ISSN : 2583-2646

Power Allocation System Using Artificial Neural Network

ESP Journal of Engineering & Technology Advancements
© 2025 by ESP JETA
Volume 5  Issue 4
Year of Publication : 2025
Authors : Ariramar C, Ramraj S
:10.56472/25832646/JETA-V5I4P102

Citation:

Ariramar C, Ramraj S, 2025. "Power Allocation System Using Artificial Neural Network", ESP Journal of Engineering & Technology Advancements  5(4): 7-12.

Abstract:

The electric vehicle (EV) and renewable energy generation have achieved considerable development due to the growing energy demand and scarcity in fossil fuels. At the same time, EVs consume a huge amount of electricity when they are clustered in a charging station. In this project we are going to create a Artificial Intelligence based Power Allocation and ev charging system. We are using a deep learning technique called artificial Neural Network and hence we can able to get an accuracy over 90%. We predict the suitable power source for charging the electric vehicles using artificial Neural Network.

References:

[1] Y. Li, T. Zhao, C. Liu, Y. Zhao, P. Wang, H. B. Gooi, K. Li, and Z. Ding, “An interactive decision-making model based on energy and reserve for electric vehicles and power grid using generalized stackelberg game,” IEEE Trans. Ind. Appl., vol. 55, no. 4, pp. 3301–3309, Jul. 2019.

[2] J. C. Mukherjee and A. Gupta, “Distributed charge scheduling of plugin electric vehicles using inter-aggregator collaboration,” IEEE Trans. Smart Grid, vol. 8, no. 1, pp. 331–341, Jan. 2017.

[3] Q. Chen, N. Liu, C. Hu, L. Wang, and J. Zhang, “Autonomous energy management strategy for solid-state transformer to integrate PV-assisted EV charging station participating in ancillary service,” IEEE Trans. Ind. Informat., vol. 13, no. 1, pp. 258–269, Feb. 2017.

[4] R. Wang, P. Wang, and G. Xiao, “Two-stage mechanism for massive electric vehicle charging involving renewable energy,” IEEE Trans. Veh. Technol., vol. 65, no. 6, pp. 4159–4171, Jun. 2016.

[5] N. Liu, Q. Chen, X. Lu, J. Liu, and J. Zhang, “A charging strategy for PV-based battery switch stations considering service availability and self-consumption of PV energy,” IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 4878–4889, Aug. 2015.

[6] S. Esmaeili, A. Anvari-Moghaddam, and S. Jadid, “Optimal operation scheduling of a microgrid incorporating battery swapping stations,” IEEE Trans. Power Syst., vol. 34, no. 6, pp. 5063–5072, Nov. 2019

[7] Q. Yan, B. Zhang, and M. Kezunovic, “Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation,” IEEE Trans. Smart Grid, vol. 10, no. 2, pp. 2096–2106, Mar. 2019.

[8] C. Luo, Y. Huang, and V. Gupta, “Stochastic dynamic pricing for EV charging stations with renewable integration and energy storage,” IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 1494–1505, Mar. 2018.

[9] S. Cui, Y. Wang, J. Xiao, and N. Liu, “A two-stage robust energy sharing management for prosumer microgrid,” IEEE Trans. Ind. Informat., vol. 15, no. 5, pp. 2741–2752, May 2019.

[10] M. Sepehry, M. H. Kapourchali, V. Aravinthan, and W. Jewell, “Robust day-ahead operation planning of unbalanced microgrids,” IEEE Trans. Ind. Informat., vol. 15, no. 8, pp. 4545–4557, Aug. 2019.

[11] M. H. K. Tushar, A. W. Zeineddine, and C. Assi, “Demand-side management by regulating charging and discharging of the EV, ESS, and utilizing renewable energy,” IEEE Trans. Ind. Informat., vol. 14, no. 1, pp. 117–126, Jan. 2018.

[12] M. Shin, D. Choi, and J. Kim, “Cooperative management for PV/ESSenabled electric vehicle charging stations: A multiagent deep reinforcement learning approach,” IEEE Trans. Ind. Informat., vol. 16, no. 5, pp. 3493–3503, May 2020.

Keywords:

EV, Power Allocation, Energy, Demand.