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Long-Term National Impacts 

of State-Level Policies 

Preprint 

N. Blair, W. Short, P. Denholm, and D. Heimiller 

To be presented at the WindPower 2006 Conference 

Pittsburgh, Pennsylvania 

June 4–7, 2006 

Conference Paper 

NREL/CP-620-40105 

June 2006 
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Long-Term National Impacts of State-Level Policies* 

 WINDPOWER 2006 

Nate Blair, Walter Short, Paul Denholm, Donna Heimiller 

National Renewable Energy Laboratory 

1617 Cole Boulevard 

Golden, Colorado 80401-3393 

nate_blair@nrel.gov

walter_short@nrel.gov 

paul_denholm@nrel.gov

donna_heimiller@nrel.gov

*This work has been authored by an employee or employees of the Midwest Research Institute under 

Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy. The United States 

Government retains and the publisher, by accepting the article for publication, acknowledges that the 

United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish 

or reproduce the published form of this work, or allow others to do so, for United States Government 

purposes. 

1
ABSTRACT 

This paper presents analysis conducted with the Wind Deployment System Model (WinDS) – a 

model of capacity expansion in the U.S. electric sector. With 358 regions covering the United 

States, detailed transmission system representation, and an explicit treatment of wind 

intermittency and ancillary services, WinDS is uniquely positioned to evaluate the market 

impacts of specific state-level policies. 

This paper provides analysis results regarding the impact of existing state-level policies designed 

to promote wind-capacity expansion, including state portfolio standards, mandates, and tax 

credits. Our results show the amount of wind deployment due to current state-level incentives as 

well as examine their lasting impact on the national wind industry. For example, state-level 

mandates increase industry size and lower costs, which result in wind capacity increases in states 

without mandates and greater market growth even after the policies expire. Although these 

policies are enacted by individual states, the cumulative effect must be examined at a national 

level. Finally, this paper examines the impact on wind-capacity growth by increasing the penalty 

associated with the state-level renewable portfolio standards (RPS). Our results show national 

and regional wind energy deployment and generation through 2050. 

2
Section 1: WinDS Overview

WinDS is a computer model that optimizes the regional expansion of electric generation and 

transmission capacity in the continental United States over the next 50 years. It employs a 

Geographic Information System (GIS) to develop region-specific data for input to a linear 

program (LP). Most of the methodology description that follows addresses the linear-program 

portion of the model and is simply referred to as WinDS. Where it is important to distinguish the 

analysis that is done in the GIS from the LP, the GIS capability is specifically identified. 

WinDS minimizes system-wide costs of meeting electric loads, reserve requirements, and 

emission constraints by building and operating new generators and transmission in 26 two-year 

periods from 2000 to 2050. The primary outputs of WinDS are the amount of capacity and 

generation of each type of prime mover—coal, gas combined cycle, gas combustion turbine, 

nuclear, wind, etc.—in each two-year period. Figure 1 shows an example of WinDS capacity 

estimates for the United States for different generation technologies over the next 50 years. 

 

Figure 1. Base Case WinDS Capacity Estimates 

3
While WinDS includes all major generator types, it was designed primarily to address the market 

issues of greatest significance to wind—transmission and intermittency. The WinDS model 

examines these issues primarily by using a much higher level of geographic disaggregation than 

other models. As Figure 2 represents, WinDS uses 358 different regions in the continental 

United States. Much of the data inputs to WinDS are tied to these regions and derived from a 

detailed GIS model/database of the wind resource, transmission grid, and existing plant data. The 

geographic disaggregation of wind resources allows WinDS to calculate transmission distances, 

as well as the benefits of dispersed wind farms supplying power to a demand region. 

Figure 2. Regions within WinDS 

4
As shown in Figure 3, WinDS disaggregates the wind resource into five classes ranging from 

Class 3 (5.4 meters/second at 10 meters above ground) to Class 7 (>7.0 m/s). WinDS also 

includes offshore wind resources and distinguishes between shallow and deep offshore wind 

turbines. Shallow-water turbines are assumed to have lower initial costs, because they employ a 

solid tower with an ocean-bottom pier; while deep-water turbines are assumed to be mounted on 

floating platforms tethered to the ocean floor. 

