Voltage Stability Assessment in Radial Distribution Systems: Leveraging Artificial Neural Networks for High Penetration of Solar PV and EVs

Kavya Suresh, Kanakasabapathy P, Saikat Chakrabarti, Sanjib Kumar Panda

Abstract


The growing penetration of electric vehicles (EVs) and solar photovoltaic (SPV) systems in radial distribution networks increases system flexibility, yet poses severe challenges to voltage stability. Conventional approaches like Continuation Power Flow (CPF) and Jacobian-based sensitivity analysis (JBSA) offer accurate evaluations, but tend to be overly slow because of resource consumption. This paper proposes a com- bination of a data-driven approach to automate the assessment of voltage stability with a neural network approach for faster evaluation using a set framework of benchmarking scenario parameters to test boundary conditions at different levels of EVs and SPV penetration focused around the core elements of thwarting the system behaviour changes. The methodology strongly focuses around three parameters—Voltage Stability Index (VSI), Loadability Margin (LAM), and Generation Admissibility Margin (GAM)—that outline the system’s ability to withstand further injections of load and generation while keeping the voltage within acceptable limits for stability analysis. The ANN model is developed with the load flow datasets obtained from the IEEE 33-bus radial distribution system as the training set. Out of all the learning algorithms used, the best accuracy was obtained with the Levenberg-Marquardt algorithm. This study is novel in its combined assessment of voltage stability under simultaneous high penetrations of SPV and EVs, which have traditionally been studied separately. The proposed Feed-forward ANN model uniquely estimates Voltage Stability Index (VSI), Loadability Margin (LAM), and Generation Admissibility Margin (GAM) concurrently, enabling adaptive, real-time monitoring. Unlike prior methods, our approach integrates data-driven machine learning with power system analysis for efficient, accurate prediction, providing practical insights for distribution network operators managing renewable and EV integration. Compared to conventional methods CPF (20.34 minutes, 7.64?MB) and JBSA (2.86 minutes, 21.90?MB), the ANN requires only 0.6 seconds and 3.03?MB, demonstrating significant gains in speed and memory efficiency. Results show the proposed ANN-VSI method effectively forecasts voltage stability margins, offering a practical alternative to traditional methods for real-time stability assessment in active distribution systems with high renewable energy and electric vehicle integration.


Keywords


Solar Photovoltaic (SPV) Penetration; Electric Vehicles (EVs); Voltage Stability; Voltage Stability Index (VSI); Loadability Margin (LAM); Generation Admissibility Margin (GAM)

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References


R. Wang, X. Bi, S. Bu, “Real-time coordination of dynamic network reconfiguration and volt-var control in active distribution network: A graph-aware deep reinforce- ment learning approach,” IEEE Trans. Smart Grid, vol. 15, no. 3, pp. 3288–3302, 2024. doi:10.1109/TSG.2023.3324474.

L. Peng, A. Zabihi, M. Azimian, H. Shirvani, and F. Shahnia, “Developing a robust expansion planning approach for transmission networks and privately-owned renewable sources,” IEEE Access, vol. 11, pp. 76046–76058, 2023, doi: 10.1109/ACCESS.2022.3226695.

W. Huang, W. Zheng, D. J. Hill, “Distribution network reconfiguration for short-term voltage stability enhancement: An efficient deep learning ap- proach,” IEEE Trans. Smart Grid, vol. 12, no. 6, pp. 5385–5395, 2021. doi:10.1109/TSG.2021.3097330.

R. Yan, T. K. Saha, “Investigation of voltage stability for residential customers due to high photovoltaic penetrations,” IEEE Trans. Power Syst., vol. 27, no. 2, pp. 651– 662, 2012. doi:10.1109/TPWRS.2011.2180741.

S. Nandi, S. R. Ghatak, P. Acharjee, “Placement of EV fast charging station in distribution system based on voltage stability index strategy,” in Proc. 6th IEEE Int. Conf. Condition Assessment Techniques in Electrical Systems (CATCON), 2022, pp. 46–51. doi:10.1109/CATCON56237.2022.10077648.

A. Tavakoli, S. Saha, M. T. Arif, M. E. Haque, N. Mendis, A. M. T. Oo, “Impacts of grid integration of solar PV and electric vehicle on grid stability, power quality and energy economics: A review,” IET Energy Syst. Integr., vol. 2, no. 3, pp. 243–260, 2020. doi:10.1049/iet-esi.2019.0047.

N. K. K., J. N. S., V. K. Jadoun, “A combined approach to evaluate power quality and grid dependency by solar photovoltaic based EV charging station using hybrid optimization,” J. Energy Storage, vol. 84, 110967, 2024. doi:10.1016/j.est.2024.110967.

N. Sanampudi, P. Kanakasabapathy, “Integrated voltage control and frequency regulation for stand-alone micro-hydro power plant,” Materials Today: Proc., vol. 46, pp. 5027–5031, 2021. doi:10.1016/j.matpr.2020.10.403.

