Long Term National Impacts of State-level Policies

WindPower 2006

Nate Blair, Walter Short, Paul Denholm, Donna Heimiller

National Renewable Energy Laboratory

Goal of Analysis

• Attempting to answer the following questions– What impact will state-level incentives have on

wind capacity growth in the near future and the distant future?

– How are state-level policies shaping the dispersal of wind deployment across the country ?

• This effect interacts with dispersal due to capacity value increase with greater dispersal.

– Could higher penalties promote greater compliance with RPS? And how high should they be?

Contents

• Brief Description of the WinDS Model

• Base Case results

• State-Level Policy Impacts– No State-Level Policy– Impact of Penalty Level on RPS

Compliance

WinDS Model(Wind Deployment Systems Model)

A multi-regional, multi-time-period model of capacity expansion in the electric sector of the U.S.

Designed to estimate market potential of wind energy in the U.S. for the next 20 – 50 years under different technology development and policy scenarios

WinDS Regions

Wind Resources in WinDS

WinDS is Designed to Address the Principal

Market Issues for Wind • Access to and cost of transmission

– Class 4 close to the load or class 6 far away?– How much wind can be transmitted on existing lines?– Will wind penetrate the market if it must cover the cost

of new transmission lines?– Will offshore wind close to seaboard loads penetrate?

• Resource Variability– How does wind capacity credit change with

penetration?– How do ancillary service requirements increase with

wind market penetration– How much would dispersal of wind sites help?– Is on-site storage cost effective?

General Characteristics of WinDS• Linear program cost minimization for each of 26 two-

year periods from 2000 to 2050

• Sixteen time slices in each year: 4 daily and 4 seasons

• 4 levels of regions – wind supply/demand, power control areas, NERC areas, Interconnection areas

• Existing and new transmission lines

• 5 wind classes (3-7), onshore and offshore shallow and deep

• All major power technologies – hydro, gas CT, gas CC, 4 coal technologies, nuclear, gas/oil steam

• Electricity storage capability

Base Case Capacity by Wind Class

0

10

20

30

40

50

60

70

80

90

100W

ind

Ca

paci

ty (

GW

)

Class 3

Class 4

Class 5

Class 6

Class 7

Base Case Generation Fractions

Basecase Wind Capacity in 2050

Legislation Leaves Modeling Questions

• How long is the final RPS fraction to be maintained?

• What is the penalty for non-compliance with an RPS?– “standard utility enforcement” = $ ???

• What RPS fraction is from wind?• How long will PTC’s and ITC’s last?• Frequently Changing Legislation

Our Modeling Assumptions for Incentives

• State Incentives based on DSIRE database and other sources.

• If no duration given, assumed incentive lasts throughout the simulation.

• Assumed complete RPS compliance for affected utilities – either by purchasing renewables or paying the penalty– Munis and coops frequently exempted

• Fraction that must be met by wind determined exogenously– Based on viability of wind resource and other state

renewable resources (solar, biomass, etc.)• Assumed that RPS requirements can be met by wind

generation transmitted in from other states.

State RPS Assumptions

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 0.0079CA 2017 1 5 20 0.63 0.0340CO 2015 100 50 10 0.69 0.0440CT 2010 100 55 10 0.94 0.0130DE 2019 100 25 10 0.75 0.0560IL 2013 100 10 15 0.92 0.0620MA 2009 100 50 4 0.85 0.0260MD 2019 100 20 7.5 0.80 0.0450MN 2015 1 10 1125MW 1.00 0.0718MT 2015 100 10 15 0.90 0.0750NJ 2008 100 50 6.5 1.00 0.0290NM 2011 100 10 10 0.53 0.0260NV 2015 100 10 20 0.89 0.1330NY 2013 1 5 25 0.84 0.0350OR 2020 100 5 Bnft Fund 1.00 0.0780PA 2020 100 45 8 0.98 0.0140RI 2019 1 55 15 0.99 0.0690TX 2009 10 50 5880 MW 1.00 0.0100VT 2012 1 10 Bnft Fund 1.00 0.0500WI 2011 1 10 2.2 0.75 0.0060

State PTC and ITC Assumptions

Wind Capacity With & W/out State Incentives (With&W/out R&D Improvements)

0

50

100

150

200

250

300

350

400

2000 2010 2020 2030 2040 2050

To

tal

Win

d C

ap

ac

ity (

GW

)

0

2

4

6

8

10

12

14

16

18

20

Delt

a W

ind

Cap

ac

ity

(G

W)

AEO2005 Reference CaseBasecase with state incentivesBasecase with no state incentivesNo Tech Improvements with State IncentivesNo Tech Improvements - no state incentivesDelta (Basecase - no state)No Tech Improvements Delta (with- no state)

Delta Wind Consumed in 2030 (MWh) (normal state incentives - no state incentives)

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

Increased Penalty Values would increase RPS compliance

0

2

4

6

8

10

12

14

16

18

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

Add

ition

al W

ind

Cap

acity

(G

W)

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

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 will most likely not continue to be a primary factor in new wind growth.

• Enhanced incentives and the spread of incentives to new states could continue to spur wind energy growth.

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

• Continued work on including additional state-level incentives and updating existing incentives is necessary for more precise near-term forecasts.

Disclaimer and Government License

This work has been authored by Midwest Research Institute (MRI) under Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy (the “DOE”). The United States Government (the “Government”) retains and the publisher, by accepting the work for publication, acknowledges that the 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 Government purposes. 

Neither MRI, the DOE, the Government, nor any other agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe any privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the Government or any agency thereof. The views and opinions of the authors and/or presenters expressed herein do not necessarily state or reflect those of MRI, the DOE, the Government, or any agency thereof.