Abstract
The storage security and privacy protection of power plant ancillary service data face severe challenges, hindering power plant operation optimization and the efficient operation of the electricity market. This research aims to construct a secure and reliable data storage system for power plant ancillary services and establish a scientific and accurate performance evaluation method. To achieve this, a multi-layer technology fusion model based on blockchain, fully homomorphic encryption (FHE), and smart contracts is proposed. The architecture integrates blockchain for trusted data provenance, FHE for privacy-preserving computation, and smart contracts for automated business logic execution, forming a coherent and secure data management framework. Specifically, the study adopts a hybrid storage mode combining blockchain structure and private database. Secure interaction and homomorphic operations of encrypted data are achieved through smart contracts. An improved approximation-ideal solution sorting method is used, combined with fuzzy hierarchical analysis to determine indicator weights. The results showed that in the ancillary business data test of a provincial power system in 2023, the proposed storage scheme achieved a data leakage rate of 1% for 10,000 pieces of data and a tampering detection success rate of 98.99%. This performance evaluation method was applied to six cross-regional power plants, effectively distinguishing the performance differences of ancillary services among different power plants. The relative similarity of the frequency regulation scenario in new energy power plants was 0.85, which was 12% higher than that in thermal power plants. This research provides a reliable and secure storage path for power plant ancillary service data, promoting the digital transformation of the power system and the standardized development of the electricity market. However, the proposed approach may face adaptability challenges in cross-regional deployment due to varying grid regulations and data standards, and the computational overhead of fully homomorphic encryption could impact real-time performance in large-scale applications. Future work will focus on optimizing algorithm efficiency, reducing computational costs, and validating the framework across diverse regional power systems to enhance its generalizability and practical deployment.
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