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The customer, end minimization process of charging cost for electric vehicles (EVs), and the increased electric vehicle fleet caused by uncertainties are the main factors to the degradation of the reliability of the distribution system. Moreover, the Vehicle-to-grid feature consideration is the major asset for discharging the EV at peak demand leading to customer satisfaction towards the economic savings of expenses (charging bills). In this paper, the reliability probability method and the failure rate evaluation have been implemented for the charging ports involved in the charging station of the 50-350 kW power distribution system. The failure rate of the 24 and 12 charging ports involved parking lot has been considered for the reliability analysis. The failure rate evaluation of the system has been estimated with an expected failure rate of 2%. The maintenance cost of the charging port has been evaluated on the basis of the regular failure rates in the charging system. The maintenance cost of the fuel system and EV charging system has been compared with consideration of efficiency. The Binomial distribution probability method is applied in this paper. It is inferred that the applied method is suitable for the estimation of failure rates of charging stations that are installed with the strategy of the batched porting system. [ABSTRACT FROM AUTHOR]

On data that are not normally distributed, classical linear regression is developed into the Generalized Linear Model (GLM). One of them is logistic regression which is used for binary discrete data. A different strategy is to consider all variables (response and predictors) to be random observations from the joint probability distribution. A model with dependencies should be used because some factors are dependent. The copula model, one of which is the Gaussian copula, is a popular technique for creating multivariate models with dependencies. The Gaussian copula regression model (GCMR) is a general structure that can be used to model any form of dependent response. The Gaussian copula blends the ease of interpretation of marginal modeling with the flexibility of the dependency structure specification. In this case, the estimated parameter is the regression coefficient and the error is expressed in the dependency structure. The model estimation uses the maximum likelihood method. This paper aims to apply the GCMR model to time series data where the dependent variable is discrete with a binomial distribution. A case study was conducted to see the effect of weather factors on Covid cases in DKI Jakarta. The regression model used is binomial data logistic regression with the dependency structure expressed in the correlation matrix with the assumption that the error is obtained from the ARMA(p,q) process. The dependent variable is the number of Covid-19 cases and the independent variable is the maximum temperature, average temperature and humidity. The data are a daily time series with a range of March 1, 2020 to April 30, 2022. Based on the GCMR model, Maximum temperature, average temperature and Humidity have a significant influence on the number of Covid-19 cases in Jakarta. For this case study, the results show that the GCMR model is better than logistic regression analysis. [ABSTRACT FROM AUTHOR]