Measuring PV technical potential

and financial feasibility for U.S.

educational buildings

Nichole Hanus

Gabrielle Wong-Parodi, Inês Azevedo, and Alex Davis

USAEE18. Solar PV: Deployment and Impacts

11/13/2017

2

The U.S. relies on fossil fuels.

Coal, 30%

Nuclear, 20%Renewables, 8%

Hydroelectric, 7%

Petroleum, 0.6%

Other, 0.7%

Natural Gas, 34%

Total U.S. Electricity Generation, 2016 [1]

3

Energy services are the largest contributor to

greenhouse gases in the U.S.

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Em

issio

ns

(mill

ion

me

tric

to

ns o

f C

O2

eq

uiv

ale

nts

)

U.S. Greenhouse Gas Emissions by Economic Sector, 1990-2015 [2]

Commercial

Agriculture

Industry

Transportation

Electricity generation

Transportation = 28%

Industry = 22%

Electricity = 30%

4

School budgets could stretch with electricity

savings.

• The Department of Energy’s Better Buildings Challenge includes

higher education facilities in their goal to reduce building

energy consumption by 20% compared to a 2010 baseline [3]

• Annual energy costs: ~ $7.7 billion, or ~225K students’ tuition [4]

Photo 1: DOE, 2016

Photo 2: AASHE, 2017

5

School budgets could stretch with electricity

savings.

• The Department of Energy’s Better Buildings Challenge includes

higher education facilities in their goal to reduce building

energy consumption by 20% compared to a 2010 baseline [3]

• Annual energy costs: ~ $7.7 billion, or ~225K students’ tuition [4]

Purchase

RECs

Install EE

equipment

Retro-

commissioning

On-site

generation

Photo 7: DOE, 2016

Photo 8: AASHE, 2017

6

Research Questions

• What is the total potential electricity generation from solar PV across

the U.S. educational sector?

• What are the total lifetime cost and benefits – both private and public

– of installing rooftop solar PV systems on all U.S. educational

institutions today?

• What are the annualized per-kilowatt costs and benefits of solar PV

systems installed on educational institutions across the U.S.? Under

what circumstances does installation of the system pay off?

• How sensitive is the CBA to inputs such as discount factor, system

size, and the value of net generation (e.g. retail vs. LMP)?

7

Electricity

Price

($/kWh)

Electricity

Generated

by the PV

Panel,

when

consuming

(kWh/yr)

Electricity

Generated

by the PV

Panel, not

consuming

(kWh/yr)

Value at

which you

sell the

electricity

back to the

grid

($/kWh)

Installation

System

Price

($/kW)

Rebates

($/kW)

System

Size

(kW)

Annual Benefits Upfront Costs

Benefit-Cost Analysis (BCA) at the school level,

to be aggregated at county- or state-level

Using the present value of benefit annuities, where i = 7% or 2% and n = 20 years:

Data and Methods: School net benefits

𝑃1 − 1 + 𝑟 −𝑛

𝑟 Equations modified from Azevedo et al. [5]

8

CO2, NOX, PM2.5,

and SO2 avoided

damages ($/MWh)

at a given seasonal

hour of day

Electricity

Generated

by the PV

Panel

(MWh/yr)

Rebates

($/kW)

System

Size

(kW)

Annual Benefits Upfront Costs

BCA still at the school level,

to be aggregated at county- or state-level

Using the present value of benefit annuities, where i = 7% or 2% and n = 20 years:

Data and Methods: Social net benefits

𝑃1 − 1 + 𝑟 −𝑛

𝑟 Equations modified from Azevedo et al. [5]

9

Data and Methods: Building counts

Integrated

Postsecondary

Education Data System

(IPEDS) [6]

Common Core

of Data (CCD) [7]

Private School Universe

Survey (PSS) [8]

Compiler

National Center for

Education Statistics

(NCES) from Department

of Education

NCES NCES

School TypePrivate/Public Higher

EducationK-12 Public K-12 Private

Entries 7,647 102,799 26,983

Year Collected 2015 2015-16 School Year 2011-2012 School Year

Rate of Collection Annually Annually Biennial

After excluding schools with reported latitude and longitude values falling outside of the U.S. boundaries, the combined

dataset includes 134,137 schools.

10

Data and Methods: LIDAR processed data

NREL linked 38,022 schools

(28% of all schools)• 41% of higher education

• 26% of K12 public

• 35% of K12 private

Output: rooftop space (SF)

K12 Public

K12 Private

Higher Education

LIDAR Measurement

11

Data and Methods: LIDAR processed data

NREL linked 38,022 schools

(28% of all schools)• 41% of higher education

• 26% of K12 public

• 35% of K12 private

Output: rooftop space (SF)

Extrapolate LIDAR outputs to

buildings outside the 128 cities• County-level predictors( e.g.

