Optimal placement and sizing of distributed energy resources in active distribution networks under uncertainty: A multi-objective approach using electric eel foraging optimization

• A novel techno-economical multi-objective function (MOF) is proposed for optimal sizing and placement of renewable energy resources (RESs) and shunt capacitors in the distribution networks. • Probabilistic models for wind, solar, and load demand uncertainty using Weibull, lognormal, and normal probability density functions are integrated with scenario generation and reduction techniques to accurately capture system variability. • The electric eel foraging optimization (EEFO) algorithm is employed to solve the complex MOF problem, demonstrating superior performance over established metaheuristics in solution quality, robustness, and convergence speed. • Comprehensive comparative analysis confirms the framework's superiority, showcasing significant improvements in voltage stability, loss and cost reduction, and renewable hosting capacity under uncertain conditions. The integration of Renewable Energy Sources (RESs) into active distribution networks presents a trade-off between loss reduction and operational uncertainties, as well as challenges to network stability. This paper proposes a novel techno-economic multi-objective function (MOF) to optimize the sizing and placement of RESs, fuel cells, and shunt capacitors. The proposed MOF maximizes the hosting capacity and voltage stability while minimizing power losses, voltage deviation, and costs. The proposed MOF utilizes the electric eel foraging optimization (EEFO) algorithm, which was benchmarked against seven metaheuristic algorithms (GA, PSO, GWO, SMA, MPA, AHA, and ARO) and tested on the modified IEEE 69-bus system under uncertainty. Simulation results demonstrate the superior performance of the EEFO-based MOF, achieving a 69.48% reduction in power losses. Under uncertain conditions, it attained the best overall performance: the lowest loss (9.46 kW), minimal cost ($226/h), least land use (5604.3 m²), and highest hosting capacity (55.1%).