Guo Yao, Wang Shaorong, Chen Dezhi
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel). 2025 Apr 3;25(7):2276. doi: 10.3390/s25072276.
The rapid growth of renewable energy and increasing electricity demand pose challenges to the reliability and flexibility of traditional distribution networks. To address these issues, the construction of AC/DC hybrid distribution networks (AC/DC-HDNs) based on existing AC grids has become a promising solution. However, planning the expansion of such networks faces challenges like complex device and line topologies, dynamic fluctuations in distributed generation (DG) and load, and high power electronics costs. This paper proposes a time- and space-integrated expansion planning method for AC/DC-HDNs. The approach builds a distribution grid model based on graph theory, integrating the spatial layouts of AC distribution lines, DGs, main grids, and loads, while capturing dynamic load and renewable energy generation characteristics through time-series analysis. A modified graph attention network (MGAT)-based deep reinforcement learning (DRL) algorithm is used for optimization, balancing economic and reliability objectives. The simulation results show that the modified algorithm outperforms traditional algorithm in terms of both training efficiency and stability, with a faster convergence and lower fluctuation in cumulative rewards. Additionally, the proposed algorithm consistently achieves higher cumulative rewards, demonstrating its effectiveness in optimizing the expansion planning of AC/DC-HDNs.
可再生能源的快速增长和不断增加的电力需求对传统配电网的可靠性和灵活性提出了挑战。为了解决这些问题，基于现有交流电网构建交直流混合配电网（AC/DC-HDNs）已成为一种很有前景的解决方案。然而，规划此类网络的扩展面临着诸多挑战，如复杂的设备和线路拓扑结构、分布式发电（DG）和负荷的动态波动以及高功率电子成本。本文提出了一种用于AC/DC-HDNs的时空一体化扩展规划方法。该方法基于图论构建配电网模型，整合交流配电线、分布式电源、主电网和负荷的空间布局，同时通过时间序列分析捕捉动态负荷和可再生能源发电特性。使用基于改进图注意力网络（MGAT）的深度强化学习（DRL）算法进行优化，平衡经济和可靠性目标。仿真结果表明，改进算法在训练效率和稳定性方面均优于传统算法，收敛速度更快，累积奖励波动更小。此外，所提算法始终能获得更高的累积奖励，证明了其在优化AC/DC-HDNs扩展规划方面的有效性。
