41
RPS Model Methodology Arne Olson, Partner Doug Allen, Consultant

RPS Model Methodology

  • Upload
    kort

  • View
    33

  • Download
    0

Embed Size (px)

DESCRIPTION

RPS Model Methodology. Arne Olson, Partner Doug Allen, Consultant. Contents. Updates to Previous 33% Implementation Analysis Resource Potential, Cost, and Performance Portfolio Selection Methodology. Updates to Previous 33% Implementation Analysis. - PowerPoint PPT Presentation

Citation preview

Page 1: RPS Model Methodology

RPS Model Methodology

Arne Olson, Partner

Doug Allen, Consultant

Page 2: RPS Model Methodology

2

Contents

Updates to Previous 33% Implementation Analysis

Resource Potential, Cost, and Performance

Portfolio Selection Methodology

Page 3: RPS Model Methodology

Updates to Previous 33% Implementation Analysis

Page 4: RPS Model Methodology

4

Current Analysis Compared to 2009 Implementation Analysis

33% RPS Implementation Analysis was focused on informing state RPS policy

Key tasks:

1. Develop “plausible scenarios” for achieving 33% by 2020

2. Estimate the net ratepayer costs associated with each plausible scenario

3. Highlight key obstacles to reaching the 33% goal, including the need for new transmission and the integration costs of new resources

The current analysis is focused on informing state RPS planning

Key tasks:

1. Develop “plausible scenarios” for achieving 33% by 2020

2. Estimate year-by-year resource build-outs under each alternative scenario

3. Feed information into LTPP proceeding to inform the Commission’s decisions regarding fossil procurement

Page 5: RPS Model Methodology

5

Weaknesses of Previous 33% Implementation Study

New transmission is assumed for most projects

No way to determine which projects could get built without new lines

Renewable projects are selected in aggregated “bundles” and could not be selected individually

Crude methodology for addressing potential out-of-state REC resources (“Out-of-State Early”, “Out-of-State Late”)

Did not look at operational impacts of renewables at such high levels of penetration

Integration costs for intermittent resources based on a rule of thumb

Lack of transparency in handling of short-listed CPUC projects

Project viability ratings not very scientific

Page 6: RPS Model Methodology

6

Key Improvements in Portfolio Development Methodology

Improved handling of transmission requirements for new renewable resources

Allows some resource to be delivered over existing transmission or with minor upgrades

Allows individual selection of Non-CREZ resources

Incorporation of unbundled REC resources

Can now model out-of-state REC-only resources as well as out-of-state CREZs

Incorporation of “Discounted Core” of commercial projects

Projects in advanced stage of permitting and development

Page 7: RPS Model Methodology

7

Other Updates to Analysis

Improved detail on Commercial projects in CPUC ED Database

CREZ assignments

2009 solicitations

Confidential projects either aggregated or excluded to improve transparency

New Aspen environmental scores

Updated resource list based on RETI Phase 2B analysis

Updated RPS Net Short based on:

2009 IEPR Load Forecast

New load decrements for EE and CHP calculated by CPUC

Existing renewable resources from 2008 Net System Power Report

Page 8: RPS Model Methodology

Resource Potential, Cost, and Performance

Page 9: RPS Model Methodology

9

General Approach

Determine renewable resource gap (GWh) in 2020

Compile database of resources available to meet RPS target

Rank available resources based on cost, commercial interest, environmental sensitivity and timeline

Select resources to fill renewable resource gap

Page 10: RPS Model Methodology

10

Determining Demand for Renewables

Demand for renewables in California is based on 2020 RPS target, equal to 33% of eligible retail sales

2020 retail sales based on CEC 2009 IEPR load forecast

Excludes retail sales by small LSEs (<200 GWh/yr)

Estimate quantity of renewable resources online in base year

Used renewable resource “claims” from CEC 2008 Net System Power Report

Added new resources online in 2009 based on ED Database

RPS resource gap is the difference between the 2020 target and the 2009 renewables claims

Page 11: RPS Model Methodology

11

Renewable Net Short

33 33 33

26

54

0

20

40

60

80

100

2009 2020, 20% RPS 2020, 33% RPS

Ren

ewab

le G

ener

atio

n (

TW

h)

