Identifying Economically Feasible Locations for Solar PV at the Click of a Button

Transcription

1 Identifying Economically Feasible Locations for Solar PV at the Click of a Button Prepared for the State of California Author: Simon Fowell Simon Fowell Master of Development Practice at U.C. Berkeley T: E: simon.fowelluk@gmail.com

2 The Challenge for a Cleaner Energy System Climate change needs little introduction. Unsustainable use of fossil fuels have led to increasing carbon dioxide levels with the repercussions being felt globally in the form of more extreme and less predictable weather. The energy use of buildings is a major contributing factor to climate change. In 2014, buildings accounted for 41% of energy use in the U.S. (EIA, 2015). Building codes and environmental building standards, such as LEED, are trying to address this problem by reducing the energy use intensity of new buildings. However, this does not address the vast number of existing buildings (commercial and residential) that are environmentally inferior. Therefore one of the solutions is to place photovoltaics on existing buildings to generate a cleaner source of energy, which can either be directly used on site or sold back to the grid. Despite solar panels often being synonymous with the green movement, businesses and households are not sure whether putting solar panels on their roofs is financially viable. Current potential customers have to actively seek out a quote from solar PV companies, or companies individually target one customer determining the potential for solar one house at a time. This is a slow process. It begs the need for a targeted approach that pre- emptively identifies specific buildings that will be financially viable. If this can be done at the click of a button, instead of property by property, the solar revolution that is really needed may be sparked. 2

3 Solution/Methodology Conceptual Model Challenge: Challenge: Effects of climate change As previously stated, Carbon Dioxide emissions from electricity generation are causing climate change, which is affecting people around the globe including those in the USA. Solar companies have to determine the economic feasibility of buildings on a case- by- case basis, which slows progress in CO2 reduction. Requirement: Requirement: Reduce CO2 in electricity As part of reducing the impacts of climate change, not only do we need to reduce the amount of energy we consume, we need to reduce the CO2 intensity of electricity we do use. A piece of this puzzle can be achieved through solar PV. Objective: Objecave: Increase solar PV uptake The question we are trying to solve is determining which properties are economically viable to house solar PV panels and how we speed uptake of solar. Soluaon: Easier idenaficaaon $ benefits High- Level Solution: The solution that is proposed in this report is to create a mechanism through which viable buildings can be identified at the click of a button. Building a model within Esri s ArcMap software using solar radiation tools is seen as the best way to achieve this. 3

4 Illustrating the Dollar Benefit of Solar PV Brainstorm: An important aspect of any problem solving is to think. One must ask: what data do we need and how must we manipulate it in order to solve the problem? To work out solar potential of buildings, it seemed necessary to find the following: building footprint; aspect of each roof face; trees that may block sunlight or reduce available roof area by overhanging onto roofs; the solar radiation of each roof; electricity price per kwh; and the cost of solar PVs per square meter. Data Collection: The outer- most circle represents data collection. Environmental, social, geo- political, and economic data is collected. The data, their source, and the format they come in are displayed in the surrounding tables. Data Processing: Different processing and mathematical operations will be applied to these data in Esri s ArcMap GIS software. These manipulations are illustrated in the inner ring. The processing will be conducted for an entire year s worth of solar radiation and then iterated for each season to determine feasibility throughout the year, not just for the entire year. Results This processing will generate the results seen in the center. We want to determine how much energy can be produced on site, how much this translates to in energy savings per year (or per season); and then potentially work out what the payback year would be. These results will be produced for a small section of the overall target area as a pilot in order to save on processing power. Once the model is refined, the process can be conducted on larger areas.. Figure 1: Conceptual Model of Solar PV viability 4

