Robust Stability Control Theory and Characteristics Analysis of Networked Energy Storage Converters under Large Disturbance Scenarios

Authors

  • Fengshun Jiao Shenzhen Power Supply Co., Ltd.
  • Guoxing Wu Shenzhen Power Supply Co., Ltd.
  • Weijie Gao Shenzhen Power Supply Co., Ltd.
  • Chuyun She Shenzhen Power Supply Co., Ltd.

DOI:

https://doi.org/10.4108/ew.14509

Keywords:

Grid-forming energy storage converter, Large disturbance scenario, Stability control, Dynamic model

Abstract

INTRODUCTION: Grid-Forming Energy Storage Converter (GFM-ESC) is a key interface equipment for the coordinated regulation of source and network in new power systems. However, the dynamic coupling mechanism between its multi-topology structures such as direct-mounted, string-type, and centralized types and large disturbance scenarios (new energy power fluctuations, power grid transient shocks) has not yet been clarified.

OBJECTIVES: Existing robust control methods do not embed topological feature constraints and are difficult to ensure the system stability margin under large disturbances under multi-topology. This theoretical gap limits the scene adaptability of GFM-ESC. In response to the above scientific issues, this paper focuses on Robust Stability Control Theory and Characteristics Analysis of Networked Energy Storage Converters under Large Disturbance Scenarios.

METHODS: First, a large disturbance dynamic model of multi-topology GFM-ESC was established. By analyzing the mapping relationship among wiring topology, component parameters and system state matrix, the energy flow mechanism and eigenvalue trajectory instability inducements of GFM-ESC under different topologies were revealed during large disturbances, and the influence of topological parameters on the system was quantified. Secondly, grounded on H∞ robust control theory, a robust stable control method for topology adaptation was proposed, a closed-loop control law embedded with topological features was derived, and Lyapunov stability theory was used to prove the robust convergence of this control method under large disturbances and topological parameter perturbation scenarios. Finally, through small signal analysis and large disturbance numerical simulation, the performance of the developed theory and method was verified.

RESULTS: Findings showed that the developed dynamic model can accurately describe the large disturbance characteristics of multi-topology GFM-ESC, and the corresponding robust control method can increase the system stability margin by 42%, and can still maintain the asymptotic stability of the closed-loop system when the topological parameters were perturbed by 15%.

CONCLUSION: The results of this study enhance the theoretical system for controlling large disturbances in the multi-topology GFM-ESC, providing a foundation for the collaborative design of GFM-ESC topologies in new power systems.

Downloads

Download data is not yet available.

References

[1] Liu Y, Chen Y, Xin H, Tu J, Zhang L, Song M, and Zhu J. System strength constrained grid-forming energy storage planning in renewable power systems. IEEE Transactions on Sustainable Energy. 2024; 16(2): 981-994.

[2] Alshahrani S, Khan K, Abido M, and Khalid M. Grid-forming converter and stability aspects of renewable-based low-inertia power networks: Modern trends and challenges. Arabian Journal for Science and Engineering. 2024; 49(5): 6187-6216.

[3] Wu X, Wu S, Wang F, Wei C, Li R, Jiang X, and Cai X. Grid-forming control and experimental validation for high voltage transformerless battery energy storage system. IEEE Transactions on Power Electronics. 2024; 40(4): 4889-4901.

[4] Zhao F, Wang X, and Zhu T. Power dynamic decoupling control of grid-forming converter in stiff grid. IEEE Transactions on Power Electronics. 2022; 37(8): 9073-9088.

[5] Favuzza S, Musca R, and Zizzo G. Comparative modeling and analysis of EMT and phasor RMS grid-forming converters under different power system dynamics. IEEE Transactions on Industry Applications. 2023; 60(2): 3613-3624.

[6] Chen M, Zhou D, Tayyebi A, Prieto-Araujo E, Dörfler F, and Blaabjerg F. On power control of grid-forming converters: Modeling, controllability, and full-state feedback design. IEEE Transactions on Sustainable Energy. 2023; 15(1): 68-80.

[7] Zhang K, Shi M, Chen X, Shao D, Xu Y, and Chen Y. Interaction modeling and stability analysis of grid-forming energy storage system based on SISO transfer functions. IEEE Transactions on Sustainable Energy. 2024; 16(1): 573-587.

[8] Wu Z, Zhang M, Fan B, Shi Y, and Guan X. Deep synchronization control of grid-forming converters: A reinforcement learning approach. IEEE/CAA Journal of Automatica Sinica. 2025; 12(1): 273-275.