M. M. Haque, P. Wolfs, “A review of high PV penetrations in LV distribution net- works: Present status, impacts and mitigation measures,” Renew. Sustain. Energy Rev., vol. 62, pp. 1195–1208, 2016. doi:10.1016/j.rser.2016.04.025.

M. Karimi, H. Mokhlis, K. Naidu, S. Uddin, A. H. A. Bakar, “Photovoltaic penetration issues and impacts in distribution network—A review,” Renew. Sustain. Energy Rev., vol. 53, pp. 594–605, 2016. doi:10.1016/j.rser.2015.08.042.

J. Suganya, R. Karthikeyan, J. Ramprabhakar, “Voltage stabilization by using buck converters in the integration of renewable energy into the grid,” in New Trends in Computational Vision and Bio-Inspired Computing. Springer, 2020, pp. 103–113. doi:10.1007/978-3-030-41860-0 10.

K. R. Bharath, H. Choutapalli, P. Kanakasabapathy, “Control of bidirectional DC– DC converter in renewable-based DC microgrid with improved voltage stability,” International Journal of Renewable Energy Research, vol. 8, no. 2, p. 7509, 2018.

A. Zabihi and M. Parhamfar, “EMPOWERING THE GRID: Toward the integration of electric vehicles and renewable energy in power systems,” International Journal of Energy Security and Sustainable Energy (IJESSE), vol. 2, no. 1, pp. 1–14, Jul. 2024, doi: 10.5281/zenodo.12751722.

R. Gadal, O. Aziz, F. Elmariami, A. Belfqih, N. Agouzoul, “Voltage stability assessment and control using indices and FACTS: A comparative review,” J. Electr. Comput. Eng., 2023, Art. ID 5419372. doi:10.1155/2023/5419372.

H. S. Salama, I. Vokony, “Voltage stability indices-a comparison and a review,” Comput. Electr. Eng., vol. 98, 107743, 2022. doi:10.1016/j.compeleceng.2022.107743.

A. Selim, S. Kamel, A. S. Alghamdi, F. Jurado, “Optimal placement of DGs in distribution system using an improved Harris Hawks optimizer based on single- and multi-objective approaches,” IEEE Access, vol. 8, pp. 52815–52829, 2020. doi:10.1109/ACCESS.2020.2980245.

A. A. Mohamed Faizal, N. Dwivedi, M. Sivasubramanian, S. Marisargunam, K. Rajesh, and N. Janaki, “Voltage stability improvement using PV coordinated control scheme in IEEE-9 bus system,” in Proc. ICRP, 2023, Springer, 2024.

A. A. Mohamed Faizal, N. Dwivedi, M. Sivasubramanian, S. Marisargunam, K. Rajesh, and N. Janaki, “A combined approach to evaluate power quality and grid dependency by solar photovoltaic based electric vehicle charging station using hybrid optimization,” J. Energy Storage, 2024.

K. Anthony and V. Arunachalam, “Voltage stability monitoring and improvement in a renewable energy dominated deregulated power system: A review,” e-Prime – Adv. Electr. Eng., Electron. Energy, vol. 11, 2025, Art. no. 100893, doi: 10.1016/j.prime.2024.100893.

S. Ly, A. Singh, P. Vorobev, Y. C. Soh, and H. D. Nguyen, “Chance-constrained solar PV hosting capacity assessment for distribution grids using Gaussian Process and Logit learning,” arXiv preprint, arXiv:2505.19839, 2025.

PV magazine International, “EV charging shapes PV investment, grid load in community study,” pv magazine International, Jun. 12, 2025. [Online]. Available: https://www.pv-magazine.com/2025/06/12/ev-charging-shapes-pv-investment-grid-load-in-community-study/

K. D. Dharmapala, A. Rajapakse, K. Narendra, Y. Zhang, “Machine learn- ing based real-time monitoring of long-term voltage stability using volt- age stability indices,” IEEE Access, vol. 8, pp. 222544–222555, 2020. doi:10.1109/ACCESS.2020.3043935.

H. H. Goh, Q. S. Chua, S. W. Lee, B. C. Kok, K. C. Goh, K. T. K. Teo, “Evaluation of voltage stability indices in power systems using an artificial neural network,” Procedia Eng., vol. 118, pp. 1127–1136, 2015. doi:10.1016/j.proeng.2015.08.454.

A. K. Sharma, A. Saxena, B. P. Soni, V. Gupta, “Voltage stability assessment using artificial neural network,” in IEEE Int. Conf. Electrical, Computer and Communication Technologies (ICECCT), 2018, pp. 1–6. doi:10.1109/ICECCT.2018.8475510.

S. Sathyan, V. Pandi, A. Antony, S. R. Salkuti, P. Sreekumar, “ANN-based energy management system for PV-powered EV charging station with battery backup and vehicle-to-grid support,” Int. J. Green Energy, vol. 21, pp. 1–16, 2023. doi:10.1080/15435075.2023.2246048.

