Skill optimization algorithm for integrated planning of renewable energy sources and electric springs under uncertainty

The growing penetration of renewable energy sources (RESs) and the rising electricity demand necessitate innovative grid management strategies. This study presents an optimization-based framework for reducing line losses and improving voltage profiles in distribution networks through the coordinated placement of electric springs (ESs) and RESs. Owing to their fast response, ESs can stabilize the grid and mitigate power fluctuations; however, their optimal placement for loss minimization has not been fully addressed. To overcome this challenge, a Skill Optimization Algorithm (SOA) is employed to determine the optimal co-location of ESs and RESs (wind and solar) within the distribution system. Six deployment scenarios are analyzed to assess system performance. The stochastic nature of wind, solar, and load demand is modelled using Weibull, lognormal, and normal probability distributions, respectively, with Monte Carlo simulation and scenario reduction applied for uncertainty handling. The proposed framework is validated on IEEE 33-bus and IEEE 69-bus systems. Results show that the SOA reduces line losses by 15–39 % in the IEEE 33-bus system and 12–23 % in the IEEE 69-bus system compared with existing methods. These findings highlight the strategic value of coordinated RES and ES deployment in enhancing grid reliability and supporting sustainable, intelligent energy systems.