Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment

Security-constrained unit commitment (SCUC) is a computationally complex process utilized in power system day-ahead scheduling and market clearing. SCUC is run daily and requires state-of-the-art algorithms to speed up the process. The constraints and data associated with SCUC are both geographically and temporally correlated to ensure the reliability of the solution, which further increases the complexity. In this paper, an advanced machine learning (ML) model is used to study the patterns in power system historical data, which inherently considers both spatial and temporal (ST) correlations in constraints. The ST-correlated ML model is trained to understand spatial correlation by considering graph neural networks (GNN) whereas temporal sequences are studied using long short-term memory (LSTM) networks. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, and synthetic South-Carolina (SC) 500-Bus system. Moreover, B-{\theta} and power transfer distribution factor (PTDF) based SCUC formulations were considered in this research. Simulation results demonstrate that the ST approach can effectively predict generator commitment schedule and classify critical and non-critical lines in the system which are utilized for model reduction of SCUC to obtain computational enhancement without loss in solution qualityComment: 8 Figures, 5 Tables, 1 Algorith

Machine Learning Assisted Approach for Security-Constrained Unit Commitment

Security-constrained unit commitment (SCUC) is solved for power system day-ahead generation scheduling, which is a large-scale mixed-integer linear programming problem and is very computationally intensive. Model reduction of SCUC may bring significant time savings. In this work, a novel approach is proposed to effectively utilize machine learning (ML) to reduce the problem size of SCUC. An ML model using logistic regression (LR) algorithm is proposed and trained with historical nodal demand profiles and the respective commitment schedules. The ML outputs are processed and analyzed to reduce variables and constraints in SCUC. The proposed approach is validated on several standard test systems including IEEE 24-bus system, IEEE 73-bus system, IEEE 118-bus system, synthetic South Carolina 500-bus system and Polish 2383-bus system. Simulation results demonstrate that the use of the prediction from the proposed LR model in SCUC model reduction can substantially reduce the computing time while maintaining solution quality.Comment: 6 Pages, 5 Figures, 3 tables, 1 algorith

A Comprehensive Assessment of Climate Change and Coastal Inundation through Satellite-Derived Datasets: A Case Study of Sabang Island, Indonesia

Climate-change-induced hazards are negatively affecting the small islands across Indonesia. Sabang Island is one of the most vulnerable small islands due to the rising sea levels and increasing coastal inundation which threaten the low-lying coastal areas with and without coastal defences. However, there is still a lack of studies concerning the long-term trends in climatic variables and, consequently, sea level changes in the region. Accordingly, the current study attempts to comprehensively assess sea level changes and coastal inundation through satellite-derived datasets and model-based products around Sabang Island, Indonesia. The findings of the study show that the temperature (both minimum and maximum) and rainfall of the island are increasing by ~0.01 °C and ~11.5 mm per year, respectively. The trends of temperature and rainfall are closely associated with vegetative growth; an upward trend in the dense forest is noticed through the enhanced vegetation index (EVI). The trend analysis of satellite altimeter datasets shows that the sea level is increasing at a rate of 6.6 mm/year. The DEM-based modelling shows that sea level rise poses the greatest threat to coastal habitations and has significantly increased in recent years, accentuated by urbanisation. The GIS-based model results predict that about half of the coastal settlements (2.5 sq km) will be submerged completely within the next 30 years, provided the same sea level rise continues. The risk of coastal inundation is particularly severe in Sabang, the largest town on the island. The results allow regional, sub-regional, and local comparisons that can assess variations in climate change, sea level rise, coastal inundation, and associated vulnerabilitie

High Rates of All-cause and Gastroenteritis-related Hospitalization Morbidity and Mortality among HIV-exposed Indian Infants

<p>Abstract</p> <p>Background</p> <p>HIV-infected and HIV-exposed, uninfected infants experience a high burden of infectious morbidity and mortality. Hospitalization is an important metric for morbidity and is associated with high mortality, yet, little is known about rates and causes of hospitalization among these infants in the first 12 months of life.</p> <p>Methods</p> <p>Using data from a prevention of mother-to-child transmission (PMTCT) trial (India SWEN), where HIV-exposed breastfed infants were given extended nevirapine, we measured 12-month infant all-cause and cause-specific hospitalization rates and hospitalization risk factors.</p> <p>Results</p> <p>Among 737 HIV-exposed Indian infants, 93 (13%) were HIV-infected, 15 (16%) were on HAART, and 260 (35%) were hospitalized 381 times by 12 months of life. Fifty-six percent of the hospitalizations were attributed to infections; gastroenteritis was most common accounting for 31% of infectious hospitalizations. Gastrointestinal-related hospitalizations steadily increased over time, peaking around 9 months. The 12-month all-cause hospitalization, gastroenteritis-related hospitalization, and in-hospital mortality rates were 906/1000 PY, 229/1000 PY, and 35/1000 PY respectively among HIV-infected infants and 497/1000 PY, 107/1000 PY, and 3/1000 PY respectively among HIV-exposed, uninfected infants. Advanced maternal age, infant HIV infection, gestational age, and male sex were associated with higher all-cause hospitalization risk while shorter duration of breastfeeding and abrupt weaning were associated with gastroenteritis-related hospitalization.</p> <p>Conclusions</p> <p>HIV-exposed Indian infants experience high rates of all-cause and infectious hospitalization (particularly gastroenteritis) and in-hospital mortality. HIV-infected infants are nearly 2-fold more likely to experience hospitalization and 10-fold more likely to die compared to HIV-exposed, uninfected infants. The combination of scaling up HIV PMTCT programs and implementing proven health measures against infections could significantly reduce hospitalization morbidity and mortality among HIV-exposed Indian infants.</p