Energy Constrained Generation Dispatch Based on Price Forecasts Including Expected Values and Risk

A number of price forecasting methods are used to forecast wholesale (spot) electricity prices. The forecasts are evaluated for both accuracy and variation in accuracy (risk). These forecasts are used to balance revenue against forecasting error risk in dispatching constrained generation. The best dispatch method found was based on the half-hours with the maximum demand

Evaluation of support vector machine based forecasting tool in electricity price forecasting for Australian national electricity market participants

In this paper we present an analysis of the results of a study into wholesale (spot) electricity price forecasting utilising Neural Networks (NNs) and Support Vector Machines (SVM). Frequent regulatory changes in electricity markets and the quickly evolving market participant pricing (bidding) strategies cause efficient retraining to be crucial in maintaining the accuracy of electricity price forecasting models. The efficiency of NN and SVM retraining for price forecasting was evaluated using Australian National Electricity Market (NEM), New South Wales regional data over the period from September 1998 to December 1998. The analysis of the results showed that SVMs with one unique solution, produce more consistent forecasting accuracies and so require less time to optimally train than NNs which can result in a solution at any of a large number of local minima. The SVM and NN forecasting accuracies were found to be very similar

Optimizing Generator Dispatch by Quadratic Programming Using the Minimum Energy State of System Method

Improved operation and comprehension of analysis tools are required in this era of rapid changes in electrical power system structure, electricity market regulations and operation. In this paper a brief introduction and worked example of a proposed formulation of generator dispatch utilizing suitable optimisation techniques to minimise an objective (energy) function while satisfying Ohm's law and Kirchhoff's Current law is presented. The formulation of this Minimum Energy State of System (MESoS) perspective can be used to solve circuit problems, load flows and optimising generator dispatch problems. The aim of this paper is to present this consistent solution perspective and so make some contribution towards improved understanding and communication of power system analysis methods and results. Finally, this MESoS concept is applied to a standard 6 bus test power systems

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19

Identification of LZTFL1 as a candidate effector gene at a COVID-19 risk locus

The severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) disease (COVID-19) pandemic has caused millions of deaths worldwide. Genome-wide association studies identified the 3p21.31 region as conferring a twofold increased risk of respiratory failure. Here, using a combined multiomics and machine learning approach, we identify the gain-of-function risk A allele of an SNP, rs17713054G>A, as a probable causative variant. We show with chromosome conformation capture and gene-expression analysis that the rs17713054-affected enhancer upregulates the interacting gene, leucine zipper transcription factor like 1 (LZTFL1). Selective spatial transcriptomic analysis of lung biopsies from patients with COVID-19 shows the presence of signals associated with epithelial–mesenchymal transition (EMT), a viral response pathway that is regulated by LZTFL1. We conclude that pulmonary epithelial cells undergoing EMT, rather than immune cells, are likely responsible for the 3p21.31-associated risk. Since the 3p21.31 effect is conferred by a gain-of-function, LZTFL1 may represent a therapeutic target

An algorithm of Katz and its application to the inverse Galois problem

SIGLEAvailable from TIB Hannover: RR 1606(99-47) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman