Colorado School of Mines, Advanced Energy Systems, USA.
Global Journal of Engineering and Technology Advances, 2026, 27(01), 238-254
Article DOI: 10.30574/gjeta.2026.27.1.0104
Received on 25 March 2026; revised on 28 April 2026; accepted on 01 May 2026
The accelerating global transition toward low-carbon energy systems has intensified the need for flexible, reliable, and scalable solutions to manage the intermittency of renewable resources such as wind and solar. Battery Energy Storage Systems (BESS) have emerged as a critical enabler within large-scale clean energy transition frameworks, offering capabilities for load balancing, frequency regulation, peak shaving, and grid resilience. This study examines the strategic optimization of BESS within integrated renewable energy networks, emphasizing system-level design, operational efficiency, and economic viability. From a broader perspective, it explores the evolving role of storage technologies in decarbonized energy systems, including policy alignment, market structures, and technological advancements in lithium-ion and next-generation batteries. Narrowing down, the paper evaluates optimization techniques such as predictive analytics, artificial intelligence-driven energy management systems, and hybrid storage configurations to enhance performance under variable demand and supply conditions. Furthermore, it highlights the importance of lifecycle assessment, degradation modeling, and cost-benefit analysis in ensuring sustainable deployment. The findings demonstrate that optimized BESS integration significantly improves renewable penetration, grid stability, and energy efficiency, thereby accelerating the transition to resilient and sustainable large-scale clean energy systems globally.
Battery Energy Storage Systems (BESS); Renewable Energy Integration; Grid Stability; Energy Optimization; Clean Energy Transition; Artificial Intelligence in Energy Systems
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Oluwaseun Alonge. Optimizing battery energy storage systems for renewable integration in large scale clean energy transition frameworks. Global Journal of Engineering and Technology Advances, 2026, 27(01), 238-254. Article DOI: https://doi.org/10.30574/gjeta.2026.27.1.0104.





