New York States Clean Energy Standard Policy: Examining

New York States Clean Energy Standard Policy:
Examining Challenges to Increasing Residential Solar Power
Laurie Buonanno, PhD - Project Advisor; Greg Rabb, MUP/JD - Second Reader

Introduction & Purpose

Literature Review

Findings & Recommendation

The power grid was originally designed around central points of
power generation. Power grid operators and policy makers need
to adjust the current system for power supply dispatch and
delivery to gain full benefits of solar power (DNV GL 2014, P1).
One theory of this change is described in Figure 1, The Power
Industry of Today. This figure represents the current state,
where energy is created at a centralized location, sent through
the power system transmission system and then to the
distribution system making it to the end consumer. In this current
system, energy could travel large distances from generation to
point of use. Figure 2, the Power Industry of Tomorrow looks to
integrate generation at the source of the load. This journey is
under way with solar PV systems having the ability to be a critical
component. In the new model, the power one can generate from
a solar PV system could be used by ones neighbor. This means
the power that historically traveled hundreds of miles will only
need to travel hundreds of feet. New Yorks Clean Energy
Standard will require 50% of New York's electricity to come from
renewable energy sources like wind and solar by 2030. This is a
study on how to increase penetration of residential solar power in
New York State.

Many researchers have examined adoption of residential solar power
with respect to uncertainty. Uncertainty can be defined as conditions
or events that are unpredictable and /or credible probabilities to
possible outcomes that cannot be assigned (Business Dictionary
2017). Research suggests that despite generous financial incentives,
the adoption rates of residential solar PV rates are low. Willingness to
adapt is high, but willingness to pay is low (Korcaj, Hahnel, and
Spada 2014 P407). Uncertainty can be a critical element that
surrounds the consumers decision to invest in a residential solar PV
system. Current research attempts to determine the optimal adaption
times, values of benefits, and adoption rates over time. Many studies
examine policy incentives, concluding that policies reducing
uncertainty will have a positive effect on incentivizing adoption.

This study found that uncertainty and break-even point are the
two main factors affecting a homeowners adoption of a residential
solar PV system, with both factors closely related. The break-even
point (economic side of installing a residential solar system) was the
most important aspect for the experts. Although the respondents
wanted to support renewable energy and going green, it was
imperative to have a reasonable payback period. Uncertainties can
be directly linked to the break-even point. As uncertainties exist,
these directly affect or shift the breakeven point. To increase
penetration of residential solar PV systems it is critical to reduce the
uncertainties that exist and provide clarity to a very difficult to
manage process.

Top 10 States by installed solar
capacity
Rank
State
Installed MWHouseholds

1

California

18,296.00

4,732,000.00

Population

39,250,017

Watts per
resident
466.1

Total incentives number
of policy and by state

Table 1 represents the top ten states by installed capacity. In these
states, over 1000 policies and incentives exist. With over 100 policy
incentives in New York, it can be difficult for the residential power
customer to understand incentives, with the main information source
for potential PV system adapters being Solar Installers. A study by
Crago and Chernyakhovskiy (2016) concluded that among financial
incentives, only rebates have a large and statistically significant
effect. They concluded that a $1 per watt rebate increased annual PV
capacity additions by 47%. (See Table 2.) Solar PV System Cost
Breakdown for a 5600 watt system (typical home size unit), is $3.40
per watt.

North Carolina

3,016.00

341,000.00

10,146,788

297.2

110

3

Arizona

2,982.00

446,000.00

6,931,071

430.2

80

4

Nevada

1,991.00

309,000.00

2,940,058

677.2

39

5

New Jersey

1,991.00

309,000.00

8,944,469

222.6

57

6

Utah

1,489.00

292,000.00

3,051,217

488.0

52

7

Massachusetts

1,487.00

244,000.00

6,811,779

218.3

113

8

Georgia

1,432.00

162,000.00

10,310,371

138.9

64

9

Texas

1,215.00

137,000.00

27,862,596

43.6

166

10

New York

927

152,000.00

19,745,289

46.9

120

Sources: Solar-Energy-Industries-Association (2016), Database of State Incentives for Renewables & Efficiency
(2017) (for policy and state incentives) & US Census (2017) .

Table 2. Solar PV System Cost Breakdown
Quote Date

Methods
Research was conducted to price out a residential solar system
for my residence. Next, nonprobability expert sampling was used,
with data collected through face-to-face semi-structured, openended interviews of experts in the energy industry. These experts
have investigated rooftop solar PV systems, but did not install
solar PV in their residences..

Feb-17

Quote Number

1

Total System Cost $19,040.00
NYSERDA Credit

268

2

Based on these findings, it is recommended that NYS consider
designating a single organization (such as New York State Energy
and Research Development Authority - NYSERDA) to establish an
entity (office/bureau) which could serve as a single source for
consumer information.

($2,160.00)

Out of pocket cost $16,880.00
Federal tax credit ($5,064.00)
NY solar credit

($4,220.00)

Source: Solar Quote for Chris Carey

Net cost after first

Referencesyear

$7,596.00

System size
(kw)
Cost per Watt

5.6
$3.40

Annual
Production
8400
kWh From
installer
From

Business Dictionary (2017). "Uncertainty" Retrieved 4/29/2017, 2017, from
http://www.businessdictionary.com/definition/uncertainty.html
Pwatts.nrel.go
6134
Crago, C. L., & Chernyakhovskiy, I. (2017). Are policy incentives for solar power effective? Evidence from
residential installations in the Northeast. Journal of Environmental Economics and Management, 81, 132-151.
v(1)
DNV, G. (2014). Energy, A Review of Distributed Energy Resources. Prepared
by DNV GL Energy for the New
York Independent System Operator.
Korcaj, L., et al. (2015). Intentions to adopt photovoltaic systems depend on homeowners' expected personal gains
and behavior of peers. Renewable Energy, 75, 407-415.
Rai, V., et al. (2016). Overcoming barriers and uncertainties in the adoption of residential solar PV. Renewable
Energy, 89, 498-505.

SRCC poster template provided by Instructional Resources and Office of Undergraduate Research

Chris Carey - MPA Project

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