Feasibility vs. Optimality in Distributed AC OPF - A Case Study Considering ADMM and ALADIN

This paper investigates the role of feasible initial guesses and large consensus-violation penalization in distributed optimization for Optimal Power Flow (OPF) problems. Specifically, we discuss the behavior of the Alternating Direction of Multipliers Method (ADMM). We show that in case of large consensus-violation penalization ADMM might exhibit slow progress. We support this observation by an analysis of the algorithmic properties of ADMM. Furthermore, we illustrate our findings considering the IEEE 57 bus system and we draw upon a comparison of ADMM and the Augmented Lagrangian Alternating Direction Inexact Newton (ALADIN) method

Using Network Dynamical Influence to Drive Consensus

Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the "steering" refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks

New results on the local linear convergence of ADMM: A joint approach

Thanks to its versatility, its simplicity, and its fast convergence, alternating direction method of multipliers (ADMM) is among the most widely used approaches for solving a convex problem in distributed form. However, making it running efficiently is an art that requires a fine tuning of system parameters according to the specific application scenario, and which ultimately calls for a thorough understanding of the hidden mechanisms that control the convergence behavior. In this framework, we aim at providing new theoretical insights on the convergence process and specifically on some constituent matrices of ADMM whose eigenstructure provides a close link with the algorithm's convergence speed. One of the key techniques that we develop allows to effectively locate the eigenvalues of a (symmetric) matrix product, thus being able to estimate the contraction properties of ADMM. In the comparison with the results available from the literature, we are able to strengthen the precision of our speed estimate thanks to the fact that we are solving a joint problem (i.e., we are identifying the spectral radius of the product of two matrices) in place of two separate problems (the product of two matrix norms)

Elastic and Predictive Allocation of Computing Tasks in Energy Harvesting IoT Edge Networks

We consider a distributed IoT edge network whose end nodes generate computation jobs that can be processed locally or be offloaded, in full or in part, to other IoT nodes and/or edge servers having the necessary computation and energy resources. That is, jobs can either be partitioned and executed at multiple nodes (including the originating node) or be atomically executed at the designate server. IoT nodes and servers harvest ambient energy and jobs have a completion deadline. For this setup, we are concerned with the temporal allocation of jobs that maximizes the minimum level among all energy buffers in the network while meeting all the deadlines, i.e., that makes the network as much as possible energy neutral. Jobs continuously and asynchronously arrive at the IoT nodes, and computing resources are allocated dynamically at runtime, automatically adapting the processing load across nodes and servers. To achieve this, we present a Model Predictive Control based algorithm, where the job scheduler solves a sequence of low complexity convex problems and exploits future job and energy arrival estimates. The proposed technique is numerically evaluated, showing excellent adaptation capabilities, and performance close to that of an offline optimal scheduler with perfect information of all processes

Optimum control of distributed energy resources in residential micro-grids

none4In micro-grids, the distributed energy resources are interfaced with the grid by electronic power processors. If these processors perform cooperatively, full exploitation of energy sources can be achieved together with distribution loss reduction and voltage stabilization. This requires optimum local control and dynamic grid mapping capability, which can easily be implemented by a distributed ICT architecture based on local data processing capability and powerline communication.Invited contribution at panel session.noneAlessandro Costabeber;Tomaso Erseghe;Paolo Tenti;Stefano TomasinCostabeber, Alessandro; Erseghe, Tomaso; Tenti, Paolo; Tomasin, Stefan