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SERVM Strategic Energy Risk Valuation Model Astrape Consulting

Nick Wintermantel

11/10/2016

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Topics

Resource Adequacy Overview

SERVM Model Overview

Reserve Margin Study (2013)

Renewable Integration Study (2015)

Effective Load Carrying Capability

Flexibility Study

Integration Costs

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Resource Adequacy Overview

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Resource Adequacy

Resource Adequacy Definition: The ability of supply-side and demand-side resources

to meet the aggregate electrical demand (NERC Definition)

Resource Adequacy Studies

Reserve Margin Study

Goal: Calculate generating capacity deficiencies and determine the amount of capacity needed to

maintain resource adequacy during peak conditions

Purpose: Input into expansion planning processes

Effective Load Carrying Capability Study

Goal: Determine the capacity contribution of intermittent resources

Purpose: Necessary to calculate the system reserve margin

Flexibility Study

Goal: Determine reliability deficiencies including both firm load shed events and renewable resource

curtailment due to system ramping/startup constraints (not capacity deficiencies)

Purpose: Provides assistance in setting appropriate parameters for resource additions and to

determine system operating reserve requirements

Integration Cost Study

Goal: Determine incremental system costs caused by adding intermittent resources

Purpose: Used in capacity procurements and in resource selection processes

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Resource Adequacy Metrics

Loss of Load Expectation (LOLECAP): Expected number of firm load shed events in a

given year due to capacity shortfalls

Loss of Load Expectation (LOLEFLEX): Expected number of firm load shed events in a

given year due to not having enough ramping capability

Loss of Load Hours (LOLHCAP): Expected number of hours of firm load shed in a given

year due to capacity shortfalls

Loss of Load Hours (LOLHFLEX): Expected number of hours of firm load shed in a given

year due to not having enough ramping capability

Expected Unserved Energy (EUECAP): Expected amount of firm load shed in MWh for a

given year due to capacity shortfalls

Expected Unserved Energy (EUEFLEX): Expected amount of firm load shed in MWh for a

given year due to not having enough ramping capability

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SERVM Model Overview

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Strategic Energy Risk Valuation Model (SERVM)

SERVM has over 30 years of use and development

Probabilistic hourly and intra-hour chronological production cost model designed

specifically for resource adequacy and system flexibility studies

SERVM calculates both resource adequacy metrics and costs

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SERVM Uses Resource Adequacy

Loss of Load Expectation Studies

Optimal Reserve Margin

Operational Intermittent Integration Studies

Penetration Studies

System Flexibility Studies

Effective Load Carrying Capability of Energy Limited Resources

Wind/Solar

Demand Response

Storage

Fuel Reliability Studies

Gas/Electric Interdependency Questions

Fuel Backup/Fixed Gas Transportation Questions

Transmission Interface Studies

Resource Planning Studies

Market Price Forecasts

Energy Margins for Any Resource

System Production Cost Studies

Evaluate Environmental/Retirement Decisions

Evaluate Expansion Plans

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Resource Commitment and Dispatch

8760 Hourly Chronological Commitment and Dispatch Model

Simulates 1 year in approximately 1 minute allowing for thousands of

scenarios to be simulated which vary weather, load, unit performance, and

fuel price

Capability to dispatch to 1 minute interval

Respects all unit constraints

Capacity maximums and minimums

Heat rates

Startup times and costs

Variable O&M

Emissions

Minimum up times, minimum down times

Must run designations

Ramp rates

Simulations are split across multiple processors linked up to the SQL

Server

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Resource Commitment and Dispatch

Commitment Decisions on

the Following Time Intervals

allowing for recourse

Week Ahead

Day Ahead

4 Hour Ahead, 3 Hour

Ahead, 2 Hour Ahead, 1

Hour Ahead, and Intra-Hour

Load, Wind, and Solar

Uncertainties at each time

interval (decreasing as the

prompt hour approaches)

Benchmarked against other

production models such as

PROSYM

47,000

48,000

49,000

50,000

51,000

52,000

53,000

0 0.5 1 1.5 2 2.5 3 3.5 4

Net

Lo

ad

MW

Hour

1 - 4 Hour Ahead Forecast Error

Actual Net Load Forecast Error Range from Hour 0

At hour 0, SERVM draws from correlated load, wind,

and solar forecast error distributions for intra-hour, 1 hr

ahead, 2 hrs ahead, 3 hrs ahead, and 4 hrs ahead

uncertainty. SERVM then makes commitment &

dispatch adjustments based on the uncertain forecast,

but ultimately must meet the net load shape that

materializes.

