Data-driven optimization of lithium battery energy storage for grid stability and renewable energy integration

The study examines lithium battery energy storage systems (ESS) to improve renewable energy use, emphasizing optimizing energy management and grid stability. This research introduces an innovative two-stage framework for implementing Energy Storage Systems (ESS) using a data-driven paradigm that significantly enhances energy storage operations compared to conventional single-stage approaches. The research employs a multi-objective control approach to regulate peak load reduction and maintain battery charge levels. Daily grid load estimates are produced via the support vector machine technique, while the constant power approach regulates battery control signals. The study validates the proposed control method through comprehensive Simulink modeling of a battery storage system, successfully implementing peak shaving (achieving 17.3 % reduction in peak loads) and valley filling (with 15.8 % improvement in load balancing) to stabilize the grid and enhance the overall efficiency of energy storage systems by 23.6 % compared to traditional methods. This study establishes a theoretical framework for prospective applications in ESS technology, highlighting the equilibrium between economic advantages and grid stability.