Abstract: With the accelerated integration of renewable energy into the grid and the intelligent transformation of the new power system, the Power Internet of Things (PIoT) has become key to realizing the intelligence of power systems. However, Power Internet of Things Devices (PIoTD) in remote areas face numerous challenges, including inadequate network coverage, limited energy harvesting, and poor communication conditions. To address these issues, a cloud-edge-device cooperation framework based on artificial intelligence is processed, which employs Unmanned Aerial Vehicle Simultaneous Wireless Information and Power Transfer (UAV-SWIPT) to provide continuous energy to energy-constrained PIoTD. Energy replenishment and communication relay frameworks for SAG-PIoT devices are facilitated by deploying SWIPT services on UAVs in a low-altitude network within the space-air-ground network. Furthermore, to optimize the collaborative work of multiple UAVs and enhance data relay, transmission power allocation, Global Energy Efficiency (GEE), and PIoTD association scheduling, a multi-agent deep reinforcement learning algorithm is introduced to tackle the problems of incomplete global information and high-dimensional variable coupling in dynamic environments. The simulation results show that the proposed algorithm converges faster and demonstrates superior energy efficiency compared to several other benchmark algorithms. On the other hand, in terms of maximizing the minimum transmission rate, MADDPG achieves the highest performance, reaching bits/s. Additionally, it is observed that the optimal SWIPT power splitting ratio is approximately 0.7, and the GEE is the highest.

摘要： 随着可再生能源的规模化并网和新型电力系统智能化转型的加速，电力物联网（Power Internet of Things, PIoT）已成为实现电力系统智能化的关键。然而，偏远地区电力物联网设备（Power Internet of Things Device, PIoTD）面临着网络覆盖不足、能量收集受限、通信条件差等诸多问题。为解决这些问题，首先提出了一种基于人工智能的云-边缘-设备合作框架，采用多无人机无线携能通信技术（UAV Simultaneous Wireless Information and Power Transfer, UAV-SWIPT）来为能量受限的PIoTD提供持续能量。通过空天地网络中低空组网的UAV搭载SWIPT服务，辅助SAG-PIoT设备能量补充和通信中继框架。此外，为优化多无人机协同工作，提升数据中继、发射功率分配、全局能耗效率（Global Energy Efficiency, GEE）及PIoTD关联调度，引入了一种多智能体深度强化学习算法以解决动态环境下全局信息不完整和高维度变量耦合问题。仿真结果表明，所提算法相比于其他几种基准算法，收敛速度快，能耗效率表现优异，另一方面，在最大化最小传输速率方面，MADDPG最高，达到了 bits/s。同时还发现SWIPT功率分割最佳比例在0.7左右，此时GEE最高。