Existing Resources RPS Net Short

Page 12: RPS Model Methodology

12

Sources of New Resources to Fill Resource Gap

1. Commercial Projects

ED Database of IOU projects

POU procurement plan data obtained from CARB

2. Additional “Theoretical” Projects

RETI pre-identified and proxy projects for California

WREZ projects for the rest of the WECC

3. Original Renewable DG resource potential estimates

Developed as part of 2010 LTPP

Page 13: RPS Model Methodology

13

Updated ED Database

Analysis incorporates the latest ED Database, including 2009 IOU solicitations

Contracted projects are included individually

Shortlisted projects are aggregated by resource type and zone in cases where there are at least three such projects to preserve confidentiality (otherwise they are left out)

Distribution of projects among zones has changed since previous analysis

CREZ MW GWh

Tehachapi 4,174 11,239

Northwest 1,805 4,137

Pisgah 1,700 3,974

NonCREZ 841 3,831

Imperial South 1,074 3,042

Riverside East 1,042 2,547

Alberta 886 2,422

Carrizo South 849 1,980

Mountain Pass 710 1,720

Nevada C 500 1,415

Colorado 420 1,301

San Diego South 415 1,293

Montana 300 820

Imperial North 109 770

Fairmont 296 752

Arizona 290 737

Solano 240 704

Kramer 250 584

Inyokern 242 566

Distributed Solar - PG&E 244 468

Distributed Solar - SCE 140 290

Round Mountain 86 281

New Mexico 32 238

Santa Barbara 83 238

Palm Springs 77 222

Nevada N 30 212

Imperial East 30 212

Utah-Southern Idaho 90 191

San Bernardino - Lucerne 49 170

Total 17,003 46,357

Page 14: RPS Model Methodology

14

Current Analysis Previous Analysis Current Analysis Previous Analysis

MW GWh MW GWh MW GWh MW GWh

Tehachapi 4,174 11,239 2,038 5,756 Palm Springs 77 222 37 107

Northwest 1,805 4,137 - - Nevada N 30 212 - -

Pisgah 1,700 3,974 800 1,954 Imperial East 30 212 560 1,367

NonCREZ 841 3,831 525 3,118 Utah-Southern Idaho 90 191 - -

Imperial South 1,074 3,042 792 1,944 San Bernardino - Lucerne 49 170 666 1,623

Riverside East 1,042 2,547 2,932 6,855 Baja - - - -

Alberta 886 2,422 - - Barstow - - - -

Carrizo South 849 1,980 - - British Columbia - - - -

Mountain Pass 710 1,720 1,480 3,523 Carrizo North - - - -

Nevada C 500 1,415 30 228 Cuyama - - - -

Colorado 420 1,301 - - Iron Mountain - - 368 897

San Diego South 415 1,293 225 650 Lassen North - - 53 384

Montana 300 820 - - Lassen South - - - -

Imperial North 109 770 569 2,472 Owens Valley - - 55 307

Fairmont 296 752 384 907 San Bernardino - Baker - - - -

Arizona 290 737 - - San Diego North Central - - 87 333

Solano 240 704 150 434 Twentynine Palms - - 67 194

Kramer 250 584 837 2,016 Victorville - - 160 342

Inyokern 242 566 - - Westlands - - - -

Distributed Solar - PG&E 244 468 - - Wyoming - - - -

Distributed Solar - SCE 140 290 - - Needles n/a n/a 747 1,911

Round Mountain 86 281 125 468 Out-of-State Early n/a n/a 2,062 6,617

New Mexico 32 238 - - Out-of-State Late n/a n/a 534 1,304

Santa Barbara 83 238 83 238 Total 17,003 46,357 16,363 45,950

CREZ CREZ

Changes in the ED Database

Page 15: RPS Model Methodology

15

Breakdown of the ED Database

Discounted Core Non-Discounted CoreExcluded from Model

to Preserve Confidentiality

Total ED Database Projects

MW GWh MW GWh MW GWh MW GWh

Biogas 12 84 28 195 - - 40 279

Biomass 158 1,176 8 56 20 149 186 1,381

Geothermal 70 494 439 3,101 336 2,370 845 5,965

Hydro - - 26 79 - - 26 79

Large Scale Solar PV 2,068 5,068 2,087 4,970 975 2,322 5,130 12,359

Small Solar PV 384 758 - - - - 384 758

Solar Thermal 2,744 6,414 1,305 3,050 153 358 4,202 9,822

Wind 3,684 10,049 3,991 10,862 1,098 2,987 8,772 23,898 Total 9,120 24,044 7,883 22,313 2,581 8,186 19,584 54,542

