An Energy Management Optimization Method for Arctic Space Environment Monitoring Buoys Based on Deep Reinforcement Learning

To address the long-term operational challenges of space environment monitoring buoys under extreme Arctic conditions, this paper proposes an energy management optimization method based on deep reinforcement learning (DRL). By constructing a buoy system model that integrates renewable energy sources, a primary lithium battery power supply, and a battery energy storage unit, combined with an Arctic environmental model incorporating low-temperature efficiency degradation, a reward function was designed to minimize power supply deficits while ensuring system reliability. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm was employed to optimize energy scheduling strategies. Simulation results based on real Arctic data (August 2024–January 2025) demonstrate that integrating wind turbines significantly reduces reliance on primary lithium batteries. Specifically, the required lithium battery capacity was reduced by 87.5% (from 61.44 kWh to 7.685 kWh), and procurement costs were lowered by approximately $68,830 compared to non-rechargeable schemes1. This method significantly enhances the buoy’s endurance and scheduling intelligence, offering valid insights into energy management in intelligent polar observation equipment.