Figure 3. Wind Resources in WinDS 

5
These different classes and types of wind have different costs and performance characteristics. 

Generally the higher wind-class sites (i.e. Class 7), are the preferred sites. However, Figure 4

shows that, at any given point in time, the wind turbines installed will be at a mix of sites with 

different wind-resource classifications. This occurs because, in selecting the installation sites, 

WinDS considers not only the resource quality, but also examines factors such as transmission 

availability, transmission costs and losses, correlation of the wind output with neighboring sites, 

environmental exclusions, site slope, and population density. 

0

10000

20000

30000

40000

50000

60000

70000

80000

2000

2004

20082012201620202024

2028

2032

2036204020442048

MW

Existing Trans Land-class3

Existing Trans Land-class4

Existing Trans Land-class5

Existing Trans Land-class6

Existing Trans Land-class7

New Lines Land-class3

New Lines Land-class4

New Lines Land-class5

New Lines Land-class6

New Lines Land-class7

Figure 4. Wind Capacity Results by Type and Class 

WinDS is also disaggregated over time—not only with the 26 two-year periods between 2000 

and 2050, but also within each year. Each year is divided into four seasons with each day of each 

season divided into four diurnal time slices. These 16 time slices during each year allow WinDS 

to capture the intricacies of meeting peak electric loads, with both conventional sources and 

intermittent wind generators. 

WinDS models the major conventional electricity generators, including: 

- pulverized coal 

- integrated gasification combined-cycle coal 

- existing unscrubbed coal boilers 

- existing scrubbed coal boilers 

- natural gas combined cycle 

6
- natural gas combustion turbines 

- nuclear 

- hydroelectricity 

Fuel costs are exogenously specified over time by NERC region, as are electric loads. WinDS is 

a national electric capacity expansion model, not a general equilibrium model. Assessing the 

potential of wind energy under any given scenario requires that the scenario be exogenously 

specified in terms of fuel costs and electric loads, and by NERC region over the 50-year time 

horizon of WinDS. 

While the focus of WinDS is on wind-energy technologies, the model does include some detail 

on other generation technologies. For example, there are four types of coal-fired power plants 

within WinDS—existing boilers without SO2 scrubbers, existing with scrubbers, new advanced 

pulverized coal plants, and new integrated-gasification combined-cycle plants. These plants can 

burn either high-sulfur or, for a cost premium, low-sulfur coal. Generation by coal plants is 

restricted to base and intermediate load, with cost penalties (representing ramping/spinning 

costs) if power production during peak load periods exceeds production in shoulder-peak hours. 

Nuclear is considered to be base load. Combined-cycle, natural-gas plants are considered capable 

of providing some spinning reserve and quick-start capability, but the primary source of peak 

power and operating reserves are combustion turbines and hydroelectricity. Hydroelectricity 

capacity is not allowed to increase, due to resource and environmental limitations. Hydro is also 

energy-constrained, due to water resource limitations. 

WinDS tracks emissions from both generators and storage technologies of carbon, sulfur dioxide, 

nitrogen oxides, and mercury. Caps can be imposed at the national level on any of these 

emissions. Alternatively, a carbon tax can be imposed that linearly escalates to the maximum tax 

level over time. 

Section 2: Base Case Results

The WinDS Base Case results are highly dependent on the primary inputs to the model. The 

conventional power costs and performance along with the fuel prices for gas, coal and nuclear 

are taken from the U.S. Energy Information Agency’s Annual Energy Outlook1. In this analysis, 

the 2005 version of the AEO was used. The wind cost and performance data comes from the 

annual benefits projection titled “Projected Benefits of Federal Energy Efficiency and 

Renewable Energy Programs – FY 2007 Budget Request”.2

 Table 1 shows a brief summary of 

the wind cost and performance data with improvements due to research and development. The 

learning-by-doing improvements are calculated endogenously within WinDS, using an 8% 

learning rate based on both U.S. and world production estimates. 