R. D. Zimmerman, C. E. Murillo-Sa´nchez, R. J. Thomas, “MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 12–19, 2011. doi:10.1109/TPWRS.2010.2051168.

R. D. Zimmerman, C. E. Murillo-Sa´nchez, MATPOWER User’s Manual, Ver. 7.1.2020. doi:10.5281/zenodo.4074122.

B. Bakhshideh Zad, J. Lobry, F. Valle´e, “A new voltage sensitivity analysis method for medium-voltage distribution systems incorporating power losses impact,” Electr. Power Components Syst., vol. 46, nos. 14–15, pp. 1540–1553, 2018. doi:10.1080/15325008.2018.1511639.

M. Tostado, S. Kamel, F. Jurado, “Developed Newton–Raphson based predic- tor–corrector load-flow approach with high convergence rate,” Int. J. Electr. Power Energy Syst., vol. 105, pp. 785–792, 2019. doi:10.1016/j.ijepes.2018.09.021.

N. Hatziargyriou et al., “Definition and classification of power system stabil- ity—revisited & extended,” IEEE Trans. Power Syst., vol. 36, no. 4, pp. 3271–3281, 2021. doi:10.1109/TPWRS.2020.3041774.

K. Suresh, P. Kanakasabapathy, “A review of voltage stability issues in distribution system influenced by high PV penetration and its mitigation techniques,” Int. J. Renew. Energy Res., vol. 13, no. 1, pp. 236–244, 2023. doi:10.20508/ijrer.v13i1.13388.g8678.

J. Modarresi, E. Gholipour, A. Khodabakhshian, “A comprehensive review of the voltage stability indices,” Renew. Sustain. Energy Rev., vol. 63, pp. 1–12, 2016. doi:10.1016/j.rser.2016.05.010.

S. Mokred, Y. Wang, “Voltage stability assessment and contingency ranking in power systems based on a modern stability assessment index,” Results Eng., vol. 23, 102548, 2024. doi:10.1016/j.rineng.2024.102548.

M. Aldeen, S. Saha, T. Alpcan, “Voltage stability margins and risk assessment in smart power grids,” IFAC Proc. Vol., vol. 47, no. 3, pp. 8188–8195, 2014. doi:10.3182/20140824-6-ZA-1003.02102.

S. Mokred, Y. Wang, T. Chen, “Modern voltage stability index for prediction of volt- age collapse and estimation of maximum load-ability,” Int. J. Electr. Power Energy Syst., vol. 145, 108596, 2023. doi:10.1016/j.ijepes.2022.108596.

S. Mokred, Y. Wang, T. Chen, “A novel collapse prediction index for voltage stability analysis and contingency ranking in power systems,” Prot. Control Mod. Power Syst., vol. 8, no. 1, pp. 1–27, 2023. doi:10.1186/s41601-023-00279-w.

J. P. Roselyn, D. Devaraj, S. S. Dash, “Multi-objective genetic algorithm for voltage stability enhancement using rescheduling and FACTS devices,” Ain Shams Eng. J., vol. 5, no. 3, pp. 789–801, 2014. doi:10.1016/j.asej.2014.04.004.

A. N. Archana, T. Rajeev, “A novel reliability index based approach for EV charging station allocation in distribution system,” IEEE Trans. Ind. Appl., vol. 57, no. 6, pp. 6385–6394, 2021. doi:10.1109/TIA.2021.3109570.

M. I. Akbar et al., “A novel hybrid optimization-based algorithm for optimal DG allocations in distribution networks,” IEEE Access, vol. 10, pp. 25669–25687, 2022. doi:10.1109/ACCESS.2022.3155484.

T. H. B. Huy, D. N. Vo, K. H. Truong, T. Van Tran, “Optimal dis- tributed generation placement in radial distribution networks using enhanced search group algorithm,” IEEE Access, vol. 11, pp. 103288–103305, 2023. doi:10.1109/ACCESS.2023.3316725.

A. K. Barnwal, L. K. Yadav, M. K. Verma, “A multi-objective approach for voltage stability enhancement and loss reduction via reconfiguration and DG allocation,” IEEE Access, vol. 10, pp. 16609–16623, 2022. doi:10.1109/ACCESS.2022.3146333.

A. M. Tahboub, V. R. Pandi, H. H. Zeineldin, “Distribution system reconfigu- ration for annual energy loss reduction considering variable distributed generation profiles,” IEEE Trans. Power Delivery, vol. 30, no. 4, pp. 1677–1685, 2015. doi:10.1109/TPWRD.2015.2424916.

S. H. Dolatabadi, M. Ghorbanian, P. Siano, N. D. Hatziargyriou, “An enhanced IEEE 33-bus benchmark test system for distribution system studies,” IEEE Trans. Power Syst., vol. 36, no. 3, pp. 2565–2572, 2021. doi:10.1109/TPWRS.2020.3038030.




DOI (PDF): https://doi.org/10.20508/ijsmartgrid.v9i3.529.g396

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