5 Spatial Data Model Geographic Boundary: This study initially focuses on a section of Alameda for the sake of processing power, but will be scaled up to the San Francisco Bay Area, and potentially further across California. Spatial Resolution: Metric Spatial resolution Interpretation NAIP 1 x 1 meter Each pixel in the NAIP imagery is 1 square meter Lidar 3 inch Each point is roughly 3 inches apart Economic 1) Parcel The results show the potential economic benefit to each property. Temporal Resolution: Solar radiation The temporal scale that we are looking at is an entire year (in this case 2016) for the solar radiation model. We all break this down into each season: spring, summer, fall, and winter. Economic model The model estimates the payback year of purchasing solar panels. Currently, it assumes that the upfront cost is paid in full in year 0 and the money saved on utilities is constant over time. As the model s sophistication improves, it may be able to incorporate electricity price inflation over time, as well as different payment schedules, thus more accurately determine the payback period. Figure 2: UTM zones for USA SF Bay Area is in zone 10. Source: (James McNutt, n.d.) Projection and Datum: This report uses the North American Datum (NAD) 1983 and is projected in Universal Transverse Mercator (UTM) Zone 10, which encompasses Alameda. All data was re- projected (if necessary) into UTM Zone 10, ensuring that everything is in the same projection. 5

6 Data Data needs and discovery Various types of data from various sources will be needed in order to produce the desired results. The following table illustrates the data that will be required. All data will be projected in UTM Zone 10 NAD 1983 and clipped by the pilot project extent before any modeling is conducted. Variable Model/format Reasoning Source City limit Vector/ shape file This frames the analysis to the location we Alameda County are focusing on website I need to know where buildings are and how Building big their footprint is in order to determine Radke Oakland Vector/ shape file footprint how much energy they use and how much data library* they can generate. Building address/ contact info Building height/ elevation Trees kwh per square foot Solar PV cost per square foot Solar PV efficiency Usable roof area Price of electricity String, as part of the building footprint layer Digital surface model in Lidar form. Vector Area solar radiation model Number in a field Number in a field Number in a field Number in a field Once we have identified profitable locations, we need a mechanism of contacting them. This will be an input into the solar radiation tool in ArcGIS. By knowing the height of objects, I will be able to estimate how much energy (in kwh) can be generated on a particular site) It is useful to know if any trees are overhanging on roofs because this will take up valuable PV space. This ArcGIS tool estimates the yearly electricity generation (kwh) for a particular specified area. This will be needed so that we can work out the payback year. PV panels cannot convert all solar radiation into electricity, and there is efficiency loss Not all the roof can be used. Perhaps there are coolers that are in the way, or there is shade. This will be needed so that we can work out the payback year. Radke Oakland data library Radke Oakland data library USGS 4 band NAIP imagery Produced in the model Industry source for solar PV costs Industry source for solar efficiency Industry source Industry source for purchase power agreement Notes: * In the future, if we are unable to acquire building footprints, we can use 4- band NAIP imagery and use ISO classification or dynamic segmentation to identify non- pervious surfaces (pavements, roads, and roofs etc.). We can then subtract the Digital Elevation Model from the Digital Surface Model to identify non- pervious surfaces that are higher in elevation (thus identifying solely roofs). Converting these from raster to polygon will give us the outline of the buildings in vector format. 6

7 Mathematical Model/Flowchart The figure below illustrates the mathematical model used to achieve the desired results. Data inputs are represented by blue; Green items represent processes; Red items represent results. Calculating the financial viability of each house is done in 6 main stages: 1) finding trees that may be in the way of roof space; 2) identifying areas of roof that have favorable aspects; and 3) erasing one from the other to find available roof area; 4) performing a solar radiation; 5) calculating energy generation for each house; and 6) performing an economic analysis. 1. Finding trees Trees are found by performing ISO unsupervised classification on 4- band NAIP imagery. Using 3 classes gave the best representation of vegetation. This is reclassified so that only the vegetation band is shown. However, we want to find trees, not just vegetation. Therefore we need to find tall vegetation. To do this, we perform a raster calculator and subtract Digital Elevation Model from Digital Surface Model in order to find the height of everything. We then reclassify this so that only objects greater than 2.5m are left. By clipping vegetation by objects that are greater than 2.5m leaves only vegetation that is taller than 2.5m, which in this case, we are calling trees. Figure 3: Finding Trees Using GIS Figure 4: Finding Trees Using GIS 7

