A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late March. As the U.S. begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing U.S. wholesale electricity markets with COVID-19 case, weather, cellular location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we uncover a significant reduction in electricity consumption across that is strongly correlated with the rise in the number of COVID-19 cases, degree of social distancing, and level of commercial activity.Comment: This paper has been accepted for publication by Joule. The manuscript can also be accessed from EnerarXiv: http://www.enerarxiv.org/page/thesis.html?id=198

Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality

The transition towards carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation. The transition to a carbon-neutral electric grid poses significant challenges to conventional paradigms of modern grid planning and operation. Much of the challenge arises from the scale of the decision making and the uncertainty associated with the energy supply and demand. Artificial Intelligence (AI) could potentially have a transformative impact on accelerating the speed and scale of carbon-neutral transition, as many decision making processes in the power grid can be cast as classic, though challenging, machine learning tasks. We point out that to amplify AI's impact on carbon-neutral transition of the electric energy systems, the AI algorithms originally developed for other applications should be tailored in three layers of technology, markets, and policy.Comment: To be published in Patterns (Cell Press

EXPLORING THE IMPACTS OF COVID-19 PANDEMIC ON OMAN'S ELECTRICITY SECTOR

This article reviews the recent trends of Oman's electricity sector before the COVID-19 pandemic outbreak. The impacts of the pandemic on the Main Interconnected System (MIS) of Oman were analyzed using hourly load data. The analysis shows that the MIS demand declined as a result of the decrease in economic activities during the lockdown. In addition, the MIS demand experienced temporal and geographical variations: the former is demonstrated by a shift in peak demand hours, while the latter is represented by a reduction in Muscat's urban areas' load compared with those of other areas

The COVID-19 pandemic's impact on U.S. electricity demand and supply: an early view from the data

After the onset of the recent COVID-19 pandemic, a number of studies reported on possible changes in electricity consumption trends. The overall theme of these reports was that ``electricity use has decreased during the pandemic, but the power grid is still reliable''---mostly due to reduced economic activity. In this paper we analyze electricity data upto end of May 2020, examining both electricity demand and variables that can indicate stress on the power grid, such as peak demand and demand ramp-rate. We limit this study to three states in the USA: New York, California, and Florida. The results indicate that the effect of the pandemic on electricity demand is not a simple reduction from comparable time frames, and there are noticeable differences among regions. The variables that can indicate stress on the grid also conveyed mixed messages: some indicate an increase in stress, some indicate a decrease, and some do not indicate any clear difference. A positive message is that some of the changes that were observed around the time stay-at-home orders were issued appeared to revert back by May 2020. A key challenge in ascribing any observed change to the pandemic is correcting for weather. We provide a weather-correction method, apply it to a small city-wide area, and discuss the implications of the estimated changes in demand. The weather correction exercise underscored that weather-correction is as challenging as it is important

The Impact of the COVID-19 Pandemic and Russia's Invasion of Ukraine on Electricity Demand: A Case Study of Southern European Countries.

Recent global events, particularly the COVID-19 pandemic and Russia's invasion of Ukraine, have been found to dramatically influence electricity consumption patterns, especially within European nations. In this study, the impacts of these consecutive crises on the electricity demand of selected EU countries: Bulgaria, Greece, Romania, and different regions of Italy were examined. The Ordinary Least Squares regression model was utilized to analyze hourly load data and air temperatures. The findings indicate that the 2020 COVID-19 lockdown reduced consumption uniformly across the studied regions, while the 2022 energy crisis led to varied impacts, with distinct patterns being exhibited in regions within Italy. Remarkably, resilience was shown by Bulgaria during both crises, whereas pronounced effects were experienced in Southern Italy in both periods. The importance of understanding these shifts for effective policymaking and future resilience planning is emphasized in this study. A limitation of the analysis is found in its sole use of aggregate power load data and its generalized modelling. It is suspected that clearer results could be obtained in each case if analyzed the electricity consumption data separated by sectors

Economic Development and Renewable Energy Nexus in Morocco: Co-Integration and Causality

Purpose The present study explores the causal relationships between economic development, renewable energy consumption, nonrenewable energy consumption, and CO2 emissions in the context of Morocco. Methods The panel unit root test, Auto Regressive Distributed Lag (ARDL), and bounds test were used to assess the co-integration of the variables in the study and the long-run relationship between them. It employs the Granger causality test using a vector error correction model to determine the existence and direction of causality among the variables. It uses Morocco's annual statistical data from 1990 through 2019. Results The co-integration of the variables in the study was confirmed, implying that a long-run relationship exists between them. The causality test results suggest that a bidirectional causality exists between renewable energy consumption and economic development, which validates the feedback hypothesis of the mutual link between renewable energy consumption and economic development. Implications These findings suggest that Morocco's economic development is critical in providing the required resources for sustainable development. It also implies that boosting renewable energy utilization would enhance Morocco's economic development and limit environmental degradation