Urban/rural distinction, median

household income, etc.)

• School-level predictors (e.g. school &

student population, NCAA affiliation,

etc.)

K12 Public

K12 Private

Higher Education

LIDAR Measurement

12

Data and Methods: Solar generation and

building load profiles• Calculated output from Typical Meteorological Year (TMY3) insolation data

using the method described in Vaishnav et al. (2017) [10]

• Department of Energy commercial reference buildings [11]

• “Secondary School” type • 210,000 SF• 2 floors• 1,200 students

• Hourly electricity load profiles (kW) – 8760 entries

TMY3 Stations

0

200

400

600

800

1000

1200

1400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Hou

rly E

lectr

icity L

oa

d (

kW

)

Hour of Day

Seattle, WA vs. Houston, TX - May 1

Seattle, WA Houston, TX

13

Data and Methods: Electricity prices and

installation costs• Electricity prices: EIA Annual Average for commercial – can use prices by state

to calculate the value of the portion displaced (or sold back) [12]

• Use the LBNL Tracking the Sun Database (latest project data: 2016) [13]

• Considering school, government, and non-profit projects • 1,046 projects out of 790,000 projects

• 11 states represented

Mean installed price $ in this dataset:

• $3,837/kW

• $559,100 overall

14

Data and Methods: PV Rebates and net-

metering• Referencing the DSIRE database: schools, rebates ($/kW) [14]

• Of the states offering such rebates:• Value ranges from $100/kW to $1,200/kW• Capacity limits range from 5 kW to 1 MW

• Currently, we are assuming that net-metering is available for schools in every state and that excess generation is valued at the LMP.

15

Data and Methods: Estimating environmental &

health benefits• Based on Siler-Evans et al.’s (2013) model in “Regional variations in the health,

environmental, and climate benefits from wind and solar generation” [15,16]

• Azevedo et al. updated model with 2016 emissions for each US fossil fuel power plant > 25 kW [17]

• Estimating Air pollution and Social Impact Using Regression (EASIUR) [18]

• Estimate the dispersion of pollutants and the resulting concentrations in all U.S. counties• Use dose-response function to estimate physical impacts• Monetize impacts (e.g. VSL $6-8.6M, market value of lost commodities, etc.)

Winter Spring Summer Fall

NH

3N

OX

SO

2P

M2.5 500k

100k

50k

10k

5k

1k

500

Ma

rgin

al S

ocia

l C

ost

[$/t

]

Photo 9: EASIUR, 2017

16

Preliminary Results: Positive social net

benefits, negative school net benefits

RQ1. We find a total installed power

electricity generation potential:

• 3,600 GW installed capacity

• 5,500 TWh of annual energy

RQ2. We arrive at the following net

benefits (LMP, 7% discount rate):

• School = -7,700 billion USD

• Social = 2,000 billion USD

17

Preliminary Results: States w/ available space

don’t always have highest energy potential

18

Preliminary Results: School benefits may vary by

retail rates; social benefits driven by energy mix

Some regions may have high

social benefits, but unfavorable

economics for school

19

Next Steps

• Refine regression analysis for available space estimation

• Aggregate results at the county- and state-level to develop

choropleth maps of school and social benefits across the

United States

20

Next Steps

• Refine regression analysis for available space estimation

• Aggregate results at the county- and state-level to develop

choropleth maps of school and social benefits across the

United States

• Further sensitivity analyses for the CBAs

• Discount factor

• Available rebates

• System size

• 20-year emissions/damages levels

• Avoided electricity charges

• Future studies might include interviews with actual decision-

makers to determine if our quantified benefits are valued

21

Thank you :)

22

Acknowledgements

This work was completed with support from the Engineering and Public Policy

Department at Carnegie Mellon. This material is also based upon work supported by

the National Science Foundation Graduate Research Fellowship Program under

Grant No. DGE1252522 and an international travel allowance through the Graduate

Research Opportunities Worldwide (GROW). Any opinions, findings, and conclusions

or recommendations expressed in this material are those of the author(s) and do not

necessarily reflect the views of the National Science Foundation.

My contact:

nhanus@andrew.cmu.edu

23

References

Photo links

1. https://betterbuildingssolutioncenter.energy.gov/sites/default/files/attachments/Winter_2016_Progress_Report_0.pdf

2. https://stars.aashe.org/institutions/participants-and-reports/

3. http://barney.ce.cmu.edu/~jinhyok/easiur/

Literature

1. Energy Information Agency (EIA). (2017). Electric Power Monthly (US Energy Information Administration, December 23, 2016). Available from: https://www.eia.gov/electricity/data/browser/

2. Environmental Protection Agency (EPA). (2017). Greenhouse Gas Inventory Data Explorer. Available from:

https://www3.epa.gov/climatechange/ghgemissions/inventoryexplorer/#allsectors/allgas/econsect/current

3. Department of Energy. (2016). Better Buildings Winter 2016 Progress Update. Available from:

https://betterbuildingssolutioncenter.energy.gov/sites/default/files/attachments/Winter_2016_Progress_Report_0.pdf

4. NCES. (2016). Fast Facts. Retrieved from https://nces.ed.gov/fastfacts/display.asp?id=76

5. Azevedo, I., Vaishnav, P., and Horner, N. (2016). Location-specific costs and benefits of rooftop solar photovoltaic. iTeam Presentation at Carnegie Mellon University.

