Privacy Preserving-Based Approach for Economic Power Dispatch in a Smart Grid

The economic power dispatch is a widely recognized optimization issue in a smart grid that intends to minimize the total cost of generation while meeting individual generator constraints and total demand. However, current algorithms fail to protect the private information of individual generators. In this paper, a privacy preserving-based optimization algorithm is proposed for the economic power dispatch problem under directed graphs. By adding carefully designed randomness into optimization dynamics, our proposed algorithm is guaranteed to protect the sensitive information of generators against internal honest-but-curious attackers while achieving the optimal solution. In the end, numerical simulations are shown to illustrate and confirm the proposed algorithm.