AI-Driven Intelligent Scheduling Framework for Hybrid Wind/PV/Battery Energy Storage Systems.

Abstract

Hybrid Wind/PV/Battery Energy Storage Systems (WPVBESS) have emerged as an effective solution for improving renewable energy integration and grid stability. The base article primarily discusses scheduling strategies such as power smoothing, tracking program output, and peak load shifting for hybrid renewable systems. However, conventional scheduling approaches suffer from limitations including fixed-rule operation, poor adaptability, and dependence on forecasting accuracy. This research proposes an intelligent Artificial Intelligence (AI)-driven scheduling framework for hybrid Wind/PV/Storage systems using Deep Reinforcement Learning (DRL). The proposed model enhances the operational strategies presented in the base paper by introducing adaptive real-time decision-making capabilities for battery charging/discharging management and renewable power allocation. The system integrates renewable forecasting, battery State of Charge monitoring, and smart grid interaction into a unified intelligent controller. Simulation analysis demonstrates improved renewable energy utilization, enhanced grid stability, reduced power fluctuations, minimized battery degradation, and effective peak load management. The proposed framework provides a scalable and intelligent solution for future smart grid and sustainable energy applications.