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Starting in early 2020, the novel coronavirus disease (COVID-19) severely attached the U.S., causing substantial changes in the operations of bulk power systems and electricity markets. In this paper, we develop a data-driven analysis to substantiate the pandemic's impacts from the perspectives of power system security, electric power generation, electric power demand and electricity prices. Our results suggest that both electric power demand and electricity prices have discernibly dropped during the COVID-19 pandemic. Geographically diverse impacts are observed and quantified, while the bulk power systems and markets in the northeast region are most severely affected. All the data sources, assessment criteria, and analysis codes reported in this paper are available on a GitHub repository. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to partial or total lockdowns in several countries during the first half of 2020, which in turn caused a depressed electricity demand. In Great Britain (GB), this low demand combined with large renewable output at times, created conditions that were not expected until renewable capacity increases to meet emissions targets in coming years. The GB system experienced periods of very high instantaneous penetration of non-synchronous renewables, compromising system stability due to the lack of inertia in the grid. In this paper, a detailed analysis of the consequences of the lockdown on the GB electricity system is provided, focusing on the ancillary services procured to guarantee stability. Ancillary-services costs increased by £200m in the months of May to July 2020 compared to the same period in 2019 (a threefold increase), highlighting the importance of ancillary services in low-carbon systems. Furthermore, a frequency-secured scheduling model is used in the present paper to showcase the future trends that GB is expected to experience, as penetration of renewables increases on the road to net-zero emissions by 2050. Several sensitivities are considered, demonstrating that the share of total operating costs represented by ancillary services could reach 35%. • The GB electricity grid experienced an operational challenge due to low inertia. • Cost of ancillary services during lockdown was 3 times higher than in 2019. • This cost could reach up to 35% of total operating costs by 2030. • Fast frequency response shows to be very beneficial in the future system. [ABSTRACT FROM AUTHOR]

• A transfer learning framework and a CNN model are proposed for the black swan small-sample events. • (BEST-L). • Experiments show that the BEST-L outperforms traditional methods. • The transfer learning method can effectively use small samples to improve the model's generalization. • performance. • The BEST-L is tested for the load forecasting case with the COVID-19 scenario in Central China. The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems. [ABSTRACT FROM AUTHOR]

• COVID-19 modified when, where, and how we use electricity. • The impact of COVID-19 varied across spatiotemporal scales. • There are multiple offsetting effects that can mask the impact of COVID-19 on electricity demand. Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implications for electricity sector investment and operating decisions that maintain reserve margins and provide grid reliability. This study reveals that understanding this uncertainty requires an understanding of the underlying drivers at the customer-class scale. This paper utilizes three datasets to compare the impacts of COVID-19 on electricity consumption across a range of spatiotemporal and customer scales. At the utility/customer-class scale, COVID-19-induced shutdowns in the spring of 2020 shifted weekday residential load profiles to resemble weekend profiles from previous years. Total commercial loads declined, but the commercial diurnal load profile was unchanged. With only total loads available at the balancing authority scale, the apparent impact of COVID-19 was smaller during the summer due in part to phased re-opening and spatial variability in re-opening, but there were still clear variations once total loads were broken down zonally. Monthly data at the state scale showed an increase in state-level residential electricity sales, a decrease in commercial sales, and a small net decrease in total sales in most states from April-August 2020. Analyses that focus on total load or a single scale may miss important changes that become apparent when the load is broken down regionally or by customer class. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel genetic algorithm-based seasonal fractional grey model is proposed. • Monthly electricity generation is forecasted since the Covid-19 lockdown started. • Seasonal grey and machine learning models are used for forecasting. • The selected countries are France, Germany, Spain, Turkey, and the UK. • The share of renewables in total electricity generation is forecasted. Balances in the energy sector have changed since the implementation of the Covid-19 pandemic lockdown in Europe. This paper analyses how the lockdown affected electricity generation in European countries and how it will reshape future energy generation. Monthly electricity generation from total renewables and non-renewables in France, Germany, Spain, Turkey, and the UK from January 2017 to September 2020 were evaluated and compared. Four seasonal grey prediction models and three machine learning methods were used for forecasting; the quarterly results are presented to the end of 2021. Additionally, the share of electricity generation from renewables in total electricity generation from 2017 to 2021 for the selected countries was compared. Electricity generation from total non-renewables in the second quarter of 2020 for France, Germany, Spain, and the UK decreased by 21%–25% compared to the same period of 2019; the decline in Turkey was approximately 11%. Additionally, electricity generation from non-renewables in the third quarter of 2020 for all countries, except Turkey, decreased compared to the same period of the previous year. All grey prediction models and support vector machine method forecast that the share of renewables in total electricity generation will increase continuously in France, Germany, Spain, and the UK to the end of 2021. The forecasting methods provided by this study open new avenues for research on the impact of the Covid-19 pandemic on the future of the energy sector. [ABSTRACT FROM AUTHOR]

