Optimal distributed generation placement and sizing using modified grey wolf optimization and ETAP for power system performance enhancement and protection adaptation

The integration of distributed generation (DG) units into electricity distribution networks (EDNs) is a key strategy for enhancing system performance, improving power quality, and increasing network reliability through effective voltage control. This paper presents a hybrid technique that integrates a modified grey wolf optimization (MGWO) algorithm, implemented in MATLAB, with the electrical transient and analysis program (ETAP) software for security analysis to achieve the optimal locations and sizing of DG units under protection adaptation. The proposed technique focuses on minimizing active (APL) and reactive (RPL) power losses, improving voltage stability (VS) and investigating the effects of fault current variations on the protection system to ensure its adaptability to the integration of DG units. The MGWO algorithm is an improved version of the conventional GWO algorithm, which is based on a hierarchical model inspired by the social behavior of grey wolves. MGWO is modified by the addition of adaptive weights and dynamic circling mechanisms, which improve the balance between exploration (searching for new regions of the solution space) and exploitation (improving known solutions). These modifications enable the wolves to adjust their position dynamically; thus, avoiding premature convergence and allowing them to escape local optima. The MGWO expands on the GWO with adaptive mechanisms that prevents it from becoming trapped in the local minima, yielding faster convergence time, better accuracy of solution, and increased immunity and robustness to solving optimization problems that are complex and multimodal. The effectiveness of the proposed approach is evaluated by simulations performed on the IEEE 33-bus test system and a large scale 114-bus distribution network to determine its reliability for different network sizes. The findings indicate that the optimal DG placement in the 33-bus system results in a 69.7% decrease in average power loss, a 69.6% decrease in real power loss, and a 7.3% enhancement in voltage stability. In the 114-bus system, APL and RPL decreased by 65.2% and 64.9%, respectively, accompanied by a 6.5% enhancement in VS. In addition, ETAP analysis was performed using Newton–Raphson (NR) for load flow analysis to assess the effects of DG integration at different capacity levels. The results indicate that the integration of DG units has a considerable effect on fault current behaviour, with the maximum fault current (I_max) increasing by up to 21.5%, while the minimum fault current (I_min) shows considerable fluctuations, requiring changes in protection strategies to manage altered fault current levels. The comparison of the results to the traditional and advanced metaheuristic algorithms confirm that the proposed technique achieved a higher power loss minimization while maintaining the system stability. Furthermore, the proposed MGWO–ETAP offers a global solution by combining DG placement and sizing optimization with adaptive protection controls, ensuring reliable and efficient DG integration in complex power systems.