<p>Numerous researchers have explored the impact of renewable energy integration on power system flexibility using various methodologies. However, a significant gap persists in the accurate estimation and comprehensive analysis of flexibility assessment indices. These indices are probabilistic in nature and, therefore, require time-series data for more accurate estimation. They quantify the uncertainty in ramping requirements by considering inputs such as generator capabilities, generation mix, energy storage, and load profiles. By comparing these factors with the system’s ramping capability, the indices assess the risk of failing to meet demand fluctuations caused by variability in generation or load. Due to their probabilistic evaluation method, flexibility assessment indices cannot be optimized like conventional system parameters. Their calculation involves the analysis of time-series data and the evaluation of system performance under varying generation and load conditions. As such, they must be tested and simulated under diverse scenarios to observe potential improvements. This research primarily focuses on the evaluation and improvement of flexibility assessment indices through the implementation of Hybrid Energy Storage Systems (HESSs) and the application of various control strategies for large-scale renewable energy integration. Simulation studies were conducted on IEEE 9 Bus, 14 Bus, 39 Bus, and 57 Bus test systems, where conventional generators were systematically replaced with renewable generators coupled with HESS units. Optimal power flow studies were carried out using 24-h time-series data, replacing one generator at a time and continuing step-by-step until maximum renewable penetration was achieved. The proposed research employs a battery–supercapacitor combination for the HESS, leveraging the complementary characteristics of high-energy storage with slow response (battery) and high-power storage with fast response (supercapacitor). Various HESS configurations were examined, including passive HESS, active HESS with PI control, active HESS with fuzzy logic control, and an ANFIS-based control strategy. Simulation outcomes included key performance indicators such as renewable energy share, operating cost, and flexibility indices for each configuration. Comparative charts were used to illustrate trends and variations across different systems and control strategies. The novelty of this work lies in the evaluation and enhancement of flexibility assessment indices using HESS, supported by diverse control strategies to identify system thresholds for each network or grid. Among all strategies tested, the ANFIS-based active control consistently delivered the most favorable results. Furthermore, the study reveals that each system has a distinct threshold for variable renewable energy (VRE) share, consistently identified around 55–60% for optimal performance. Below this threshold, operating costs tend to increase marginally, flexibility indices remain stable, and VRE integration improves significantly. This study provides valuable insights into the optimal configuration and control strategies of HESS for enhancing flexibility assessment indices under large-scale renewable energy penetration.</p>