Impact of State Policies on Diffusion of Solar PV in the US:

A Sector Specific Empirical Analysis

Gireesh Shrimali, Monterey Institute of International Studies (gshrimali@miis.edu; +1-650-353-8221)

Steffen Jenner, German Ministry of Environment

Motivation• Price of solar PV systems needs be brought down to $1/W to make them

cost-competitive (DOE, 2010). In 2009: – $7.5/W for residential (<10kW)/commercial (10-100kW) (TSIII, 2010)– $3.75/W for utility (RMI, 2010)

• Would like to understand how – i.e., what are the drivers?

• Module vs. Balance of System (BOS): Modules are global commodity– BOS:

• Electric system (inverters, wiring, transformers, installation) • Structural system (site prep, attachments, racking, installation)• Business processes (siting, permitting, interconnection, O&M, margin)

– BOS costs account for ~50% of system cost• This share is increasing over time!• BOS costs vary across jurisdictions

Motivation: US vs. Germany Residential BOS Costs (2009 US$)

Langen (2010): Installer + wholesaler offers from Sovello‘s partner network, Interviews with installers

Motivation (Continued)• Module is a global commodity, whereas BOS is a local commodity

– Can influence BOS with local policy– Of course, there are (can be) other drivers

• [Q1] Would like to understand if state policies have impacted:– [Q1a] BOS (NOT system) costs (actually price): Primary objective

• Directly: Via barrier removal or increased competition• Indirectly: Via learning-by-doing or scale effects

– [Q1b] Deployment: Secondary objective

• [Q2] Are results different based on application-sector? Define:– 0-10kW systems: Residential sector– 10-100kW systems: Commercial sector

Policies: Financial IncentivesPolicy Description Example

Income tax Credit/deduction from Personal/corporate state taxes

30/100% credit/deduction

Cash Rebate/grant claimed either at time of purchase or shortly afterwards

30/50% rebate/grant

Sales tax Exemption/refund of the state sales tax 100% exemption

Property tax Solar value exempted from property value 5 years/life-time exemption

Policies: Command-and-controlPolicy Description Notes

Renewable portfolio standards

• Specified amount of renewable sales/capacity within specified timeframe• Unbundled renewable energy certificates common• Some states allow solar carve-outs

• Could be Mandatory or Voluntary• Examples: Sales (0.5-30%); Capacity (50-2280 MW)

Required green power option

• Utilities offer renewable electricity to customers: Can charge a public utility commission approved premium • Block and kWh premium rates

• Mandatory• Block premium rate: $2 per 100 kWh

Government green power purchasing

• Specified % of a state government purchases from renewables• Some states allow local governments to aggregate loads

• Voluntary• 2.5-50%

Policies: SupportPolicy Description

Contractor licensing Specific licensing that guarantees that contractors have the necessary – defined minimum – knowledge and experience for installing solar systems

Equipment certification

Requires that equipment meets minimum performance standards

Solar access Protect consumers’ right to install and operate (e.g., access to sunlight) a solar system – e.g., secured access to renewable resource from future neighborhood developments

Net metering Allows for a two-directional flow of electricity, allowing consumers to sell back to the grid in times of over-capacity

Interconnection standard

Specification of technical, contractual, metering, and rate rules for connecting to the grid

Theory: BOS Cost• Financial incentives and command & control policies:

– These policies would result in increased demand as well as increased (anticipated) supply for renewable energy solutions in the market

– BOS cost (margin) would increase if demand increases more than supply; it would decrease if otherwise

– Issue: What are we capturing – short-term vs. long-term effects?

• Support policies:– These policies would remove barriers and hence reduce transaction costs

• e.g., interconnection standards would increase system reliability, reducing risk, and making investment more economical

– BOS cost would change in response to changing transaction costs

• BOS costs would decrease due to cumulative deployment (learning-by-doing)

Theory: Annual Deployment

• Financial incentives and command & control policies: – These policies typically target deployment– Therefore, they would increase deployment

• Support policies:– These policies remove barriers to installation– Deployment would increase if barriers are reduced; it would

decrease if otherwise

• Deployment would increase due to decreased system price

Method• Use program-level data: Link policy variation to objective

– Issue: Will detect only variation after start of programs– Will miss any “other” variation: Under-stating of results

• Sector specific, fixed effects – program and year – panel regressions– 4 dependent variables (BOS cost; annual capacity) x (residential; commercial)– 12 policy variables (kept 9) 4 financial incentives; 3 command-and-control policies; 5

support policies (kept 2)

