多块ADMM用于智能电网中的大数据优化
Multi-Block ADMM for Big Data Optimization in Smart Grid
AI总结：
本文综述了基于交替方向乘子法的并行和分布式优化算法，用于解决智能电网通信网络中的大数据优化问题，介绍了多块ADMM的扩展和改进方法及其在电力系统中的应用。
摘要：
本文回顾了基于交替方向乘子法（ADMM）的并行和分布式优化算法，用于解决智能电网通信网络中的大数据优化问题。我们首先介绍了大规模优化问题的规范形式。接下来，我们描述了ADMM的一般形式，然后聚焦于从2块到N块设置的几种直接扩展和复杂修改方法，以处理优化问题。给出了每种扩展/修改的迭代方案和收敛性质，并展示了在大规模计算设施上的实现。最后，我们列举了电力系统中分布式鲁棒状态估计、网络能源管理和安全约束最优功率流问题的应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
