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ABSTRACT
The transportation sector is undergoing an ever-increasing fast development, shifting from traditional fossil fuel-based transport to ultra-low emission electrified transport. To accelerate this transformation, appropriate planning and operation of charging stations (CSs) especially fast and ultra-fast CSs is of utmost importance. The initial and most important step toward optimum planning of CSs is to develop models for the prediction of electric vehicle (EV) load demand in order to estimate future charging profiles. Thus, a stochastic EV load model should be employed to get an accurate estimation of the total EV charging load regrading numerous interdependent uncertainties. The paper specially provides a critical review regarding different uncertainties related to EV load demand and various stochastic modeling approaches. For this purpose, related research works reported between 2005 and 2023 were gathered, screened, and summarized. Then, selected research papers are evaluated in terms of stochastic modeling of EV load demand. The stochastic approaches were categorized in two main groups of conventional and fast charging modes with regard to probability density functions, Monte Carlo Simulation, Fuzzy method, Markov chain, artificial neural networks, Copulas, and hybrid modes. Next, details of each methodology by highlighting related cons and pros were provided. It was obtained that most research works took into account three to five random variables (RVs) in-average for stochastic studies. In addition, various test and real-world networks throughout the world were employed to validate the obtained results. Finally, some potential future research areas in the field of stochastic CS planning and operation are presented.
Graphical Abstract
HIGHLIGHTS
A comprehensive review of the different types of EVs and CSs,
A technical review of different RVs and uncertainties related to stochastic EV load demand,
A complete review of various stochastic methods for EV load demand modeling, and
An outlook on identifying future research perspectives in the field.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Nomenclature
| Abbreviations and Acronyms | = | |
| ABS | = | Activity-based model |
| AEV | = | All electric vehicles |
| ATUS | = | American time use surveys |
| BEV | = | Battery electric vehicle |
| BES | = | Battery energy storage |
| BSI | = | Battery swapping infrastructure |
| BSS | = | Battery swapping station |
| CP | = | Charging profile |
| CS | = | Charging station |
| CSP | = | Charging service provider |
| DCFCS | = | Direct current fast charging station |
| DOD | = | Depth of discharge |
| DNO | = | Distribution network operator |
| DSO | = | Distribution system operator |
| DUOATS | = | Direct use of observed activity-based schedule |
| ESS | = | Energy storage system |
| EV | = | Electric vehicle |
| EVCS | = | Electric vehicle charging station |
| ET | = | Electric taxi |
| EGR | = | Emissions gap report |
| FC | = | Fuel cell |
| FCS | = | Fast charging station |
| FCEV | = | Fuel cell electric vehicle |
| GEV | = | Generalized extreme value |
| GHG | = | Greenhouse gases |
| GDP | = | Gross domestic product |
| G2V | = | Grid to vehicle |
| HEV | = | Hybrid electric vehicle |
| HTS | = | Household travel survey |
| IEA | = | International energy agency |
| IEC | = | International Electrotechnical Commission |
| ICE | = | Internal combustion engine |
| ICEV | = | Internal combustion engine vehicle |
| LV | = | Leisure and vacation |
| MCS | = | Mobile charging station |
| MCS | = | Monte Carlo Simulation |
| NTS | = | National travel survey |
| PAPR | = | Peak-to-average power ratio |
| = | Probability distribution function | |
| PEM | = | Point estimation method |
| PEV | = | Plug-in electric vehicle |
| PHEV | = | Plug-in hybrid electric vehicle |
| PM | = | Probabilistic modeling |
| RE | = | Renewable energy |
| REEV | = | Range-extended electric vehicle |
| RER | = | Renewable energy resource |
| RCS | = | Rapid charging station |
| SB | = | Shopping and business |
| SOC | = | State of charge |
| ToU | = | Time of use |
| UFCS | = | Ultra-fast charging station |
| V2G | = | Vehicle to grid |
| WHO | = | World Health Organization |
| WS | = | Work and school |
| XFC | = | Extreme fast charging |
| Sets, Indices, Parameters, Variables, and Functions | = | |
| Cj | = | Battery capacity of the jth EV for 100 km |
| Cn | = | On-board battery capacity of the nth EV |
| Ej | = | Energy required to recharge the ith EV |
| En | = | Electricity consumption of the nth EV per 100 km |
| k | = | Decreasing rate of charging power after the critical point |
| MaxDOD Maximum depth of discharge in the EV battery | = | |
| = | Charging power of the jth EV | |
| = | Operating power of the nth charger in fast charging mode | |
| = | Operating power of the nth charger in slow charging mode | |
| s | = | Daily mileage of an EV |
| = | Initial SOC of the jth EV | |
| = | Final SOC of the jth EV | |
| = | Desired battery SOC of the nth EV | |
| = | Battery SOC of the nth connected EV | |
| = | Battery SOC of the nth EV at the end of 1st phase | |
| ts | = | Charging start time of an EV |
| te | = | Charging end time of an EV |
| TC | = | Charging time duration of an EV |
| = | Charging time duration of the jth EV | |
| Tn | = | Total charging time of the nth EV |
| Tnf | = | Charging time of the nth EV in fast charging mode, 1st phase |
| Tns | = | Charging time of the nth EV in fast charging mode, 2nd phase |
| uTC | = | Expected value of charging time duration of an EV |
| = | Expected value of charging end time of an EV | |
| = | Expected value of charging start time of an EV | |
| = | Expected value of ln(s) | |
| = | Energy consumption of the jth EV for 100 km | |
| = | Standard deviation of ln(s) | |
| = | Time period of the nth connected EV | |
| = | Time period of the nth disconnected EV | |
| σTc | = | Standard deviation of charging time duration of an EV |
| = | Standard deviation of charging end time of an EV | |
| = | Standard deviation of charging start time of an EV | |
| = | Charging efficiency of the jth EV battery pack | |
| = | Charging efficiency of the nth charger |