Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow

The future power grid may rely on distributed optimization to determine the set-points for huge numbers of distributed energy resources. There has been significant work on applying distributed algorithms to optimal power flow (OPF) problems, which require separate computing agents to agree on shared boundary variable values. Looser tolerances for the mismatches in these shared variables generally yield faster convergence at the expense of exacerbating constraint violations, but there is little quantitative understanding of how the convergence tolerance affects solution quality. To address this gap, we first quantify how convergence tolerance impacts constraint violations when the distributed OPF generator dispatch is applied to the power system. Using insights from this analysis, we then develop a bound tightening algorithm which guarantees that operating points from distributed OPF algorithms will not result in violations despite the possibility of shared variable mismatches within the convergence tolerance. We also explore how bounding the cumulative shared variable mismatches can prevent unnecessary conservativeness in the bound tightening. The proposed approach enables control of the trade-off between computational speed, which improves as the convergence tolerance increases, and distributed OPF solution cost, which increases with convergence tolerance due to tightened constraints, while ensuring feasibility

PowerModelsADA: A Framework for Solving Optimal Power Flow using Distributed Algorithms

This paper presents PowerModelsADA, an open-source framework for solving Optimal Power Flow (OPF) problems using Alternating Distributed Algorithms (ADA). PowerModelsADA provides a framework to test, verify, and benchmark both existing and new ADAs. This paper demonstrates use cases for PowerModelsADA and validates its implementation with multiple OPF formulations.Comment: This work has been submitted to the IEEE for possible publicatio

Statistical Properties of Avalanches in Networks

We characterize the distributions of size and duration of avalanches propagating in complex networks. By an avalanche we mean the sequence of events initiated by the externally stimulated `excitation' of a network node, which may, with some probability, then stimulate subsequent firings of the nodes to which it is connected, resulting in a cascade of firings. This type of process is relevant to a wide variety of situations, including neuroscience, cascading failures on electrical power grids, and epidemology. We find that the statistics of avalanches can be characterized in terms of the largest eigenvalue and corresponding eigenvector of an appropriate adjacency matrix which encodes the structure of the network. By using mean-field analyses, previous studies of avalanches in networks have not considered the effect of network structure on the distribution of size and duration of avalanches. Our results apply to individual networks (rather than network ensembles) and provide expressions for the distributions of size and duration of avalanches starting at particular nodes in the network. These findings might find application in the analysis of branching processes in networks, such as cascading power grid failures and critical brain dynamics. In particular, our results show that some experimental signatures of critical brain dynamics (i.e., power-law distributions of size and duration of neuronal avalanches), are robust to complex underlying network topologies.Comment: 11 pages, 7 figure

Is there a correlation between infection control performance and other hospital quality measures?

Quality measures are increasingly reported by hospitals to the Centers for Medicare and Medicaid Services (CMS), yet there may be tradeoffs in performance between infection control (IC) and other quality measures. Hospitals that performed best on IC measures did not perform well on most CMS non–IC quality measures

Analysis of the Human Mucosal Response to Cholera Reveals Sustained Activation of Innate Immune Signaling Pathways

To better understand the innate immune response to Vibrio cholerae infection, we tracked gene expression in the duodenal mucosa of 11 Bangladeshi adults with cholera, using biopsy specimens obtained immediately after rehydration and 30 and 180 days later. We identified differentially expressed genes and performed an analysis to predict differentially regulated pathways and upstream regulators. During acute cholera, there was a broad increase in the expression of genes associated with innate immunity, including activation of the NF-kappaB, mitogen-activated protein kinase (MAPK), and Toll-like receptor (TLR)-mediated signaling pathways, which, unexpectedly, persisted even 30 days after infection. Focusing on early differences in gene expression, we identified 37 genes that were differentially expressed on days 2 and 30 across the 11 participants. These genes included the endosomal Toll-like receptor gene TLR8, which was expressed in lamina propria cells. Underscoring a potential role for endosomal TLR-mediated signaling in vivo, our pathway analysis found that interferon regulatory factor 7 and beta 1 and alpha 2 interferons were among the top upstream regulators activated during cholera. Among the innate immune effectors, we found that the gene for DUOX2, an NADPH oxidase involved in the maintenance of intestinal homeostasis, was upregulated in intestinal epithelial cells during cholera. Notably, the observed increases in DUOX2 and TLR8 expression were also modeled in vitro when Caco-2 or THP-1 cells, respectively, were stimulated with live V. cholerae but not with heat-killed organisms or cholera toxin alone. These previously unidentified features of the innate immune response to V. cholerae extend our understanding of the mucosal immune signaling pathways and effectors activated in vivo following cholera

A Case Tracking System with Electronic Medical Record Integration to Automate Outcome Tracking for Radiologists

Radiologists make many diagnoses, but only sporadically get feedback on the subsequent clinical courses of their patients. We have created a web-based application that empowers radiologists to create and maintain personal databases of cases of interest. This tool integrates with existing information systems to minimize manual input such that radiologists can quickly flag cases for further follow-up without interrupting their clinical work. We have integrated this case-tracking system with an electronic medical record aggregation and search tool. As a result, radiologists can learn the outcomes of their patients with much less effort. We intend this tool to aid radiologists in their own personal quality improvement and to increase the efficiency of both teaching and research. We also hope to develop the system into a platform for systematic, continuous, quantitative monitoring of performance in radiology