Development and Demonstration of an Integrated Energy Management System for Energy-Efficient Cold-Chain Logistics Centers

초록

With the rapid expansion of the cold-chain logistics industry, energy consumption in low-temperature logistics centers has increased significantly due to the growing number and scale of refrigeration facilities. In particular, refrigeration and cooling systems account for approximately 60–70% of the total electricity consumption in cold storage facilities. As a result, they are major contributors to rising operational costs and greenhouse gas emissions. Accordingly, the development of an energy management system (EMS) specifically tailored to cold-chain logistics centers has become increasingly important to mitigate rising operational costs and greenhouse gas emissions. This study presents a cold-chain-specialized energy management system, referred to as the cold-chain EMS (CEMS), which integrates real-time energy monitoring with an AI-driven framework that combines three-dimensional (3D) temperature mapping, deep reinforcement learning-based unit cooler control, intelligent defrost initiation, and a digital twin-based energy management system. The proposed CEMS enables coordinated control of refrigeration equipment while maintaining strict temperature requirements for product quality preservation. Experimental testbed and field demonstration results show that the proposed integrated framework improves energy efficiency while maintaining temperature stability in cold-chain logistics centers. Furthermore, this study discusses future research directions for next-generation cold-chain energy management systems leveraging artificial intelligence and digital twin technologies.

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