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Evaluating the role of behavior and social class in electric vehicle adoption and charging demands.

ABSTRACT: Understanding electric vehicle (EV) adoption rates and charging patterns is critical in enabling grid operators to maintain quality of supply and offers the potential to procure network services and avoid or postpone capital investments. Agent-based models have separately been shown to be useful in modeling EV adoption, policy options, behavioral influences, and grid impacts. In this work, we bring together these threads with real world travel data to present a multi-scale, behaviour-based EV adoption and use model able to replicate historical changes in vehicle fleets and match the most recent real world EV charging profile data. We have shown how our model can be used to simulate the impact of policies and consumer behavior on the rate of EV adoption across socio-economic groups and the locational grid impacts of EV charging, and as such we believe it to be of value to policy makers, grid operators, and demand response aggregators.

SUBMITTER: Lee R

PROVIDER: S-EPMC8361267 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Evaluating the role of behavior and social class in electric vehicle adoption and charging demands.

Lee Rachel R Brown Solomon S

iScience 20210728 8

Understanding electric vehicle (EV) adoption rates and charging patterns is critical in enabling grid operators to maintain quality of supply and offers the potential to procure network services and avoid or postpone capital investments. Agent-based models have separately been shown to be useful in modeling EV adoption, policy options, behavioral influences, and grid impacts. In this work, we bring together these threads with real world travel data to present a multi-scale, behaviour-based EV ad ...[more]

PMID: 34409273

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Project description:Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

Dataset Information

Evaluating the role of behavior and social class in electric vehicle adoption and charging demands.

Publications

Evaluating the role of behavior and social class in electric vehicle adoption and charging demands.

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