Jacob Gross

4-23-2020

Professor Xiaojiang Li

Analyzing Temple University’s Solar Power Potential

Introduction

In 2019, Temple University published its revised Climate Action Plan. As a part of their mission to make Temple a greener campus, the plan contains several goals related to renewable energy, two of these being to “Develop at least 100+ kW of renewable energy systems at Temple’s facilities by 2022,” and to “Reduce energy use in existing building stock by 18% in a typical climatic year by 2033.” In light of these goals, I wondered how GIS could be used to fulfill the proposed mission of the Climate Action Plan. Since I am very interested in solar and its potential and an alternative, sustainable energy source, I was driven to determine the solar capacity of Temple University. Therefore, my research question for this project was, “If Temple University were to invest in solar panels as part of its Climate Action Plan, where would be the optimal locations to place the panels, how much energy could be generated, and would it eventually be worth the cost of installation?”

Data and Methods

A tutorial posted by ESRI was used as guidance throughout the process of this research project. The first step for this project was acquiring the LIDAR data required to create a DSM. This was gathered from the PASDA website using the file ‘Philadelphia LiDAR - LAS Files 2018.’ With this, a DSM was created from all first-return points, and a hillshade was subsequently created from the DSM for analysis purposes. The tutorial called for building footprints, so next, a polygonal shapefile was created, and building footprints were laid on top of all Temple University-owned rooftops that appeared suitable for solar panel placement (figure 1). Google maps and a campus map were used to identify all university-owned, suitable rooftops. Next, the Area Solar Radiation Tool was used in ArcMap to analyze how much solar energy each suitable rooftop would receive. This was run calculating solar energy over the course of a year, at 1.5 hr. intervals. Also, I used the building footprint shapefiles as a mask to reduce calculation time. (I recommend not running this on a slower computer. My laptop with an i5 processor and SSD took an entire day to run the calculations.)

After receiving solar radiation (figure 2), the information was in Wh/m2. The next step was to convert the data to kWh/m2, so I simply used the Raster Calculator tool was used to divide solar radiation by 1000. This new data representation was then symbolized using a blue to red symbology. Next, all optimal rooftops needed to be identified out of all the building footprints. For this there were three criteria: 1.) Suitable rooftops should have a slope of 45 degrees or less, 2.) Suitable rooftops should receive at least 800 kWh/m2 of solar radiation, and

3.) Suitable rooftops should not face north, as north-facing rooftops in the northern hemisphere receive less sunlight.

To identify roofs that fulfilled these criteria, first a slope layer was created based on DSM. The DSM was put into ArcMap’s slope tool. This gave a layer representing all slopes in the DSM. Next, an aspect layer was created again with the DSM. This represented all directions, with 0 representing absolute north, and 180 for absolute south. The Con tool was used to make the final layer showing optimal roofs. This tool allows you to make a conditional statement. If the value is true, it will input information from one layer. If false, it can input nothing or input information from another layer. The first Con expression was for slope. The conditional statement was if the roof slope is less than or equal to 45 degrees, input the solar radiation data. If false, the field was left null. Next, the con tool was used to identify all areas with more than 800 kWh/m2 of radiation, with the expression If VALUE (Solar Radiation) is greater than or equal to 800, input the solar radiation data. If false, the field was null. Finally the Con tool was used again for direction aspect, where all areas that fell within the range of “North” were eliminated from the solar radiation layer.

After this process of selecting the optimal roofs, I had a shapefile of all optimal locations for solar panels (figure 3). Next, the Zonal Statistics as Table tool was used to calculate the average solar radiation per building. Using this tool, the mean solar radiation was calculated for each rooftop polygon. The resulting table was joined to the building footprint shapefile, with the join field being each rooftop unique FID. Included in this table was each rooftops area and mean solar radiation. Then, each rooftop with less than 30 m2 of area was removed, since this is considered too small for solar panel installation. For this, the Select Layer by Attribute tool was used.

The final steps were to create a solar radiation field to represent the total solar radiation received per year by each building’s usable area. For this, a new field was added to the building footprint shapefile with the joined solar radiation and area table. Then, I multiplied the solar radiation (in kwh/m2) by its area (m2), so just kWh was left. This figure was again divided by 1000 so results were in mWh, thus reducing number size. According to the tutorial, the EPA estimate of 15 percent efficiency and 86 percent performance ratio. Therefore, the solar radiation in mWh was multiplied by .15, then .86 to account for inefficiencies in the solar panels.

The last thing to do was symbolize the data. This is shown in the resulting maps (figure 4 and figure 5).

Results

The results of this project showed that a.) Temple’s greatest solar power generators would be the Liacouras Center and Pearlman Hall. b.) The selected roof areas suitable for solar panels covered 316,702 m2 or 78 acres, and c.) Altogether, if solar panels were placed within all selected locations, 47,306 MWh could be produced within a year. The resulting maps can be seen in figures 4 and 5 at the end of this report.

Conclusion

This figure is reasonable for the area given but is an immense amount of energy (the average U.S. home uses around 10 mWh yearly. It is likely that it would not be possible to place solar panels on all of the selected locations. It would be interesting for another project to see how solar panels are placed, and if certain obstacles like rooftop ventilation can get in the way of solar panel placement. My original research question asked, “where would be the optimal locations to place the panels, how much energy could be generated, and would it eventually be worth the cost of installation?” This project clearly answered my question on the optimal locations. It also showed me the energy generated from this area, at 47,306 mWh. However, I could not answer the question on if installation would be worth the cost. This is because it is not that simple and clear to price solar panels. They are often priced in kW, not area. kW is a measure of power, which is not easily converted to energy (kWh). Also, the price depends on if the system is grid attached, the panels used, as well as post-installation tax deductions and grid buyback. However, most importantly, this project proved that at 316,702 m2 (78 acres), Temple has plenty of sunny, open roof space for solar panels, passive solar heating systems, or perhaps even green roofs. Given this fact, in terms of sustainable energy, I believe Temple University could have a bright future.

Tables & Figures

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5