## 3 Methods
### Estimation of the PV Potential from Solar EVs on Donghae Expressway
#### 3.2.1 Weather Characteristics  
This study predicted the expected annual potential in the near future rather than targeting a specific year. For meteorological data, typical meteorological year (TMY) data were adopted, which are long-term observations that provide a good hourly representation of the climate characteristics of the region. TMY data have been used in various studies to evaluate PV power potential and are therefore considered valid input. However, as shown in Figure 1, the Donghae expressway, which is approximately 120 km long, is located in the north-south direction; therefore, weather characteristics may differ depending on the location. Herein, TMY data containing hourly insolation values for three cities, Sokcho, Gangneung, and Donghae, which are the main cities through which the Donghae expressway passes, were obtained from the SolarGIS website ([[https://www.solargis.com/](https://www.solargis.com/)]([https://www.solargis.com/](https://www.solargis.com/)) (accessed on 15 July 2024)) [40] (TMY data for Sokcho, Gangneung, and Donghae, which have existing meteorological observatories, were obtained from the SolarGIS website and are referred to as meteorological observation points in this study). To construct meteorological data for the Gangwon state, data from the Korea Meteorological Administration's Weather Data Open Portal ([[https://data.kma.go.kr/](https://data.kma.go.kr/)]([https://data.kma.go.kr/](https://data.kma.go.kr/)) (accessed on 15 July 2024)) [41] also were reviewed but were not adopted because of missing data and errors.  
To reflect the weather characteristics corresponding to the driving location and time of solar EVs, the nearest neighbor classification method was applied between the Donghae expressway and three weather stations. This classification methodology is one of the most basic classification rules in machine learning and is a methodology for finding the closest point or data to a specific point based on the Euclidean distance or a specific objective function. As shown in Figure 4, this study used the TMY data from the closest of the three observation points (Sokcho, Gangneung, and Donghae) to the point where the solar EV was located at the time of driving. The Donghae expressway was categorized into three sections considering the location of the latest weather observation point from the vehicle. The red section was termed Section A (reflecting the weather characteristics of Sokcho), the green section was termed Section B (reflecting the weather characteristics of Gangneung), and the blue section was termed Section C (reflecting the weather characteristics of Donghae). For example, if a solar EV is driven or parked in the red section, it can be considered to reflect the Sokcho weather characteristics.  
When traveling between two or more sections in a drive exceeding one hour, the weather characteristics change because of time and location variations. In this case, the average value of the weather characteristics of the two cities (e.g., solar irradiation at the nearest hour to the departure city and solar irradiation at the nearest hour to the arrival city) is applied, as shown in Equation (2). For example, if a solar EV departs from Sokcho at 8:00 a.m. and arrives in Gangneung one hour later, the average of the weather characteristics of Sokcho at 8:00 a.m. and those of Gangneung at 9:00 a.m. is used. If the departure or arrival time is not on time, the traveling or stopping time is calculated in minutes, and the weather characteristic value is applied using the ratio of minutes to hours.  
(2)  
Hourly weather characteristics for traveling between cities (place, time) =[weather characteristics(City A, t) + weather characteristics(City B, t or t+1)] \(\div\) 2