Preventive Synaptic Learning Smart Grid Optimization for Intelligent Energy Management and Loss Minimization
Document Type : Original Research Article
Received: 10 February 2026
|
Revised: 02 March 2026
|
Accepted: 16 March 2026
|
Published: 24 June 2026
Authors
1
Professor & Dean, IT, Bharath Institute of Higher Education And Research, Chennai, India
2
Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamilnadu,, India
*
Author to whom correspondence should be addressed.
Abstract
Great difficulties are encountered in the modern smart grid system when aims of reducing losses, stabilizing voltage and properly incorporating renewable energy resources need to be achieved for a dynamic operation of the system. Current optimization methods primarily target single-objective control strategies and do not include adaptive preventive learning features to support real-time control of smart grid. To overcome these drawbacks, this research is proposing a Preventive Synaptic Learning Smart Grid Optimization (PSLSGO) framework to optimize the utilization of smart energy management and power distribution. The proposed adaptive framework brings together adaptive synaptic learning, preventive fault prediction, intelligent power routing, reactive power compensation, adaptive load balancing and renewable energy coordination in a single optimization framework. The real-time electrical parameters such as voltage, current, frequency, load demand, transformer temperature, and renewable energy generation were gathered using IoT-enabled monitoring systems and analyzed with the help of the IEEE 33-bus smart distribution network. The experimental results showed that the proposed PSLSGO framework was able to achieve 98.4% energy efficiency, 98.1% reliability, 48.9% reduction in transmission losses, 97.2% voltage stability and 92.5% renewable energy utilization. The obtained results were statistically validated using ANOVA and confidence interval analysis to ensure the significance and reliability of the results. The adaptive synaptic learning mechanism has been found to enhance the prediction accuracy and capability of preventive control, in the presence of varying operating conditions. In conclusion, the proposed PSLSGO framework offers a smart, scalable, and energy-efficient approach for future smart grid optimization and sustainable power distribution system.
Keywords:
Adaptive Synaptic Learning
,
Energy Loss Reduction
,
IoT-enabled Smart Grid
,
Preventive Control Mechanism
,
Renewable Energy Integration
,
Smart Grid Optimization.
References
[1]
Adegoke, S. A., Sun, Y., Adegoke, A. S., & Ojeniyi, D. (2024). Optimal placement of distributed generation to minimize power loss and improve voltage stability. Heliyon, 10(21), e39298.
https://doi.org/10.1016/j.heliyon.2024.e39298
[2]
Hany, R. M., Mahmoud, T., Osman, E. S. A. E. A., Abd El Rehim, A. E. F., & Seoudy, H. M. (2024). Optimal allocation of distributed energy storage systems to enhance voltage stability and minimize total cost. PLOS ONE, 19(1), e0296988.
https://doi.org/10.1371/journal.pone.0296988
[3]
Islam, A., Rudra, S., & Kolhe, M. L. (2025). Optimizing the placement of distributed energy storage and improving distribution power system reliability via genetic algorithms and strategic load curtailment. Neural Computing and Applications, 37, 17589–17608.
https://doi.org/10.1007/s00521-025-11037-4
[4]
Anteneh, T., & Bimrew, T. (2024). Robust distribution networks reconfiguration considering the improvement of network resilience considering renewable energy resources. Scientific Reports, 14, 23041.
https://doi.org/10.1038/s41598-024-73928-1
[5]
Ayanlade, S. O., Ariyo, F. H., Jimoh, A., Akindeji, K. T., Adetunji, A. O., Ogunwole, E. I., & Owolabi, D. E. (2024). Navigating the complexity of photovoltaic system integration: an optimal solution for power loss minimization and voltage profile enhancement considering uncertainties and harmonic distortion management. Electrical Engineering, 106, 5765–5786.
https://doi.org/10.1007/s00202-024-02693-1
[6]
Gupta, S. C., & (co-authors). (2024). Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms. Frontiers in Energy Research, 12, 1378242.
https://doi.org/10.3389/fenrg.2024.1378242
[7]
Asabere, P., et al. (2024). Optimal capacitor bank placement and sizing using particle swarm optimization for power loss minimization in distribution network. Journal of Engineering Research.
