# Solar Electric Vehicles as Energy Sources in Disaster Zones: Physical and Social Factors

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## Abstract

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## 1. Introduction

_{2}footprint [31].

## 2. Methods

#### 2.1. Resilience Scenario

- A significant earthquake occurs in a scenario city (“PV City”) (radius 5 km). In the first few hours, the PV City local government transitions schools, community centers, and care centers to evacuation centers equipped with spot coolers at six locations (4 hp each). In addition, the PV City local government calls for temporary first-aid stations and multiple charging stations for mobile devices for local people to help access disaster information over and above access to conventional disaster infrastructure. The number of temporary charging stations accessible within a 1 km walk is 25. Because such facilities require electricity, the PV City local government calls for a voluntary donation of electricity from PV-equipped vehicles; a certain percentage of drivers check the charging status (SoC) of their vehicles (every hour), and if SoC is over 90%, they decide to go to one of the use points and provide electricity until the charge of the battery falls to 50% (vehicle batteries are recharged by PV on vehicles). Other efforts are in place to help keep disaster prevention equipment and facilities functioning. Seven days later, the PV City local government is notified that regional lifelines have been restored.

#### 2.2. Demand—Supply Balance

- Energy requirements of critical facilities, like headquarters of the disaster response and evacuation centers (based on conventional infrastructure);
- Establishment of temporary evacuation centers essential and suitable for energy supply from VIPV; and
- The need for air conditioning in hot weather (a critical need that is usually ignored in conventional disaster response planning).

- The density and distribution of the SEV;
- Distribution of solar irradiance and climate;
- VIPV-specific losses;
- Energy consumption by SEV to carry the donated energy to the saving points and return; and
- Voluntary donation of VIPV energy to shared facilities (probabilities, conditions, and incentives).

#### 2.3. Demand Model

- Temporary shelter with medical care;
- Mobile device charging station within walking distance; and
- Backup power for air conditioning.

#### 2.4. Supply Model

- The orientation angle of the VIPV is not fixed but, rather, frequently changes during driving;
- A moving VIPV has a higher probability of shading than does a fixed-station PV;
- Although the shading objects, such as street trees and traffic signals, are relatively small, they have some effect on VIPV performance;
- Curved surfaces of the vehicle;
- The impacts mentioned above interact and depend on the vehicle’s local coordinates;
- Rapid solar irradiance fluctuation, generally in milliseconds, results from dynamic partial shading;
- Rapid fluctuations in the solar spectrum; and
- Temperature variation between parking and driving.

- Solar irradiance on VIPV (orthogonal 5 axes);
- Distribution of shaded objects used to estimate solar irradiance and irradiation on an arbitrary reference plane tangible to the curved surface of the VIPV;
- Distribution of edges of the shading objects that results in partial shading; and
- Dynamic spectrum fluctuation.

#### 2.5. Curve Correction of VIPV

#### 2.6. Shading Correction of VIPV Performance

#### 2.7. Monte-Carlo Simulation with Social Activities

- SEV drivers check the state of charge (SoC) hourly;
- If the SoC does not exceed 90%, then the SEV driver does not consider energy donation;
- If the SoC exceeds 90%, 5% of drivers in this situation will donate energy, coin toss to randomly determine (5% probability) determine if a driver will donate; and
- The SEV drivers who decide to donate energy to resilient facilities supported by the VIPV, consume energy to move to the facility, donated energy (up to 50% SoC), and return to their original places.

## 3. Results

#### 3.1. Measurement and Modeling of the Power of VIPV Affected by Shading Objects

- Area of each surface element of the hemispherical sky;
- The aperture and shading ratio varied according to the grazing and orientation angles, given by the matrix form:
- Cosine of ray from each surface element, cosine of ray relative to unit vector of the surface element of the absorber; and
- Area of the surface element of the absorber.

- Direct normal irradiance (constant value);
- Aperture probability (=0 when shaded and =1 when not shaded);
- Cosine of the ray to the surface element of the absorber; and
- Area of the surface element of the absorber.

- Reflection of the building wall by direct sunlight, the orientation of the normal vector of the wall is within ±90 ° of the direct ray of sunlight;
- Reflection of the building wall by the diffused sunlight; and
- The reflection from the road is given by the product of the reflectivity of the road surface and the horizontal irradiation affected by shading and reflection from the building wall.

- Direct indication of solar irradiance for PV panels on the roof and side(s) of the vehicle;
- Shading detection was compared with the direction information obtained by comparing four monitored irradiances on the side(s) of the vehicle;
- Three-dimensional solar-resource monitoring, including the angular distribution of solar resources on the roof of a vehicle, is essential for characterizing curved modules; and
- The performance and solar irradiance models for the VIPV were validated by checking more than two orthogonal axes using monitored data in five orthogonal directions.