8 2. Finding favorable roof aspects Favorable aspects are roof faces that face NE, E, SE, S, and SW as these are likely to catch the most sun. Therefore, one would only like to put PV panels on these faces. As the model expands to other areas, it may be necessary to include westerly- facing roofs. These favorable roof aspects are found by clipping the DSM by building footprints to just leave the DSM of buildings. We can perform an aspect function in ArcMap, which reveals each pixel s aspect. We can then reclassify these so that only favorable aspects are left, then we can convert it to a polygon. Figure 6: Finding Sunny Aspects of Roofs Figure 5: Finding Sunny Aspects of Roofs 8

9 3. Finding available roof area 5. Calculate electricity generation for each house Available roof area to house PVs is found by erasing trees from the favorable/sunny aspects. This leaves roof areas that face the sun and do are not covered by tree canopy. 4. Solar radiation Use zonal statistics to calculate the sum of the solar radiation for each building and spatially join this to the building shapefile. As solar PV panels are ~15% efficient (Kelly- Detwiler, 2013), we multiply incoming solar radiation by 15% to calculate the electricity generation of the available roof area. We perform an area solar radiation tool to the DSM to calculate the incoming solar radiation in kwh for each square meter. 6. Economic analysis As electricity is roughly $0.10 per kwh (EIA, 2016), we multiply the kwh generated by $0.10 to calculate the annual savings from housing PV panels. This is iterated for spring, summer, fall, and winter to illustrate any seasonal effects. For the yearly savings, we also include the cost of installing PV panels at $75 per square meter(gogreensolar, 2016). By dividing the total installation cost by the annual savings, we can calculate a year that the PV panels pay for themselves. Figure 7: The Whole Model to Determine Economic Viability of Solar PV Figure 8: The Whole Model to Determine Economic Viability of Solar PV 9

10 Results Seasonal results Figure 9 illustrates the savings each building will generate per season at a price of electricity of $0.10 per kwh. It is important to note that the landscape used in this pilot is fairly homogenous with little variation in slope, building height, or natural features. As a result, although there are differences in the amount each building saves over each season, the differences are fairly subtle. Nevertheless, due to the variation in building size in our sample, some buildings generate up to $6,000- $16,000 in savings in some per season, whereas others are generating less than $25. As we look across each month, we can see that the color generally gets warmer as we move from Spring to Summer, and then gets cooler as we move to Fall and then on to Winter. Looking at the statistics, the average savings for this sample in Spring is $838 per building, in Summer it equals $857, Fall is $326, and Winter is $322. We can see that there is quite a dramatic difference in the savings, even though it may not be immediately evident from the picture. Mean savings by season ($ per building) Figure 9: Seasonal Savings ($ Saved per Building) $900 $800 $700 $600 $500 $400 $300 $200 $100 $- Spring Summer Fall Winter Spring Summer Fall Winter 10

