This study introduces an improved version of the gray wolf optimizer (I-GWO) to determine the optimal allocation and sizing of distribution static compensators (DSTATCOMs) in radial distribution systems, considering the uncertainties associated with renewable energy sources and load fluctuations. The I-GWO algorithm incorporates a dimension learning-based hunting (DLH) strategy to enhance population diversity, balance exploration and exploitation, and prevent premature convergence. The effectiveness of the proposed method is demonstrated through tests on three IEEE radial distribution systems: 33-bus, 69-bus, and 85-bus systems. The results show that the I-GWO-based approach significantly reduces power losses, improves voltage profiles, and enhances voltage stability compared to other optimization techniques such as bat algorithm (BA), chaotic salp swarm algorithm (CSSA), multi-objective sine-cosine approach (MOSCA), and multi-objective particle swarm optimization (MOPSO). The study also considers the presence of renewable energy sources (RESs) and load uncertainties, using the fast scenario reduction (FSR) method to reduce the number of scenarios. The optimal locations and sizes of DSTATCOMs are determined for each system, showing significant improvements in power loss reduction, voltage stability, and cost savings. The proposed method is validated through simulations and compared with existing algorithms, demonstrating its superiority in terms of efficiency and applicability.This study introduces an improved version of the gray wolf optimizer (I-GWO) to determine the optimal allocation and sizing of distribution static compensators (DSTATCOMs) in radial distribution systems, considering the uncertainties associated with renewable energy sources and load fluctuations. The I-GWO algorithm incorporates a dimension learning-based hunting (DLH) strategy to enhance population diversity, balance exploration and exploitation, and prevent premature convergence. The effectiveness of the proposed method is demonstrated through tests on three IEEE radial distribution systems: 33-bus, 69-bus, and 85-bus systems. The results show that the I-GWO-based approach significantly reduces power losses, improves voltage profiles, and enhances voltage stability compared to other optimization techniques such as bat algorithm (BA), chaotic salp swarm algorithm (CSSA), multi-objective sine-cosine approach (MOSCA), and multi-objective particle swarm optimization (MOPSO). The study also considers the presence of renewable energy sources (RESs) and load uncertainties, using the fast scenario reduction (FSR) method to reduce the number of scenarios. The optimal locations and sizes of DSTATCOMs are determined for each system, showing significant improvements in power loss reduction, voltage stability, and cost savings. The proposed method is validated through simulations and compared with existing algorithms, demonstrating its superiority in terms of efficiency and applicability.