# Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis

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## Abstract

**:**

## 1. Introduction

## 2. Wind Data and Preprocessing

## 3. Methodology

#### 3.1. Wind Turbine Selection

#### 3.2. Wind Energy Calculation

_{0}is the measured speed, height z

_{0}is known, and $\alpha $ is the power law exponent (shear exponent or Hellman exponent) describing the terrain topology, varying with atmosphere stability (temperature changes), which is commonly set to 1/7 or 0.143 for open land [31]. However, in this study the exponent $\alpha $ parameter was of less concern than the stability of the wind speed itself for charging EVs. Therefore, $\alpha =0.143$ was chosen for this assessment. The instantaneous output power of a wind turbine ${P}_{w}\left(t\right)$ as a function of the wind speed $v$ at a turbine hub height $z$, given the power curve ${P}_{c}$, is described by:

Algorithm 1: EV wind power supply | ||

Inputs: | ${P}_{w}$, | The instantaneous wind power; |

${t}_{charge}$, | EV battery time to charge; | |

${t}_{ov}$, | Overlap time of intervals; | |

${P}_{charger},$ | EV charger power; | |

Output: | ${E}_{nov},$ | Constant supplied energy; |

$E{V}_{no}$, | Number of EVs | |

/* Calculate the number of overlap intervals */ | ||

1: ${t}_{no}=\left(length\left({P}_{w}\right)-{t}_{charge}\right)/\left({t}_{charge}-{t}_{ov}\right)$ | ||

2: for $i:=2$ to ${t}_{no}$ step $\left({t}_{charge}-{t}_{ov}\right)$ do | ||

3: ${P}_{ov1}={P}_{w}\left[i-1:{t}_{charge}-1\right]$ | ||

4: ${P}_{ov2}={P}_{w}\left[i:{t}_{charge}\right]$ | ||

/* Check the stability of wind power*/ | ||

5: if std$\left({P}_{ov1}\right)>$ 0.1 or std$\left({P}_{ov2}\right)>$0.1 then | ||

6: Continue | ||

7: end if | ||

/* Find non-overlap intervals*/ | ||

8: if $\left({P}_{ov1}{\displaystyle \cap}{P}_{ov2}=\varphi \right)$ then | ||

9: ${P}_{nov}\left[i\right]={P}_{ov2}$ | ||

10: end if | ||

11: end for | ||

/* Calculate the total energy*/ | ||

12: ${E}_{nov}=\left({t}_{charge}/60\right){\displaystyle \sum}{P}_{nov}\left[i\right]$ | ||

/* Calculate number of EVs*/ | ||

13:$E{V}_{no}=\left({E}_{nov}/{P}_{charger}\right)$ |

#### 3.3. Charging Station Capacity

## 4. Results

#### 4.1. Wind Speed Data Averaging

#### 4.2. Wind Turbine Selection

#### 4.3. Charging Station Capacity

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 2.**Schematic view of the data analysis procedure for off-grid wind-to-EV charging stations, where std is the sample standard deviation, P

_{charger}is the charging point avg power, P

_{w}is the instantaneous output power of a wind turbine.

**Figure 5.**Windrose plots and wind speed distributions for M2 (

**a**–

**f**) and M4 (

**g**–

**l**) when averaged over one (

**a**,

**d**,

**j**,

**i**), two (

**b**,

**e**,

**h**,

**k**), and three (

**c**,

**f**,

**i**,

**l**) minutes. (

**d**) Wind speed distribution for M2 averaged over one minute and fitted on Weibull distribution of scale = 4.0849 and shape = 1.3948. (

**e**) Two minutes averaged wind speed distribution of the M2 tower data with Weibull scale = 4.0930 and shape = 1.410. (

**f**) The M2 tower wind speed data averaged over three minutes interval with Weibull scale = 4.0986 and shape = 1.421. (

**j**) The M4 tower wind speed of one minute average with Weibull distribution scale = 4.8212 and shape = 1.3444. (

**k**) The M4 wind speed data when averaging over two minutes with Weibull scale = 4.8299 and shape = 1.3576. (

**l**) The wind speed distribution of the M4 tower averaged over three minutes with Weibull distribution scale = 4.8357 and shape = 1.3669.