A Wavelet Analysis of the Bitcoin-Hashrate Nexus Accounting for the Effects of Energy Commodities

This study investigates the relationship between the growth rates of Bitcoin and Bitcoin hashrate while controlling for the effect of energy commodities, specifically two-month futures on Brent crude oil, coal, and natural gas. Based on daily data from January 2013 until December 2022, we utilize the wavelet methodology to analyze dynamics both in time and frequency. Building on the previous work of Rehman and Kang (2021), this study extends the sample period and improves the replicability of their findings. Controlling for the effect of energy commodities, our analysis reveals several interesting results, highlighting the temporal and dynamic nature of these relationships. Our most significant observation that was discovered in both bi- and multivariate forms of the wavelet methodology is the low-frequency in-phase coherence between bitcoin's returns and hashrate growth rates, which persists from the beginning of 2020 until the end of our sample period in 2023, with hashrate growth rates leading bitcoin returns. These findings suggest that the link between the returns on bitcoin and hashrate growth rates while considering the impact of the energy commodities is complex and context-dependent, and further research is needed to fully understand the underlying mechanisms driving these relationships. Our study contributes to the existing literature on the Bitcoin-hashrate nexus by providing a more comprehensive analysis that accounts for the dynamic nature of these relationships, and by improving the replicability of previous research

Economic and Social Consequences of the COVID-19 Pandemic in Energy Sector

The purpose of the Special Issue was to collect the results of research and experience on the consequences of the COVID-19 pandemic for the energy sector and the energy market, broadly understood, that were visible after a year. In particular, the impact of COVID-19 on the energy sector in the EU, including Poland, and the US was examined. The topics concerned various issues, e.g., the situation of energy companies, including those listed on the stock exchange, mining companies, and those dealing with renewable energy. The topics related to the development of electromobility, managerial competences, energy expenditure of local government units, sustainable development of energy, and energy poverty during a pandemic were also discussed

A Wavelet Analysis of the Bitcoin- Hashrate Nexus Accounting for the Effects of Energy Commodities

This study investigates the relationship between the growth rates of Bitcoin and Bitcoin hashrate while controlling for the effect of energy commodities, specifically two-month futures on Brent crude oil, coal, and natural gas. Based on daily data from January 2013 until December 2022, we utilize the wavelet methodology to analyze dynamics both in time and frequency. Building on the previous work of Rehman and Kang (2021), this study extends the sample period and improves the replicability of their findings. Controlling for the effect of energy commodities, our analysis reveals several interesting results, highlighting the temporal and dynamic nature of these relationships. Our most significant observation that was discovered in both bi- and multivariate forms of the wavelet methodology is the low-frequency in-phase coherence between bitcoin's returns and hashrate growth rates, which persists from the beginning of 2020 until the end of our sample period in 2023, with hashrate growth rates leading bitcoin returns. These findings suggest that the link between the returns on bitcoin and hashrate growth rates while considering the impact of the energy commodities is complex and context-dependent, and further research is needed to fully understand the underlying mechanisms driving these relationships. Our study contributes to the existing literature on the Bitcoin-hashrate nexus by providing a more comprehensive analysis that accounts for the dynamic nature of these relationships, and by improving the replicability of previous research

An Analytic and Systemic View of the Digital Transformation of Healthcare

Industry 4.0 represents a digital revolution that is driven by technologies that blur the lines between the physical and digital worlds. Industry 4.0, the latest industrial revolution, is poised to have a profound impact on all aspects of society. In order to understand how the healthcare industry is being transformed by the convergence of the physical and digital realms, a systems perspective is taken in this study. Two research questions are addressed regarding the opportunities and interventions that can be provided by both analytical and systems conceptions of digital transformation. I use a systemic literature review approach to address the research questions. A sample of studies between 2000 and 2022 is analyzed. Existing studies mostly examine the effects of new digital technologies on healthcare providers. However, digital transformation also presents significant challenges, such as data privacy, ethical concerns related to AI-based automated decision-making, and equity issues related to e-health. Solutions to major challenges at both micro and macro levels can be derived from the existing theories and tools of systems thinking. For instance, systems thinking\u27s continuous learning and adaptation capabilities can be useful for healthcare organizations to develop the required digital capabilities. Furthermore, the interconnectedness of subsystems and stakeholders in systems thinking can be combined with digital twin technology to investigate the dynamic interactions among key stakeholders, leading to the development of new regulatory policies