A local oriented frilled lizard optimizer (LO-FLO) for enhanced loading margin stability in power systems with integrated wind-sources and FACTS devices

Abstract Modern power systems face critical operational challenges due to increasing renewable energy integration, load growth, and system faults. This paper proposes the local oriented frilled lizard optimizer (LO-FLO) to address optimal power management and security enhancement. The algorithm incorporates an adaptive local search mechanism that dynamically balances exploration and exploitation to enhance convergence efficiency under a restricted computational budget of 100 iterations. Multiple STATCOM and thyristor controlled series capacitors devices are optimally coordinated to provide flexible reactive power support and improve system loadability, with wind power intermittency modeled via the Weibull distribution. The proposed LO-FLO is validated across 23 benchmark functions and two practical power systems: the modified IEEE 30-Bus and the Algerian 114-Bus system. To verify the robustness and particularity of the algorithm, a comprehensive statistical analysis was conducted, including the Wilcoxon rank-sum test and boxplot distribution analysis over 50 independent trials. For the IEEE 30-Bus system, LO-FLO achieves superior performance with a total power loss of 16.1195 MW and loading margin stability (L_M.S) of 1.86 p.u. For the Algerian system, the method achieves an L_M.S of 1.32 p.u., significantly outperforming the improved mountain gazelle optimization. Statistical results confirm that LO-FLO provides higher solution quality and superior convergence stability ( p < 0.05) compared to standard metaheuristics. With integrated wind farms, power loss further reduces to 93.5797 MW. The results demonstrate that LO-FLO effectively enhances modern power system security through coordinated FACTS management and renewable integration under stringent stability constraints.