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The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is due to the mitigation measures that include lockdown and Work from Home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-COVID-19 business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result to a net benefit for flexible stakeholders, such as consumer on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

This study is an effort to cover and interconnect multiple aspects of the fabrication of the yttria-stabilized zirconia (YSZ) from powder preparation to a solid electrolyte suitable for utilization in solid oxide cells. Thus, a series of YSZ electrolytes was prepared, differing in the content of the Y2O3 dopant and in the method of preparation. Combustion synthesis along with the thermal decomposition of precursors was used for YSZ powder synthesis with a dopant content of 8 to 18 mol.%. Post-synthesis treatment of the powder was necessary for achieving satisfactory quality of the subsequent sintering step. The morphology analyses of the YSZ powders and sintered electrolytes produced proved that small particles with a uniform size distribution are essential for obtaining a dense electrolyte. Furthermore, the conductivity of YSZ electrolytes with different Y2O3 contents was examined in the temperature range of 400 to 800 °C. The lowest conductivity was found for the sample with the highest Y2O3 content. The obtained results enable the preparation methods, YSZ powder morphology, and composition to be connected to the mechanical and electrochemical properties of the YSZ electrolyte. Thus, this study links every step of YSZ electrolyte fabrication, which has not been sufficiently clearly described until now.

Although both aggregate and per capita energy consumption in Bangladesh is increasing rapidly, its per capita consumption is still one of the lowest in the world. Bangladesh gradually shifted from petroleum-based energy to domestically sourced natural-gas-based energy sources, which are predicted to run out within next two decades. The present study first identified the determinants of aggregate commercial energy and its three major components of oil, natural gas, and coal demand for Bangladesh using a simultaneous equations framework on an annual database covering a period of 47 years (1972–2018). Next, the study forecast future demand for aggregate commercial energy and its three major components for the period of 2019–2038 under the business-as-usual and ongoing COVID-19 pandemic scenarios with some assumptions. As part of a sensitivity analysis, based on past trends, we also hypothesized four alternative GDP and population growth scenarios and forecast corresponding changes in total energy demand forecast. The results revealed that while GDP and lagged energy demand are the major drivers of energy demand in the country, we did not see strong effects of own- and cross-price elasticities of energy sources, which we attributed to three reasons: subsidized low energy prices, time and cost required to switch between different energy-mix technologies, and suppressed energy demand. The aggregate energy demand is expected to increase by 400% by the end of the forecasting period in 2038 from its existing level in 2018 under the business-as-usual scenario, whereas the effect of COVID-19 could suppress it down to 300%. Under the business-as-usual scenario, the highest increase will occur for coal (3.94-fold), followed by gas (2.64-fold) and oil (2.37-fold). The COVID-19 pandemic will suppress the future demand of all energy sources at variable rates. The ex ante forecasting errors were small, varying within the range of 3.6–3.7% of forecast values. Sensitivity analysis of changes in GDP and population growth rates showed that forecast total energy demand will increase gradually from 3.58% in 2019 to 8.79% by 2038 from original forecast values. Policy recommendations include capacity building of commercial energy sources while ensuring the safety and sustainability of newly proposed coal and nuclear power installations, removing inefficiency of production and distribution of energy and its services, shifting towards renewable and green energy sources (e.g., solar power), and redesigning subsidy policies with market-based approaches.

The challenges of the world economy and their societies, after the outbreak of the COVID-19 pandemic have led policy-makers to seek for effective solutions. This paper examines the oil price volatility response to the COVID-19 pandemic and stock market volatility using daily data. A general econometric panel model is applied to investigate the relationship between COVID-19 infection and death announcements with oil price volatility. The paper uses data from six geographical zones, Europe, Africa, Asia, North America, South America, and Oceania for the period 21 January 2020 until 13 May 2021 and the empirical findings show that COVID-19 deaths affected oil volatility significantly. This conclusion is confirmed by a second stage analysis applied separately for each geographical area. The only geographical area where the existence of correlation is not confirmed between the rate of increase in deaths and the volatility of the price of crude oil is Asia. The conclusions of this study clearly suggest that COVID-19 is a new risk component on top of economic and market uncertainty that affects oil prices and volatility. Overall, our results are useful for policy-makers, especially in the case of a new wave of infection and deaths in the future.