Signed - ApprovedSigned - Pending

ApprovalIn Negotiations

Total Projects Included in RES

CalculatorMW GWh MW GWh MW GWh MW GWh

Biogas 21 144 19 135 - - 40 279

Biomass 89 659 77 573 - - 166 1,232

Geothermal 219 1,547 290 2,048 - - 509 3,595

Hydro - - 26 79 - - 26 79

Large Scale Solar PV 1,138 2,724 1,421 3,591 1,596 3,722 4,155 10,037

Small Solar PV 7 14 268 536 109 209 384 758

Solar Thermal 1,615 3,775 2,434 5,689 - - 4,049 9,464

Wind 2,950 8,034 814 2,249 3,910 10,629 7,675 20,911 Total 6,039 16,896 5,349 14,900 5,615 14,560 17,003 46,357

Page 16: RPS Model Methodology

16

Treatment of Commercial Projects

Commercial projects are divided into two categories:

1. Discounted Core: Project has obtained or has made significant progress towards obtaining a permit

2. Non-Discounted Core: Project has made limited progress towards obtaining a permit

Discounted Core projects are given first priority in the resource selection sorts, reflecting their high probability of development

Non-Discounted Core projects are given priority over generic resources but are not guaranteed development

POU planned resources treated as Non-Discounted core

Page 17: RPS Model Methodology

17

POU Resources

E3 has included POU resource procurement plans in the development of the resource portfolios based on CEC data

POU resources are included as Non-Discounted Core commercial resources

In-State Out-of-State

MW GWh MW GWh

Biogas 145 1,013 - -

Biomass - - 2 12

Geothermal 550 3,884 42 299

Hydro - Small - - 156 478

Solar Thermal 358 836 - -

Solar PV - - - -

Wind 504 1,455 648 1,871

Total 1,557 7,188 848 2,660

Page 18: RPS Model Methodology

18

RETI Phase 2B Database

The updated RETI Phase 2B Database contains site-specific information for renewable resource potential, cost, and performance in California

Out-of-state resources from the WREZ Transmission Model have been incorporated with the RETI data

WREZ estimates of potential represent high-quality remote renewable resources that would require significant transmission upgrades to reach load centers

Page 19: RPS Model Methodology

19

Out of State Renewable Resource Data from E3 Models E3 maintains a database of renewable resource

cost and performance data in the West

Wind and solar data based on NREL GIS modeling

Geothermal and hydro data from EIA

Biomass aggregated from various sources

Additional resource data for BC and Alberta

Used to supplement WREZ data for out-of-state resources (Montana, Colorado, BC, Alberta)

Page 20: RPS Model Methodology

20

Estimates of Statewide DG Potential

As part of the 2010 LTPP, E3 and Black & Veatch collaborated to develop original estimates of the statewide potential of solar PV

The RPS model integrates these estimates, allowing it to evaluate the viability of the development of these resources

RegionEasy to

InterconnectHarder to

InterconnectTotal

North Coast 1148 1440 2588Central Valley 1237 5693 6931South Coast 1890 433 2323Mojave Desert 694 1600 2294TOTAL 4970 9167 14136

Page 21: RPS Model Methodology

21

Resource Cost and Performance

E3 used site-specific data on resource cost and performance where available (RETI and WREZ projects)

Generic assumptions were developed for resources without specific information based on averages of the RETI data (shown below)

Biogas - Landfill

Biogas - Other

Biomass GeothermalHydro - Small

Large Scale Solar

PV

Small Solar PV

Solar Thermal

Wind

Capital Cost ($/kW) 2,750$ 5,500$ 4,522$ 6,379$ 3,300$ 4,500$ 4,500$ 5,300$ 2,371$

Fixed O&M ($/kW-yr) 130$ 165$ 72$ -$ 25$ 50$ 50$ 66$ 60$

Variable O&M ($/MWh) -$ -$ 17$ 38$ -$ -$ -$ -$ -$

Heat Rate (Btu/kWh) 12,070 13,200 14,800 - - - - - -

Capacity Factor (%) 80.0% 80.0% 85.0% 80.6% 35.0% 27.8% 23.7% 26.7% 32.9%

On-Peak Availability (%) 100% 100% 100% 100% 65% 65% 65% 78%* 11%**

LCOE ($/MWh) 92$ 121$ 106$ 148$ 161$ 142$ 216$ 202$ 95$

* On-peak availability for solar thermal varies between 70% and 85% based on CREZ