https://doi.org/10.1016/j.jer.2024.03.007
[8]
Hassan, M., Raza, T., & Ahmad, F. (2025). Optimizing capacitor bank placement in distribution networks using a multi-objective particle swarm optimization approach for energy efficiency and cost reduction. Scientific Reports, 15, 12016.
https://doi.org/10.1038/s41598-025-96341-8
[9]
Chen, M., Ma, S., Soltani, Z., Ayyanar, R., Vittal, V., & Khorsand, M. (2023). Optimal placement of PV smart inverters with volt-VAr control in electric distribution systems. IEEE Systems Journal, 17(3), 3436–3446.
https://doi.org/10.1109/JSYST.2023.3256121
[10]
Bhattacharya, S., & (co-authors). (2024). A supervisory volt/var control scheme for coordinating voltage regulators with smart inverters on a distribution system. Frontiers in Smart Grids, 3, 1356074.
https://doi.org/10.3389/frsgr.2024.1356074
[11]
Eissa, M. M., Swief, R. A. W., & Abdel Salam, T. S. (2025). Demand side management with electric vehicles and optimal renewable resources integration under system uncertainties. Scientific Reports, 15, 18543.
https://doi.org/10.1038/s41598-025-00752-6
[12]
Alaas, Z., Moustafa, G., & Mansour, H. (2025). Performance of pelican optimizer for energy losses minimization via optimal photovoltaic systems in distribution feeders. PLOS ONE, 20(3), e0319298.
https://doi.org/10.1371/journal.pone.0319298
[13]
Elnaggar, M. F., Péné, A. D., Boussaibo, A., Tsegaing, F., Tchouli, A. F., Barro, F. I., & Basetti, V. (2024). Optimal sizing and power losses reduction of photovoltaic systems using PSO and LCL filters. PLOS ONE, 19(4), e0301516.
https://doi.org/10.1371/journal.pone.0301516
[14]
Ibarra-Ontiveros, M., et al. (2024). Advanced optimization of renewables and energy storage in power networks using metaheuristic technique with voltage collapse proximity and dynamic thermal rating technology. Journal of Energy Storage, 106, 114831.
https://doi.org/10.1016/j.est.2024.114831
[15]
Boubaker, S., Kraiem, H., Ghazouani, N., Kamel, S., Mellit, A., & Alsubaei, F. S. (2025). Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm. Scientific Reports, 15, 13343.
https://doi.org/10.1038/s41598-025-97473-7
[16]
Zangmo, R., Sudabattula, S. K., Mishra, S., Dharavat, N., Golla, N. K., Sharma, N. K., & Jadoun, V. K. (2024). Optimal placement of renewable distributed generators and electric vehicles using multi-population evolution whale optimization algorithm. Scientific Reports, 14, 28447.
https://doi.org/10.1038/s41598-024-80076-z
[17]
Ragab, M. M., Ibrahim, R. A., Desouki, H., & Swief, R. (2023). Optimal design of off-grid hybrid system using a new zebra optimization and stochastic load profile. Scientific Reports, 13, 15000.
https://doi.org/10.1038/s41598-023-41929-1
[18]
Chabok, B. S., Sadegh-Samiei, M., Jalilvand, A., & Bagheri, A. (2024). Optimal operation of the smart electrical network considering energy management of demand side. Science and Technology for Energy Transition, 80, 2025.
https://doi.org/10.2516/stet/2025001
[19]
El-Seheimy, R., Kamel, S., & (co-authors). (2024). Enhancement of distribution system performance with reconfiguration, distributed generation and capacitor bank deployment. Heliyon, 10(7), e28745.
https://doi.org/10.1016/j.heliyon.2024.e28745
[20]
Rasheed, M., Hussain, B., Al-Sumaiti, A. S., & Abid, M. (2025). Stability improvement of grid-connected DFIG wind farm with STATCOM compensated power network using RL-based coordinated transient controller. IEEE Access.
https://doi.org/10.1109/ACCESS.2025.3584719
[21]
Zhang, J., Wu, H., Akbari, E., Bagherzadeh, L., & Pirouzi, S. (2025). Eco-power management system with operation and voltage security objectives of distribution system operator considering networked virtual power plants with electric vehicles parking lot and price-based demand response. Computers & Electrical Engineering, 121, 109895.