 

1

 http://www.eia.doe.gov/oiaf/archive/aeo05/index.html 

2

 http://www1.eere.energy.gov/ba/pdfs/39684.pdf 

7
8

Table 1: Onshore Turbines Cost and Perf. Due to R&D 

Resource 

Class Install Year Capacity 

Factor 

Capital cost 

($/kW)* 

Fixed O&M

($/kW-yr) 

Variable 

O&M 

($/MWh) 

3 2000 32.9% 1136 7.76 5.15 

3 2010 34.9% 1112 7.76 4.53 

3 2020 37.6% 1082 7.76 4.04 

3 2050 37.7% 1049 7.76 3.84 

4 2000 34.8% 1136 7.76 5.15 

4 2010 37.4% 1112 7.76 4.53 

4 2020 40.9% 1082 7.76 4.04 

4 2050 41.4% 1049 7.76 3.84 

5 2000 40.2% 1081 7.76 5.15 

5 2010 42.4% 1058 7.76 4.53 

5 2020 44.2% 1020 7.76 4.04 

5 2050 45.1% 995 7.76 3.84 

6 2000 44.0% 1081 7.76 5.15 

6 2010 45.8% 1058 7.76 4.53 

6 2020 47.6% 1020 7.76 4.04 

6 2050 48.3% 995 7.76 3.84 

7 2000 54.4% 1044 7.76 5.15 

7 2010 55.4% 980 7.76 4.53 

7 2020 56.2% 963 7.76 4.04 

7 2050 56.3% 963 7.76 3.84 

 * Overnight capital cost 

Figure 5 indicates the average coal and natural gas costs currently used within the model, based 

on AEO 2005. Higher gas prices will be a minor driver but higher coal prices would dramatically 

increase the amount of wind capacity deployed in the future due to the prevalence of new coal 

capacity being built. 

0

3

6

9

12

15

2000 2020 2040 2060 2080

Year

Fuel Price ($/mmBTU)

Average U.S. Gas Price

Average U.S. Coal Price

Figure 5. AEO 2005 Natural Gas and Coal Prices used in WinDS 
As Figure 6 indicates, the fraction of total national generation from wind power increases to 

roughly 10% by 2050. It also shows that various types of coal generation—including integrated 

gasification combined cycle (IGCC), new pulverized coal plants (coal-new), and existing coal 

plants (coal-old-1 and coal-old-2)—combine to make up more than 80% of the national 

generation by 2050. This wind fraction includes the impact of the state-level incentives discussed 

below. It indicates that additional state incentives, federal incentives, or improved R&D would 

help wind reach a higher fraction of national generation. Figures 1 and 4 (above) also are from 

this same base case model run. They indicate the overall capacity from each generation source 

(including wind), as well as the amount of wind by class and transmission type (existing grid 

lines or new lines, which include new lines to serve load within the same region). However, the 

state incentives examined in this paper are generally concerned with generation by wind. 

Figure 6. Base Case Generation Fractions (including state-level incentives) 

Also of interest to states is the location of the wind capacity. Wind capacity can secure state 

benefits such as economic development, out-of-state investment, energy payments to local land 

owners and tax revenue to the states. Figure 7 indicates where the wind capacity is built in the 

base case by 2050. As expected, the states with strong wind resource and load are the site for 

much of the future wind deployment. In Figure 9, the amount of wind generation that is due to 

state-level incentives is also mapped. Note that, in the base case, roughly 260 GWs of wind are 

deployed by 2050, compared to roughly 9 GWs currently. 

9
Figure 7. Base Case Wind Capacity in 2050 

Section 3: Treatment of State-Level Incentives

WinDS currently models three state-level incentives—state-level renewable portfolio standards 

(RPS), state-level production tax credits (PTC), and state-level investment tax credits (ITC). 

There are a multitude of other local and state incentives, including benefits such as property and 

sales tax reductions. These incentives, which have not been modeled yet, are in addition to the 

federal PTC (scheduled to expire in 2007). 

The information about the state-level incentives was gleaned from the Database of State 

Incentives for Renewable Energy (DSIRE) database3

 and other sources such as the Union of 

Concerned Scientists.4 For WinDS, several different pieces of information were needed 

including: 

• When the incentive takes effect. Typically, this is when the legislation is initiated. 

• For an RPS, when the incentive reaches its full level. 

• The level of incentive—either the dollar amount of the PTC or ITC, or the generation 

fraction associated with the RPS. 

• For an RPS, what generation fraction is can be, or must be, met by wind. 

• The duration of the incentive—how long it lasts once it reaches full enforcement. 

• In the case of the RPS, the penalty for noncompliance. 

