Lyu Pengpeng, Bu Qiangsheng, Liu Yu, Jing Jiangping, Hu Jinfeng, Su Lei, Chu Yundi
State Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, China.
College of Artificial Intelligence and Automation, Hohai University, Nanjing 210098, China.
Biomimetics (Basel). 2025 Sep 14;10(9):618. doi: 10.3390/biomimetics10090618.
During feeder outages in the distribution network, localized power restoration using distribution resources (e.g., PVs) can ensure supply to critical loads and mitigate adverse impacts, especially when main grid support is unavailable. This study presents a power restoration strategy aiming at maximizing the restoration duration of critical loads to ensure their prioritized recovery, thereby significantly improving power system reliability. The methodology begins with load enumeration via breadth-first search (BFS) and utilizes a long short-term memory (LSTM) neural network to predict microgrid generation output. Then, an adaptive multipoint crossover genetic solving algorithm (AMCGA) is proposed, which can dynamically adjust crossover and mutation rates, enabling rapid convergence and requiring fewer parameters, thus optimizing island partitioning to prioritize critical load demands. Experimental results show that AMCGA improves convergence speed by 42.5% over the traditional genetic algorithm, resulting in longer restoration durations. Compared with other strategies that do not prioritize critical load recovery, the proposed strategy has shown superior performance in enhancing critical load restoration, optimizing island partitioning, and reducing recovery fluctuations, thereby confirming the strategy's effectiveness in maximizing restoration and improving stability.
在配电网馈线停电期间，利用分布式资源（如光伏）进行局部供电恢复可确保关键负荷的供电并减轻不利影响，尤其是在主电网支持不可用时。本研究提出了一种供电恢复策略，旨在最大化关键负荷的恢复持续时间，以确保其优先恢复，从而显著提高电力系统可靠性。该方法首先通过广度优先搜索（BFS）进行负荷枚举，并利用长短期记忆（LSTM）神经网络预测微网发电输出。然后，提出了一种自适应多点交叉遗传求解算法（AMCGA），该算法可动态调整交叉和变异率，实现快速收敛且所需参数较少，从而优化孤岛划分以优先满足关键负荷需求。实验结果表明，AMCGA比传统遗传算法的收敛速度提高了42.5%，从而实现了更长的恢复持续时间。与其他未优先考虑关键负荷恢复的策略相比，所提出的策略在增强关键负荷恢复、优化孤岛划分和减少恢复波动方面表现出卓越性能，从而证实了该策略在最大化恢复和提高稳定性方面的有效性。
