# Modeling Photovoltaic Potential for Bus Shelters on a City-Scale: A Case Study in Lisbon
## 2 Materials and Methods
### Bus Shelter Photovoltaic Potential
#### 2.2.2 Modeling the Solar Bus Shelters' Photovoltaic Potential  
After modeling the solar resource at the city's surface, the next step is to identify the best bus shelters located in the public space (Figure 3):  
* The first assumption made is that to be considered for PV potential analysis, a bus shelter must not be under any type of vegetation (e.g., trees) [25] or urban structures, such as bridges or balconies. The analysis begins with the examination of the point shapefile with all the bus stops available in the city (raw data). The goal is to eliminate all locations under trees. For this task, a Very-High-Resolution (VHR) image is used. Through an object-oriented classification, a map with the tree coverage of the city is produced and used to determine each shelter's suitability, following the methodology proposed by [26]. The locations that are away from trees are then subject to visual identification based on Google Street View information. The aim is to produce a polygon layer with the eligible bus shelters (i.e., locations away from trees and with shelters).  
Figure 3: Solar resource analyses at the city scale and selection of the solar bus shelters.  
This layer already has the dimensions of each shelter, a key element when performing PV system design and sizing.
* After modeling the solar resource and mapping the suitable bus shelters, the amount of energy that reaches each bus shelter is quantified through an overlay analysis.
* The methodology continues with the assessment of the technical potential of the shelters with the best geographic conditions. To select the shelters with higher irradiation, and consequently less shadowing, the Direct Irradiance Fraction (DIF) is proposed. This quality factor is used to describe the amount of insolation that an array will capture (as a percentage), relative to what an optimally oriented, unshaded array would capture in the same location. Therefore, the DIF is calculated for each bus shelter, and only those having a value higher than 70% are found to meet the geographic criteria making them suitable for a PV panel. Note that this threshold is adjustable upon financial analysis. In fact, according to the PV installation cost, the value can be more or less restrictive. The result is a layer with the location of shelters with higher solar radiation in the city. Applying the PV efficiency, the energy produced at each bus shelter is calculated. The final output is the map with the best production sites according to geographical and technical criteria, i.e., the city's solar bus shelters.  
The PV potential of each solar bus shelter is then compared with the energy required by some typified applications:  
* Level 1 applications require less energy and include remote small-scale sensors. Based on the concept of the Internet of Things (IoT), such sensors can deliver public information or gather data about the frequency of commuters in a shelter at a certain time, increasing the quality of the bus service.
* Level 2 includes non-refrigerated vending machines or LCDs up to 20'. These amenities can provide personal hygiene items or real-time information on a digital screen.
* Level 3 applications include larger digital displays and ticket machines.
* Level 4 includes the most energy-demanding applications like refrigerated vending machines that offer commuters hot or cold drinks, snacks or food.
* Additionally, the illumination capacity of each shelter is investigated.  
All these applications constitute added value initiatives that can improve the quality of the public space and the services offered to the users.