• Control for potential confounders: – 4 economic controls electricity price, natural gas price, GDP, cumulative deployment or

system price– 3 structural controls (kept 2) solar potential (FE), lcv score, #natural resource employees

• Multiple (7) specifications, all with state/time FE: Controls (none, exogenous, partial, all); CA (w/, w/o); method (OLS w/ state clustering, PCSE)

Data• Using LBNL Tracking the Sun III (program-level) data 27 different

incentive programs in 16 states– Installed-Cost + Capacity: 1998-2009– Calculated: BOS cost = installed-cost minus module-price

• Used Navigant Wholesale Index as a proxy for module price• May not capture local variation: Mixing of impact

• Coverage: Data not comprehensive by any means!– Approximately 78 K residential and commercial systems– 70% (~874MW) of all grid-connected PV capacity (~1.2GW) installed in the

United States through 2009– Issue: Selection Bias(es)?

• Still, this is the best dataset that we have

Results: Summary• We correlate presence of policy to change in BOS cost and annual deployment

• Not all policies have a statistically significant impact, and we observe differential impacts:– Across BOS costs and annual deployment– Across residential and commercial sectors: In size not direction

• For BOS costs:– Financial incentives and support policies reduce BOS costs

• In residential sector, cash/property-tax incentives reduce BOS cost by $0.7/0.9• In commercial sector, interconnection standards reduce BOS cost by $1.6

– Command-and-control policies increase BOS costs

• For annual deployment: – All types of policies increase deployment

Results: Residential Sector BOS Cost

cash in

centive

income t

ax in

centive

propert

y tax

incen

tive

sales

tax in

centive RPS

reqd gr

een power

option

govt

green

purchase

net mete

ring

interco

nnection st

d

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Impact of policy on BOS cost

Policy

Impa

ct ($

/W)

95% confidence interval. Specification (4)

Results: Commercial Sector BOS Cost

95% confidence interval. Specification (4)

cash in

centive

income t

ax in

centive

propert

y tax

incen

tive

sales

tax in

centive RPS

reqd gr

een power

option

govt

green

purchase

net mete

ring

interco

nnection st

d

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Impact of policy on BOS cost

Policy

Impa

ct ($

/W)

Future Work• Diagnosis: Why do we get these results?

– Already have some intuition– Can do more

• Effects of support policies: Net metering, interconnection standards, permitting (e.g. Dong and Wiser, 2013), , etc

• Breakdown of value chain and impact of policy via pathways• Data from TS IV-VI

• Methods– Regressions: Declining returns

• Major data issues need to be resolved!– Case studies: How have programs done differently? Why?

• These (27) programs• Solar City America programs (DOE): Old and the new

BACKUP

Programs vs. Policy• Definitions

– Programs are focused efforts in a state• e.g., California Solar initiative (CSI) offers $0.2-$3.25/W

– Policies are typically state-wide• e.g., interconnection standards may apply to the whole state of CA, including CSI

• No clear order– Policies may change before/after programs start

• e.g., property tax incentives change after AZ Solar and Renewables Incentive Program

– Programs may change existing policies or (even) start new policies• e.g., modification of existing cash incentives – CSI replacing existing CA programs

• A policy may affect many programs– e.g., interconnection standards example above

Method: Policy Variable Coding• Which date to use: Enacted vs. effective?

– Using effective date for now– May miss impact of “announcement effect”: Under-stating of

impact– Ideal: Differentiate based on type of policy

• e.g., for RPS enactment date would make more sense, whereas for cash incentive effective date would

• What policy stringency to use?– Using policy dummies for now: “1” when policy becomes effective– Assign equal importance to strong/weak policies: Mixing of impact– Ideal: Use more realistic measures

• e.g., the actual cash incentive in a state

Method: Other Issues

• Data availability: Reliable BOS cost hard to get– Price available NOT cost

• Use price as a proxy for cost• Could over-state impact due to over-stating of price during times of under-

supply

– Individual components costs not available• Focus on total BOS cost• Under-stating of impact due to individual effects canceling out

• Do not correct for endogeneity– Controls themselves impacted by dependent variables

• e.g., cumulative deployment, system-price, electricity price, lcv score, #natural resource employees

– Good instruments hard to find: Future work!

Other (Potential) Data Sources

• Open PV: Project-level data– Indicators: Size (kW DC); date installed; zip code; cost– 78, 948 installs overall: But, where is the missing data? Selection Bias?

