1

Integrated Resource Plan Presentation

Las Cruces, NM

April 11, 2012

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Introduction

• The energy & load forecast is used to project energy sales and peak load for 20 years.

• Peak load forecast is used to determine how much Generation and Transmission capacity will be needed over time.

• The system expansion profile is used to plan for the capital expenditures required to meet the future system load

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Introduction (cont.)

• The energy forecast is used to determine the expected level of energy sales and revenue, usually for 2-3 years.

• This information is used by Finance to balance cash flow and financing needs, as well as proving guidance to outside investors and analysts.

4

Load & Energy Forecasts Serve Competing Purposes

• Electric utilities need to have adequate capacity available to meet peak conditions at any point in time, thus capacity planners want an aggressive load forecast.

• Electric utilities need short-term forecast of energy sales and revenues. This is used to provide guidance to senior management, outside investors, analysts and government agencies. Thus, financial planners want a conservative energy forecast.

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Model Enhancements

• Monthly sales model developed to replace annual forecasting model.

• This gives EPE the ability to model the effects of weather on energy sales, adjust the sales forecast for average weather patterns, and explain the impact of abnormal hot or cool weather on system energy sales.

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Model Enhancements (cont.)

• Customer growth is explicitly tied to energy sales.

• The effect of increasing refrigerated air conditioning saturation rate on residential use per customer is modeled.

• Interactive weather terms are used to pool monthly data into one equation, increasing the degrees of freedom 12X.

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Energy Model

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Texas Residential Model

Texas Residential Use per Customer Equation

UPC_TXres = +rpi *EP_Real_Pers_Inc+acsat +* AC_Sat_Dum + m01*EP_M01HDD_2ma + m02*EP_M02HDD_2ma + m06*EP_M06CDD_2ma + m07*EP_M07CDD_2ma + m08*EP_M08CDD_2ma + m09*EP_M09CDD_2ma + m10*EP_M10CDD_2ma + m12*EP_M12HDD_2ma + AR(1)

Texas Residential Customer Equation

CUS_TXres = + HH *EP_Households + AR(1)

Texas Residential kWh Forecast

KWH_TXres = UPC_TXres * CUS_TXres

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New Mexico Residential Model

New Mexico Residential Use Per Customer Equation

UPC_NMres = +rpi *LC_Real_Pers_Inc+ m01*LC_M01HDD_2ma + m02*LC_M02HDD_2ma + m06*LC_M06CDD_2ma + m07*LC_M07CDD_2ma + m08*LC_M08CDD_2ma + m09*LC_M09CDD_2ma + m10*LC_M10CDD_2ma + m12*LC_M12HDD_2ma + AR(1)

New Mexico Residential Customer Equation

CUS_NMres = + HH *LC_Households + AR(1)

New Mexico Residential kWh Forecast

KWH_NMres = UPC_NMres * CUS_NMres

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Texas Commercial Model

Texas Commercial Use per Customer Equation

UPC_TXcom = +rpi *EP_Nonfarm_Empl + m02*EP_M02HDD_2ma + m06*EP_M06CDD_2ma + m07*EP_M07CDD_2ma + m08*EP_M08CDD_2ma + m09*EP_M09CDD_2ma + m10*EP_M10CDD_2ma + Trend90+ AR(1)

Texas Commercial Customer Equation

CUS_TXcom = + HH *EP_Households + AR(1)

Texas Commercial kWh Forecast

KWH_TXcom = UPC_TXres * CUS_TXres

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New Mexico Commercial Model

New Mexico Commercial Use Per Customer Equation

UPC_NMcom = +rpi *LC_Real_Pers_Inc+ m01*LC_M01HDD_2mam01 + m02*LC_M02HDD_2ma + m04*LC_M04HDD_2ma + m05*LC_M05CDD_2ma + m06*LC_M06CDD_2ma + m07*LC_M07CDD_2ma + m08*LC_M08CDD_2ma + m09*LC_M09CDD_2ma + m10*LC_M10CDD_2ma + m12*LC_M12HDD_2ma + AR(1)

New Mexico Commercial Customer Equation

CUS_NMcom = + HH *LC_Households + AR(1)

New Mexico Commercial kWh Forecast

KWH_NMcom = UPC_NMcom * CUS_NMrom

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Calibrating the UPC Forecasts

• The forecasted level of energy sales from the model equations were either high or low compared to 2011 due to forecast error.

