Multi-Block ADMM for Big Data Optimization in Smart Grid
For researchers and practitioners in smart grid optimization, this paper provides a survey of ADMM variants for large-scale problems, but it is incremental as it reviews existing methods without proposing new ones.
This paper reviews parallel and distributed ADMM-based optimization algorithms for big data problems in smart grid communication networks, covering extensions from 2-block to N-block ADMM and their convergence properties, with applications in power system state estimation, energy management, and optimal power flow.
In this paper, we review the parallel and distributed optimization algorithms based on alternating direction method of multipliers (ADMM) for solving "big data" optimization problem in smart grid communication networks. We first introduce the canonical formulation of the large-scale optimization problem. Next, we describe the general form of ADMM and then focus on several direct extensions and sophisticated modifications of ADMM from $2$-block to $N$-block settings to deal with the optimization problem. The iterative schemes and convergence properties of each extension/modification are given, and the implementation on large-scale computing facilities is also illustrated. Finally, we numerate several applications in power system for distributed robust state estimation, network energy management and security constrained optimal power flow problem.