RENEWABLE GALVESTON: SOLAR
PHOTOVOLTAIC FEASIBILITY STUDY
Prepared for the Galveston Housing Authority by Stanford Students
Environmental Consulting
1. EXECUTIVE SUMMARY
The Galveston Housing Authority (GHA) has requested that Stanford Students
Environmental Consulting (SSEC) determine whether or not solar photovoltaic (PV)
systems are a viable technology to reduce electricity bills for tenants living with
assistance from or in properties owned by the GHA. This document outlines the
analyses performed by SSEC’s engineering and financial teams. Although GHA’s
facilities are capable of accommodating PV systems, we have concluded that an
investment into solar PV is not financially attractive or advisable at this time.
2. ENGINEERING ANALYSIS
We assessed the solar panel installation capacity of each building using roof
schematics and satellite imagery. This information, along with local meteorological
data, allowed us to simulate hourly electricity generation from each PV system using
PVsyst, the industry-standard software package for this type of analysis.
2.1 DATA USED FOR ANALYSIS
2.1.1 GHA building schematics
GHA requested that we study 21 houses with 34 units in total from mixed-income
communities. The addresses of the houses are summarized in the appendix Table
A1. Representative housing plans, including Roof Plan, Landscape Plan, Survey and
Elevation Plan, were also provided to extract information such as Length, Width,
Azimuth and Roof Tile for sizing analysis. Those houses can be separated into 3
kinds of housing: Single-family, Duplex and Quadplex. Houses were classified
according to the type and labeled with specific building number accordingly, as
shown in Table A1.
2.1.2 Google Earth imagery
After analyzing the roof plans, we used Google Earth to observe if the surrounding
foliage or larger houses might shade the solar panels. Shading the solar panels
would make them less efficient. Although the images of Galveston were taken on
February 15, 2011, we assumed that no abnormal events since Hurricane Ike have
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drastically altered Galveston’s environment. From an aerial view, we could survey
nearby houses and potentially large trees. If a house had potential obstructions, we
used “Street View” to compare the house to the height of neighboring houses and
trees.
2.2 METHODOLOGY
2.2.1 Google Earth shading estimates
We used the GHA housing schematics and Google Earth imagery to estimate where
solar panels should be located on each roof. Housing schematics were provided for
most addresses. Roof tilt angle, azimuth and area data is compiled in Table A2. We
used Google Earth to compare similar houses for which schematics were not
provided. If the unknown houses had similar exteriors to ones laid out in the
schematics, we assumed that the unknown houses had identical roof area, tilt and
azimuth.
Figure 2.1: Illustrating the solar panel azimuth angle (ΦC) and tilt angle (Σ) along
with the solar azimuth angle (ΦS) and altitude angle (β). Courtesy Masters, 2005
The exterior elevation plans given in the housing schematics indicated that all of the
houses had a 4:12 ratio as seen in Figure 2.1. Thus, every roof is tilted 18 degrees
from the ground.
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Figure 2.2: Exterior elevation plans indicates the roof’s tilt angle. Courtesy GHA
Solar panels are generally installed on south-facing sides of roofs where they receive
the greatest exposure to sunlight. We found that duplexes typically had two sides
whereas singles and quads had four sides. For two-sided roofs, we only calculated
the southern-most side’s azimuth. For four-sided roofs, we calculated the azimuths
for the two adjacent sides facing closest to south. We used each property’s
orientation – recorded the housing schematic’s “Survey” – to calculate the azimuth.
The roof plans in the housing schematics indicated the dimensions of the roof, which
allowed us to calculate each roof’s surface area. We used Google Earth to observe
each house’s surroundings, thus allowing us to calculate how much roof area the
solar panels would occupy. We searched for tall neighboring houses and trees, as
those obstructions could shade parts of the south-facing roofs and reduce the
performance of the solar panels. Based on the number of possible obstructions and
their proximities to the houses, we estimated how much of the south-facing roof
area would be useful for solar energy by multiplying the roof area by a percentage
between 0 and 100%. For example, if a house had one tall tree in the south yard, its
useable roof area might be 70% of the area. We rounded these percentage roof
areas down to the nearest multiple of 5%. The resulting useable surface area
estimates are listed in Table A2.
2.3 PVSYST MODELING
First, we uploaded NREL meteorological data for Galveston to simulate local
conditions such as temperature and cloudiness. Next, we classified roofs with
approximately the same azimuth and area into a “roof type.” There are a total of 17
roof types in this study.
We then conducted 17 PVsyst modeling runs, one for each different roof type. In the
system design for each roof type, we chose among the SunPower solar panels that
would better fit the roof area, and chose an inverter that would meet all the
constraints in the PVsyst modeling. In this study, we have tried to minimize the
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solar panel and inverter types to two, as shown in Table A2. The number of solar
panels and inverters were calculated automatically in PVsyst.
