The growing energy demand has highlighted the need for reliable, decentralized, and self-sustained power systems. In this context, optimal integration of distributed energy resources (DERs) into distribution networks is vital for improving voltage stability and reducing power losses. However, conventional DER planning methods are computationally intensive and may produce inconsistent solutions under varying load conditions. This paper presents a sensitivity-based Generalized Particle Swarm Optimization (GEPSO) framework for optimal siting and sizing of DERs in radial distribution systems. The methodology is implemented in two stages. First, site-specific geographical and meteorological data are evaluated to determine realistic DER capacity limits. Second, these feasibility constraints are embedded within the GEPSO algorithm to identify optimal DER locations and sizes. Load flow analysis is performed using the forward–backward sweep method on a real 69-node Ramchandrapura feeder and the IEEE 33-node test system under multiple loading and DER penetration scenarios. Simulation results demonstrate an average active power loss reduction of approximately 36%, with up to 3.3% improvement compared to conventional PSO, while enhancing the minimum bus voltage by about 3–4%. The key contribution of this work is the integration of site-specific feasibility assessment with a sensitivity-guided GEPSO framework for efficient DER planning applications.
Sharma et al. (Thu,) studied this question.
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