6. NCES. (2015a). Common Core of Data. Retrieved from https://nces.ed.gov/ccd/pubschuniv.asp

7. NCES. (2015b). Integrated Postsecondary Education Data System. Retrieved from https://nces.ed.gov/ipeds/Home/UseTheData

8. NCES. (2015c). Private School Universe Survey. Retrieved from https://nces.ed.gov/surveys/pss/tableswhi.asp

9. Phillips, C., Melius, J., Gagnon, P., Margolis, R., & Elmore, R. (2016). A Data Mining Approach to Estimating Rooftop Photovoltaic Potential in the U . S ., 1–25.

10. Vaishnav, P., Horner, N., & Azevedo, I. L. (2017) Was it worthwhile? Where have the benefits of rooftop solar photovoltaic generation exceeded the cost? Environmental Research Letters.

11. DOE. (2017). Commercial Reference Buildings. Retrieved from https://energy.gov/eere/buildings/commercial-reference-buildings%0A

12. OpenEI. (2017). Utility Rate Database. Retrieved from http://en.openei.org/wiki/Utility_Rate_Database

13. Barbose, G. L., & Darghouth, N. R. Tracking the Sun IX: The Installed Price of Residential and Non-residential Photovalic Systems in the United States (Washington: US Department of

Energy, 2016). available at emp. lbl. gov/publications/trackin.

14. DSIRE database. Available at: http://ncsolarcen-prod.s3.amazonaws.com/wp-content/uploads/2017/03/Renewable-Portfolio-Standards.pdf

15. Siler-Evans, K., Azevedo, I. L., Morgan, M.G, Apt, J. (2013). Regional variations in the health, environmental, and climate benefits from wind and solar generation, Proceedings of the

National Academy of Sciences, 110 (29), 11768-11773

16. Siler-Evans. K., Azevedo, I.L., Morgan, M.G., (2012). Marginal emissions factors for the US electricity system. Environmental Science & Technology, 46 (9): 4742–4748.

17. Azevedo IL, Horner NC, Siler-Evans K, Vaishnav PT (2017). Electricity Marginal Factor Estimates. Center For Climate and Energy Decision Making. Pittsburgh: Carnegie Mellon University.

http://cedmcenter.org

18. Heo, J., Adams, P. J., & Gao, H. O. (2016a). Public Health Costs of Primary PM2.5 and Inorganic PM2.5 Precursor Emissions in the United States. Environmental Science and Technology,

50(11), 6061–6070. https://doi.org/10.1021/acs.est.5b06125

24

What role do schools play? [3]

Office20%

Mercantile17%

Food sales & service

11%

Education11%

Health care9%

Public8%

Other8%

Lodging7%

Warehouse & Storage

7%

Religious worship

2%

U.S. Electricity Consumption, 2012 (kWh)

25

What role do schools play? [3]

Office20%

Mercantile17%

Food sales & service

11%

Education11%

Health care9%

Public8%

Other8%

Lodging7%

Warehouse & Storage

7%

Religious worship

2%

U.S. Electricity Consumption, 2012 (kWh)

Office19%

Mercantile13%

Food sales & service

4%

Education14%

Health care5%

Public8%

Other10%

Lodging7%

Warehouse & Storage

15%

Religious worship

5%

U.S. Floor Space, 2012 (ft2)

26

Data overview

Building Counts

Integrated Postsecondary Education

Data System (IPEDS)

Common Core of Data (CCD)

Private School Universe Survey (PSS)

Solar Irradiance Data

NREL TMY3 Data – 936 locations

Building Load Profile

DOE – 936 locations

Cost & Rebates

LBNL Tracking the Sun

DSIRE

Electricity Prices

EIA 2015 Avg. for Commercial

Roof Data

NREL LIDAR Data

Google Earth

Quantified Damages

EASIUR Model

AP2 Model

27

What role does solar play?