Next, we simulated hourly electricity production from the full PV system and
compiled the raw data into hourly averages for each month. A sample for Roof Type
1 is shown in Table A3. We also extracted the system size from the PVsyst report
and summarized them in Table A2.
Finally, we estimated the electricity production for each building by scaling the
electricity production for its Roof Type by the individual buildings’ system size.
System size and electricity generation for each building is summarized in Table A4.
3. FINANCIAL ANALYSIS
We built a financial model that could be used to estimate the economic payback of
individual PV systems. The model accepts various inputs and predicts the economic
payback of each candidate system by computing probable hourly electricity output
over a 30-year time period
3.1 MODEL DEVELOPMENT
3.1.1 Overview of Inputs
Inputs to the model can be broken down into three broad categories:
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Financial: capital costs of solar systems, applicable federal and state
rebates and incentives, incentives offered by local utility, electricity
rates from local utility for both consumption (buying) and generation
(selling) power to and from the grid.
Performance: amount of electricity generated by the solar systems,
amount of electricity consumed by each tenant family.
Assumptions: O&M costs, system degradation, inflation rate, utility
escalation rate, GHA cost of capital (discount rate)
3.1.2 Financial Inputs
Capital costs of installing a PV system were referenced from Tracking The Sun IV
(Wiser et al, 2011), using the data for Texas rather than the national average.
Installed costs ranged from $8.81/W for systems less than 1kW in size, down to
$5.91/W for systems larger than 10kW but smaller than 30kW.
Despite extensive research, we were unable to find any applicable tariffs or rebates
beyond the 30% Federal Investment Tax Credit. It remains unclear whether the
GHA would in fact qualify for this incentive, though we have assumed that they
would. Several municipalities in the area around Galveston have utilities that offer a
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rebate on system capital cost on the order of $1.50/W (around 25%).
Unfortunately, Reliant Energy, the utility provider for tenants, does not offer such a
rebate.
Reliant Energy does offer a feed-in tariff for electricity generated by residential PV
systems and fed into the grid. The tariff is equal to the applicable purchase price of
electricity for the first 500kWh/month fed into the grid, and a flat $0.05/kWh
thereafter.
The aforementioned purchase price of electricity varies depending on which rate
plan the GHA homes are contracted under. We assumed the most expensive rate
plan, for lack of information regarding the exact contracted rate plan. This gives the
solar systems the best chance of being competitive. The rate consists of a fixed
generation charge if the monthly demand is below 800 kWh, a per kWh usage
charge, a fixed transmission charge and a per kWh transmission charge. We
assumed that revenue from selling surplus energy back to the grid consisted of only
the variable charges (per kWh) and not the fixed components.
3.1.3 Performance Inputs
The financial model took the electricity production data from PVsyst as an input.
These data took the form of hourly electrical output from the PV systems for each
hour of the year. This electrical production was also reduced by 0.5% per year in
the model across the 30-year projected system lifetime to account for system
degradation.
The model also took as an input the estimated amount of electricity consumed by
each GHA tenant each day for each month of the year. To get this data, first we took
back-casted load profile data from ERCOT’s website and compared it to sample GHA
tenant electricity bills. We used the data for ERCOT’s residential coastal zone, high
winter scenario (RESHIWR_COAST) as the monthly pattern of this load profile most
closely matched the sample bills we had received from the GHA. The scenario
assumes that buildings use an electric heater, which is in fact the case for GHA’s
facilities. Next, we used a simple floor plan area ratio method to scale the electricity
demand from the sample bills to all the other properties, depending on their size
and number of bedrooms. Armed with all this data, we were able to estimate hourly
electricity consumption for each of the candidate properties in the study.
3.1.4 Assumptions
It was necessary to make some assumptions in order to complete the data input.
The following list documents the most important assumptions:
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Annual maintenance costs: $27/kW/year (source: EPRI)
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Average utility rate escalation: 5.0% / year
Average inflation rate: 3.0% / year
GHA cost of capital (discount rate): 4% / year
The values given above were taken as base values but our analysis included
sensitivity studies to investigate the effect of varying these assumptions.
3.2 OUTPUTS
3.2.1 Energy Consumption and Production
The financial model subtracts the electrical demand for each property for each hour
of the year from the electrical output of the PV system at that hour, and thus
calculates the change in net electricity demand from the grid to the case without
solar system installed (see Figure 3.1). The upper panel shows hourly energy
produced by a 10.8kW PV system for a July day compared to the estimated energy
consumption data for the building. The lower panel shows the net electricity flow
between the property and the grid: red bars indicate that electricity is being bought
from the grid, blue bars indicate there is a net export of electricity back to the grid
(i.e., the property’s entire electrical needs are being supplied by the PV system, and
there is some electricity left over which can be sold to the grid at the feed-in rate).
