Toward Carbon-Neutral Electric Power Systems in the New York State: a Novel Multi-Scale Bottom-Up Optimization Framework Coupled with Machine Learning for Capacity Planning at Hourly Resolution

In this work, we propose a novel multi-scale bottom-up optimization framework for the carbon-neutral transition planning of the electric power sector, which incorporates hourly time scale and electricity storage to address the reliability and energy balance issues of the future deep-decarbonized power systems. In addition to the technology and capacity information for each facility, the proposed framework also accounts for facility ages, which are usually omitted in the literature, without significantly increasing the computational demand. To reduce the computational requirement of simultaneously optimizing capacity planning and hourly systems operations over the next few decades, a reduced model is developed based on representative days, using a novel approach that integrates multiple machine learning techniques. Based on the optimal transition pathways, hourly operational simulations are conducted for every year within the planning horizon to obtain detailed optimization results. To illustrate the applicability of the proposed framework, a case study for the New York State is presented through two cases, with and without electricity storage capacity expansion. The proposed approach using principal component analysis coupled with K-means outcompetes multiple conventional approaches of using clustering techniques directly. The transition planning results show that the total generation capacity for the case with electricity capacity expansion is 39% higher than the other case, while the latter case has 200% more generation capacity from non-intermittent sources. Detailed hourly operational simulation results indicate that offshore wind, hydro, and utility solar are the primary power sources by 2040 for the case with electricity storage capacity expansion, while hydro, offshore wind, and nuclear are the main electricity sources for the other case

Regression results of mechanism analysis.

Based on empirical analysis of Chinese listed companies from 2010 to 2018, we demonstrate that enterprise digital transformation has a significant impact on improving capacity utilization. Digital transformation is a significant driving force behind enterprise-specific production and innovation. Furthermore, enterprise innovation and enterprise-specialized production play a mediating role in the impact of enterprise digital transformation on capacity utilization. Based on these baseline findings, heterogenous analysis reveals that the impact of digital transformation on capacity utilization is significant for firms with larger capital scales or poor governance and manufacturing abilities. However, it is less important for enterprises with small- and medium-sized capital scales or with more standardized governance, as well as non-manufacturing (service) enterprises.</div

Variable definitions.

Based on empirical analysis of Chinese listed companies from 2010 to 2018, we demonstrate that enterprise digital transformation has a significant impact on improving capacity utilization. Digital transformation is a significant driving force behind enterprise-specific production and innovation. Furthermore, enterprise innovation and enterprise-specialized production play a mediating role in the impact of enterprise digital transformation on capacity utilization. Based on these baseline findings, heterogenous analysis reveals that the impact of digital transformation on capacity utilization is significant for firms with larger capital scales or poor governance and manufacturing abilities. However, it is less important for enterprises with small- and medium-sized capital scales or with more standardized governance, as well as non-manufacturing (service) enterprises.</div

Collecting data.

Based on empirical analysis of Chinese listed companies from 2010 to 2018, we demonstrate that enterprise digital transformation has a significant impact on improving capacity utilization. Digital transformation is a significant driving force behind enterprise-specific production and innovation. Furthermore, enterprise innovation and enterprise-specialized production play a mediating role in the impact of enterprise digital transformation on capacity utilization. Based on these baseline findings, heterogenous analysis reveals that the impact of digital transformation on capacity utilization is significant for firms with larger capital scales or poor governance and manufacturing abilities. However, it is less important for enterprises with small- and medium-sized capital scales or with more standardized governance, as well as non-manufacturing (service) enterprises.</div

Descriptive statistics results.

Based on empirical analysis of Chinese listed companies from 2010 to 2018, we demonstrate that enterprise digital transformation has a significant impact on improving capacity utilization. Digital transformation is a significant driving force behind enterprise-specific production and innovation. Furthermore, enterprise innovation and enterprise-specialized production play a mediating role in the impact of enterprise digital transformation on capacity utilization. Based on these baseline findings, heterogenous analysis reveals that the impact of digital transformation on capacity utilization is significant for firms with larger capital scales or poor governance and manufacturing abilities. However, it is less important for enterprises with small- and medium-sized capital scales or with more standardized governance, as well as non-manufacturing (service) enterprises.</div

Heterogeneity analysis results.

