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Optimal capacity configuration and scheduling of Wind-PV-battery-based EV charging stations using advanced Optimized Deep Learning Considering Time-of-Use Pricing and Demand Response with Performance Benchmarking
10.1016/j.est.2025.119931
2026-01-21
0
PRE
AI
Abstract
En 中文
Electric vehicles (EVs) are becoming more and more popular due to their substantial contribution to lowering CO2 emissions and the use of fossil fuels. However, there is a chance that the network will become overloaded and the power industry will be severely burdened if millions of EVs' shifting needs are met directly from the grid. In this manuscript, optimal capacity configuration and scheduling of Wind-PV-battery-based EV charging stations (CS) using advanced Optimized Deep Learning Considering Time-of-Use Pricing and Demand Response with Performance Benchmarking is proposed. The novel approach is used in this work like as Granger Causality-Inspired Graph Neural Network (GCIGNN) and Wolf-Bird Optimizer (WBO). Input data is collected from EV Charging Load Dataset. The photovoltaic and wind turbine and battery is a major power source of the proposed method. The primary aim of the proposed technique is used to minimize the cost, emission and improve the efficiency of the EV charging. The GCIGNN method utilized to forecast the EV load demand and the WBO technique is employed to optimize the EV charging point. The proposed technique is implemented and compare with other exiting approaches in the MATLAB platform like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Multi-Objective Particle Swarm Optimization (MOPSO). The proposed technique efficiency 99.2 % and cost of electricity (COE) is 0.286 Yuan/kWh is better than exiting methods.
Keywords:
Electric vehicles
Photovoltaic
Wind Turbine
Battery
Load Demand
Charging station
Journal
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9.8
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Citations: 10.1W
Researchers
A
Arjun, M. S.
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M
Mohan, N.
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Nagaraj, C.
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Papers: 1
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Sathish, K. R.
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Papers: 1
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S
Somashekar, D. P.
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View moreOrganization
P
presidency university, bangalore
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353
Papers: 321
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R
Ramaiah Institute of Technology
Scholars:
715
Papers: 671
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S
Sri Jayachamarajendra College of Engineering
Scholars:
281
Papers: 244
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Citations: 0
Cited Papers