Figure 3.1a-b PV system performance on a July day
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3.2.2 Levelized Cost of Energy
The model calculates the change in monthly electricity bill for each tenant that
would be brought about by installing a solar PV system. It does this on a monthly
basis for the projected 30-year project lifespan. The model calculates a net present
value (NPV) by discounting future savings at the assumed cost of capital, subtracting
the similarly discounted operations and maintenance (O&M) costs, and subtracting
the capital cost of installing the system. A NPV greater than zero indicates that the
investment is worthwhile, whereas a NPV less than zero indicates a bad investment.
We also used the discounting principle to calculate a levelized cost of energy (LCOE).
LCOE spreads the capital, O&M and fuel costs (which are zero for solar) across all
the energy produced by the systems over its lifetime, and discounts to the present
day to give a single $/kWh figure. If this figure is lower than the price currently paid
to the utility to purchase electricity, then it can be concluded that the solar system is
a good investment. If it is higher, then it is likely a bad investment.
3.2.3 Model Structure
The model structure is shown in Figure 3.2, which shows data for the first year of
the system life. This analysis repeated for the entire 30-year projected system
lifetime. The NPV and LCOE values are computed from the data in the table.
Figure 3.2 Financial model structure
3.3 RESULTS
3.3.1 Summary
A summary of the results for six selected GHA properties is shown in Table 3.1. It
can be seen that, unfortunately, none of the systems are financially attractive. All
systems have negative NPVs and their LCOEs are more than double the average
electricity price from Reliant Energy (~$0.10/kWh). It can also be seen that the
larger the system size, the more financially unattractive it is.
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Table 3.1: Select model output
There are three main explanations for this disappointing result. First, the solar
resource in Galveston, while good, is not excellent, due to its proximity to the ocean
and thus the relativity high percentage of cloudy days compared to other places at
similar latitudes. Second the lack of any state or local incentives means that system
purchasers must pay the full install price for their systems (less the 30% federal
investment tax credit). Third, and perhaps most importantly, the low retail
electricity rates in Texas, and Galveston specifically, make it hard for solar to be
competitive with grid electricity in comparison to other states where the electricity
prices are higher. We explored the second and third drivers for poor economics in
the sensitivity analysis section.
3.3.2 Comparison to GHA Utility Subsidies
GHA provides its tenants with partial or full assistance on their electricity bills,
depending on unit type and size. We estimated the savings on monthly electricity
bills over the lifetime of the PV system in order to determine whether or not it
would make sense as a replacement for GHA’s existing subsidies. Figure 3.3 shows
that GHA would only begin saving a small amount of money on its monthly
electricity bill by 2030, while real savings only begin to accrue after 2040.
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Figure 3.3 Monthly savings on electricity bills over 30 years
3.3.3 Sensitivity Analysis
In order to fully understand the results of the model, we carried out several
sensitivity analyses to understand what effect certain parameters had on the output.
Looking first at installed cost of the system, we found that the capital cost per watt
would have to drop to around $2/W before the solar systems would return a
positive NPV (Figure 3.4a). This compares to a current real price of close to $6/W.
Put another way, it would require a capital rebate of more than $4/W to result in a
positive NPV (Figure 3.3b). This is unlikely in the near term.
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Figure 3.4a-b Sensitivity analysis on PV system capital costs and rebate
Next, we examined the role that retail electricity price plays in the economic
performance of a solar system. Electricity prices in Galveston ($0.10/kWh) are far
lower than those in three other states that currently have the highest installed solar
capacity: California, New Jersey and Hawai’i. Figure 3.5shows the effect of varying
the electricity rate in the absence of any state subsidies or incentives (and also
assuming Galveston’s solar resource). It is seen that the higher electricity prices in
California and New Jersey improve the economics, but these states still rely on
incentives to make solar systems financially attractive (notwithstanding the
somewhat better solar resource in California, and worse resource in New Jersey).
Only Hawai’i, with its extremely high average electricity price of $0.25/kWh, sees a
positive economic return from solar in the absence of subsidies and incentives.
Figure 3.5 Sensitivity analysis on electricity rate
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Finally, we conducted a simple what-if analysis: what if Galveston were subject to
the same $0.13/kWh average electricity prices as California, and a $2/W state or
local utility rebate was offered? It can be seen in Figure 3.6 that under those
conditions, all sizes of solar system would generate a positive NPV, making such an
investment financially attractive. This also suggests that if solar costs continue to
fall as rapidly as they have done for the last few years, and retail electricity prices
continue to rise, it may be worthwhile repeating this analysis in the near future as
the economics of solar in Galveston are likely to improve rapidly over the coming
years.
Figure 3.6 “What if” scenario of PV system installation in California.
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