The rapid development of energy storage power stations plays a significant role in the widespread adoption of the energy internet. Anomaly detection in these stations, as a critical component of daily operation and maintenance, holds great importance for ensuring the normal operation of energy storage systems. Currently, station monitoring primarily relies on preset fixed threshold-based alerts combined with manual supervision. However, this approach is unable to detect abnormal states below the threshold and poses a risk of missing certain anomalies. This study employs an unsupervised deep learning model based on variational autoencoders (VAEs) to perform anomaly detection on real operational data. By training the model on normal operational data, the model learns the distribution of data in the latent space under normal conditions. Experimental results demonstrate that the VAE-based model is capable of effectively detecting abnormal data segments and outliers in electricity power real-world data. Compared to classical machine learning algorithms such as Isolation Forest and Support Vector Machine, the detection performance of the VAE-based model demonstrates superiority, indicating its practical value and research significance.