Distributed optimization for economic dispatch with acceleration and privacy preservation over unbalanced directed networks

As one of the basic problems of smart grids, economic dispatch problems (EDPs) have aroused extensive research interest with the expansion of network scale and the improvement of system complexity. The problems aim to ensure that the overall electricity demand and generating capacity are met while minimizing the overall cost of power generation by optimizing the output power of individual generators. In order to solve constraint-coupled EDPs over unbalanced directed networks, a new privacy-preserving distributed accelerated random sleep algorithm is proposed. This algorithm incorporates conditional noise in information transmission, effectively ensuring the privacy preservation. Meanwhile, the addition of the momentum acceleration mechanism can accelerate the convergence of the algorithm, and the random sleep strategy can improve computational efficiency of the proposed algorithm. In addition, theoretical analysis is conducted to ensure the convergence and privacy of the proposed algorithm. Finally, simulation experiments are carried out to prove the effectiveness of the algorithm.