Based on empirical analysis of Chinese listed companies from 2010 to 2018, we demonstrate that enterprise digital transformation has a significant impact on improving capacity utilization. Digital transformation is a significant driving force behind enterprise-specific production and innovation. Furthermore, enterprise innovation and enterprise-specialized production play a mediating role in the impact of enterprise digital transformation on capacity utilization. Based on these baseline findings, heterogenous analysis reveals that the impact of digital transformation on capacity utilization is significant for firms with larger capital scales or poor governance and manufacturing abilities. However, it is less important for enterprises with small- and medium-sized capital scales or with more standardized governance, as well as non-manufacturing (service) enterprises.</div

Results of robustness tests.

Based on empirical analysis of Chinese listed companies from 2010 to 2018, we demonstrate that enterprise digital transformation has a significant impact on improving capacity utilization. Digital transformation is a significant driving force behind enterprise-specific production and innovation. Furthermore, enterprise innovation and enterprise-specialized production play a mediating role in the impact of enterprise digital transformation on capacity utilization. Based on these baseline findings, heterogenous analysis reveals that the impact of digital transformation on capacity utilization is significant for firms with larger capital scales or poor governance and manufacturing abilities. However, it is less important for enterprises with small- and medium-sized capital scales or with more standardized governance, as well as non-manufacturing (service) enterprises.</div

Benchmark regression results.

Based on empirical analysis of Chinese listed companies from 2010 to 2018, we demonstrate that enterprise digital transformation has a significant impact on improving capacity utilization. Digital transformation is a significant driving force behind enterprise-specific production and innovation. Furthermore, enterprise innovation and enterprise-specialized production play a mediating role in the impact of enterprise digital transformation on capacity utilization. Based on these baseline findings, heterogenous analysis reveals that the impact of digital transformation on capacity utilization is significant for firms with larger capital scales or poor governance and manufacturing abilities. However, it is less important for enterprises with small- and medium-sized capital scales or with more standardized governance, as well as non-manufacturing (service) enterprises.</div

Cell Chemistry of Sodium–Oxygen Batteries with Various Nonaqueous Electrolytes

Development of the nonaqueous Na–O₂ battery with a high electrical energy efficiency requires the electrolyte stable against attack of highly oxidative species such as nucleophilic anion O₂^•–. A combined evaluation method was used to investigate the Na–O₂ cell chemistry with various solvents, including ethylene carbonate/propylene carbonate (EC/PC)-, <i>N</i>-methyl-<i>N</i>-propylpiperidinium bis­(trifluoromethansulfonyl) imide (PP13TFSI)-, and tetraethylene glycol dimethyl ether (TEGDME)-based electrolytes. It is found that the TEGDME-based electrolytes have the best stability with the predominant yield of NaO₂ upon discharge and the largest electrical energy efficiency (approaching 90%). Both EC/PC- and PP13TFSI-based electrolytes severely decompose during discharge, forming a large amount of side products. Analysis of the acid dissociation constant (p<i>K</i>_a) of these electrolyte solvents reveals that the TEGDME has the relatively large value of p<i>K</i>_a, which correlates with good stability of the electrolyte and high round-trip energy efficiency of the battery

Table_1_Comprehensive bioinformatics analysis reveals common potential mechanisms, progression markers, and immune cells of coronary virus disease 2019 and atrial fibrillation.DOCX

ObjectivesAtrial fibrillation (AF) is the most common arrhythmia in coronary virus disease 2019 (COVID-19) patients, especially in severe patients. A history of AF can exacerbate COVID-19 symptoms. COVID-19 Patients with new-onset AF have prolonged hospital stays and increased death risk. However, the mechanisms and targets of the interaction between COVID-19 and AF have not been elucidated.Materials and methodsWe used a series of bioinformatics analyses to understand biological pathways, protein-protein interaction (PPI) networks, gene regulatory networks (GRNs), and protein-chemical interactions between COVID-19 and AF and constructed an AF-related gene signature to assess COVID-19 severity and prognosis.ResultsWe found folate and one-carbon metabolism, calcium regulation, and TFG-β signaling pathway as potential mechanisms linking COVID-19 and AF, which may be involved in alterations in neutrophil metabolism, inflammation, and endothelial cell function. We identified hug genes and found that NF-κb, hsa-miR-1-3p, hsa-miR-124-3p, valproic acid, and quercetin may be key regulatory molecules. We constructed a 3-gene signature consisting of ARG1, GIMAP7, and RFX2 models for the assessment of COVID-19 severity and prognosis, and found that they are associated with neutrophils, T cells, and hematopoietic stem cells, respectively.ConclusionOur study reveals a dysregulation of metabolism, inflammation, and immunity between COVID-19 and AF, and identified several therapeutic targets and progression markers. We hope that the results will reveal important insights into the complex interactions between COVID-19 and AF that will drive novel drug development and help in severity assessment.</p