As electric vehicle adoption accelerates, utilities face mounting complexity in coordinating power flows between charging stations, grid infrastructure, and renewable generation. A new adaptive control strategy tackles this challenge by automating the coordination of vehicle batteries with solar and wind resources to stabilize the grid and reduce costs.
The approach combines two computational techniques: a goat optimization algorithm that decides when vehicles should charge or discharge, and a convolutional neural network that predicts near-term power demand patterns based on weather conditions and traffic. Working together, these algorithms adjust charging schedules in real time to match renewable generation cycles, reducing reliance on fossil fuel plants during peak EV demand periods.
In simulation tests on MATLAB, the method achieved notable improvements over existing control systems. Operating costs fell to $1,563 per test cycle, while power losses dropped to 15.6 kilowatts. System efficiency reached 99.2%, and carbon emissions were cut to 60.6 kilograms per cycle. Performance gains came from more intelligent dispatch of vehicle batteries—using EVs as distributed storage to absorb excess solar output midday and provide power during evening peaks.
The research demonstrates how advanced computing can unlock the grid-stabilizing potential of millions of connected vehicles. However, real-world deployment requires integration with utility control systems, standardized communication protocols between vehicle manufacturers and grid operators, and regulatory frameworks that permit and incentivize V2G participation. Current barriers remain significant: many utilities lack the sensors and software to monitor distributed vehicle charging, and most EV charging networks operate independently of grid operators.
The authors tested their algorithm against five competing methods and report clear technical superiority, but implementation success will depend equally on business models and infrastructure investment rather than optimization alone.
