ﻻ يوجد ملخص باللغة العربية
This paper proposes a fully distributed reactive power optimization algorithm that can obtain the global optimum of non-convex problems for distribution networks without a central coordinator. Second-order cone (SOC) relaxation is used to achieve exact convexification. A fully distributed algorithm is then formulated corresponding to the given division of areas based on an alternating direction method of multipliers (ADMM) algorithm, which is greatly simplified by exploiting the structure of active distribution networks (ADNs). The problem is solved for each area with very little interchange of boundary information between neighboring areas. The standard ADMM algorithm is extended using a varying penalty parameter to improve convergence. The validity of the method is demonstrated via numerical simulations on an IEEE 33-node distribution network, a PG&E 69-node distribution system, and an extended 137-node system.
High penetration levels of distributed photovoltaic (PV) generation on an electrical distribution circuit may severely degrade power quality due to voltage sags and swells caused by rapidly varying PV generation during cloud transients coupled with t
Large scale, non-convex optimization problems arising in many complex networks such as the power system call for efficient and scalable distributed optimization algorithms. Existing distributed methods are usually iterative and require synchronizatio
We consider a distributed optimization problem over a network of agents aiming to minimize a global objective function that is the sum of local convex and composite cost functions. To this end, we propose a distributed Chebyshev-accelerated primal-du
This paper considers decentralized stochastic optimization over a network of $n$ nodes, where each node possesses a smooth non-convex local cost function and the goal of the networked nodes is to find an $epsilon$-accurate first-order stationary poin
Information compression is essential to reduce communication cost in distributed optimization over peer-to-peer networks. This paper proposes a communication-efficient linearly convergent distributed (COLD) algorithm to solve strongly convex optimiza