#### 3.2. Survey of the Local Community: Willingness to Donate for Disaster Resilience

#### 3.3. Monte-Carlo Simulation Results

## 4. Discussion

## 5. Conclusions

- Lower risk of an empty battery in VIPV vehicles;
- In BEV vehicles, once the battery is drained, the possibility of energy donation is nil; and
- VIPV makes possible self-generating energy for transportation to the point of relief.

- Lower risk of damage (direct or indirect);
- No trouble with the utility grid or electric cables;
- Avoids reliance on a single centralized PV system (“all or nothing”);
- Units can move to areas of higher irradiance;
- Units can transport both energy and goods; and
- Energy accommodation is possible by carrying energy by vehicles.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Standard resilience facilities with their energy source requirement and estimated energy consumption during disaster response.

**Figure 2.**Resilience facilities in addition to the standard facilities in Figure 2, powered by voluntary electricity contributions from SEVs and VIPVs.

**Figure 5.**Multi-pyranometer irradiance measurement on the 5-axis: (

**a**) definition of the coordinate system; (

**b**) position of the pyranometers (red circles).

**Figure 7.**Calculation of the 3D shading and aperture matrix from the fisheye image: (

**a**) fisheye image; (

**b**) 2D histogram of the shading probability calculated by binarization of the image; (

**c**) structure of the aperture matrix obtained by the 2D histogram of the shading probability; the shading matrix is calculated by 1–

**E**.

**Figure 8.**Shading distribution and irradiance onto VIPVs (5-axis) of the open zone: (

**a**) fisheye image; (

**b**) shading probability as the function of the grazing angle; (

**c**) comparison of the measured (red bars) and calculated (blue bars) irradiance onto 5-axis planes.

**Figure 9.**Shading distribution and irradiance onto VIPVs (5-axis) of the residential zone: (

**a**) fisheye image; (

**b**) shading probability as the function of the grazing angle; (

**c**) comparison of the measured (red bars) and calculated (blue bars) irradiance onto 5-axis planes.

**Figure 10.**Shading distribution and irradiance onto VIPVs (5-axis) of the building zone: (

**a**) fisheye image; (

**b**) shading probability as the function of the grazing angle; (

**c**) comparison of the measured (red bars) and calculated (blue bars) irradiance onto 5-axis planes.

**Figure 11.**Shading distribution on three zones used for the resilience probability; blue curves correspond to the x-direction (left–right direction), and the red curves correspond to the y-direction (front-rear).

**Figure 12.**Response to community survey regarding willingness to donate energy during disaster relief.

**Figure 13.**Monte Carlo simulation result: A time-series plot of the total battery charge remaining in the facilities supported by SEVs.

**Figure 14.**Solar irradiation of Miyazaki, Japan, the community on which the Monte Carlo simulation was based.

**Figure 15.**Number of SEVs vs. probability of continuous electricity supply for seven days, until the lifeline recovers (residential zone in Miyazaki).

**Figure 16.**Probability of sustaining resilience energy for a week in open zone: (

**a**) fisheye image; (

**b**) probability curves.

**Figure 17.**Probability of sustaining resilience energy for a week in residential zone: (

**a**) fisheye image; (

**b**) probability curves.

**Figure 18.**Probability of sustaining resilience energy for a week in building zone: (

**a**) fisheye image; (

**b**) probability curves.

**Figure 19.**Histogram of the estimated solar irradiance on a car roof in Japan from 830 sites (N45° to N24°, ranging from subpolar to subtropical zone) in three zones.

Type of Facility | HQ, Disaster Response | Evacuation Center | Community Hall | Care House |
---|---|---|---|---|

Floor area (m^{2}) | 10,000 | 7000 | 950 | 4200 |

Capacity (persons) | 250 | 3000 | 200 | 100 |

Required energy capacity | 294.4 | 262.1 | 54.8 | 363.5 |

Non-emergency air conditioning capacity (kW) | 200 | 200 | 50 | 300 |

On-site battery (kWh) | 264 | 133 | 43 | 44 |

PV (kW) | 165 | 61 | 10 | 32 |

Co-generator | -- | -- | -- | 0.7 kW |

Emergency generator (kW) | 200 | 120 | 38.4 | |

Fuel stock (hr) | 72 | 3 | 5 |

Type of Facility | Time Period | Average Energy Demand (kW) |
---|---|---|

Headquarters of disaster response | 06:00–17:00 | 34.6 |

17:00–21:00 | 34.6 | |

21:00–06:00 | 32.2 | |

Evacuation center | 06:00–17:00 | 13.2 |

17:00–21:00 | 23.1 | |

21:00–06:00 | 13.2 | |

Community hall | 06:00–17:00 | 0.8 |

17:00–21:00 | 1.2 | |

21:00–06:00 | 0.7 | |

Care house | 06:00–17:00 | 23.7 |

17:00–21:00 | 25.9 | |

21:00–06:00 | 23.1 |

Type of Facility | Period | Average Power Demand (kW) |
---|---|---|

Temporary shelter with medical care | 06:00–17:00 | 34.6 |

17:00–21:00 | 34.6 | |

21:00–06:00 | 32.2 | |

Common charging station for mobile devices | 06:00–06:00 | 0.2 kW× 25 |

Backup power for air conditioning | 06:00–16:00 | 2.2 kW× 6 |

Parameter | Parameter Description | Variable Type | Distribution |
---|---|---|---|

