Privacy-Preserving Distributed Optimization for Economic Dispatch Over Balanced Directed Networks

Economic dispatch problems (EDPs), as a basic issue of smart grids, have appealed to a wide range of research interests owing to the expansion of network scales and the increase of system complexity. The flexibility of economic dispatch algorithms puts forward urgent requirements of distributed optimization methods dependent on information exchanges, which may lead to the leakage of private information. To solve this problem, a privacy-preserving strategy in a distributed paradigm is proposed by adding artificial sequences to the transmitted multi-step gradient information. In light of such a strategy, a new distributed privacy-preserving optimization approach in light of multi-step gradient information is developed to handle the addressed EDPs. When introduced parameter sequences satisfy suitable conditions, both the convergence to the optimal solution and the privacy of sensitive parameters in the generator cost are effectively guaranteed. Finally, an illustrative simulation is specially offered to verify the validity of the developed strategy.