 

3

 DSIRE – Database of State Incentives for Renewable Energy http://www.dsireusa.org/ 4

 http://www.ucsusa.org/clean_energy/clean_energy_policies/clean-energy-policies-and-proposals.html 

10
• What fraction of the state electricity load is actually subject to the incentive. In the case 

of the RPS, small utilities (such as municipalities and cooperatives) often are exempt or 

allowed to opt-out of the RPS. 

As shown in Table 2 and Table 3, 20 states with an RPS and eight states with either a PTC or 

ITC have been modeled. It should be noted that these incentives are in addition to the standard 

federal incentives (including the federal PTC that ends in 2007, accelerated depreciation, and 

others). In Table 2, the “Year Fraction Reached” indicates the year at which the state will reach 

the “Legislated RPS Fraction (%).” The “Yrs to Maintain” indicates how long the RPS fraction 

must be maintained after the “Year Fraction Reached.” For states in which a duration was not 

indicated, or the legislation indicated that the RPS would not end, a value of 100 years was used 

to maintain the fraction throughout the simulation. The penalty (in $/MWh) is the cost for each 

MWh that the load-serving entities in the state fall short of the wind RPS fraction of generation. 

The “State Load Fraction Included” indicates the amount of electricity consumed within the state 

that is subject to the RPS. 

The “Final Fraction due to Wind” is the fraction of total state electricity consumption that must 

be met by wind by the “Year Fraction Reached.” This value accounts for the legislated RPS 

fraction, the state load fraction included in the RPS, and a decision on the amount of RPS that 

will be met by wind. Currently, the WinDS model does not accurately model biomass, 

geothermal, photovoltaics, or landfill gas generation capacity expansion. Several of these sources 

are expected to be significant contributors to these state RPSs. Therefore, the amount that must 

be met by wind was decided on exogenously based on the relative attractiveness of the biomass, 

geothermal, and wind resources for each state. For example, if the biomass resource was 

excellent and the wind resource was average, then a smaller fraction of the RPS was assumed to 

be required from wind power in the future. This exogenous assessment produced estimates from 

85% down to 50% of the RPS requirement that must be met by wind. WinDS assumes complete 

compliance with this net RPS either through building wind or by paying the penalty. 

Although many states allow wind generation to be shipped into the state in contribution to the 

RPS, a minority of states require that renewable energy, used to meet their RPS, must be 

generated from within the state. It is expected that such requirements will eventually be ruled in 

violation of interstate commerce laws5

. Thus, WinDS assumes that the wind contributions to 

state RPSs can be transmitted in from other states. 

 

5

 Renewable Orphans: Adopting Legal Renewable Standards at the State Level, Steven Ferrey, 

The Electricity Journal, Volum 19, Issue 2, Pages 52-61

11
Table 2. State-level Renewable Portfolio Standards as Implemented in this Study 

Year 

Fraction 

Reached

Yrs to 

Maintain

Penalty 

($/Mwh)

Legislated 

RPS 

Fraction (%)

State Load 

Fraction 

Included

Final 

Fraction due 

to Wind

AZ 2025 1 50 1.1 1.00 0.0079

CA 2017 1 5 20 0.63 0.0340

CO 2015 100 50 10 0.69 0.0440

CT 2010 100 55 10 0.94 0.0130

DE 2019 100 25 10 0.75 0.0560

IL 2013 100 10 15 0.92 0.0620

MA 2009 100 50 4 0.85 0.0260

MD 2019 100 20 7.5 0.80 0.0450

MN 2015 1 10 1125MW 1.00 0.0718

MT 2015 100 10 15 0.90 0.0750

NJ 2008 100 50 6.5 1.00 0.0290

NM 2011 100 10 10 0.53 0.0260

NV 2015 100 10 20 0.89 0.1330

NY 2013 1 5 25 0.84 0.0350

OR 2020 100 5 Bnft Fund 1.00 0.0780

PA 2020 100 45 8 0.98 0.0140

RI 2019 1 55 15 0.99 0.0690

TX 2009 10 50 5880 MW 1.00 0.0100

VT 2012 1 10 Bnft Fund 1.00 0.0500

WI 2011 1 10 2.2 0.75 0.0060

Table 3. State-level Production Tax Credit (PTC) and Investment Tax Credits (ITC) as 

Used in this Study 

There are several issues that make modeling these state-level incentives quite difficult. First, 

many laws do not explicitly outline how long the final RPS fraction must be maintained. In 

reality, once the RPS fraction is met, if it is not continually enforced and wind is not yet 

economical on its own, the generation fraction from wind will start to fall. This is because the 

load continues to grow, and that growth is met by another generation source—typically new coal 

12
plants. Also, if developers know that they will only need to pay the RPS penalty for one year, 

then they would rather do that than build more wind capacity. Therefore, specification of the 

RPS duration is critical. 

