Assessing uncertainty in the optimal placement of distributed generators in radial distribution feeders.

• Introduces an innovative framework integrating deterministic and probabilistic simulation techniques to address the critical research gap concerning variability in minimal power loss and voltage profile alterations in DGS placement. • Quantifies uncertainty levels in power loss and voltage profiles through Monte Carlo simulations and probability density functions, offering valuable insights for decision-makers to optimize DGS placement and ensure consistent voltage profiles, enhancing grid resilience. • Demonstrates substantial reductions in power loss and consistent adherence to desired voltage limits in the case study feeder, highlighting the significance of considering uncertainties in DGS placement strategies for achieving optimal outcomes. • Contributes to establishing a more resilient and sustainable power distribution network by addressing the critical aspect of uncertainty in DGS placement, paving the way for informed decision-making and a cleaner, more reliable energy future. The surge in renewable energy sources (RESs) and decentralized generation has reshaped power distribution, with distributed generation systems (DGS) like solar photovoltaic (PV) and wind turbines offering advantages including reduced losses and improved grid resilience. However, optimal placement of DGSs within radial distribution feeders faces challenges due to uncertainties in minimal power loss and voltage profile improvements tied to load variations, RES generation, and system parameters. Despite previous investigations, a research gap stays concerning variability in minimal power loss and voltage profile alterations beyond specified limits, hampering well-informed decision-making and potentially leading to sub-optimal solutions and reduced grid reliability. This research paper introduces an uncertainty analysis framework to address this gap, integrating deterministic and probabilistic simulation techniques. Validated initially in the IEEE 33 bus system, the framework is then applied to the Katunje Distribution Feeder in Bhaktapur, Nepal. Utilizing Monte Carlo simulations and probability density functions, the framework quantifies uncertainty levels in power loss and voltage profiles, offering valuable insights for decision-makers. The study underscores the importance of incorporating uncertainty into DGS placement strategies to enhance grid resilience and ensure consistent voltage profiles, even under fluctuating conditions. [ABSTRACT FROM AUTHOR]