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This paper reviews distributed/decentralized algorithms to solve the optimal power flow (OPF) problem in electric power systems. Six decomposition coordination algorithms are studied, including analytical target cascading, optimality condition decomposition, alternating direction method of multipliers, auxiliary problem principle, consensus+innovations, and proximal message passing. The basic concept, the general formulation, the application for dc-OPF, and the solution methodology for each algorithm are presented. We apply these six decomposition coordination algorithms on a test system, and discuss their key features and simulation results. [ABSTRACT FROM AUTHOR]

Distributed optimization for solving nonconvex optimal power flow (OPF) problems in power systems has attracted considerable attention in the last decade. Most studies are based on the geographical decomposition of the IEEE test systems for verifying the feasibility of the proposed approaches. However, it is not clear if one can extrapolate from these studies that similar approaches can also be applied to very large-scale real-world systems. In this paper, we show, for the first time, that distributed optimization can be effectively applied to a large-scale real transmission network, namely, the Polish 2383-bus system for which no predefined partitions exist, by using a recently developed partitioning technique. More specifically, the problem solved is the ac OPF problem with geographical decomposition of the network using the alternating direction method of multipliers (ADMM) method in conjunction with the partitioning technique. Through extensive simulations and analytical studies, we show that with the presented partitioning technique the convergence performance of the ADMM method can be improved substantially, which enables the application of distributed approaches to very large-scale systems. [ABSTRACT FROM PUBLISHER]

Distributed optimization techniques in electric power systems have drawn increased attention as they provide a scalable way to handle the increasingly complex and large-scale optimization problems associated with the optimal operation of the system. However, little effort has been reported on how to optimally partition the overall optimization problem into subproblems, which significantly affects the efficiency and convergence speed of distributed methods. To address this issue, this paper focuses on how to determine the optimal partition for a given system and optimization problem, and quantify the improvement obtained with the optimal partition in terms of number of iterations and convergence time for solving the ac optimal power flow problem. The proposed approach is based on spectral clustering using a combination of the Hessian matrix of the optimization problem and the admittance matrix as the affinity matrix. Simulation results for the IEEE test systems with 14, 30, 57, 118, and 300 buses confirm the effectiveness of the proposed partitioning method, and the robustness of the performance of a certain partition with respect to the operating point of the system. [ABSTRACT FROM PUBLISHER]

In electric power systems, multiple entities are responsible for ensuring an economic and reliable way of delivering power from producers to consumers. With the increase of variable renewable generation it is becoming increasingly important to take advantage of the individual entities’ (and their areas’) capabilities for balancing variability. Hence, in this paper, we employ and extend the approximate Newton directions method to optimally coordinate control areas leveraging storage available in one area to balance variable resources in another area with only minimal information exchange among the areas. The problem to be decomposed is a model predictive control problem including generation constraints, energy storage constraints, and AC power flow constraints. Singularity issues encountered when formulating the respective Newton–Raphson steps due to intertemporal constraints are addressed and extensions to the original decomposition method are proposed to improve the convergence rate and required communication of the method. [ABSTRACT FROM PUBLISHER]