Accelerated Hybrid Consensus Alternating Direction Method of Multipliers for Distributed Economic Power Dispatch Problem with Adaptive Penalty

Economic power dispatch (EPD) is a significant managing problem in power systems. Control and management programs in large-scale systems, especially the power system, can be run centrally or in a distributed manner, each with its own advantages and disadvantages. If the power system is managed as a central fusion center, it will be vulnerable to natural disasters and cyber-attacks on the control center, despite its fast processing speed. On the other hand, if power system is managed in a distributed manner, information will be exchanged between all buses, and there is a risk that the security of the system will be compromised and intercepted by spreading information on all buses. The combination of security and system speed at the same time can ensure that power system needs are met. This paper addresses the solution of the EPD by defining a novel computational architecture, namely hybrid network architecture that merges both distributed and centralized architectures to speed up obtaining the solution of the EPD in large-scale power systems, while maintaining the necessary security level. This proposed method allows adjusting the convergence speed of the operating system along with setting the cybersecurity of the distributed system. This can play a key role in operating system reliability, which includes speed and security. To perform the optimization step, using the alternating direction method of multipliers (ADMM) framework and hybrid network architecture, a decentralized algorithm, named hybrid ADMM (HADMM), is developed to be used in solving the EPD problem. Besides, an acceleration scheme that exploits the relationship between ADMM and Douglas–Rachford splitting is proposed, which results in a faster algorithm called accelerated hybrid ADMM (AHADMM). The simulation results of the proposed algorithm applied to solve the EPD problem on the IEEE 118-bus system show the acceptable performance of the proposed algorithm under different scenarios.