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The optimal siting and sizing of DGs are vital for the efficient operation of both radial and microgrid distribution systems. From an operational perspective, minimizing real power loss, reducing voltage deviation, and improving voltage stability index are the three primary objectives considered in this study. This manuscript addresses these issues by proposing a novel quasi-oppositional forensic-based investigation (QOFBI) algorithm, an evolutionary meta-optimization technique designed to optimize the location and sizing of DGs under various operating conditions, while adhering to system constraints. The approach introduces a weighting factor-based multiobjective formulation, where optimal weighting factors are computed dynamically. This ensures a balanced approach to minimizing power loss, voltage deviation, and enhancing voltage stability. Extensive simulations were conducted on the IEEE 33-bus and IEEE 69-bus standard distribution systems, evaluating the impact of DG placement with varying power factors under operational constraints. The results demonstrate the superiority of the proposed approach in terms of faster convergence, reduced complexity, and improved performance compared to existing optimization methods. The QOFBI algorithm achieves a 94.44% reduction in active power loss, highlighting its robust performance across different operational scenarios. These findings underscore the potential of QOFBI as a highly effective tool for DG optimization in modern distribution systems, offering both operational efficiency and system reliability.
Malika et al. (Mon,) studied this question.