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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.

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.

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.

The reclosing time of a transmission line has a distinct influence on system stability. A new method of calculating the optimal reclosing time based on the transient energy function (TEF) is presented in this paper. It has been shown that an optimal reclosing time can be used to set the autoreclosing device so that the system stability or power-transfer capability can be significantly improved. Though power-system TEF is based on classical models, the results from simulation studies of a real system (46 generators and 230 buses) with complex models show that the optimal reclosing time calculated using TEF can be used effectively.

The reclosing time of a transmission line has a distinct influence on system stability. A new method of calculating the optimal reclosing time based on the transient energy function (TEF) is presented in this paper. It has been shown that an optimal reclosing time can be used to set the autoreclosing device so that the system stability or power-transfer capability can be significantly improved. Though power-system TEF is based on classical models, the results from simulation studies of a real system (46 generators and 230 buses) with complex models show that the optimal reclosing time calculated using TEF can be used effectively.

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.

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.