<p>This paper presents a comprehensive flexibility assessment of the Telangana state power grid under progressively increasing renewable energy penetration through the systematic replacement of conventional generators with renewable generation units, each integrated with an individual Hybrid Energy Storage System (HESS) and a dedicated control strategy. As conventional generators are progressively replaced by renewable generators, each integrated with a dedicated Hybrid Energy Storage System (HESS) and independently controlled, the grid evolves into a multi-renewable, multi-HESS system characterized by heterogeneous control schemes. Initially, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control strategy was implemented for HESS management, and key flexibility assessment indices were evaluated. Subsequently, a hybrid Differential Evolution–Evolutionary Particle Swarm Optimization (DEEPSO) algorithm was employed as an alternative control strategy to enhance system flexibility. Comparative analyses between the two control approaches were conducted to quantify their effectiveness and identify the system flexibility threshold. The Telangana practical power grid was adopted as a real-world case study, and simulations were performed using one day of high-resolution time-series demand and generation data. The results demonstrate that the DEEPSO-based control strategy consistently outperforms the ANFIS-based approach in improving flexibility indices, particularly at higher renewable penetration levels. Based on the observed trends, the system flexibility threshold of the Telangana grid was estimated to be in the range of 60–65% renewable energy integration. The findings provide practical insights into optimal control strategy selection and storage deployment for enhancing flexibility in real-world power systems with high renewable energy penetration.</p>