Energy Management Method for Fast-Charging Stations with the Energy Storage System to Alleviate the Voltage Problem of the Observation Node

Large-scale fast charging of electric vehicles (EVs) probably causes voltage deviation problems in the distribution network. Installing energy storage systems (ESSs) in the fast-charging stations (FCSs) and formulating appropriate active power plans for ESSs is an effective way to reduce the local voltage deviation problem. Some deterministic centralized strategies used for ESSs at FCSs are proposed to solve the voltage deviation problem mentioned above. However, the randomness of the EV load is very large, which can probably reduce the effects of deterministic centralized strategies. A fast and reliable centralized strategy considering the randomness of the EV load for ESSs is a key requirement. Therefore, we propose in this paper a day-ahead scheduling strategy with the aim of maximizing the probability of the nodal voltage change being smaller than a preset limit at the observation node. In the proposed strategy, the uncertainty of EV load is taken into account and the probability of the voltage change of an observation node is quantified by a proposed analytic assessment model (AMM). Furthermore, a voltage change optimization model (VCOM) based on a novel control parameter β is proposed, where β can be used as a constraint to suppress the nodal voltage change at the observation node. Finally, the IEEE 33-bus test system is used to verify the effectiveness of the proposed day-ahead ESS strategy.

Visibility Detection Algorithm of Single Fog Image Based on the Ratio of Wavelength Residual Energy

Different visibilities and different wavelength attenuations can cause color deviation problem in some dehazing algorithms. A visibility detection algorithm based on a single fog image is proposed. First, the visibility range of the image is preliminarily determined according to the transmissivity; then, the normalized differences between the residual energy ratios of different wavelengths of RGB channels are calculated, and the pixels with large gray deviation of a single channel are filtered to improve the calculation accuracy; finally, the image visibility detection value is calculated. The experimental results show that the proposed algorithm not only effectively reflects the fog image visibility but is also well suited for evaluating the effectiveness of the image defogging algorithms and the restoration degree of the defogging color difference.

Optimal Parameters of Volt–Var Function in Smart Inverters for Improving System Performance

This paper proposes a method to improve the performance of a distribution system by optimizing volt–var function of a smart inverter to alleviate the voltage deviation problem due to distributed generation connection. In order to minimize voltage deviation and line losses which represent the performance of a distribution system, this paper proposes an algorithm that optimally sets the parameters of the volt–var function. In the process of optimizing the parameters of the volt–var function, the algorithm proposed in this paper considers minimizing the contribution of the reactive power in order not to affect the output of the distributed generation. In order to apply to the field, the distribution system in South Korea considering the configuration and operation regulation was selected as a test model for algorithm verification. As a result, the system performance was successfully improved by optimally setting the volt–var function of the smart inverter which is an effective way to solve the over-voltage problem caused by distributed generation connection. This paper verified the proposed method through OpenDSS, a quasi-static time-series simulation, for the test model considering the characteristics of the distribution system in South Korea.

An Exploratory Examination of the Use of Co-Occurrence Network Analysis to Assess the Anxiety and Beliefs in College Students When Performing Mathematical Computation

This study used used co-occurrence network analysis to study the experience of college students when solving statistics or algebra problems. Students generated words to describe their perceptions of solving a standard deviation problem and a quadratic computation. Results indicated that the statistics problem was associated with words indicating "anxiety" and "confusion". Conversely, words, such as "relaxed and "familiar" were used to describe solving the quadratic computation. Additionally, rating scales measuring statistics anxiety, math anxiety, and math helplessness, showed that meth anxiety tended to predict statistics anxiety for all students, above and beyond math helplessness. <br>

An Exploratory Examination of the Use of Co-Occurrence Network Analysis to Assess the Anxiety and Beliefs in College Students When Performing Mathematical Computation

This study used used co-occurrence network analysis to study the experience of college students when solving statistics or algebra problems. Students generated words to describe their perceptions of solving a standard deviation problem and a quadratic computation. Results indicated that the statistics problem was associated with words indicating "anxiety" and "confusion". Conversely, words, such as "relaxed and "familiar" were used to describe solving the quadratic computation. Additionally, rating scales measuring statistics anxiety, math anxiety, and math helplessness, showed that meth anxiety tended to predict statistics anxiety for all students, above and beyond math helplessness. <br>

Cramér type large deviations for trimmed L-statistics

CRAMÉR TYPE LARGE DEVIATIONS FOR TRIMMED L-STATISTICSIn this paper, we propose a new approach to the investigationof asymptotic properties of trimmed L-statistics and we apply it to the Cramér type large deviation problem. Our results can be compared with those in Callaert et al. 1982 – the first and, as far as we know, the single article where some results on probabilities of large deviations for the trimmed L-statistics were obtained, but under some strict and unnatural conditions. Our approach is to approximate the trimmed L-statistic by a non-trimmed L-statistic with smooth weight function based onWinsorized random variables. Using this method, we establish the Cramér type large deviation results for the trimmed L-statistics under quite mild and natural conditions.