Optimization of battery charging and discharging strategies in substation DC systems using the dual self-attention network-N-BEATS model

With the rapid pace of urbanization and industrialization, the demand for electricity has surged, placing immense pressure on power management systems. Substation DC systems play a crucial role in managing these fluctuations to ensure a stable and reliable power supply. However, existing battery charging and discharging strategies often suffer from inefficiencies, which can negatively impact overall system performance and sustainability. In this study, we introduce a novel approach that leverages artificial intelligence and time series predictive analytics through the dual self-attention network-neural basis expansion analysis for time series (DSAN-N-BEATS) model. This model integrates the self-attention network with the neural basis expansion analysis for time series (N-BEATS) model to accurately capture time-series data and optimize battery management. Our experimental results demonstrate that the DSAN-N-BEATS model significantly enhances battery state prediction accuracy, achieving a 95.84% accuracy rate, and improves charging and discharging efficiency by 20% compared to traditional methods. These improvements contribute to the overall reliability and sustainability of power systems. This research provides innovative methods for optimizing battery strategies, supporting sustainable development in the power industry, and enhancing system stability and reliability.