Multidimensional Data Aggregation of Flexible Resources Based on Fog Computing

The increasing imbalance between power supply and demand in new electricity system, alongside the integration of a high proportion of renewable energy sources, has led to a significantly increased demand for flexible resources. To more accurately evaluate the role of flexibility resources in power scheduling, this paper proposes a privacy-preserving data aggregation scheme for multidimensional flexibility resource aggregation. By incorporating the Paillier homomorphic encryption algorithm, superincreasing sequences, and blind factors, this approach enables multidimensional data transmission while ensuring data privacy. Additionally, a signature authentication method based on the Elliptic Curve Discrete Logarithm Problem is employed to ensure data integrity and verify data sources. The introduction of Horner rules reduces data transmission volume, enhancing system efficiency by providing a parsable structure that enables the system to obtain aggregate data based on load classification across all users. Security and performance analysis demonstrate that the proposed scheme not only ensures data privacy for grid users but also achieves superior computational and communication efficiency compared to existing schemes.