https://doi.org/10.1016/j.compeleceng.2024.109895
[22]
Li, H., et al. (2024). Optimal scheduling of microgrids considering real power losses of grid-connected microgrid systems. Frontiers in Energy Research, 11, 1324232.
https://doi.org/10.3389/fenrg.2023.1324232
[23]
Salem, M., et al. (2025). Optimizing sustainable energy management in grid-connected microgrids using quantum particle swarm optimization for cost and emission reduction. Scientific Reports, 15, 6354.
https://doi.org/10.1038/s41598-025-90040-0
[24]
Agouzoul, N., Oukennou, A., Elmariami, F., Ebeed, M., Boukherouaa, J., Gadal, R., Aly, M., & Mohamed, E. A. (2025). Optimization of the stochastic optimal reactive power dispatch with renewable energy resources using a modified dandelion algorithm. PLOS ONE, 20(7), e0328170.
https://doi.org/10.1371/journal.pone.0328170
[25]
Sima, C. A., et al. (2024). Efficient design of energy microgrid management system: A promoted Remora optimization algorithm-based approach. Heliyon, 10(2), e24045.
https://doi.org/10.1016/j.heliyon.2024.e24045
[26]
Cikan, N. N., & Cikan, M. (2024). Reconfiguration of 123-bus unbalanced power distribution network analysis by considering minimization of current & voltage unbalanced indexes and power loss. International Journal of Electrical Power & Energy Systems, 157, 109796.
https://doi.org/10.1016/j.ijepes.2024.109796
[27]
Mahdavi, M., Awaafo, A., Dini, S. M., Moradi, F., Jurado, F., & Vera, D. (2024). A flexible loss reduction formulation for simultaneous capacitor placement and network reconfiguration in distribution grids. Electric Power Systems Research, 235, 110708.
https://doi.org/10.1016/j.epsr.2024.110708
[28]
Kandel, A. A., et al. (2024). Efficient reduction of power losses by allocating various DG types using the zebra optimization algorithm. Results in Engineering, 23, 102560.
https://doi.org/10.1016/j.rineng.2024.102560
[29]
Gupta, A., et al. (2025). Flexible renewable integrated energy system capabilities to improve voltage stability with power quality and economic environmental operation of smart grid. Scientific Reports, 15, 7492.
https://doi.org/10.1038/s41598-025-29052-9
[30]
Mossie, M. A., Yetayew, T. T., Bitew, G. T., Beza, T. M., & Yenealem, M. G. (2025). A computational approach to voltage stability enhancement and loss reduction in distribution systems using PSO-optimized STATCOM and DG. Scientific Reports, 15, 48821.
https://doi.org/10.1038/s41598-025-30235-7
[31]
Wen, X., Li, H., Wu, X., Li, Y., Liu, S., Huang, G., & Crisostomi, E. (2024). Multi-objective optimal decision for orderly power utilization based on improved ε-constraint method in active distribution networks. PLOS ONE, 19(10), e0309437.
https://doi.org/10.1371/journal.pone.0309437
[32]
Peng, B., & Wang, Y. (2024). Coordinated active-reactive power optimization considering photovoltaic abandon based on second order cone programming in active distribution networks. PLOS ONE, 19(9), e0308450.
https://doi.org/10.1371/journal.pone.0308450
[33]
Zhang, Z., Dou, C., Yue, D., Xue, Y., Xie, X., Deng, C., & Zhang, B. (2024). Voltage sensitivity-related hybrid coordinated power control for voltage regulation in active distribution networks. IEEE Transactions on Smart Grid, 15(2), 1388–1398.
https://doi.org/10.1109/TSG.2023.3292939
[34]
Li, J., Mo, H., Sun, Q., Wei, W., & Yin, K. (2024). Distributed optimal scheduling for virtual power plant with high penetration of renewable energy. International Journal of Electrical Power & Energy Systems, 160, 110103.
https://doi.org/10.1016/j.ijepes.2024.110103
[35]
Vellingiri, M., et al. (2025). Adaptive energy loss optimization in distributed networks using reinforcement learning - enhanced crow search algorithm. Scientific Reports, 15, 12165.
https://doi.org/10.1038/s41598-025-97354-z