• Actual Energy Sales2011 = Predicted Sales2011 + Error2011

• The 2011 error was then deducted from the forecast going forward to calibrate the forecast for the last known year.

• Predicted Energy Sales2011 = Actual Sales2011

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Texas Other Public Authority Model

Texas Public Authority Energy

KWH_TXOPA = +GMP *EP_GMP + m05*EP_M05CDD + m06*EP_M06CDD + m07*EP_M07CDD + m08*EP_M08CDD + m09*EP_M09CDD + m10*EP_M10CDD + Bliss_Growth_Dum +AR(1)

Texas Commercial Customer Equation

CUS_TXOPA = + nfe *EP_Nonfarm_Empl + AR(1)

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NM Other Public Authority Model

New Mexico Public Authority Energy

KWH_NMOPA = +GMP*LC_GMP + m05*LC_M05CDD + m06*LC_M06CDD + m07*LC_M07CDD + m08*LC_M08CDD + m09*LC_M09CDD + m10*LC_M10CDD

NM Public Authority Customer Equation

CUS_NMOPA = + nfe*LC_Nonfarm_Empl + AR(1)

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Industrial Model

• Industrial Customers are assumed to remain constant.

• Industrial load is not weather sensitive.

• Monthly industrial loads vary significantly month to month making it difficult to use statistical methods.

• Decided to estimate industrial energy sales on an annual basis.

• Energy sales are estimated directly, rather than using use per customer equations.

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Industrial Model

Texas Industrial KWH Equation (annual)

KWH_TXind = + mfg * EP_Mfg_Empl + Dum98 + AR(1)

Texas Industrial Customer Equation (Constant)

CUST_TXind = 41

New Mexico Industrial KWH Equation (annual)

KWH_NMind = + mfg * LC_Mfg_Empl(-1) + AR(1)

New Mexico Industrial Customer Equation (Constant)

CUS_NMind = 7

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Street & Highway Lighting Model

Texas Street & Highway KWH Equation

KWH_TXs&h = + pop* EP_population + effic* effic_dum+AR(1)

Texas Street & Highway Customer Equation (Constant)

CUST_TXs&h = 212

New Mexico Street & Highway KWH Equation

KWH_NMs&h = + pop* LC_population + AR(1)

NM Street & Highway Customer Equation (Constant)

CUS_NMs&h = 18

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Weather

• El Paso weather has been warming over time.

• The summer weather in El Paso has been extremely hot, in terms of CDD, in 2010 & 2011

• Since weather changes, sometimes dramatically from year to year, it is necessary to use the average weather for several years to smooth out the annual variability and use the average weather in the forecasting equation.

• For the purpose of generating the energy forecast, ten-year average weather for El Paso and Las Cruces is used in the forecast.

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1,500

1,700

1,900

2,100

2,300

2,500

2,700

2,900

3,100

3,300

De

gre

e D

ay

s P

er

Ye

ar

El Paso Annual CDD & HDD

CDD

HDD

Linear (CDD)

Linear (HDD)

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Out of Model Adjustments

• Military Activity

• Fort Bliss

• White Sands

• Conservation & Load Management

• Losses

• Rio Grande Electric Cooperative

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Fort Bliss Assumptions

• Reduced incremental load forecast (-42 MW from 2015 forward vs. 2011 Forecast)

• Troop strength at build out levels

• Delays in construction projects

• Uncertain impact of “Net Zero” Program (could lower future energy sales to the base).

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Cumulative Difference in Out-of-Model Adjustment (2012-2011)

0

5

10

15

20

25

30

35

40

45

50

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

MW

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Energy & Customer Forecast Summary

YearNative System Energy

(GWh) Percent Growth Customers

2011 8,362 378,547

2012 8,303 -0.71% 385,406

2013 8,507 2.46% 392,539

2014 8,706 2.34% 399,679

2015 8,888 2.09% 406,837

2016 9,065 1.99% 413,968

2017 9,211 1.61% 420,697

2018 9,359 1.61% 427,081

2019 9,507 1.58% 433,385

2020 9,679 1.81% 439,738

2021 9,819 1.45% 446,000

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Historical Accuracy of Energy Forecast from Annual Models

• The forecasts generated from the historical annual models tended to over forecast energy.

