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This paper deals with the medium between two reconfigurable sensor nodes characterized by radio interfaces that support multiple channels for exchanging real-time messages under energy constraints. These constraints are violated if the consumed energy in transmission is higher than the remaining quantity of energy. A reconfiguration, i.e., any addition or removal of tasks in devices and consequently of messages on the medium, can cause the violation of real-time or energy constraints at run time. To achieve a feasible scheduling in time (i.e., message deadlines will be respected) and energy (i.e., there is available energy) on the medium, we propose new dynamic solutions: Balance, Dilute, and a Combination of them to manage any addition or removal of messages. The proposed approach utilizes the energy harvesting techniques and the PowerControl algorithm to reduce the nonharvested consumed energy. The proposed strategies achieve significant improvement over existing methods and provide the highest percentage of adding messages, with a lower average in response time and energy consumption. They reach a percentage of success in adding the highest priority messages while meeting deadlines up to 85%.

The potential of renewable energy should be fully exploited in the transportation sector to achieve a cleaner production. Therefore, this paper proposes an on-grid hybrid hydrogen refueling and battery swapping station powered by wind energy. This novel concept can promote the development of low-carbon emission vehicles including hydrogen-based vehicle and battery electric vehicle. During the daily operation of the station, the multiple uncertainties may lead to a higher operational cost. To address this problem, a hybrid stochastic/distributionally robust optimization method is proposed to handle different uncertainties for the energy management problem. The first type of uncertainties can be depicted by a certain distribution, i.e. electricity price and wind power, which is processed by a stochastic optimization method. The second type of uncertainties is associated with human behaviors and is difficult to find its probability distribution, i.e. the hydrogen demand of hydrogen-based vehicles, so the second type is processed by a distributionally robust optimization method. The overall objective is to minimize the total operational cost of the station, which also considers the battery swapping station overstock punishment. Because a reasonable battery swapping scheduling can reduce the waiting time of users and operational cost of the station. The results indicate that the proposed method can effectively address the conservatism of solutions as its total operational cost is 4.4% lower than that of the hybrid stochastic/robust optimization method under a high confidence level.

Nanosecond-level transient electromagnetic disturbance (TED), including very fast transient overvoltage caused by operation of disconnectors, high-altitude electromagnetic pulse, and many other fast transients may interfere or even damage the electrical equipment. As one of the main overvoltage protective equipment, the protective performance of metal-oxide surge arresters (MOAs) under nanosecond-level TED should be investigated and then compared with that under microsecond-level TED, especially the lightning impulse. Based on a testing platform containing a 400-kV pulse generator with adjustable rise time from 5 to 100 ns, the behaviors of nonlinearity, fast impulse response, and converting impedances of three types of 10-kV MOAs under TED with different rise time were explored experimentally in this article. The peak residual voltages of 10-kV MOAs under TED with the rise time of 5 ns at 5 kA are 50.2–60.7% higher than those under the lightning impulse. The rise time of TED has significant influence on the peak voltage and impedance converting behaviors of MOAs. A circuit model of 10-kV MOAs under nanosecond-level TED is built and validated by experimental results, which can be applied in insulation coordination and design of protective devices against TED.

In order to achieve the Paris Agreement goals of keeping the temperature rise well below 2 °C or even 1.5 °C, all countries would need to make fair and ambitious contributions to reducing emissions. A vast majority of countries have adopted reduction targets by 2030 in their Nationally Determined Contributions (NDCs). There are many alternative ways to analyze the fairness of national mitigation contributions. This article uses a model framework based on six equity principles of effort-sharing, to allocate countries’ reduction targets under global emissions scenarios consistent with meeting the Paris climate goals. It further compares these allocations with the NDCs. The analysis shows that most countries need to adopt more ambitious reduction targets by 2030 to meet 2 °C, and even more for 1.5 °C. In the context of 2 °C, the NDCs of the United States of America and the European Union lack ambition with respect to the approaches that emphasize responsibility; China's NDC projection falls short of satisfying any approach in 2030. In the context of 1.5 °C, only India, by implementing its most ambitious efforts by 2030, could be in line with most equity principles. For most countries, the NDCs would use most of their allowed emissions space for the entire 21 st century by 2030, posing a major challenge to transform to a pathway consistent with their fair contributions in the long-term.

Residential energy consumption in China increased dramatically over the period of 2002-2010. In this paper, we undertake a decomposition analysis of changes in energy use by Chinese households for five energy-using activities: space heating/cooling, cooking, lighting and electric appliances. We investigate to what extent changes in energy use are due to changes from appliances and to change in floor space, population and energy mix. Our decomposition analysis is based on the logarithmic mean Divisia index technique using data from the China statistical yearbook and China energy statistical yearbook in the period of 2002-2010. According to our results, the increase in energy-using appliances is the biggest contributor to the increase of residential energy consumption during 2002-2010 but the effect declines over time, due to energy efficiency improvements in those appliances. The second most important contributor is floor space per capita, which increased with 28%. Of the four factors, population is the most stable factor and energy mix is the least important factor. We predicted electricity use, with the help of regression-based predictions for ownership of appliances and the energy efficiency of appliances. We found that electricity use will continue to rise despite a gradual saturation of demand

Multi-layer microgrids cluster (MLMC) has been formed in some certain areas with microgrids (MGs) merging into smart distributed system. It relies on an energy management system (EMS) to manage and schedule the optimal power flow among MGs and public power system in a stable and balanced way. Compared with EMS for single MG, energy management optimization for MLMC is much more complex that the interactions with other MGs require to be considered. In this paper a multi-time-scales EMS is proposed to minimize the total operating cost of MLMC. It consists of two parts: day-ahead static scheduling and real-time dynamic compensation. The day-ahead scheduling is an economic optimization based on the forecasting data within 24 hours. The real-time dynamic compensation is presented to compensate scheduling signal based on the real-time operating information, which is caused by the predictive error. Furthermore, charge-discharge protections of energy storage system (ESS) for each single MG are taken into account. A general-structure MLMG is studied as an example, and this problem is solved by mixed integer linear programming (MILP). The effectiveness of proposed EMS is verified in the MG Research Laboratory in Aalborg University, Denmark. The hardware-in-loop simulation results show the proposed EMS has a good convergence and performance in grid-connected mode.

The technological development of preparing bio-oil from low-temperature hydrothermal conversion of agricultural and forestry waste has positive significance for alleviating the shortage of oil energy supply and reducing environmental pollution. This paper selects typical oxides (Al2O3, CeO2, MgO, SiO2, TiO2, and ZnO) as catalysts to set up a low-temperature (220 °C) hydrothermal conversion process of cotton stalk containing pretreatment processes including chopping. For moderate amplification estimation, lab-scale experimental data is used as a benchmark for calculation, and the functional unit for this study is set to be a 1 kg bio-oil product. The results suggest that the cerium dioxide-involved process with the highest bio-oil yield and highest synthetic consumption, and the silica-involved process with the lowest bio-oil yield, caused the highest environmental impact, resulting in greenhouse gas (GHG) emissions of 67.729 kg CO2e/kg and 60.001 kg CO2e/kg, respectively. It indicates that catalysts need to consider the balance between synthetic consumption and catalytic performance. Magnifying lab-scale data to an industrial scale using scale-up frameworks introduces a low model uncertainty, as the practical value had little effect on the overall evaluation results. However, existing equipment data should be used to reduce the uncertainty of the model itself. The environmental sustainability of bio-oil production by low-temperature hydrothermal liquefaction still needs to be improved, especially by catalyst recovery and bio-oil yield improvement.