• Navigant (Paula Mints): Project-level data– Apparently has all the data, but it is proprietary, and a huge price tag, which

went up over time (>25K … > 50K … > 100K)

• IREC (Larry Sherwood): State-level data 1996-2009– Had cost data “where available” referred us to LBNL study/NREL data– Delivered sector specific data on annual deployment– In the end, not enough data points

Data (Continued)• Data points: Out of 324 total, we have

– Residential: 126 for cost (61% missing); 151 for deployment (54% missing)– Commercial: 89 for cost (72% missing); 151 for deployment (54% missing) – Utility: 25 for cost (92% missing) – can’t detect >20 coefficients

• Implications of missing data/unbalanced panels on inference– Need results from the same sample for cost and deployment: Eventually use the

same dataset– STATA should be able to handle but we need to be careful since sophisticated

methods may not work!

• Panel data issues: Heteroskedasticity, panel correlation, serial correlation, etc.– Estimator: OLS, FGLS, PCSE – use primarily OLS, supported by PCSE– Method: 2SLS, SUR, bootstrapping – future work!

Regressions• Variables: Endogenous and exogenous

– Exogenous variables: natural gas price, gdp, solar potential– Endogenous variables: electricity price, lcv score, #natural resource employees

• Methods: OLS and PCSE– Robust OLS with program and time fixed effects, clustered on states– PCSE with program and time fixed effects, and heteroskedasticity and serial correlation correction

• Specifications: Robustness checks– (1) OLS with only policy– (2) OLS with policy + only exogenous control variables– (3) OLS with all variables except cumulative deployment/system price– (4) OLS with all variables– (6) PCSE with all variables except cumulative deployment/system price: Similar to (3)

• Removed contractor licensing, equipment certification, solar access due to no/too-few changes

Policy Variability Within ProgramsPolicy Total changes Changes after program

cash incentives 8 4

income tax incentives 16 6

property tax incentives 11 6

sales tax incentives 7 6

RPS 24 8

required green power option 2 2

government green purchase 14 4

contractor licensing 1 1

Equipment certification 0 0

solar access 1 1

net metering 12 3

interconnection standards 23 8

Red: Low/no confidence; Orange: Medium confidence; Red: High confidence … all relative!

Results: Robust SignificanceVariable BOS cost

(residential)BOS cost(commercial)

Deployment(residential)

Deployment(commercial)

income tax incentives

cash incentives Negative (-$0.7)

property tax incentives Negative (-$0.9)

sales tax incentives

RPS

required green power option

government green purchase

net metering

interconnection standards Negative (-$1.6)

• Residential sector: cash incentives and property tax incentives reduce the BOS cost by $0.7 and $0.9, respectively• Commercial sector: interconnection standards reduces the BOS cost by $1.6, respectively

Results: Potential SignificanceVariable BOS cost

(residential)BOS cost(commercial)

Deployment(residential)

Deployment(commercial)

income tax incentives

cash incentives Negative Negative (2, 3, 6) Positive (1-3, 6)

property tax incentives Negative Positive (6)

sales tax incentives

RPS Positive (6)

required green power option

Positive (2, 3)

government green purchase Positive(6)

net metering

interconnection standards Negative Positive (3, 4, 6)

Numbers in brackets are specifications mentioned earlier

Discussion: BOS Cost• Decrease due to financial incentives:

– cash incentives (residential and commercial)– property tax incentives (residential)

• Increase due to command and control policies: – RPS (residential)– government green purchase (commercial)

• Decrease due to support policies: – interconnection standards (commercial)

• Directional impact consistent even if not statistically significant – e.g., interconnection standards reduces residential BOS costs by about $0.4

• Overall impact may be mixed/under-stated– e.g., overall $1 reduction in residential BOS costs

• Policy implication: Financial incentives and support policies should be used to induce eventual reduction in BOS cost– However, need to do cost-benefit analysis: Financial incentives are typically more expensive than

support policies

Discussion: Annual Deployment• Increase due to financial incentives:

– cash incentives (commercial)– property tax incentives (commercial)

• Increase due to command and control policies: – required green power option (commercial)

• Increase due to support policies: – interconnection standards (residential)

• Direction impact not consistent independent of statistical significance– Overall impact may be mixed

Discussion (Continued)• Sectors are impacted by different policies?

– Residential: • BOS cost reduction due to: cash incentives, property tax incentives• BOS cost increase due to: RPS• Deployment increase due to: interconnection standards

– Commercial: • BOS cost reduction due to: cash incentives, interconnection standards• BOS cost increase due to: government green purchase• Deployment increase due to: cash incentives, property tax incentives, required green power

option

– For these policies, same directional impacts across sectors, independent of statistical significance

• No impact of learning-by-doing and systems cost– Learning is yet to impact BOS cost– System cost is yet to impact deployment

• Not much change in system cost!