 

Another issue is determining each state’s exact penalty for RPS noncompliance. Several states 

impose “standard utility enforcement” mechanisms to enforce compliance, which makes it 

difficult to determine the future costs for noncompliance. As shown below, the penalty level 

directly impacts the level of compliance with the RPS. 

 

A third issue is determining what fraction of the RPS will be met by wind. As discussed, WinDS 

does this exogenously, because the model does not currently handle biomass, geothermal, or 

other sources that could meet the RPS. Ideally, the state RPS guidelines would specify the 

breakdown between wind and other renewables in meeting the RPS. 

 

The duration of the state-level PTC and ITC also is an issue. As with the federal PTC, the current 

status is often determined for a short period of time (in terms of the modeling horizon) and is 

often expected to be renewed. Nonetheless, WinDS assumes that the state-level credits expire at 

the time cited by current legislation. 

 

Finally, the legislation is frequently changing. Between the time that this study was done and the 

AWEA conference, several states added one of these state incentives and several others (notably 

Arizona, Minnesota, New Jersey, and Wisconsin) made significant increases to their incentives. 

This frequent legislative changes and extensions can be good for wind, but are always difficult to 

predict and model. 

Section 4: State-Level Incentives Results

Two scenarios were examined to determine the additional wind capacity due to state-level 

incentives. The first scenario is the base case, which includes the state-level incentive 

requirements. WinDS was also run with the base case assumptions but without the state-level 

incentive requirements of Tables 2 and 3. These two runs are shown in Figure 8 by two blue 

lines (referencing the left y-axis), which resulted in just more than 250 GWs of wind capacity in 

2050. A third blue line indicates the difference between these two runs (and references the right 

y-axis). This third line shows that the difference due to state incentives remains roughly 8 GWs 

after 2012. Thus much of the impact of state-level incentives occurs early on. 

The second scenario determines the impact of state-level incentives if wind costs and 

performance did not improve with future research and development. These runs result in roughly 

50 GWs of wind in 2050 and are shown by two red lines in Figure 8. This is a significantly 

lower value than the base case, as expected. However, this scenario still results in more wind 

capacity being present than in the AEO 2005 reference case, which is shown for comparison. By 

looking at the red delta line for “no technology improvements,” one can see that the additional 

wind capacity due to state incentives continues to move upward, in excess of the base case delta, 

to a typical value of 12 GWs—and eventually up to almost 18 GWs. The lesser impact of stateincentives in the base case is because the incentives are not needed as much in the base case with 

its lower wind energy costs. 

13
0

50

100

150

200

250

300

350

400

2000 2010 2020 2030 2040 2050

Total Wind Capacity (GW)

0

2

4

6

8

10

12

14

16

18

20

Delta Wind Capacity (GW)

AEO2005 Reference Case

Basecase with state incentives

Basecase with no state incentives

No Tech Improvements with State Incentives

No Tech Improvements - no state incentives

Delta (Basecase - no state)

No Tech Improvements Delta (with- no state)

Figure 8. Wind Capacity With and Without State Incentives (with and without R&D 

improvements) 

The next question to examine is whether the state incentives actually change the deployment 

pattern of the wind and its use. Figures 9 and 10 examine the geographic location of windgeneration use. Both graphs compare the wind generation consumed in each region with state 

incentives and without state incentives, for the case without research and development 

improvements. If the region is red, that indicates that it consumes more wind energy generation 

with state incentives enacted. If the region is blue, then the region consumes more wind energy 

generation without the state incentives enacted. Figure 9 does this for 2030, while Figure 10

does this for 2050—both graphs are reflective of the state incentives. As expected, the regions 

that are red (more wind with incentives) are generally located in the states that have attractive 

incentives, e.g. Nevada (RPS 13%), New Mexico (PTC), Texas (mandate for wind). Continuing, 

other red states have RPS’s including Illinois, Oregon, New Jersey, and New York. 

