Data twin system based on high-dimensional data analysis

The integration of distributed massive renewable energy, energy storage, and electric vehicles has altered the original operation mode of the distribution network, transforming it from deterministic to stochastic, and from unidirectional to bidirectional power flow. Phenomena such as voltage exceeding limits, forward and reverse overloads, and reverse power flow in transformer zones have become increasingly frequent. Therefore, it is crucial to accurately and promptly grasp the operational status of the distribution network, eliminate potential safety hazards and operational risks, and ensure the safe and stable operation of the power grid. However, the distribution network faces challenges such as a wide distribution of points, complex structure, and incomplete coverage of measurement and acquisition points. With the construction of a new power system, the number of electrical equipment, generator nodes, and load nodes has multiplied, resulting in a surge in the amount of data and increasing demands for computational efficiency. Additionally, the complexity of the distribution network and the uncertainty of distributed power sources pose challenges for data identification and analysis. To address these issues, this paper proposes a high-dimensional data analysis technology based on digital twins, which is used to analyze the operational status of the distribution network and the interactive and collaborative relationship between power sources, loads, and storage. This approach enhances the management and control capabilities of optimal scheduling, maximum consumption, and safe and stable operation of the distribution network.