distribution Mixed Mutation and Its Application in Energy Storage System Capacity Optimization. Biomimetics 2025, 10, 244.
2. Fletcher, N.A.; Krebs, M.D. Sustained delivery of anti-VEGF from injectable hydrogel systems provides a prolonged decrease of endothelial cell proliferation and angiogenesis in vitro. RSC Adv. 2018, 8, 8999–9005.
3. Sun, J.; Zhang, J.; Gu, W.; Long, Y.; Guo, C. Experimental Investigation on Diamond Band Saw Processing of Resin Mineral Composites. Materials 2024, 17, 1814.
4. Xu, J.; Zhao, S.; Jiang, L.; Cen, L.; Shi, L.; Gao, M. Porosity formation and suppression mechanisms in oscillating laser-arc hybrid welding of aluminum alloys. Int. J. Heat Mass Transf. 2026, 261, 128528.
5. Gudauskis, M.; Žvironas, A.; Plikynas, D. Indoor Navigation Systems for Visually Impaired Persons: A Comprehensive Review of Technologies and Approaches. In Mobility of Visually Impaired People. Signals and Communication Technology; Pissaloux, E., Velazquez, R., Eds.; Springer: Cham, Switzerland, 2026.
6. Liu, Z.; Wang, Y.; Song, K.; Zhou, K.; Jiang, R.; Liao, Z.; Zou, L.; Huang, Y. Achieving Finished Surface and Anti-Fatigue Performance Improvements of Titanium Alloy through Precision Abrasive Belt Grinding-Polishing Process. Tribol. Int. 2026, 215, 111467.
7. Piscopo, G.; Salmi, A.; Atzeni, E. Investigation of dimensional and geometrical tolerances of laser powder directed energy deposition process. Precis. Eng. Int. Soc. Precis. Eng. Nanotechnol. 2024, 85, 217–225.
8. D’Mello, S.; Kory, J. A Review and Meta-Analysis of Multimodal Affect Detection Systems. ACM Comput. Surv. 2015, 47, 43.
9. Russell, S.; Norvig, P. Artificial Intelligence: A Modern Approach, 4th ed.; Pearson: London, UK, 2021.
10. Rosiak, M.; Kawulok, M.; Maćkowski, M. The Effectiveness of UWB-Based Indoor Positioning Systems for the Navigation of Visually Impaired Individuals. Appl. Sci. 2024, 14, 5646.
11. Awd, M.; Walther, F. AI-Powered Very-High-Cycle Fatigue Control: Optimizing Microstructural Design for Selective Laser Melted Ti-6Al-4V. Materials 2025, 18, 1472.
12. Nguyen, H.-L.; Holderbaum Do Amaral, R.; Lerouge, S.; De Roo, A.-K.; Zehtabi, F.; Vikkula, M.; Soulez, G. Injectable chitosan hydrogel effectively controls lesion growth in a venous malformation murine model. Diagn. Interv. Imaging 2024, 105, 430–438.
13. Laskowska, D.; Balasz, B.; Zawadka, W. Microstructure and Mechanical Properties of As-Built Ti-6Al-4V and Ti-6Al-7Nb Alloys Produced by Selective Laser Melting Technology. Materials 2024, 17, 4604.
14. Gao, X.; Ye, X.; He, Y.; Ma, S.; Liu, P. Mechanical Properties and Fatigue Life Estimation of Selective-Laser-Manufactured Ti6Al4V Alloys in a Comparison Between Annealing Treatment and Hot Isostatic Pressing. Materials 2025, 18, 3475.
15. Jobin, A.; Ienca, M.; Vayena, E. The Global Landscape of AI Ethics Guidelines. Nat. Mach. Intell. 2019, 1, 389–399.
16. Xue, J.; Shen, B. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. J. Supercomput. 2023, 79, 7305–7336.
17. Beam, A.L.; Kohane, I.S. Big Data and Machine Learning in Health Care. JAMA 2018, 319, 1317–1318.
18. Fitzpatrick, K.K.; Darcy, A.; Vierhile, M. Delivering Cognitive Behavior Therapy to Young Adults with Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. JMIR Ment. Health 2017, 4, e19.
19. Kozior, T.; Bochnia, J.; Jurago, A.; Jedrzejewski, P.; Adamczyk, M. Evaluation of Selected Quality Characteristics of Thin-Walled Models Manufactured Using Powder Bed Fusion Technology. Materials 2025, 18, 1134.
20. Moughames, J.; Martinez, J.A.I.; Ulliac, G.; Sylvestre, T.; Barbot, A.; Andre, J.-C.; Qi, H.J.; Demoly, F.; Kadic, M. Topology-optimized multimaterial 4D-printed Fabry-Perot filter with enhanced thermal stability using two-photon polymerization. Thin-Walled Struct. 2025, 209, 112900.
21. Singh, S.; Shah, P.L. Safe and Efficient Practice of Bronchoscopic Sampling from Mechanically Ventilated Patients: A Structured Evaluation of the Ambu Bronchosampler-Ascope 4 Integrated System. Respiration 2021, 100, 27–33.
22. Floridi, L.; Cowls, J.; Beltrametti, M.; Chatila, R.; Chazerand, P.; Dignum, V.; Luetge, C.; Madelin, R.; Pagallo, U.; Rossi, F.; et al. AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds Mach. 2018, 28, 689–707.
23. Obermeyer, Z.; Emanuel, E.J. Predicting the Future—Big Data, Machine Learning, and Clinical Medicine. N. Engl. J. Med. 2016, 375, 1216–1219.
24. Meng, Y.; Hou, Z.; Wang, L.; Deng, A.; Hu, D.; Yang, X.; Yang, Z.; Zhu, Z.; Chen, H. Oscillating laser-arc hybrid welding of aluminum alloy via synchronous wire-powder feeding: Microstructure evolution and mechanical properties. Mater. Des. 2026, 265, 116056.
25. Acherjee, B. Hybrid laser arc welding: State-of-art review. Opt. Laser Technol. 2018, 99, 60–71.
26. Gu, Y.; Chen, M.; Wang, L. A self-learning discrete salp swarm algorithm based on deep reinforcement learning for dynamic job shop scheduling problem. Appl. Intell. 2023, 53, 18925–18958.
27. Lane, N.D.; Georgiev, P.; Qendro, L. DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7–11 September 2015.
28. Pektaş, A.; Hacıbeyoğlu, M.; İnan, O. Hybridization of the Snake Optimizer and Particle Swarm Optimization for continuous optimization problems. Eng. Sci. Technol. Int. J. 2025, 67, 102077.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), unless otherwise stated.