**Figure 6.**Selected turbines and total number of EVs calculated over different averaging intervals for M2 (

**a**–

**c**) and M4 (

**d**–

**f**) data. (

**a**,

**d**) The monthly estimated total EVs when using one-minute wind speed averaging. (

**b**,

**d**) The number of EVs using two-minute averaging intervals. (

**c**,

**f**) The three-minute averaging results of total EVs.

**Figure 7.**Wind energy EV charging conversion results for M2 tower data during January 2018 for selected turbine no16. (

**a**) Wind speed (average ± standard deviation is 5.6898 ± 4.1389 m/s) heat map rearranged to show daily 21-min intervals. (

**b**) The corresponding wind turbine power output for each 21-min interval. (

**c**) The usable interval for EV charging. (

**d**) The total number of 21-min intervals per day. (

**e**) Total available daily energy for full-EV charging. (

**f**) Total number of EVs per day.

**Figure 8.**Wind energy EV charging conversion results for the M4 tower data during January 2014 for selected turbine no16. (

**a**) Wind speed (average ± standard deviation is 8.3917 ± 6.2554 m/s) heat map rearranged to show daily 21-min intervals. (

**b**) The corresponding wind turbine power output for each 21-min interval. (

**c**) The usable interval for EV charging. (

**d**) Total number of 21-min intervals per day. (

**e**) Total available daily energy for full-EV charging. (

**f**) Total number of EVs per day.

Parameter | Description or Value |
---|---|

Battery pack capacity (${B}_{c}$) | 50 kW |

Driving range | Between 225–465 km |

Fast charging range | From 10–80% |

Charging point | Supercharger v3 (250 kW DC) |

Charging point max power | 170 kW |

Charging point avg power (${P}_{charger}$) | 100 kW |

DC charging time (${t}_{charge}$) | 21 min |

Real energy consumption | 10.2–21.1 kWh/100 km |

**Table 2.**Selected wind turbines output power stability (%) using M2 and M4 wind speed data with different averaging intervals.

ID * | Height (m/s) | Cut-In (m/s) | Rated (m/s) | Cut-Out (m/s) | Power | M4 Tower Data (%) | M2 Tower Data (%) | ||||
---|---|---|---|---|---|---|---|---|---|---|---|

(kW) | 1 Min | 2 Min | 3 Min | 1 Min | 2 Min | 3 Min | |||||

no16 | 134 | 3 | 10 | 20 | 3300 | 9.41 | 14.34 | 18.35 | 5.22 | 8.28 | 9.78 |

no17 | 114 | 3 | 10 | 20 | 3000 | 9.22 | 14.44 | 17.74 | 5.22 | 7.74 | 9.64 |

no44 | 119 | 3 | 10 | 22 | 3000 | 12.61 | 17.69 | 21.26 | 6.82 | 9.12 | 10.49 |

no67 | 129 | 3 | 10 | 23 | 3150 | 13.41 | 17.94 | 22.20 | 7.20 | 9.41 | 10.87 |

no73 | 99.5 | 3 | 10 | 25 | 2300 | 14.96 | 18.73 | 22.20 | 6.92 | 8.97 | 10.21 |

no94 | 139 | 3 | 10 | 22 | 3200 | 12.42 | 17.05 | 21.45 | 7.10 | 9.41 | 11.01 |

no95 | 136 | 3 | 10 | 22 | 3000 | 12.33 | 17.00 | 21.31 | 7.10 | 9.46 | 10.91 |

no124 | 137 | 3 | 10 | 25 | 3500 | 16.04 | 20.21 | 23.76 | 7.86 | 9.96 | 11.34 |

no128 | 99 | 3 | 10 | 25 | 2200 | 14.96 | 18.73 | 22.20 | 6.86 | 8.97 | 10.21 |

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## Share and Cite

**MDPI and ACS Style**

Noman, F.; Alkahtani, A.A.; Agelidis, V.; Tiong, K.S.; Alkawsi, G.; Ekanayake, J.
Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis. *Appl. Sci.* **2020**, *10*, 5654.
https://doi.org/10.3390/app10165654

**AMA Style**

Noman F, Alkahtani AA, Agelidis V, Tiong KS, Alkawsi G, Ekanayake J.
Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis. *Applied Sciences*. 2020; 10(16):5654.
https://doi.org/10.3390/app10165654

**Chicago/Turabian Style**

Noman, Fuad, Ammar Ahmed Alkahtani, Vassilios Agelidis, Kiong Sieh Tiong, Gamal Alkawsi, and Janaka Ekanayake.
2020. "Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis" *Applied Sciences* 10, no. 16: 5654.
https://doi.org/10.3390/app10165654