Stochastic Modeling and Forecasting of Load Demand for Electric Bus Battery-Swap Station

Electric-vehicle (EV) battery-swap stations (BSSs) have become important infrastructures for the development of EVs to extend their driving range. Due to the randomness of batteries' swapping and charging patterns, the load demand of the BSS has a stochastic nature. It is necessary to investiga...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2014-08, Vol.29 (4), p.1909-1917
Main Authors: Dai, Qian, Cai, Tao, Duan, Shanxu, Zhao, Feng
Format: Article
Language:English
Subjects:
Applied sciences
Batteries
Battery-swap station (BSS)
Charging
charging load forecasting
charging load model
Computer simulation
Demand
Direct energy conversion and energy accumulation
Electric vehicles
electric vehicles (EVs)
Electrical engineering. Electrical power engineering
Electrical power engineering
Electrochemical conversion: primary and secondary batteries, fuel cells
Estimation
Exact sciences and technology
Forecasting
Ground, air and sea transportation, marine construction
Indexes
Load modeling
Mathematical models
Miscellaneous
Monte Carlo simulation
Operation. Load control. Reliability
Power networks and lines
Predictive models
probability interval forecast
Road transportation and traffic
Stations
Stochastic processes
Stochasticity
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Summary:Electric-vehicle (EV) battery-swap stations (BSSs) have become important infrastructures for the development of EVs to extend their driving range. Due to the randomness of batteries' swapping and charging patterns, the load demand of the BSS has a stochastic nature. It is necessary to investigate the charging load characteristics of BSS to guide the coordinated battery charging for mitigating the impact of disorderly charging behaviors on the distribution network. Under the uncontrolled swapping and charging scenario, four variables are essential: 1) hourly number of EVs for battery swapping; 2) the charging start time; 3) the travel distance; and 4) the charging duration. Taking these factors into account, a novel model based on Monte Carlo simulation is presented to estimate uncontrolled energy consumption of the BSS. Then, a generic nonparametric method for the estimation of prediction uncertainty of charging load demand is introduced. Adopting an actual typical BSS as an example, the simulation results show that the proposed prediction methods of the BSS charging load and probabilistic interval are suitable for forecasting the horizon 24 h ahead.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2014.2308990