Oxygen electrode has a crucial impact on the performance of reversible solid oxide cells (RSOC), especially in solid oxide electrolysis cell (SOEC) mode. Herein, Sm0.5Sr0.5Co0.5O3-δ@Sm0.2Ce0.8O1.9 (5SSC@5SDC) composite material has been fabricated by the in-situ synthesis method and applied as the oxygen electrode for RSOCs with scandium stabilized zirconia (SSZ) electrolyte. The phase structures, thermal expansion coefficients, and micromorphologies of 5SSC@5SDC have all been further analyzed and discussed. 5SSC@5SDC is composed of a skeleton with large SDC particles in the diameter range of 200~300 nm and many fine SSC nanoparticles coated on the skeleton. Thanks to the special microstructure of 5SSC@5SDC, the electrolyte-supported RSOC with SSC@SDC oxygen electrode shows a polarization resistance of only 0.69 Ω·cm2 and a peak power density of 0.49 W·cm−2 at 800 °C with hydrogen as the fuel in solid oxide fuel cell (SOFC) mode. In addition, the electrolysis current density of RSOC with SSC@SDC can reach 0.40 A·cm−2 at 1.30 V in SOEC model, being much higher than that with the SSC-SDC (SSC and SDC composite prepared by physical mixing). RSOC with 5SSC@5SDC shows an improved stability in SOEC model comparing with that with SSC-SDC. The improved performance indicates that 5SSC@5SDC prepared by the in-situ synthesis may be a promising candidate for RSOC oxygen electrode.

The dual-ion “Saltwater Battery” based on aqueous electrolyte containing sodium ions and lithium ions is believed to be one of the safest and environmentally friendliest battery technologies. The anode consists of sodium titanium phosphate, whereas the cathode is spinel lithium manganese oxide. It has been reported that both materials can intercalate sodium and lithium ions depending on their availability in the electrolyte. This study aims to identify the dominant active species in “Saltwater Batteries” with dual-ion electrolyte. Therefore, cyclic voltammetry of single electrodes as well as full cell measurements are performed with electrolyte containing lithium sulfate, sodium sulfate or a mixture. Moreover, the study is complemented by ion analysis of the electrolyte, as well as X-ray diffraction of fresh and cycled electrodes at different states of charge. The results show that the cathode only (de)intercalates lithium ions, revealing that Li is the dominant active cathode species. The anode can react with both ions and undergoes a formation reaction accompanied by partial dissolution of sodium titanium phosphate. Nevertheless, cyclic voltammetry and full cell measurements indicate that lithium is also the dominant active species on the anode side. In conclusion, the dual-ion battery is dominated by lithium and shows a superior performance when removing sodium from electrolyte.

Vehicle-to-grid services make use of the aggregated capacity available from a fleet of vehicles to participate in energy markets, help integrate renewable energy in the grid and balance energy use. In this paper, the critical components of such a service are described in the context of a commercial service that is currently under development. Key among these components is the prediction of available capacity at a future time. In this paper, we extend a previous work that used a deep learning recurrent neural network for this task to include online machine learning, which enables the network to continually refine its predictions based on observed behaviour. The coronavirus pandemic that was declared in 2020 resulted in closures of the university and substantial changes to the behaviour of the university fleet. In this work, the impact of this change in vehicles usage was used to test the predictions of a network initially trained using vehicle trip data from 2019 with and without online machine learning. It is shown that prediction error is significantly reduced using online machine learning, and it is concluded that a similar capability will be of critical importance for a commercial service such as the one described in this paper.

Over 5.5 million tons of plastic waste are generated globally from the research sectors. A university laboratory, e.g., pathology, can generate 250 tons of clinical plastic waste annually. The UK National Health Service (NHS) generates 133 kilotons (kt) of clinical plastic waste annually. Healthcare facilities in the US generate 1.7 million tons of clinical plastic waste annually. In addition, 95% of the clinical plastics are single-use plastics derived from fossil resources, i.e., crude oils. These single-use clinical plastic wastes are incinerated, contributing to global warming, or go to the landfill, contributing to resource depletion. Plastic leakage is a major threat to the environment. This linear plastics economy model, take-make-dispose, must be replaced by a circular plastics economy, i.e., sort plastic wastes, wash, decontaminate, recover materials, blend with bio-based compounds as necessary and circulate recyclate plastics, for holistic systemic sustainability. While there are multi-faceted environmental drivers for a circular plastics economy, there are many uncertainties in the economic attributes, electricity price, labor cost and chemical cost being the primary ones influencing the cost of production of secondary or recyclate plastics, requiring government and policy support, such as a gate fee on plastic waste by the generators to the recyclers. An essential macroeconomic condition for techno-economically (or micro-economically) feasible plastic waste recycling is low oil and gas prices that influence the recyclate plastics and electricity prices. It is essential to de-fossilize the economy by decoupling renewable electricity generation from natural gas consumption and fossil-independent biopolymer productions displacing fossil-derived plastics to stimulate the circular economy. This study shows a comprehensive and robust technoeconomic analysis of mechanical recycling of clinical plastic wastes into secondary plastics recovery.