• The exception to this rule occurred in 2010 & 2011 when the three most recent annual forecasts under projected the actual energy due to the extremely hot summers.

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Demand Model

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Demand Model

• Constant System Load Factor Method

• LF = Energy / (Demand x Hours)

• Demand = Energy / (LF x Hours)

• Demand is estimated based on the Constant System Load Factor and the Native System Energy Forecast

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System Load Factor

• System load factor has been declining since 2000, resulting in a tendency for annual forecasts to under predict future peak demand levels.

• Historically, the annual forecasts used a 5 year average system load factor to project demand, given the year to year fluctuation in its level.

• Subsequently the forecast moved to a 3 year average then to a 1 year average to compensate for under prediction of peak demand due to the declining system load factor.

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Factors in System Load Factor Decline

• Increasing share of residential sales

• Increasing saturation rate for refrigerated air conditioning

• Loss of manufacturing employment

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0.500

0.520

0.540

0.560

0.580

0.600

0.620

0.640

0.660

0.680

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Sy

ste

m L

oa

d F

act

or

EPE System Load Factor

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13.50%

10.20%

13.70%

7.90%

54.90%

35.50%

0%

10%

20%

30%

40%

50%

60%

1990 1995 2000 2005 2010 2015

Refrigerated Air Conditioning Saturation Rate

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0

1

2

3

4

5

6

7

1:00

2:00

3:00

4:0

0

5:00

6:0

0

7:00

8:0

0

9:0

0

10:0

0

11:00

12:00

1:00

2:00

3:00

4:0

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5:00

6:0

0

7:00

8:0

0

9:0

0

10:0

0

11:00

12:00

Av

g k

W/c

ust

om

er

EPE's Average Residential Cooling Coincident Demand

Evaporative AC Refrigerated AC

Native Peak08/23/201016:00 Hours

2.33 kW

5.54Average Sales for Peak Day:Refrigerated: 65.45 kWhEvaporative: 44.23 kWh

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0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

199

019

90

199

019

91

199

119

92

199

219

92

199

319

93

199

419

94

199

519

95

199

519

96

199

619

97

199

719

97

199

819

98

199

919

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200

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00

200

020

01

200

120

02

200

220

02

200

320

03

200

420

04

200

520

05

200

520

06

200

620

07

200

720

07

200

820

08

200

920

09

2010

2010

2010

2011

2011

Em

plo

ym

en

t

El Paso Manufacturing Employment

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Historical Accuracy of Load Forecast from Annual Models

• The load forecasts from annual forecasting models used in previous EPE forecasts have tended to under forecast future peak loads.

• This is a consequence of using a constant system load factor assumption during a time of decreasing system load factor.

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Decision to Use 2011 Adjusted Demand Forecast

• Changed to monthly energy forecast model methodology which is untested

• This lowered energy growth forecast to more reasonable levels.

• Lower 2012 energy forecast led to a 2012 demand forecast that is lower than the 2011 demand forecast.

• Historically, the load forecasts from the annual models have already tended to under forecast peak demand

• EPE is already planning large generation and Transmission projects based on the 2011 demand forecast

• Based on these considerations, the 2011 demand forecast was selected as the 2012 demand forecast, but is reduced to reflect the MW difference 2011 & 2012 out of model adjustments.

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Demand Forecast Summary

• Same demand forecast as 2011 forecast, except demand was lowered to reflect cumulative difference in out of model demand adjustments between 2012 and 2011 forecasts

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Demand Forecast Summary

YearNative System Peak Demand

(MW) Percent Growth

2011 1,711

2012 1,698 -0.76%

2013 1,746 2.83%

2014 1,797 2.92%

2015 1,849 2.89%

2016 1,913 3.46%

2017 1,967 2.82%

2018 2,019 2.64%

2019 2,063 2.18%

2020 2,111 2.33%

2021 2,172 2.89%

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Future Monthly Model Refinements

• Develop methodology to predict system load factor base on factors such as: refrigerated air conditioning saturation, manufacturing employment, customer mix, energy efficiency, etc.

• Observe monthly model accuracy and correct problem areas.