As the field of modern high-rise buildings advances, the demand for the high-speed elevator increases. The high-speed elevator car vibration directly affects the riding comforts, among which the high-speed elevator car horizontal vibration has the most pronounced effect. Previous researches indicate that the semi-active vibration reduction method is an effective horizontal vibration reduction method for elevator car, which has the characteristics of low energy consumption, suitable for high-speed elevator, while its adaptability is relatively weak. To address the adaptability issue, we proposed a semi-active high-speed elevator car horizontal vibration reduction method based on the improved particle swarm algorithm. To verify the feasibility of the proposed method, it is applied with a magneto-rheological damper in the KLK2-type elevator from Canny Elevator Co, Ltd The horizontal vibration results are compared with ones of the passive vibration reduction method and other algorithm-based vibration reduction methods. The vibration reduction method proposed in this paper is superior to all other mentioned methods in terms of the peak-to-peak value and the A95 value of the vibration acceleration. The horizontal vibration acceleration performance of the HSEC is improved by about 70%.

The optimal planning of electric vehicle charging infrastructure has attracted extensive research interest in recent years. Most of the optimization problems were formulated by assuming that the configurations will be fixed at the optimal solution while overlooking the fact that the charging stations and the electric vehicles are “evolving” over time and have mutual impacts. On the other hand, little attention has been paid to evaluate the performance of the solutions in such a dynamic environment. Motivated by these gaps, this work develops a simulation model that captures the interactions between charging station configurations and electric vehicle population (and the preference of electric vehicles when choosing charging station). This modeling framework is then implemented to evaluate the performance of planned charging infrastructure in providing services to electric vehicles. Two indicators are calculated, i.e., usage rate and rejection rate. The former measures the “waste” due to abundant facilities installed; the latter measures the inadequacy of planned facilities, especially when the electric vehicle population is larger. The simulation results presented in this work validate the model and show the potential of the model not only to evaluate designs but also to be used for optimal planning in subsequent works.

Currently, there are three major barriers toward a greener energy landscape in the future: (a) Curtailed grid integration of energy from renewable sources like wind and solar; (b) The low investment attractiveness of large-scale battery energy storage systems; and, (c) Constraints from the existing electric infrastructure on the development of charging station networks to meet the increasing electrical transportation demands. A new conceptual design of mobile battery energy storage systems has been proposed in recent studies to reduce the curtailment of renewable energy while limiting the public costs of battery energy storage systems. This work designs a logistics system in which electric semi-trucks ship batteries between the battery energy storage system and electric vehicle charging stations, enabling the planning and operation of power grid independent electric vehicle charging station networks. This solution could be viable in many regions in the United States (e.g., Texas) where there are plenty of renewable resources and little congestion pressure on the road networks. With Corpus Christi, Texas and the neighboring Chapman Ranch wind farm as the test case, this work implement such a design and analyze its performance based on the simulation of its operational processes. Further, we formulate an optimization problem to find design parameters that minimize the total costs. The main design parameters include the number of trucks and batteries. The results in this work, although preliminary, will be instrumental for potential stakeholders to make investment or policy decisions.

The operational reserve demand curve is a scarcity pricing mechanism adopted in energy-only electricity markets to address the shortage of reserves and incentivize the generators. It is constructed based on the assumption of the normality of loss of load probability, which is estimated from reserve error data. In this Letter, the historical record of reserve error is collected and analyzed to test the assumption and find the fittest probability distribution. It is found that the normal distribution is generally not as good as other distributions like log-logistic, general gamma, and Weibull. Using a dynamic simulation framework, the installed reserve margins with different distributions are evaluated over a fifty-year period, demonstrating that the choice of distribution could significantly impact the subsequent pricing calculations and therefore the reserve margin in an energy-only electricity market. This work is of potential importance and immediate relevance to system reliability and resource adequacy in power grids.

Physical layer security is a crucial technology to achieve secure information transmission. However, emerging technologies, such as machine learning, impose significant challenges on existing security systems. In this paper, we propose a new relay system to achieve physical layer secure transmission and construct a deep learning model on the relay to design secure beamforming vector. This design is able to adapt to the current channel of the legitimate user and resist the eavesdropping from eavesdroppers. The model takes the channel’s statistical characteristic into account and it can learn the channel state information (CSI) to design secure beamforming in order to against eavesdropping effectively. The performance of bit error rate (BER) is close to the Shannon limit when the receiver demodulates the received signals, and we also ensure that the eavesdroppers cannot demodulate the transmitted signals. In addition, we design a power allocation algorithm, which considers the eavesdropper’s CSI on the relay. Simulation results show that the proposed model is able to reach the pre-defined BER requirements. The model can maximize the system security rate by optimizing power allocation at the relay.

The branching of negative downward lightning leaders is of both theoretical insterest and practical importance. In the lightning protection of ground structures, a clearer understanding of the mechanism and dynamics of leader branching is needed to identify possible lightning strike locations as well as vulnerable parts of the protection systems. In this work, we start with the image analysis of a lightning leader process recorded with a high-speed camera and to show that the anode-directed streamer zone developed from space stems in front of a leader tip needs be smaller in size (<10 m) to make branching more likely to occur. Then, we propose a kinetic model to describe the growth of the meter-scale streamer zone, which essentially states that the growth speed decreases as the streamer zone expands. The exponent in the kinetic model can be estimated by comparing the numerical and observational results. Based on the model, the streamer zone dynamics on the meter-scale differs from that of a single streamer in that the slowing down of the growth is accompanied by the recovery of the electric field at the discharge front, which may result in new bursts of streamers. This model, if further verified, could be the foundation of inferring the structure and dynamics of the streamer zone from the morphology of the leader channel, which would significantly advance the knowledge of the streamer zone in negative lightning leaders.