A Privacy-Preserving Distributed Economic Dispatch Method for Integrated Port Microgrid and Computing Power Network

Abstract

As the number of ships docking at ports and using onshore power grows, there is a pressing need for efficient economic dispatch within the port microgrid (PMG). The computing power network (CPN) leverages ubiquitous computing resources, presenting new opportunities for handling complex tasks, such as the economic dispatch problem (EDP) more efficiently. However, integrating CPNs into PMGs raises significant concerns about data security and privacy. To address these issues, this article proposes a distributed prescribed-time optimization (PPDPTO) algorithm specifically designed for the EDP in the integrated PMG and CPN. This algorithm ensures both high computation efficiency and enhanced privacy security. Specifically, this algorithm combines the distributed prescribed-time optimization theory and the privacy-preserving technique, which can protect the sensitive energy information of the berthing ships while ensuring fast convergence. Moreover, based on the proposed mask function protecting the power demand information of the berthing ships, the optimal solution of the EDP can be obtained with limited decoding resources. A smooth piecewise mask function is proposed to promote the nonvulnerability of the system and the bias in the optimal steady states of the proposed algorithm is reduced. Furthermore, the prescribed-time convergence of the PPDPTO algorithm is proven; meanwhile, the supply and demand balance constraints of the EDP can be guaranteed under the mask function. Finally, simulations undermine the effectiveness of the PPDPTO algorithm.