Energy Scheduling of Hydrogen Hybrid UAV Based on Model Predictive Control and Deep Deterministic Policy Gradient Algorithm  
[PERSON]  
1 State Grid Changzhou Power Supply Company, Changzhou 213200, China; [EMAIL_ADDRESS] (H.L. )1  
[PERSON]  
2 National Engineering Research Center of Power Generation Control and Safety, Liyang Research Institute, Southeast University, Liyang 213300, China; [EMAIL_ADDRESS] (C.W. )1  
[PERSON]  
2 National Engineering Research Center of Power Generation Control and Safety, Liyang Research Institute, Southeast University, Liyang 213300, China; [EMAIL_ADDRESS] (C.W. )1  
[PERSON]  
1 State Grid Changzhou Power Supply Company, Changzhou 213200, China; [EMAIL_ADDRESS] (H.L. )1  
[PERSON]  
1 State Grid Changzhou Power Supply Company, Changzhou 213200, China; [EMAIL_ADDRESS] (C.W. )1  
[PERSON]  
1 State Grid Changzhou Power Supply Company, Changzhou 213200, China; [EMAIL_ADDRESS] (H.L. )1  
[PERSON]  
2 National Engineering Research Center of Power Generation Control and Safety, Liyang Research Institute, Southeast University, Liyang 213300, China; [EMAIL_ADDRESS] (C.W. )1  
[PERSON]  
2 National Engineering Research Center of Power Generation Control and Safety, Liyang Research Institute, Southeast University, Liyang 213300, China; 220  
Energy scheduling for hybrid power systems can be broadly categorized into three primary approaches: rule-based methods, optimization-based methods, and learning-based methods [6]. Rule-based methods manage energy allocation based on predefined rules and logical frameworks, including time-based, state-based, and priority-based strategies. These methods operate by comparing real-time variable values against set thresholds, allowing integrated energy systems to adapt and function effectively under varying conditions. For example, [PERSON] et al. [7] proposed a rule-based energy management strategy that utilizes experimental data analysis to develop a control method for fuel cell-based compensation of battery charge in hybrid fuel cell trucks, achieving a 24.8% reduction in hydrogen consumption and a 20% reduction in battery state of charge (SOC) variation. In addition, [PERSON] et al. [8] implemented a rule-based power allocation strategy, considering power demand, remaining capacity, and power capacity, which improved fuel efficiency, power, and extended the lifespan of hybrid energy systems. Optimization-based methods employ mathematical optimization techniques to solve energy scheduling challenges in integrated energy systems [9]. By formulating objective functions, defining constraints, and selecting suitable optimization algorithms, these methods aim to achieve optimal or near-optimal solutions. Such approaches are extensively applied in UAV operations to optimize flight paths, task assignments, flight speed, and altitude adjustments, thereby enhancing overall system efficiency and performance. Recently, [PERSON] et al. [10] developed an off-grid biofuel micro-CHP system with hybrid energy storage, using an optimization-based energy management strategy that increased energy efficiency from 45.77% to 57.97% with diesel biofuel. Additionally, [PERSON] et al. [11] proposed a model to optimize both the energy system configuration--comprising fuel cells, batteries, electric motors, and thermal engines--and the flight trajectory of UAVs, using a nonlinear programming approach solved by decomposition techniques in GAMS. Learning-based methods utilize machine learning and artificial intelligence techniques to facilitate energy scheduling in integrated energy systems [12]. These methods use various machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning, to train models or develop strategies. By optimizing based on predefined objectives, learning-based approaches enable adaptive and intelligent energy management in complex and dynamic system environments.