[9] Zhang Z, Schuerhuber R, Fickert L, and Chen G. Dynamic modelling and equilibrium manifold of multi-converter systems: A study on grid-forming and grid-following converters in renewable energy power plants. IET Renewable Power Generation. 2024; 18(15): 3253-3267.

[10] Elshenawy MA, Radwan A, and Mohamed YARI. Unified sequence impedance models of synchronous generator- and virtual oscillator-based grid-forming converters. IEEE Transactions on Power Delivery. 2023; 39(1): 56-70.

[11] Pouresmaeil M, Sangrody R, Taheri S, Montesinos-Miracle D, and Pouresmaeil E. Enhancing fault ride through capability of grid-forming virtual synchronous generator susing model predictive control. IEEE Journal of Emerging and Selected Topics in Industrial Electronics. 2024; 5(3): 1192-1203.

[12] Suvorov A, Askarov A, and Kievets A. A freely configurable structure of virtual synchronous generator for grid-forming converters. Electrical Engineering. 2023; 105(3): 1331-1345.

[13] Ali AJ, Abbas HH, and Bevrani H. Comparative analysis of H2 and H∞ robust control design approaches for dynamic control systems. Journal of Engineering. 2023; 29(08): 1-15.

[14] Martínez-Turégano J, Sala A, and Blasco-Gimenez R. Grid forming H∞ control for HVDC diode rectifier-connected wind power plants. CSEE Journal of Power and Energy Systems. 2023; 10(6): 2384-2397.

[15] Rathnayake DB, Me SP, Razzaghi R, and Bahrani B. H∞-based control design for grid-forming inverters with enhanced damping and virtual inertia. IEEE Journal of Emerging and Selected Topics in Power Electronics. 2022; 11(2): 2311-2325.

[16] Hossain MI, Anonto HZ, Shufian A, Pathik BB, Mannan MA, Rashid A, and Biswas P. Enhancing microgrid resilience through integrated grid-forming and grid-following inverter strategies for solar PV battery control and fault ride-through. Scientific Reports. 2025; 15(1): 40078-40106.

[17] Çelebi M and Başçi A. Parameter optimization of bidirectional three-phase DC-AC power inverter by an improved particle swarm optimization based fractional order PI controller for the grid forming operation. International Journal of Engineering Technologies IJET. 2022; 8(1): 25-30.

[18] Qi Y, Che Y, Liu L, Ni J, and Zhang S. Optimization configuration method for grid-side grid-forming energy storage system based on genetic algorithm. Energy Engineering: Journal of the Association of Energy Engineers. 2025; 122(10): 3999-4017.

[19] Teng Y, Deng W, Pei W, Li Y, Dingv L, and Ye H. Review on grid-forming converter control methods in high-proportion renewable energy power systems. Global Energy Interconnection. 2022; 5(3): 328-342.

[20] Wang S, Wei J, Zhang L, Li H, Fernandez C, and Blaabjerg F. Improved harmonic loss-history gated unit recycling for online state of charge and state of energy co-estimation of lithium-ion batteries for large-scale energy storage stations. Energy. 2025; 340: 139225.

[21] Wang S, Dang Q, Gao Z, Li B, Fernandez C, and Blaabjerg F. An innovative square root-untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries. Journal of Energy Storage. 2024; 104: 114555.

[22] Wang S, Hu B, Zhang Q, Dang Q, and Fernandez C. Critical review on improved hybrid deep learning modeling strategies for online battery state of charge estimation in large-scale energy storage systems. Journal of Power Sources. 2026; 673: 239714.

[23] Shen C, Gu W, Shen X, and Xu Y. Lyapunov method-based coherent aggregation of grid-forming converters for transient stability equivalents. IEEE Transactions on Smart Grid. 2025; 16(2): 1462-1479.

[24] Zhu N, Hu P, Jiang C, Yu Y, and Jiang D. Comparative analysis and optimal allocation of virtual inertia from grid-forming and grid-following controlled ESSs. IET Renewable Power Generation. 2024; 18(14): 2416-2429.

Downloads

Published

09-11-2026

Issue

Section

AI-Powered Hybrid Energy Storage Optimization for Grid Cost-Efficiency and Stability

How to Cite

1.
Jiao F, Wu G, Gao W, She C. Robust Stability Control Theory and Characteristics Analysis of Networked Energy Storage Converters under Large Disturbance Scenarios. EAI Endorsed Trans Energy Web [Internet]. 2026 Nov. 9 [cited 2026 Nov. 9];13. Available from: https://publications.eai.eu/index.php/ew/article/view/14509