• NREL reports that rooftop PV can deliver 39% of the total national electric-sector sales [6]

• 1,118 GW of installed capacity

• 1,432 TWh of annual energy

• In an economic analysis, NREL finds that building types with the highest breakeven price are small offices, warehouses, and schools [7]

• Barbose and Darghouth found that installed prices are higher for tax-exempt customer sites than for for-profit commercial sites [8]

• <= 500 kW: Δ $0.3/W

• > 500 kW: Δ $1.1/W

28

Model

overview

29

Available roof space

• NREL received LIDAR data from the Department

of Homeland Security (DHS) Homeland Security

Infrastructure Program (HSIP) [13]

• Covers 128 cities – representing 23% of U.S.

buildings and 40% of the U.S. population [13]

• Data gathered between 2006 and 2014 using

airplanes and drones [13]

30

LIDAR method

• NREL received LIDAR data from the Department of Homeland Security (DHS) Homeland Security Infrastructure Program (HSIP) [18]

• Covers 128 cities – representing 23% of U.S. buildings and 40% of the U.S. population [18]

• Data gathered between 2006 and 2014 using airplanes and drones [18]

• LIDAR uses light in the form of a pulsed laser to measure variable distances to earth [19]

• NREL uses LIDAR data in combination with GIS methods to characterize suitable rooftop space [11]

31

Electricity Prices - EIA

0

5

10

15

20

25

30

OK

TX ID

WA

VA

AR

LA

WV

NC

UT

ND

OR IL

NE

SD

MO IA

WY

NV

KY

MN IN PA

FL

GA

CO

OH

KS

SC

MT

TN

DE

NM AZ

MI

US

MS

AL

US

WI

MD

DC

NJ

ME

VT

NH

NY

MA

CA RI

CT

AK HI

Ele

ctr

icity P

rice (

cents

/kW

h)

Commercial Retail Electricity Price - 2015

EIA Annual Average for commercial – can use prices by state to calculate the

value of the portion displaced (or sold back) [16]

32

Net Metering - DSIRE• Net metering agreements vary widely across the U.S. [23]

• Could assume two scenarios for the electricity sold back to the grid:• Retail prices• Local marginal price (LMP)

Photo 13: DSIRE

33

Installation costs and rebates

• Use the LBNL Tracking the Sun Database (latest project data: 2015) [13]

• Considering school, government, and non-profit projects • 600 projects out of 800,000 projects

• 10 states represented

Mean installed price $ in this dataset:

• $4,080/kW

• $750,600 overall

Mean rebate or grant $ in this dataset:

• $184/kW

• $8,492 overall

34

Quantified costs and benefits

𝑆𝑐ℎ𝑜𝑜𝑙𝐶𝑜𝑠𝑡𝑠𝑠 = 𝑖𝑠 − 𝑔𝑠

𝑆𝑜𝑐𝑖𝑎𝑙𝐶𝑜𝑠𝑡𝑠𝑠 = 𝑔𝑠

𝑆𝑐ℎ𝑜𝑜𝑙𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠𝑠 = 𝑦=2016

𝑦=2036

ℎ=1

ℎ=8760

𝑜𝑠,ℎ × 𝑝𝑠 + ℎ=1

ℎ=8760

𝑛𝑠,ℎ × 𝑙𝑠,ℎ ( 1 + 𝑑 𝑦−2016

𝑆𝑜𝑐𝑖𝑎𝑙𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠𝑠 = 𝑦=2016

𝑦=2036

ℎ=1

ℎ=8760

𝑜𝑠,ℎ + 𝑛𝑠,ℎ × 𝑚(2016 𝑠,ℎ /(1 + 𝑑 𝑦−2016

is = total system, s, installation cost

gs = total system, s, rebate

os,h = portion of the output, in kWh, of the electricity generated by a system, s, in hour, h, of a TMY

ps,y = is the state annual-average commercial retail rate of electricity in year y (set to latest available: 2015)

ns,h = output in kWh of the electricity generated by system of a TMY that exceeds consumption and is sold back to the grid

ls,h = average LMP that prevailed in the state in which the system was installed in hour, h, of 2015

m(2016)s,h = marginal health and environmental damage, expressed in 2016 $ per kWh, avoided by offsetting

a kWh of fossil fuel generation in hour h of year y in the eGrid region where the system s is

located

d = annual discount factor (discount rate set to 7%); therefore, factor is set to (1/1.07)

𝑆𝑐ℎ𝑜𝑜𝑙𝑁𝑒𝑡𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 =

𝑠

𝑆𝑐ℎ𝑜𝑜𝑙𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠𝑠 − 𝑆𝑐ℎ𝑜𝑜𝑙𝐶𝑜𝑠𝑡𝑠𝑠 ∀ 𝑎𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑖𝑜𝑛 𝑢𝑛𝑖𝑡

𝑆𝑜𝑐𝑖𝑎𝑙𝑁𝑒𝑡𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 =

𝑠

𝑆𝑜𝑐𝑖𝑎𝑙𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠𝑠 − 𝑆𝑜𝑐𝑖𝑎𝑙𝐶𝑜𝑠𝑡𝑠𝑠 ∀ 𝑎𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑖𝑜𝑛 𝑢𝑛𝑖𝑡