** On-peak availability shown is for in-state resources; out-of-state wind resources have an on-peak availability value of 20% in California

Page 22: RPS Model Methodology

22

Generic Resource Cost Assumptions

$92 $95 $106$142 $148 $161

$202 $216

0

50

100

150

200

250

Biogas -Landfill

Wind Biomass LargeScale

Solar PV

Geo-thermal

Hydro -Small

SolarThermal

SmallSolar PV

20

20

LC

OE

in

20

08

$/M

Wh

Page 23: RPS Model Methodology

23

Transmission and Geographic Classification of Resources

Each resource is assigned one of three classifications

1. CREZ: resources located within one of the 48 Competitive Renewable Energy Zones (either in California or in other states)

2. Non-CREZ: resources in California or directly across the border that are not located within a CREZ and can be delivered with minor transmission upgrades

3. Out-of-State REC: out-of-state resources that would deliver energy to the local market

Page 24: RPS Model Methodology

24

Transmission Bundles

Resources in CREZs are aggregated into transmission bundles in the following order:

1. Existing transmission bundle

2. Minor upgrade bundle

3. New transmission bundle

Discounted core projects given first priority to fill each transmission bundle

Non-core Commercial projects given next priority to fill the New Transmission bundle

Any remaining transmission capacity in the bundle is allocated to the lowest-scoring generic projects

Up to 3000 MW of new transmission allowed for each CREZ

Page 25: RPS Model Methodology

25

Examples of Transmission Capacity Allocation

300

600

1500

400 361

1000

0

200

400

600

800

1000

1200

1400

1600

(1)

Ex

isti

ng

Tra

ns

mis

sio

n

(2)

Inc

rem

en

tal

Up

gra

de

(3)

Ne

wT

ran

sm

iss

ion

(1)

Ex

isti

ng

Tra

ns

mis

sio

n

(2)

Inc

rem

en

tal

Up

gra

de

(3)

Ne

wT

ran

sm

iss

ion

Example Buildup 1 Example Buildup 2

Av

aila

ble

Ca

pa

cit

y (

MW

)

Discounted Core Commercial Projects Theoretical Projects

Page 26: RPS Model Methodology

26

Examples of Transmission Capacity Allocation

0

500

1000

1500

2000

2500

3000

Example 1 Example 2 Example 3

Av

aila

ble

Ca

pa

cit

y (

MW

)

Existing Transmission Minor Upgrades New Line

Discounted Core Commercial Projects Theoretical Projects

Page 27: RPS Model Methodology

27

Out-of-State REC Resources

Out-of-State RECs previously included in two “zones” (“Out-of-State Early” and “Out-of-State Late”)

New model can select REC resources individually

Assume physical limitations on wind integration for each region

REC resources priced at long-run “Green Premium” or Cost minus Value

REC resources optimized for access to transmission, not for resource quality – RECs are allowed for average quality resources, not best sites

Pricing based on long-run cost, not REC market price forecast (analogous to in-state resources)

No pricing distinction between different types of RECs (bundled vs. unbundled, with or without delivery requirement)

Page 28: RPS Model Methodology

28

Unbundled Out-of-State REC-Only Transaction

Leg 1: Developer to Mid-C

Mid-C Market

Leg 2: CAISO to load

Pure REC transaction with no energy purchase requirement and no delivery requirement

Developer sells energy at Mid-C

California LSE purchases REC from developer at LCOE minus Mid-C price

Separately, California LSE arranges for energy transaction from CAISO market to load

California LSE never owns energy

No incremental imports to California

Page 29: RPS Model Methodology

29

Out-of-State REC with Delivery Requirement

Leg 1: CA utility to

Mid-C

Mid-C Market

Leg 2: CAISO to load

REC transaction with energy purchase requirement and delivery requirement

California LSE purchases energy and REC from developer at LCOE of wind facility and sells energy at Mid-C

Separately, California LSE arranges for energy transaction from CAISO market to load

California LSE rebundles REC with transaction from Mid-C to CAISO that would have occurred anyway!

