Data-driven power system security assessment using high content database during the COVID-19 pandemic

As the coronavirus disease (COVID-19) broke out in late 2019, the electricity sector was significantly impacted. Hence, the effects of the pandemic and restricting measures in power system operation are investigated during pandemic circumstances. The secure operation of the power system is a fundamental requirement. Appropriate procedures should be taken to mitigate these effects and ensure the power system's security. Accordingly, in this study, the authors determine that the COVID-19 pandemic can change the system's operating conditions in the first stage. Since data-driven security assessment methods require the training database to learn about Security constraints, this paper proposes an efficient database generation strategy respecting the consequences of the COVID-19 outbreak. The proposed strategy provides a training set with high information content compatible with the operating conditions. To this end, the method consists of a characteristics extraction approach and updating scheme. The characteristics should be extracted to represent the operating conditions of the system. Further, the similarity of intervals is compared using characteristics in updating scheme. The copula-based sampling approach is provided to generate the random samples. The proposed strategy generates a database for data-driven methods. Therefore, it can be utilized in various applications of security assessment. Real-world data is mapped to the IEEE 39-bus system to illustrate the framework efficiency. The outcomes indicate that a classification using the proposed strategy outperforms conventional methods in terms of evaluation metrics. © 2017 Elsevier Inc. All rights reserved. © 2023 Elsevier Ltd

Smart Short-Term Load Forecasting through Coordination of LSTM-Based Models and Feature Engineering Methods during the COVID-19 Pandemic

Short-term load forecasting is essential for power companies because it is necessary to ensure sufficient capacity. This article proposes a smart load forecasting scheme to forecast the short-term load for an actual sample network in the presence of uncertainties such as weather and the COVID-19 epidemic. The studied electric load data with hourly resolution from the beginning of 2020 to the first seven days of 2021 for the New York Independent Operator is the basis for the modeling. The new components used in this article include the coordination of stacked long short-term memory-based models and feature engineering methods. Also, more accurate and realistic modeling of the problem has been implemented according to the existing conditions through COVID-19 epidemic data. The influential variables for short-term load forecasting through various feature engineering methods have contributed to the problem. The achievements of this research include increasing the accuracy and speed of short-term electric load forecasting, reducing the probability of overfitting during model training, and providing an analytical comparison between different feature engineering methods. Through an analytical comparison between different feature engineering methods, the findings of this article show an increase in the accuracy and speed of short-term load forecasting. The results indicate that combining the stacked long short-term memory model and feature engineering methods based on extra-trees and principal component analysis performs well. The RMSE index for day-ahead load forecasting in the best engineering method for the proposed stacked long short-term memory model is 0.1071. © 2023 Taylor & Francis Group, LLC.

A Systematic Two-Phase Study of COVID-19 Pandemic Impact on Sustainable Development Goals: A Case Study of Iran

To respond to the urgent call for preventive action against COVID-19 pandemic implications for societies, this research is carried out. The main aim of our research is providing a new insight for the effects of the newly emerged restrictions by COVID-19 on the SD Goals (SDGs). This research, for the first time applied a two-phase qualitative approach for supporting the SDGs achievement post-COVID in Iran, as a developing country in the Middle East. In the first phase, using a fuzzy Delphi method, the SDGs affected by COVID-19 were identified. In the next phase, a fuzzy cognitive map, as a qualitative system dynamics modeling, was conducted to specify the key interconnections among the SDGs post COVID-19. Finally, three strategies including focus on people in vulnerable situation, support for industrial units and small and medium-sized enterprises, and national aggregation to Fight COVID-19 were examined. As a result, different scenarios associated with the three proposed strategies were tested based on the identified interconnections among the SDGs to reduce the potential negative effects of COVID-19 crisis on the achievement of the SDGs. The results provide a decision support for stakeholders and policy makers involved in SD action plan. © Iran University of Science and Technology 2022.

Liver enzymes and inpatient deaths in COVID-19 hospitalized patients

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [COVID-19] quickly turned into a pandemic. Gastrointestinal involvement, especially liver diseases, is one of the main complications of COVID-19 patients. Objectives: The current study aimed to evaluate the high incidence of liver involvement in COVID-19 hospitalized patients and its association with mortality. Methods: A total of 560 hospitalized patients with a confirmed diagnosis of COVID-19 were included. Death was considered as the outcome. In addition to liver enzymes, demographic, clinical, and other laboratory data were also collected. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels_ 40 were considered as abnormal. To investigate the association between abnormal levels of liver enzymes and death, multiple regression logistic was used. Results: According to the findings, 29.1% (95% CI = 25.3% - 32.9%) of patients had high levels (_ 40 IU) of ALT, and 45.1% (95% CI = 40.9% - 49.3%) had high levels of AST (_ 40 IU). The frequency (based on %) of high levels of AST (_ 40 U/liter) was significantly higher in patients who died [67.3% (95% CI = 54.5% - 80.1%] of COVID-19 than those who survived [44.9% (95% CI = 39.7% - 50.0%)] (Pvalue < 0.001). No significant difference was detected in ALT between expired [29.1% (95% CI = 16.7% - 41.5%)] and survived patients [30.7% (95% CI = 25.9% - 35.5%] (P-value = 0.791). AST was found to have an independent association with death in multiple logistic regression (Wald = 4.429, OR (95% CI) = 1.014 (1.008 - 1.020), P-value = 0.035). Conclusions: Liver involvement is a common finding in COVID-19 hospitalized patients. Higher levels of AST were significantly associated with an increased mortality rate in COVID-19 patients.