Dispatch management of portable charging stations in electric vehicle networks

Summary Today, charging electric vehicles (EVs) from the utility grid has resulted in high electricity costs besides enhanced emissions. On the other hand, increased renewable energy penetration has caused curtailing a considerable share of the produced energy. This paper aims at bridging the gap between these challenges utilizing mobile charging stations (MCS). Integrated with battery energy storage, the MCS shifts the curtailed renewable energy spatially and temporally for EV charging. To this end, a novel model is proposed for joint MCS spatio‐temporal status and battery power‐energy scheduling in the presence of fixed charging stations (FCSs). Besides, a new paradigm is introduced for EV charging management and queuing in both MCS and FCS. The excess curtailed renewable energy is stored in the MCS battery for EV charging later and at a different location. The numerical testing of the model denotes a 9.63% reduction in the daily operation cost. Besides, wind and solar energy curtailment are reduced by 15.60% and 54.60%, respectively. Additionally, the EV charging queue represents a 90% reduction while FCS empty sockets increase by 80%. The benefits for the grid include peak shaving, improved voltages, and increased renewable penetration.

Section: Modeling Of Ev Charging and Queuing In Mcs And Fcsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mobile battery‐integrated charging station for reducing electric vehicles charging queue and cost via renewable energy curtailment recovery

Saboori

Jadid

2021

“…12 Conversely, private chargers are not extensively used at home in China due to constrained residential and traffic conditions. 13 In China, EV drivers mostly use public charging stations. The authors stated that different regions have varied charging behavior characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…In the USA, most drivers charge their EVs at home using a private charger 12 . Conversely, private chargers are not extensively used at home in China due to constrained residential and traffic conditions 13 . In China, EV drivers mostly use public charging stations.…”

Section: Introductionmentioning

confidence: 99%

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

Ullah

Liu

Yamamoto

et al. 2022

Summary 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.

An optimization framework for capacity allocation and energy management of fast electric vehicle charging stations‐wind photovoltaic energy using artificial transgender longicorn algorithm

Arivalahan

Balaji

2022

Summary This paper proposes the optimal design for fast electric vehicle charging stations for the capacity configuration of components and the power scheduling strategy. The proposed approach is the artificial transgender longicorn algorithm, which is a simple and powerful controller. The optimal capacity of the system components and the optimal power scheduling approach are determined. First, an electric vehicle charging simulation model is built considering the demand response, which dynamically modified the charging expectation based on the electricity price at the time of use. Second, based on the system design, a multi‐objective optimization model is proposed with minimal objectives for the electricity cost and polluting emissions. Finally, a scenario analysis is carried out and it is concluded that renewable energy supplies, connection to the utility grid and demand response may help improve the performance of the optimization objectives. By then, the performance of the proposed technique is implemented in the MATLAB platform and compared with several existing approaches.