An AI-Driven Integrated Framework for Construction, Operation, And Grid Optimization of Battery Energy Storage Systems
Abstract
The rapid expansion of renewable energy systems has intensified the need for advanced Battery Energy Storage Systems capable of supporting grid stability, operational efficiency, and resilient infrastructure development. This paper proposes an AI-driven integrated framework for the construction, operation, and grid optimization of BESS, addressing limitations in existing fragmented approaches that treat design, control, and grid interaction as isolated processes. The proposed next-generation architecture introduces a multi-layered system that unifies construction design, digital monitoring, artificial intelligence optimization, and grid integration into a cohesive framework. The construction layer emphasizes modular design principles and advanced thermal safety systems to enhance scalability, reliability, and lifecycle performance. The digital layer incorporates real-time monitoring and digital twin models, enabling continuous system representation, predictive simulation, and performance tracking. The AI layer leverages machine learning algorithms for predictive dispatch, fault detection, and adaptive control, ensuring efficient energy utilization and proactive system maintenance. The grid layer focuses on frequency regulation and voltage stabilization, enabling seamless integration with renewable energy sources and enhancing overall grid resilience. A key contribution of this study is the development of a holistic BESS architecture that integrates AI into construction-informed design, allowing operational insights to influence structural and system configurations. This bidirectional interaction between design and operation improves system optimization and reduces long-term operational risks. Furthermore, the framework establishes a foundation for smart grid resilience by enabling real-time decision-making, automated control, and adaptive response to grid disturbances. The proposed model advances the field by providing a unified approach to BESS deployment, offering practical implications for energy providers, infrastructure developers, and policymakers seeking to enhance sustainability and reliability in modern power systems.
Keywords
Battery Energy Storage SystemsArtificial Intelligence OptimizationDigital Twin SystemsSmart Grid ResiliencePredictive Energy DispatchModular Energy Infrastructure. IJEMT
References
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