No incremental imports to California

Leg 3: Mid-C to CAISO

Page 30: RPS Model Methodology

30

Pricing of Out-of-State REC vs. In-State Resource

In-State Resource priced at LCOE, ratepayer impact is cost relative to market value or “Green Premium”

Out-of-State REC priced directly at “Green Premium”

Energy and capacity values vary by market (higher in California)

Pricing is the same for all flavors of RECs

In-State Wind Resource

Out-of-State Wind REC

Levelized Cost of Wind Energy $ 90.00 $ 75.00

Integration Costs in Local Market $ 6.00 $ 6.00

Energy Value in Local Market (Mid-C or Palo Verde) $ (55.00) $ (45.00)

Capacity Value in Local Market (Mid-C or Palo Verde) $ (5.00) $ -

Net Cost to CA Ratepayers ("Green Premium") $ 36.00 $ 36.00

REC Price $ - $ 36.00

Example of In-State vs. REC-only Pricing

Page 31: RPS Model Methodology

31

Physical Limits on Out-of-State REC SupplyM

arke

t V

alu

e o

f W

ind

En

erg

y $/

MW

h

MW of Intermittent Renewables

Initially, wind displaces gas resources

More wind reduces market prices and raises integration costs

Value decreases significantly as wind displaces baseload

There is a practical limit to how much intermittent energy each zone can easily accept

Page 32: RPS Model Methodology

32

Physical Limits on Out-of-State REC Supply

There is a practical limit to how much intermittent energy each zone can easily accept

Mar

ket

Val

ue

of

Win

d E

ner

gy

$/M

Wh

MW of Intermittent Renewables

Initially, wind displaces gas resources

More wind reduces market prices and raises integration costs

Value decreases significantly as wind displaces baseload

E3 limited the REC supply based on a simplified representation

Page 33: RPS Model Methodology

33

Limits on Wind Penetration

Ability to easily absorb wind is limited to load served with flexible generation

E3 estimated hourly flexible generation in each zone:

Load – Nuclear – Coal – Base Hydro + Export Transmission Capacity

Wind limit is min value

Other regions also have RPS requirements – assume CA can soak up 50% of each region’s limit

6,461 MW2,257 MW

738 MW404 MW

1,231 MW461 MW

229 MW47 MW

0 MW0 MW

3,665 MW1,371 MW

3,968 MW1,939 MW

2,135 MW 947 MW

1,700 MW850 MW

2,211 MW808 MW

13,745 MW

Total limit on wind in regionWind available to California

Page 34: RPS Model Methodology

Portfolio Selection Methodology

Page 35: RPS Model Methodology

35

Resource Selection Methodology

1. Calculate project score for each resource

2. Allocate lowest-scoring out-of-state theoretical projects to other states until all non-CA WECC RPS targets for 2020 are satisfied

3. Rank remaining CREZ projects and select to fill transmission bundles

4. Calculate aggregate score for each transmission bundle

5. Rank transmission bundles against individual non-CREZ and REC resources

6. Select resources and bundles to meet 33% RPS target in 2020

Page 36: RPS Model Methodology

36

Detailed Portolio Development

Resource Sort for Local Use

Potential CREZ ResourcesPotential Non-CREZ and

REC Resources

Resource Sort for CA Use as RECs

Resource Sort for CA Use Towards RPS

Resources Remaining After Local Sort

Resource Sort for Existing Tx

Resources on Existing Transmission

Resources Remaining After Existing Tx Sort

Resource Sort for New Tx

New Transmission Bundles

Resources Remaining After Local Sort

Non-CREZ and REC Resource Rankings

Resources Selected for Local Use

Resources Selected for CA RPS Portfolio

Page 37: RPS Model Methodology

37

Project Scoring Methodology

Each project is scored on a 0-100 scale based on four metrics (0 is better):

Net Cost

Environmental Score

Commercial Interest Score

Timing Score

Final score for each project is a weighted average of the four individual metrics

Weights are user-defined and vary by scenario

Page 38: RPS Model Methodology

38

Net Cost Score

Cost score is based on a modified version of the RETI Ranking Cost

Includes integration and T&D avoided costs

Scores are converted to 0 – 100 scale, bounded by the model’s lowest and highest net cost resources

Modified RETI Ranking Cost

+ Levelized cost of energy

+ Interconnection (gen-tie) costs

+ Deemed integration costs

+ Levelized, per-MWh incremental transmission costs

– T&D avoided costs

– Energy value

– Capacity value

= Final project ranking cost

Page 39: RPS Model Methodology

39

Environmental Score

Handicaps resources in areas where environmental issues might hinder development

Considers a variety of factors:

