Abstract
This paper presents a novel approach to optimizing vehicle-to-grid (V2G) enhanced energy management in microgrid systems through machine learning-based forecasting. The proposed system utilizes the Random Forest algorithms to predict energy consumption and renewable energy generation patterns, enabling intelligent decision-making for V2G operations. The proposed methodology incorporates temporal features including hourly, daily, and monthly patterns to create accurate 24-hour forecasts for both load demand and renewable energy generation. The developed V2G optimization strategy then uses these forecasts to make informed decisions about the charge and discharge timing of electric vehicles, maintaining a balance between immediate grid requirements and anticipated future needs. The performance of the proposal is evaluated using real-world microgrid data and demonstrates significant improvements against traditional V2G management approaches. The studies demonstrate that the proposed model uses battery cycles more efficiently, prevents unnecessary energy transfers, and reduces battery degradation by minimizing excessive charging. The numerical studies show that the proposed technique maintains the energy deficit at a lower rate by 6.4% fewer charge-discharge operations. Furthermore, the proposed approach relies on easily accessible data rather than difficult-to-obtain weather variables, enhancing its practicality and ease of implementation. This makes the system more applicable in real-world scenarios without requiring complex meteorological data collection and enables the proposed system to adapt to varying renewable energy generation patterns and consumption behaviors, particularly suitable for microgrids with high renewable energy penetration. This research contributes to the growing intelligent energy management systems field and provides a practical framework for implementing machine learning in V2G applications.
