Flexible Power Supply and Distribution Based on FDN for Photovoltaic Tobacco Logistics Warehouses

Abstract

Industrial plants should implement sophisticated renewable-energy-based distribution systems to guarantee a continuous and reliable power supply and enhanced operational efficiency under varying loading conditions. A Flexible Power Supply and Distribution System (FPSDS) utilize the concept of Flexible Distribution Networks (FDN) for photovoltaic-contracted tobacco warehouse facilities. The integrated power system connects PV panels to a multi-port bidirectional DC-DC converter, which operates with a BESS and a full-bridge PWM inverter to provide bi-directional energy management and increased power reliability. The system achieves enhancement by integrating three control mechanisms, which include DLD for real-time energy distribution, IPR for loss minimization and Multi-Agent Deep Reinforcement Learning (MADRL) for adaptive system optimisation. The system manages dynamic voltage and power regimes and current movements through predictive behaviour prediction, which utilizes Model Predictive Control (MPC). Simulation results demonstrate enhanced electric power management that leads to better voltage systems alongside lower transmission losses and superior peak demand control. Such a solution enables both real-time capabilities and scalability benefits while enhancing operational durability and suits warehouses with energetic systems that function dynamically. The research creates a smart solution to incorporate renewable power sources within logistics operations that builds sustainable energy frameworks for decentralizing power systems. The proposed system introduces an integrated control framework combining Dynamic Load Distribution, Intelligent Power Routing, and Multi-Agent Deep Reinforcement Learning within an FDN for photovoltaic-integrated warehouses. The approach demonstrates a 15–20% reduction in transmission losses, a 12–18% increase in overall energy efficiency, and improved voltage stability by maintaining deviations within 5%. These results confirm the novelty of this research in achieving adaptive, intelligent, and scalable power distribution for logistics facilities.

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