Advanced optimization of renewables and energy storage in power networks using metaheuristic technique with voltage collapse proximity and dynamic thermal rating technology

Integrating renewable energy sources and energy storage systems into power networks is trending due to its potential technical and economic benefits. However, it poses significant challenges in the power system, such as the complexity of integration, stability, and efficiency. To efficiently integrate renewable energy-based distributed generation (RE-DG) and energy storage system (ESS) and determine the optimal location and capacity from an analytical point of view, this paper attempts to carry out site selection and capacity planning using a novel Fractional Order Whale Optimization Algorithm (FWOA). In this study, the site selection for the installation of RE-DG and ESS is executed through the assessment of vulnerability analysis, and the proposed FWOA optimization is used for the optimal placement of DGs and ESS. Moreover, extreme weather conditions are considered in this analysis, and the assessment factors used in this research are voltage stability, the possibility of line overload, and fault conditions. Simulations of the proposed FWOA approach are performed on the IEEE 69 bus system, and vulnerability evaluation is carried out using Monte Carlo Simulation, as well as the deployment of dynamic thermal rating technology to minimize the vulnerability effectively. The outcomes of the proposed method are compared with those of other metaheuristic methods. The findings show that the developed approach significantly enhances the voltage collapse proximity index values for the buses, improves the voltage profile, and decreases the power losses. Consequently, it can be considered as a promising solution for further smart grid sustainable energy systems applications.