This paper presents a new method based on coyote algorithm (COA) which is inspired from the social life of coyotes for the problem of simultaneous network reconfiguration and distributed generation (DG) placement to reduce real power loss. The effectiveness of the proposed COA method has been evaluated on two distribution systems consisting of 69-node and 119-node systems at two scenarios consisting of reconfiguration only and simultaneous reconfiguration and DG placement. The result analysis has indicated that network reconfiguration combination with optimization of location and size of distributed generation (DGs) is more effective for power loss reduction than network reconfiguration only. About the network reconfiguration only, for the 69-node and 119-node systems COA can search out the optimal solution that reduce power loss by 56.16% and 32.86%, respectively. Meanwhile, the optimal solution obtained by the network reconfiguration combination with optimization of location and size of DGs using COA helps to reduce power loss of two aforementioned systems by 84.37% and 55.31%, respectively. The result comparisons with other methods in the literature have also shown that COA has ability to obtain the better network configuration and location and size of DGs than other methods. Therefore, the proposed COA can be a promising method for the problem of simultaneous reconfiguration and DG placement.

本文提出了一种基于土狼算法（COA）的新方法，该方法从土狼的社交生活中得到启发，解决了同时进行网络重新配置和分布式发电（DG）的问题，以减少实际功率损耗。在仅由重新配置以及同时重新配置和DG放置组成的两种情况下，已经在由69个节点和119个节点组成的两个配电系统上评估了所提出的COA方法的有效性。结果分析表明，将网络重新配置与分布式发电（DG）的位置和大小进行优化相结合，比仅通过网络重新配置更有效地降低了功耗。仅关于网络重新配置，对于69节点和119节点的系统，COA可以搜索出最佳解决方案，这些解决方案可将功率损耗分别降低56.16％和32。分别为86％。同时，通过网络重新配置结合使用COA优化DG的位置和大小所获得的最佳解决方案有助于分别将上述两个系统的功耗降低84.37％和55.31％。与文献中其他方法的结果比较还表明，与其他方法相比，COA能够获得更好的网络配置以及DG的位置和大小。因此，提出的COA可能是解决同时重新配置和DG放置问题的一种有前途的方法。与文献中其他方法的结果比较还表明，与其他方法相比，COA能够获得更好的网络配置以及DG的位置和大小。因此，提出的COA可能是解决同时重新配置和DG放置问题的一种有前途的方法。与文献中其他方法的结果比较还表明，与其他方法相比，COA能够获得更好的网络配置以及DG的位置和大小。因此，提出的COA可能是解决同时重新配置和DG放置问题的一种有前途的方法。