Parallel and Distributed Optimization Method With Constraint Decomposition for Energy Management of Microgrids

Energy management in power systems is a thorny optimization problem. With the sizes of rising, centralized methods are restricted by their complexities communications, while distributed have emerged as powerful tool for dealing with increasingly complex systems. However, convergence rates some widely used methods, such standard alternating direction method multipliers (ADMM), still room improvement. In this paper, parallel and energy microgrids (MGs) proposed to boost rate without sacrificing accuracy optima, which agents calculate, exchange update parallel. At first, decomposition presented, where objective functions constraints an original problem separable variables decomposed into local agents, key our method. Further, solve problems independently then determined optima neighbors. Finally, evaluated economic dispatch demand response microgrids. The simulation results show that compared ADMM, given accuracy, number iterations only one third or even less than ADMM. Furthermore, can minimize cost generation on supply side maximize profit flexible loads side.