Abstract
With the increasing development of smart grid, multiparty cooperative
computation between several entities has become a typical characteristic
of modern energy systems. Traditionally, data exchange among parties is
inevitable, rendering how to complete multiparty collaborative
optimization without exposing any private information a critical issue.
This paper proposes a fully privacy-preserving distributed optimization
framework based on secure multiparty computation (SMPC) secret sharing
protocols. The framework decomposes the collaborative optimization
problem into a master problem and several subproblems. The process of
solving the master problem is executed in the SMPC framework via the
secret sharing protocols among agents. The relationships of agents are
completely equal, and there is no privileged agent or any third party.
The process of solving subproblems is conducted by agents individually.
Compared to the traditional distributed optimization framework, the
proposed SMPC-based framework can fully preserve individual private
information. Exchanged data among agents are encrypted and no private
information disclosure is assured. Furthermore, the framework maintains
a limited and acceptable increase in computational costs while
guaranteeing optimality. Case studies are conducted to demonstrate the
principle of secret sharing and verify the feasibility and scalability
of the proposed methodology.