[Display omitted] • Clean energy transitions based on the circular economy (CE) are analysed for a town in China. • This could reduce energy use, CO 2 emissions and PM 2.5 emissions compared to the BAU scenario. • COVID pandemic is a new opportunity to introduce circular economy measures. • Circular economy measures could accelerate clean energy transitions. This paper models the energy and emissions scenarios for a circular economy based clean energy transitions in a 140,000-population town in China, taking into account the new situation encountered by the COVID-19 pandemic. The modelled scenarios propose new clean energy transition roadmaps towards a sustainable urban system through the implementation of circular economy strategies. This is represented by the cascading use of industrial excess heat to form symbiosis between factories and to cover the growing building heat demand, as well as by the electrification of the transport sector and reusing the batteries for a second life as energy storage devices. The results show that for a circular economy scenario, during 2020–2040, an accumulated saving of 7.1 Mtoe final energy use (34%), a decline in 14.5 Mt CO 2 emissions (40%) and 592 t PM 2.5 emissions (43%) could be achieved compared with the business-as-usual scenario. The outcomes of the circular economy strategies are at least 7% better than the new policy scenario which simply has energy efficiency improvements. The outbreak of the COVID-19 tremendously impacts the socio-economic activities in the town. If taking the pandemic as an opportunity to enhance the circular economy, by 2040, compared with the scenario without introducing circular economy measures, the extra avoided final energy use, CO 2 emissions and PM 2.5 emissions could be 1.6 Mtoe (8%), 3.8 Mt (11%) and 229 t (17%) respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Pre-COVID-19 negative trends in coal markets are accelerated by pandemic. • Design of COVID-19 recovery policies have large influence on long-term coal outlook. • Trade restrictions change dramatically the composition of international coal trade. • COVID-19 driven coal decline scenarios do not comply with global climate targets. • Targeted policies to curb coal emissions are needed in pandemic aftermath. This paper examines medium and long-term perspectives for global steam coal production and trade, considering the potential impacts of the COVID-19 pandemic and plausible recovery scenarios in its aftermath. We use an interdisciplinary approach to develop a range of stylized global coal demand scenarios until 2040 depicting the influence of the COVID-19 pandemic and resulting recovery stimuli. Additional insights are gained by adjusting trade restrictions of key countries within the coal sector model COALMOD-World to resemble plausible post-COVID-19 policy and market dynamics. Results indicate that the COVID-19 pandemic might cause an "L" or "\" shape instead of the hoped for "V" or "U" shaped recovery of the coal industry. Regional effects vary, as the Atlantic market dries out first, causing shifts in trade patterns in the Pacific market. Moreover, announced trade restrictions could change dramatically the composition of the international steam coal trade. However, even significantly reduced coal consumption levels in low-coal post-COVID-19 scenarios would still be too high to comply with the global climate targets. This emphasizes the importance of concentrated policy efforts in the pandemic aftermath to manage a coal decline consistent with global climate targets while bringing just transitions efforts substantially forward, in particular in vulnerable coal-dependent countries and regions. [ABSTRACT FROM AUTHOR]