11 Identifying Economically Feasible Locations for Solar PV at the Click of a Button Yearly results Figure 10: Annual Savings from Solar PV Figure 11: Payback Year of Solar PVs The results of the model for the yearly savings yield some useful results. Figure 10 illustrates the annual savings that can be generated at $0.10 per kwh, and Figure 11 shows the payback year (cost of installation divided by annual savings) with an installation cost of $75 per square meter of solar PV. From this sample, the mean annual savings per year from owning solar PVs is $2,931 (median = $2,023). Given these figures, overall there seems to be a fair amount of potential for properties to house solar PV panels. At the low end of the spectrum, some properties are generating less than $250 per year. However, at the high end of the spectrum, some properties are generating more than $20,000 worth of electricity per year. When we look at the payback year, none of the potential properties to house solar PVs can pay for themselves before 5 years. However, there are many houses where the payback year is between 5 and 7 years. For an average homeowner on a 25- year mortgage, this probably represents a good investment. The worst payback in our sample is around 14 years, given our assumptions of the available roof area, electricity price and cost of solar PVs. We can see from the graphics that some buildings that look similar in size may generate different amounts of electricity, and thus have different viabilities. It would be interesting to determine whether there are some underlying characteristics that make one house more economically feasible than a seemingly similar house. Without doing an in- depth econometric analysis, we can still determine a strong hypothesis by visually assessing the GIS outputs. It seems that properties with more southerly- facing roof faces, and those without trees overhanging the roof have considerably higher economic viability than houses that are similar in size but have less south- facing aspects. 11

12 Conclusion Merits and long term effects Although the sample that I used was very small (due to processing time needed), there are some useful take- aways from a planning context. This model could be scaled up fairly easily to identify which houses are more economically feasible than others. Whoever has this information, it is hoped that this tool catalyzes a quicker change in the solar industry so that more people go toward solar. Government: From the public policy viewpoint, it could be used by local/ regional/ federal governments to identify properties (or types of properties) with which to give subsidies in order to push them over the edge toward solar. For a solar company: If this information was in the hands of a solar company, they would be able to have a far more targeted approach to customer acquisition saving time by being able to identify customers where it is actually worth their while. Individual property owner: If individuals have access to this information they would be able to decide for themselves whether or not they should go solar. Build an Online Community Outreach Tool Moving forward from this model, it would be great if I could build an interactive online platform. The vision for this would be a slick- looking system where you type in your address and your monthly energy bill/usage and then a NAIP image of the building shows up with some fast facts about the property, such as: available roof area; kwh potential; size of solar installment required; potential cost of installment; annual savings; and expected payback year. Furthermore, it would be good if it also had links to local solar companies so you could call them up after seeing your results. Lessons learned This modeling project took a lot of thinking power. That is where most of the time was spent just thinking conceptually and logically about what data was required and what should be done to that data in order to get a useful result. For this project, I was keen to try and get results early in order to save time. However, this actually slowed me down because these early results were inadequate and my logic was flawed. Therefore, for future research projects, I will not get so worried about spending more time making sure the logic of my model is sound, and that I have all the data I need before starting to try and generate results. 12

13 References EIA. (2015). How much energy is consumed in residential and commercial buildings in the United States? Retrieved from EIA. (2016). Table 5.6.A. Average Price of Electricity to Ultimate Customers by End- Use Sector. Retrieved May 8, 2016, from GoGreenSolar. (2016) Watt (5kW) DIY Solar Install Kit w/microinverters. Retrieved from diy- solar- panel- kit- microinverter James McNutt. (n.d.). Navigation using UTM. Retrieved March 3, 2016, from Kelly- Detwiler, P. (2013). As Solar Panel Efficiencies Keep Improving, It s Time To Adopt Some New Metrics. Retrieved from solar- panel- efficiencies- keep- improving- its- time- to- adopt- some- new- metrics/#793f260f49d7 Further resources: Jill B. Kjellsson Michael E. Webber, The Energy- Water Nexus: Spatially- Resolved Analysis of the Potential for Desalinating Brackish Groundwater by Use of Solar Energy. Resources, vol: 4 (3) pp: Easan Drury, Anthony Lopez, Paul Denholm, Robert Margolis, Relative performance of tracking versus fixed tilt photovoltaic systems in the USA. Progress in Photovoltaics: Research and Applications. Volume 22, Issue 12, pages Joshua D. Rhodes, When Will Rooftop Solar Be Cheaper Than the Grid? US News. 31/when- will- rooftop- solar- be- cheaper- than- the- grid 13