Those regions that are blue are in states with no or less attractive incentives. These state with 

no/less attractive incentives can showmore use in the no-incentives case because it is no longer 

economic to ship the power to a nearby state that has good incentives in the base case. Instead, 

the power is used in the producing state. For example, without the state incentives in Nevada, 

more wind generation is used in California which is more economical. One might note that both 

California and Arizona have an RPS but neither is very large (3.4% in CA and .7% in AZ) and 

aren’t driving the situation as much as the neighboring state provisions do. 

14
Figure 9. Delta Wind Consumed in 2030 (MWh) (normal state incentives - no state incentives) 

Figure 10. Delta Wind Consumed in 2050 (MWh) (normal state incentives - no state incentives) 

15
The final question examined is the impact of the RPS penalty on the amount of additional wind 

added. The base case state incentives have two states with an RPS penalty of only $5/MWh, and 

seven states with a penalty of $10/MWh, which might be too low to enforce compliance. 

Therefore, a series of runs were done with varying levels of RPS penalty (applied to all states 

with an RPS as described above). As Figure 11 shows, the increasing penalty levels significantly 

increase the amount of wind capacity added by the RPS. Note that this graph only extends to 

2030 to examine the near-term impacts more easily. The base case level (the combination of all 

values in Table 2) has a trajectory between $5/MWh and $10/MWh. Once the parametrically 

imposed RPS penalty gets above $20/MWh, no additional wind capacity is added. Therefore, if 

the penalties for each state were increased to at least $20/MWH, on average, more wind capacity 

will be installed. 

0

2

4

6

8

10

12

14

16

18

20002002

2004

2006200820102012

2014201620182020

2022

2024202620282030

Additional Wind Capacity (GW)

No State Incentives

0$/Mwh

5$/Mwh

10$/Mwh

Basecase (No Tech. Improve)

20$/Mwh

30$/Mwh

40$/Mwh

50$/Mwh

60$/Mwh

20 - 60 $/Mwh

Figure 11. Increased Penalty Values vs. Additional Wind Capacity Due to state RPS 

Compliance 

Section 5: Conclusions

• State-level incentives drive a significant fraction of the early growth in wind installations. 

• In the second decade of the 21st century, current incentives most likely will not continue 

to be a primary factor in new wind growth. This is especially true if R&D leads to a 

better general economic position for wind through improved cost and performance. If 

wind technology fails to improve, then state incentives could continue to provide support 

to the wind industry. 

• Enhanced incentives and the increase of incentives to new states could continue to spur 

wind energy growth. Additional RPS, PTC, and ITC legislation is being considered and 

16
added to at this time. This new legislation and increased targets could significantly alter 

the results of this study. 

• Higher penalty amounts and enforcement are critical to reaching expected RPS 

penetration levels. A penalty of at least $20/MWH in all states is required to achieve 

compliance with the amount desired by the RPS legislation. 

• Continued work on including additional state-level incentives and updating existing 

incentives is necessary for more precise near-term analysis. The WinDS team will 

continue to update the state-level incentives within the model on a regular basis. 

17
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14. ABSTRACT (Maximum 200 Words)

This paper presents analysis conducted with the Wind Deployment System Model (WinDS) – a model of capacity 

expansion in the U.S. electric sector. With 358 regions covering the United States, detailed transmission system 

representation, and an explicit treatment of wind intermittency and ancillary services, WinDS is uniquely positioned to 

evaluate the market impacts of specific state-level policies. This paper provides analysis results regarding the impact 

of existing state-level policies designed to promote wind-capacity expansion, including state portfolio standards, 

mandates, and tax credits. The results show the amount of wind deployment due to current state-level incentives as 

well as examine their lasting impact on the national wind industry. For example, state-level mandates increase 

industry size and lower costs, which result in wind capacity increases in states without mandates and greater market 

growth even after the policies expire. Although these policies are enacted by individual states, the cumulative effect 

must be examined at a national level. Finally, this paper examines the impact on wind-capacity growth by increasing 

the penalty associated with the state-level renewable portfolio standards (RPS). The results show national and 

regional wind energy deployment and generation through 2050. 

15. SUBJECT TERMS

Wind Deployment System Model; WinDS; wind; deployment; wind capacity; state policies; national energy policy; 

renewable portfolio standards; RPS; state portfolio standard; wind generation; Nate Blair, Walter Short; Paul 

Denholm; Donna Heimiller 

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