Date/time of the disaster | Step by 1 h starting from 1 January, 00:00–01:00, end on 31 December, 23:00–24:00 | Integer | Ranged uniform (0, 365 × 24 − 1) Once at the beginning |

SoC at the disaster | Each vehicle | Vector (double-precision float) (Vector size) = (Number of SEV) | Ranged uniform (0%, 100%) Once at the beginning |

Irradiation deviation | 1: Best year 0: Average year −1: Worst year | Double-precision float | Ranged uniform (−1, 1) Once at the beginning |

Degree of intention for donation | If (value) < x% and satisfies other conditions, the driver donates the energy. | Vector (double-precision float) (Vector size) = (Number of SEV) × 24 × 7 | Ranged uniform (0, 1) |

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

Site | Miyazaki, Japan (N31.938°, E131.413°) | 87376 (METPV index number) |

Road reflectance | Reflectance from the road to the vertical plane of vehicles | 0.08 |

Road reflectance (snow) | (snow cover) > 10 cm, Reflectance from the road to the vertical plane of vehicles | 0.9 |

The reflectance of the vertical plane of the shading objects | buildings, etc. | 0.25 |

Number of SEV | Input at the beginning (Integer) | -- |

Number of mobile charging stations | Distributed by walking distance (1 km) | 25 |

The required power for a mobile charging station | 24 h (constant) | 0.2 kW each |

Number of sites demanding energy supply of air conditioning | 6 | |

The required power for air conditioning | 24 h (constant) | 2.2 kW each (6 horsepower each) |

Number of temporal shelters with medical care | 1 | |

The required energy for the temporal shelter with medical care | Required power varies by time zone. | 0.36 kW 00:00–06:00 1.61 kW 06:00–17:00 4.47 kW 17:00–21:00 0.36 kW 21:00–24:00 |

Drive distance at delivering energy to the public good | 5 km | |

Electric milage | 8.33 km/kWh | |

Battery capacity | 40 kWh | |

Energy management efficiency of EV | 93% | |

MPPT ^{1} efficiency for PV power conversion | 95% | |

VIPV efficiency | 22% | |

VIPV area | Projected area to a horizontal plane | 1.8 m^{2} |

Performance ratio of VIPV | Temperature correction is done separately | 90% |

Temperature coefficient | Varies by irradiance level | −0.328%/K @ 1 kW/m^{2} of solar irradiance |

Type of Facilities | Value |
---|---|

Place | Miyazaki, Japan, N |

Climate | Semi-tropical |

Zone | Residential zone |

Population density | 624 /km^{2} |

Assumed area for calculation | 5 km radius |

Drive distance at delivering energy to the public good | 5 km |

Electric mileage (average) | 8.33 km/kWh |

Battery capacity (average) | 40 kWh |

Energy management efficiency of EV (average) | 93% |

MPPT ^{1} efficiency for PV power conversion (average) | 95% |

VIPV efficiency (average) | 22% |

VIPV area (projected area, average) | 1.8 m^{2} |

Performance ratio of VIPV (*) | 90% |

Temperature coefficient (**) | −0.328%/K @ 1 kW/m^{2} of solar irradiance |

Calculation result: Number of required SEVs (residential zone) | 720 in a 5 km radius, 9.2 per km^{2} |

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## Share and Cite

**MDPI and ACS Style**

Araki, K.; Ota, Y.; Maeda, A.; Kumano, M.; Nishioka, K.
Solar Electric Vehicles as Energy Sources in Disaster Zones: Physical and Social Factors. *Energies* **2023**, *16*, 3580.
https://doi.org/10.3390/en16083580

**AMA Style**

Araki K, Ota Y, Maeda A, Kumano M, Nishioka K.
Solar Electric Vehicles as Energy Sources in Disaster Zones: Physical and Social Factors. *Energies*. 2023; 16(8):3580.
https://doi.org/10.3390/en16083580

**Chicago/Turabian Style**

Araki, Kenji, Yasuyuki Ota, Anju Maeda, Minoru Kumano, and Kensuke Nishioka.
2023. "Solar Electric Vehicles as Energy Sources in Disaster Zones: Physical and Social Factors" *Energies* 16, no. 8: 3580.
https://doi.org/10.3390/en16083580