Current Position: t = 0

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Ancillary Service Modeling

Ancillary Services Captured

Regulation Up Reserves

Regulation Down Reserves

Spinning Reserves

Non Spinning Reserves

Load Following Reserves

Co-Optimization of Energy and Ancillary Services

Each committed resource is designated as serving energy or energy plus one of the

ancillary services for each period

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SERVM Framework

Base Case Study Year

Weather (35 years of weather history)

Impact on Load

Impact on Intermittent Resources

Economic Load Forecast Error (distribution of 5 points)

Unit Outage Modeling (thousands of iterations)

Multi-State Monte Carlo

Frequency and Duration

Base Case Total Scenario Breakdown: 35 weather years x 5 LFE points = 185 scenarios

Base Case Total Iteration Breakdown: 185 scenarios * 100 unit outage iterations = 18,500 iterations

Reserve Margin Study/ELCC Study: Hourly Simulations

Flexibility and Integration Cost Studies: Intra Hour Simulations

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Reserve Margin Study (2013)

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Load Modeling: Summer Peak Weather Variability 2013 Reserve Margin Study

-6%

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

% F

rom

No

rma

l W

eath

er

Year

PNM-North PNM-South

15

0%

10%

20%

30%

40%

50%

60%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Av

era

ge

Ca

pa

city

Fa

cto

r

Hour of Day

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sept

Oct

Nov

Dec

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 1112131415161718192021222324

Av

era

ge

Ca

pa

city

Fa

cto

r

Time of Day

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Wind

Solar

Renewable Shapes: 30 + Years 2013 Reserve Margin Study

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Economic Load Forecast Error 2013 Reserve Margin Study

Using CBO GDP approach and assuming 30% multiplier for electric

load growth compared to GDP growth

Load Forecast Error Multipliers Probability %

0.95 2.7%

0.97 14%

0.99 23.8%

1.00 19.1%

1.01 23.8%

1.03 14%

1.05 2.7%

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Unit Outage Modeling

Full Outages

Time to Repair

Time to Failure

Partial Outages

Time to Repair

Time to Failure

Derate Percentage

Startup Failures

Maintenance Outages

Planned Outages

Created Based on NERC GADS Data

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Multi State Frequency and Duration Modeling vs Convolution

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 100 200 300 400 500 600 700 800 900

Per

cen

t o

f T

ime

System MWs Offline due to Forced Outages

System Forced Outages 2013 Reserve Margin Study

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BA = PNM + Tri-State

Regions

Committed and

Dispatched as a single

region

100

130 50

100

107

50

610

1200

100

610

1300

230

60 Bi-directional

204

64 25

141

50

0

50

200 0

100

80 80

0

300

Arizona

Arizona Entities

(APS, AEPCO, TEP,

Salt River Project,

Gila River Power

Station)

El Paso Electric

Southwestern Public Service

Company

PNM - Four Corners

PNM ownership of

PV 1 - 3, FC 4 - 5,

SJ 1 - 4

PNM - North

Reeves 1 - 3,

Rio Bravo, Valencia,

Renewables

PNM - South

Afton CC,

Lordsburg1,

Lordsburg 2, PNM

portion of LUNA 1

Public Service Company of

Colorado

Tri - State North

Tri - State South

Study Topology and Market Assistance 2013 Reserve Margin Study

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Emergency Operating Procedures 2013 Reserve Margin Study

Demand Response

Firm load shed to maintain reserves equal to 4% of load

Power Saver

Program

Peak Saver

Program

Capacity (MW) 45 20

Season June-Sept June-Sept

Hours Per Year 100 100

Hours Per Day 4 6

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LOLECAP and LOLHCAP Results 2013 Reserve Margin Study

Events averaged 2 hours

Industry Standard: 1 day in 10 year standard = 0.1 LOLE = 21% reserve margin

-

0.20

0.40

0.60

0.80

1.00

-

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

9% 10% 11% 12% 13% 14% 15% 16% 17% 18% 19% 20% 21% 22%

LO

LH

(H

ou

rs P

er Y

ear)

LO

LE

(E

ven

ts P

er Y

ear)

Poly. (LOLE)

Poly. (LOLH)

LOLE

LOLH

Reserve Margin

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30

35

40

45

50

55

60

10% 11% 12% 13% 14% 15% 16% 17% 18% 19% 20% 21% 22% 23%

To

tal

Rel

iab

ilit

y C

ost

s M

$

Reserve Margin

80th Percentile

85th Percentile

90th Percentile

95th Percentile

21.8%

19.0%

15.5%

17.8%

Total Reliability Costs =

CT Carrying cost +

Production Costs above a CT+

Purchases above a CT+

Unserved Energy Costs

Economic Optimal Reserve Margin 2013 Reserve Margin Study

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Renewable Integration Study: Effective Load

Carrying Capability Study

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Incremental Effective Load Carrying Capability Generic Example Only

Simulate Base Case:

LOLECAP = .20

Add 50 MW Incremental Wind

LOLECAP = .19

Add 50 MW GT Capacity

LOLECAP = .15

Wind Resource reduced LOLE by 0.01 while GT resource reduced LOLE by .05

ELCC = .01/.05 = 20%

Incremental ELCC can also be approximated by calculating average output during

EUE events.

