Abstract
This study suggests an enhanced metaheuristic method based on the Symbiotic Organisms Search (SOS) algorithm, namely, Quasioppositional Chaotic Symbiotic Organisms Search (QOCSOS). It aims to optimize the network configuration simultaneously and allocate distributed generation (DG) subject to the minimum real power loss in radial distribution networks (RDNs). The suggested method is developed by integrating the Quasiopposition-Based Learning (QOBL) as well as Chaotic Local Search (CLS) approaches into the original SOS algorithm to obtain better global search capacity. The proposed QOCSOS algorithm is tested on 33-, 69-, and 119-bus RDNs to verify its effectiveness. The findings demonstrate that the suggested QOCSOS technique outperformed the original SOS and provided higher-quality alternatives than many other methods studied. Accordingly, the proposed QOCSOS algorithm is favourable in adapting to the DG placement problems and optimal distribution network reconfiguration.
Highlights
In distribution networks, reconfiguration is a traditional technique to minimize power loss in the system by opening/ closing switches to establish the latest optimal network structure
For Case 1, the Quasioppositional Chaotic Symbiotic Organisms Search (QOCSOS) method obtained the opened switches: 7-9-14-32-37, where the real power loss of the network was decreased from 202.67 kW to 139.5513 kW in relation to 31.14% of the power loss reduction (PLR). e system’s minimum voltage amplitude was raised from 0.9131 p.u. to 0.9378 p.u
The improved QOCSOS is successfully implemented to solve the simultaneous problems of network reconfiguration and distributed generation (DG) allocation in RDNs to reduce the real power loss. e efficacy of QOCSOS has been carried out on the 33-bus, 69-bus, and 119-bus RDNs
Summary
Reconfiguration is a traditional technique to minimize power loss in the system by opening/ closing switches to establish the latest optimal network structure. It is realized that the combination of the optimal network reconfiguration and DG placement problems would significantly decrease power loss and enhance the performance of the distribution network. The distribution network reconfiguration (DNR) is a complex optimization problem since this problem has 2n candidate solutions (n is the number of switches) [2]. The optimal DG placement (ODGP) problem refers to a complex mixed-integer nonlinear optimization problem. This is deemed as an obstacle for optimization methods. This is deemed as an obstacle for optimization methods. erefore, the issues with ODGP and DNR in combination (DNG-DG) become a more complex optimization problem for the optimization solving approach
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