Confidentiality-preserving optimal power flow for cloud computing

Abstract

In the field of power system engineering, the optimal power flow problem is essential in planning and operations. With increasing system size and complexity, the computational requirements needed to solve practical optimal power flow problems continues to grow. Increasing computational requirements make the possibility of performing these computations remotely with cloud computing appealing. However, power system structure and component values are often confidential; therefore, the problem cannot be shared. To address this issue of confidential information in cloud computing, some techniques for masking optimization problems have been developed. The work of this paper builds upon these techniques for optimization problems but is specifically developed for addressing the DC and AC optimal power flow problems. We study the application of masking a sample OPF using the IEEE 14-bus network.