Average ELCC is calculate by removing entire wind portfolio and then adding it back

to understand its LOLE reduction compared to GT Resources

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EUE and Renewable Profiles by Hour of Day 2015 RIS Study

0%

5%

10%

15%

20%

25%

30%

35%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

% o

f E

UE

% o

f R

en

ew

ab

le O

utp

ut

Hour of Day

Wind Profile by hour of day (Secondary Axis)

PV Profile by hour of day: (Secondary Axis)

PV Tracking by hour of day (Secondary Axis)

EUE

hours

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2018/2023 Average and Incremental ELCC Values 2015 RIS Study

PV Fixed PV SAT Wind

2018 average 47.2% 62.1% 21.9%

2018 incremental 43.0% 57.2% 14.2%

PV Fixed PV SAT Wind

2023 average 46.9% 61.2% 21.7%

2023 incremental 38.9% 52.1% 13.7%

SAT: Single Axis Tracking

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Renewable Integration Study: Flexibility

Study

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What Does the Flexibility Problem Look Like?

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load

(M

W)

Hour of day

Renewable Curtailment

Loss of Ramping

Renewables

Combustion Turbine

Steam

Hydro

Base Load

Load

Possibility of Adequate Total Capacity, but

Inadequate Ramping Capability. Results in

LOLEFLEX event

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Increase Load Following Reserves to Reduce LOLEFLEX Events

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Load

(M

W)

Hour of day

Renewable Curtailment

Loss of Ramping

Renewables

Combustion Turbine

Steam

Hydro

Base Load

Load

Over Commit

Conventional

Resources and

Increase Curtailment, but Avoid LOLEFLEX

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Flexibility Study Approach

Identify LOLEFLEX events and renewable curtailment (overgen) events

Solve the deficiencies using the following approaches and calculate

costs:

Change operating procedures (i.e. raise load following requirement)

Swap or add existing capacity with flexible capacity (multiple

technologies)

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Base Case Physical Reliability Results Varying Operating Reserve Levels 2015 RIS Study

2018: 16% Reserve Margin

Spin + Reg Requirement = Varied

from 8% to 16% of Load

LOLECAP is near previous LOLE

study which did not take into

account flexibility problems

LOLEFLEX adds more events but

are extremely low in magnitude

and in duration (<10 min)

10%- reg + spin target is likely

reasonable given the size and

duration of the LOLEFLEX

2018 Study Year

Reg + Spin Target 8% of Load 10% of Load 12% of Load 16% of Load

2018 LOLECAP

0.21

0.21

0.21

0.21

2018 LOLEFLEX

7.15

0.74

0.07

0.03

2018 Curtailment MWh

21,246

23,708

32,178

118,189

System Production Cost M$

289.04

294.09

301.02

322.35

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LOLEFLEX Across Different Operating Reserve Requirements 2015 RIS Study

Note: Largest decrease in LOLEFLEX

moving from 8% of Load to 10% Reg +

Spin target. Slight benefit thereafter

-

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

6% 8% 10% 12% 14% 16% 18%

LO

LE

FL

EX

(E

ve

nts

Pe

r Y

ear)

Reg + Spin Target (% of Load)

2018

2023

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Production Costs Across Different Operating Reserve Requirements 2015 RIS Study

-

50.00

100.00

150.00

200.00

250.00

300.00

350.00

400.00

450.00

500.00

6% 8% 10% 12% 14% 16% 18%

To

tal P

rod

uc

tio

n C

osts

(M

$)

Reg + Spin Target (% of Load)

2018

2023

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Renewable Curtailment Across Different Operating Reserve Requirements 2015 RIS Study

-

50,000

100,000

150,000

200,000

250,000

300,000

350,000

6% 8% 10% 12% 14% 16% 18%

Re

ne

wa

ble

Cu

rta

ilm

en

t (M

Wh

)

Reg + Spin Target (% of Load)

2018

2023

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Base Case (Monthly Basis) 2015 RIS Study

Month LOLECAP LOLEFLEX

Jan - 0.02

Feb - 0.05

Mar - 0.21

Apr - 0.17

May - 0.07

Jun 0.06 0.02

Jul 0.10 0.01

Aug 0.05 0.01

Sep 0.00 0.03

Oct - 0.08

Nov - 0.04

Dec - 0.02

Total 0.21 0.74

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Integration Cost Study

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2018 Wind Integration Cost Adder Calculation

Simulate Base Case:

LOLECAP = .21; LOLEFLEX = .07

Add 50 MW Incremental Wind/Remove 6.5 MW CT (.13 ELCC * 50

MW):

LOLECAP = .21; LOLEFLEX = .20

Add Reserve MW until LOLEFLEX = .07

Additional Reserves = 4 MW

Calculate System Cost Impact of Additional 4 MW Reserves

System Cost = +$794,161

Divide by Renewable Energy

Integration Cost Adder = $794,160 / 133,152 MWh = $5.96/MWh

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2018 Integration Cost Results

Technology Incremental

Gen (MWh) Required Spin Increase to Maintain

Base Case Reliability Cost Increase $/MWh

WIND 133,152 4 MW

794,161

5.96

PV 108,011 15% of Incremental Solar Output

489,772

4.53

PV SAT 126,144 15% of Incremental Solar Output

489,772

3.88