• The analysis splits customers into clusters based on the consumption behavior. • The disaggregated analysis shows the behavior and resilience of various sectors. • The impact of the COVID-19 measures is evaluated at short and mid terms. • The proposed approach is able to show which sub-sectors have been the vulnerable. Since the emergence of the virus that causes COVID-19 (the SARS-CoV-2) in Wuhan in December 2019, societies all around the world have had to change their normal life patterns due to the restrictions and lockdowns imposed by governments. These changes in life patterns have a direct reflection on energy consumption. Thanks to Smart Grid technologies, specifically to the Advance Metering Infrastructure at secondary distribution network, this impact can be evaluated even at the customer level. Thus, this paper analyzes the consumption behavior and the impact that this crisis has had using Smart Meter data. The proposed approach includes the selection and normalization of features, automatic clustering, the obtaining of the estimated consumption without considering the crisis (at short and mid-terms) and the impact evaluation. The proposed approach has been tested on a case with a real Smart Meter infrastructure from Manzanilla (Huelva, Spain). The results of this use case showed that residential customers have increased their consumption around 15% during full lockdown and 7.5% during the reopening period. In contrast, globally, non-residential customers have decreased their consumption 38% during full lockdown and 14.5% during the reopening period. However, referring to non-residential customers, five different consumption profiles were found with different short-term and mid-term behaviors during the COVID crisis. The different behavior found shows customers who have maintained their normal consumption during the lockdown, others who have reduced it (to a greater or lesser extent) and have not recovered it after the removal of the restrictions, and others who have reduced the consumption but then they recovered it when the restrictions were removed. The metadata of the customers in each behavior cluster found are highly correlated to the restrictions imposed to control the spread of the virus. This study shows evidence about the proposed approach usefulness to analyze the behavior and the impact at customer level during the COVID-19 crisis. [ABSTRACT FROM AUTHOR]

With the coronavirus pandemic wreathing havoc around the world, power industry has been hit hard due to the proposal of lockdown policies. However, the impact of lockdowns and shutdowns on the power system in different regions as well as periods of the pandemic can hardly be reflected on the foundation of current studies. In this paper, a prediction-based analysis method is developed to point out the electricity consumption gap resulted from the pandemic situation. The core of this method is a novel optimized grey prediction model, namely Rolling IMSGM(1,1) (Rolling Mechanism combined with grey model with initial condition as Maclaurin series), which achieves better prediction results in the face of long-term emergencies. A novel initial condition is adopted to track data with various characteristics in the form of higher-order polynomials, which are then determined by intelligent algorithms to realize accurate fitting. Historical power consumption data in China are utilized to carry out the monthly forecasts during COVID-19. Compared with other competitive models' prediction results, the superiority of IMSGM(1,1) are demonstrated. Through analyzing the gap between predicted consumption values and the actual data, it can be found that the impact of the pandemic on electricity varies in different periods, which is related to its severity and the local lockdown policies. This study helps to understand the impact on power industry in the face of such an emergency intuitively so as to respond to possible future events. [ABSTRACT FROM AUTHOR]

When COVID-19 pandemic spread in Europe, governments imposed unprecedented confinement measures with mostly unknown repercussions on contemporary societies. In some cases, a considerable drop in energy consumption was observed, anticipating a scenario of sizable low-cost energy generation, from renewable sources, expected only for years later. In this paper, the impact of governmental restrictions on electrical load, generation and transmission was investigated in 16 European countries. Using the indices provided by the Oxford COVID-19 Government Response Tracker, precise restriction types were found to correlate with the load drop. Then the European grid was analysed to assess how the load drop was balanced by the change in generation and transmission patterns. The same restriction period from 2020 was compared to previous years, accounting for yearly variability with ad hoc statistical technique. As a result, generation was found to be heavily impacted in most countries with significant load drop. Overall, generation from nuclear, and fossil coal and gas sources was reduced, in favour of renewables and, in some countries, fossil gas. Moreover, intermittent renewables generation increased in most countries without indicating an exceptional amount of curtailments. Finally, the European grid helped balance those changes with an increase in both energy exports and imports, with some net exporting countries becoming net importers, notably Germany, and vice versa. Together, these findings show the far reaching implications of the COVID-19 crisis, and contribute to the understanding and planning of higher renewables share scenarios, which will become more prevalent in the battle against climate change. • During COVID-19 spread, energy load dropped significantly in most European countries. • Only some restrictions strongly correlate with load drop. • Generation mix and exchange changed in most countries. • The biggest decrease in generation was from nuclear, fossil coal and oil. • Intermittent renewable generation continued to increase also in 2020. [ABSTRACT FROM AUTHOR]

