Optimal location of electric vehicle charging station and its impact on distribution network: A review

A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD

Electric vehicles (EVs) are the most important components of smart transportation systems. Limited driving range, prolonged charging times, and inadequate charging infrastructure are the key barriers to EV adoption. To address the problem of prolonged charging time, the simple approach of developing a new charging station to enhance the charging capacity may not work due to the limitation of physical space and strain on power grids. Prediction of precise EV charging time can assist the drivers in effective planning of their trips to alleviate range anxiety during trips. Therefore, this study employed four different ensemble machine learning (EML) algorithms: random forest, extreme gradient boosting (XGBoost), categorical boosting, and light gradient boosting machine, for predicting EVs' charging time. The prediction experiments were based on 2 years of real‐world charging event data from 500 EVs in Japan's private and commercial vehicles. The study emphasized predicting charging time for different charging modes, that is, normal and fast charging operations. The results indicate that EML models performed well under various scenarios, with the XGBoost model having the highest accuracy. Moreover, we also employ the newly developed Shapley additive explanation (SHAP) approach to tackle the non‐interpretability issues of the ML algorithm by interpreting the XGBoost model outputs. The obtained SHAP value plots demonstrated the nonlinear relationship between explanatory variables and EV charging time.

Prediction of electric vehicle charging duration time using ensemble machine learning algorithm and Shapley additive explanations

Metaheuristic algorithms are extensively utilized to find solutions and optimize complex industrial systems’ performance. In this paper, metaheuristic algorithms are utilized to predict the optimum value of the operational availability of a cooling tower in a steam turbine power plant. These techniques have some flaws like poor convergence speed, being stuck in local optima, and premature convergence. For this purpose, a novel efficient stochastic model is proposed for a cooling tower that is configured with six subsystems. The Markovian birth-death process is utilized to develop the Chapman-Kolmogorov differential-difference equations. All the random variables are statically independent, and repairs are perfect. Failure rates are exponentially distributed, while repair rates follow the arbitrary distribution. Steady-state availability (SSA) of the system is derived concerning various failure and repair rates. The sensitivity analysis of SSA is also performed to identify the most critical component. Further, system availability is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) because they are found to be more suitable for such types of problems. It is revealed that the PSO outperforms GA in predicting the availability of cooling towers used in steam turbine power plants. 

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Xuefei Wang

Papers

Public charging station location determination for electric ride-hailing vehicles based on an improved genetic algorithm

Parametric study of hybrid monopile foundation for offshore wind turbines in cohesionless soil