Disturbed lands

Right-of-Way

Significant species

Air quality

Others

Scores for each resource in each CREZ on 0-100 scale

CREZ NameTotal for Biomass

Total for Geotherm

alTotal for Solar PV

Total for Solar

ThermalTotal for

WindPV Rural

DGPV Urban Ground

PV Urban Roof

Barstow 40.3 34.2 34.2 35.3 28.9 24.5 9.9 0.0Carrizo North 32.1 27.1 25.5 26.5 24.4 21.1 8.8 0.0Carrizo South 31.5 26.9 25.1 25.8 25.6 22.0 10.0 0.0Cuyama 35.5 28.8 29.0 30.8 27.2 24.5 10.0 0.0Fairmont 30.9 30.1 35.7 36.4 29.0 24.8 9.4 0.0Imperial East 41.1 31.2 26.2 27.2 25.4 22.5 9.9 0.0Imperial North-A 35.9 26.1 21.4 21.8 24.5 18.1 7.8 0.0Imperial North-B 49.9 34.8 28.6 29.5 34.7 22.4 8.5 0.0Imperial South 46.2 31.7 24.1 24.7 27.5 19.9 8.5 0.0Inyokern 30.9 29.4 33.4 33.9 26.3 24.6 9.9 0.0Iron Mountain 38.5 28.6 22.4 22.5 22.0 21.1 10.0 0.0Kramer 22.8 22.2 27.2 27.6 19.7 17.5 6.2 0.0Lassen North 30.5 31.1 33.0 34.2 31.7 25.6 10.0 0.0Lassen South 24.7 32.5 45.7 52.9 49.7 20.3 5.0 0.0Mountain Pass 40.3 33.5 35.8 36.3 29.4 25.8 9.7 0.0Owens Valley 38.3 30.5 25.4 26.7 24.4 20.9 10.0 0.0Palm Springs 50.5 33.0 29.2 31.8 29.1 21.3 6.8 0.0Pisgah 37.5 27.2 21.1 21.3 20.8 20.5 10.0 0.0Riverside East 28.9 23.8 20.6 20.7 20.5 20.1 9.8 0.0Round Mountain-A 23.1 22.5 23.7 24.3 23.1 21.8 10.0 0.0Round Mountain-B 40.3 36.3 39.5 43.7 35.4 27.1 10.0 0.0San Bernardino - Baker 38.3 29.3 23.5 24.2 22.8 20.9 10.0 0.0San Bernardino - Lucerne 42.4 38.0 43.0 44.1 31.9 27.9 9.8 0.0San Diego North Central 57.3 51.9 69.1 75.3 73.6 41.1 9.5 0.0San Diego South 34.1 31.2 34.3 37.0 29.5 24.6 9.9 0.0Santa Barbara 27.1 23.8 26.9 31.1 22.7 15.4 4.9 0.0Solano 34.6 25.0 25.0 26.6 36.3 16.6 5.0 0.0Tehachapi 29.8 25.6 23.2 23.8 23.5 20.6 9.7 0.0Twentynine Palms 38.9 29.6 24.4 25.1 23.2 21.5 10.0 0.0Victorville 39.2 34.2 38.4 38.9 28.0 25.8 9.4 0.0Westlands 26.4 16.7 10.5 10.9 10.1 9.5 4.5 0.0Arizona 36.2 27.8 23.1 23.5 22.7Nevada 36.8 37.4 43.1 46.2 41.9Oregon 45.9 39.6 36.9 37.6 36.3Baja 45.7 41.6 51.7 56.2 51.5Other 45.9 39.6 36.9 37.6 36.3

Median for CA NonCREZ 35.9 29.6 26.9 27.6 26.3 21.5 9.8 0.0

Page 40: RPS Model Methodology

40

Commercial and Timing Scores

Commercial Score: Scale of 0-100 reflecting contracting activity of California utilities

Commercial projects receive a score of 0, while generic projects receive a score of 100

POU-planned projects considered “Commercial” and receive score of 0

Timing Score: Gives better score to resources that can be developed on a relatively short time scale

Online date < 2010 gets 0, > 2021 gets 100

For ED database projects, online dates filed with the applications

For other resources, dates based on size and type of project

Page 41: RPS Model Methodology

41

Selection of RPS Portfolio

Each transmission bundle is assigned an aggregate score based on an average of the constituent resources and compared against individual non-CREZ and RECs resources

Discounted Core Projects are selected first unless in New Transmission bundle

After Discounted Core, resources & bundles with the lowest score are selected to fill the 2020 RPS gap