Modern smart grid prospects necessitate handling abnormal operating conditions besides conventional demands for improving power systems capabilities. Uncertain load and generation, and line outages during contingency conditions of electric power systems should be properly and efficiently dealt with. Lately, lockdown situations because of COVID-19 pandemic have greatly influenced energy demands in many areas in the World. Vulnerable operation of power networks, especially in either isolated microgrids or large-scale smart grids can be significantly avoided through proposing optimal reconfigurable network. In this paper, employing the distribution network (DN) reconfiguration is deeply studied for achieving fault-tolerance and fast recovery to reliable configurable DN in smart grids. Since radiality is among crucial properties of DN topology, searching for feasible configuration of DN is NP-hard optimization problem. Therefore, the recent Manta Ray Foraging Optimization (MRFO) is considered for solving such DN optimization instance. Performance of MRFO is examined against two common optimizers: the Particle Swarm Optimization (PSO) and the Grey Wolf Optimization (GWO). Different operating conditions for both the IEEE 33-bus and IEEE 85-bus systems are analysed using these optimization techniques. The goal is to search for feasible reconfigured DN with the minimum power losses and the optimal enhanced voltage profile. Simulation results reveal that the proposed MRFO approach provides efficient and outstanding behaviour in various operation scenarios. The efficiency and the robustness of the proposed MRFO approach are verified, the power loss reduction ratio ranges between 21% and 41% in different studied scenarios and adequate voltage profile enhancement is achieved.

除了提高电力系统能力的传统需求外，现代智能电网的前景还需要处理异常运行条件。应妥善有效地处理电力系统意外情况下的不确定负荷和发电以及线路中断。最近，由于 COVID-19 大流行而导致的封锁情况极大地影响了世界许多地区的能源需求。通过提出最优可重构网络，可以显着避免电力网络的脆弱运行，特别是在孤立的微电网或大规模智能电网中。在本文中，深入研究了采用配电网 (DN) 重新配置以实现容错和快速恢复到智能电网中可靠的可配置 DN。由于径向性是 DN 拓扑的关键特性之一，寻找 DN 的可行配置是一个 NP-hard 优化问题。因此，最近的蝠鲼觅食优化（MRFO）被考虑用于解决此类 DN 优化实例。MRFO 的性能针对两种常见优化器进行了检查：粒子群优化 (PSO) 和灰狼优化 (GWO)。使用这些优化技术分析了 IEEE 33 总线和 IEEE 85 总线系统的不同操作条件。目标是寻找具有最小功率损耗和最佳增强电压分布的可行的重新配置 DN。仿真结果表明，所提出的 MRFO 方法在各种操作场景中提供了高效且出色的行为。验证了所提出的 MRFO 方法的效率和稳健性，