• Proposing a framework to analyze emission patterns of buses and changes in post-COVID-19. • 2056 buses with 1.5 million ridership and 7589 taxis with 0.2 million trips are used for analysis. • 224 social surveys are collected and show a 56.3% ridership reduction in post-COVID-19. • We find buses cannot be "greener" travel modal than cars if ridership reduces by more than 40%. Emission benefits of transit buses depend on ridership. Declines in ridership caused by COVID-19 leads uncertainty about the emission reduction capacity of buses. This paper provides a method framework for analyzing spatio-temporal emission patterns of buses in combination with real-time ridership and potential emission changes in the post-COVID-19 future. Based on GPS trajectory and Smart Card data of 2056 buses from 278 routes covering 1.5 million ridership in Qingdao, China, spatio-temporal emissions characteristics of buses are studied. 7589 taxis with 0.2 million passengers' trips are used for acquiring private cars' emissions to evaluate the emissions difference between buses and cars. Empirical results show that the average difference between buses and cars with 2 persons can reach up to 117 g/km-person during 7:00–8:59 and 115 g/km-person during 17:00–18:59. However, buses have various emission benefits around the city at different periods. A double increase in emissions during non-rush hours can be observed compared with rush hours. 224 online survey data are used to study the potential ridership reduction trend in post-COVID-19. Results show that 56.3% of respondents would decrease the usage of buses in the post-COVID-19 future. Based on this figure, our analysis shows that per kilometer-person emissions of buses are higher than cars during non-rush hours, however, still lower than cars during rush hours. We conclude that when ridership reduces by more than 40%, buses cannot be "greener" travel modal than cars as before. Finally, several feasible policies are suggested for this potential challenge. Our study provides convincing evidence for understanding the emission patterns of buses, to support better buses investment decisions and promotion on eco-friendly public transport service in the post-COVID-19 future. [ABSTRACT FROM AUTHOR]

• It simulates impact of COVID-19 on energy demand for a building mix at a district. • Confinement scenarios are proposed based on a new district design in Sweden. • Confinement measures increase electricity demand of buildings in the district. • Confinement measures reduce thermal energy demand of buildings in the district. The COVID-19 outbreak is exacerbating uncertainty in energy demand. This paper aims to investigate the impact of the confined measures due to COVID-19 outbreak on energy demand of a building mix in a district. Three levels of confinement for occupant schedules are proposed based on a new district design in Sweden. The Urban Modeling Interface tool is applied to simulate the energy performance of the building mix. The boundary conditions and input parameters are set up according to the Swedish building standards and statistics. The district is at early-design stage, which includes a mix of building functions, i.e. residential buildings, offices, schools and retail shops. By comparing with the base case (normal life without confinement measures), the average delivered electricity demand of the entire district increases in a range of 14.3% to 18.7% under the three confinement scenarios. However, the mean system energy demands (sum of heating, cooling, and domestic hot water) decreases in a range of 7.1% to 12.0%. These two variation nearly cancel each other out, leaving the total energy demand almost unaffected. The result also shows that the delivered electricity demands in all cases have a relatively smooth variation across a year, while the system energy demands follow the principle trends for all the cases, which have peak demands in winter and much lower demands in transit seasons and summer. This study represents a first step in the understanding of the energy performance for community buildings when they confront with this kind of shock. [ABSTRACT FROM AUTHOR]