### REDACTED COPY



# Oklahoma Gas & Electric Co. 

# Integrated Resource Plan

Prepared May, 2011 by:

Oklahoma Gas & Electric Co.

321 N Harvey

Oklahoma City, Oklahoma 7310 1

### 2011 Integrated Resource Plan

### 201 1 Integrated Resource Plan



## EXECUTIVE SUMMARY

INTRODUCTION

OG&E submits this interim Integrated Resource Plan ( “ IRP ” ) pursuant to the joint

stipulation and settlement agreement for Oklahoma Corporation Commission Cause No.

PUD 201000037.

DEMAND RESPONSE AND ENERGY EFFICIENCY

OG&E last updated its IRP in January 2010. This update builds on the conclusion s in

the January submittal t hat actions such as the timely termination of wholesale contracts,

encouraging energy efficiency and demand response programs and other programs

enabled by the smart grid offer benefits to customers. These actions are expected to

reduce peak demand and, wh en combined with actions identified in the 2009 IRP, are

projected to defer the need for new fossil fuel generation beyond the year 2020

.

Actions to Reduce Peak Demand (MW)

2012 2013 2014 2015 2016

Demand Retail Peak Forecast 5,823 5,898 5,989 6,073 6,123

Wholesale Peak Forecast 281 274 239 243

-

Total Peak Forecast 6,104 6,172 6,228 6,317 6,123

Energy

Efficiency

and

Demand

Response Energy Efficiency 38 38 39 39 39

Distribution Automation 8 17 26 37 48

Residential DR 71 143 216 218 219

Commercial & Industrial DR

- - 23 47 70

Load Curtailment 105 120 135 150 151

Total Peak Reduction 222 318 439 491 527

System Peak Demand 5,882 5,853 5,789 5,826 5,596



### RENEWABLE ENERGY

With the completion of the Crossroads Wind Farm, OG&E will have added 611 MW

since 2008. OG&E has made no decision whether to issue a RFP for additional wind

resources but will continue to monitor the market for renewable projects that benefit

customers wh ile contributing to the State ’s renewable energy goal.

TRANSMISSION

The 2010 SPP Transmission Expansion Plan ( “ STEP ” ) has identified projects that will

be constructed over the next five years for reliability and economic purposes, including

new generati on additions such as wind. OG&E plans t o participate in this expansion by

constructing some of the projects that have been approved for construction by the SPP

Board of Directors as listed in Schedule J

.

INTEGRATED MARKETPLACE

The SPP is still developin g the rules for an Integrated Marketplace concept to provide

efficiencies and transparency to serving customers’ energy needs throughout the SPP.

This concept is expected to impact the way OG&E’s generation units operate.

### 201 1 Integrated Resource Plan

MECHANICAL INTEGRITY

The Mechanical Integrity ( “ MI ” ) Plan is the formalization, development and

standardization of plant maintenance and reliability procedures, in an effort to mitigate

safety risks and improve reliability of the Company’s generation assets.

EMISSION CONTROL OPT IONS

On March 7, 2011, the EPA issued a proposed rule in which the Agency rejected the

Oklahoma State Implementation Plan (“ SIP ”) for SO 2 

### BART determinations and instead

proposed a Federal Implementation Plan (“FIP”) with a SO 2 

### emission limit of 0.06

lb /MM BTU . The FIP provides that this proposed emission limit can be achieved by

either the installation of four scrubbers on the four affected coal

- fired units or conversion

of those four units to natural gas

- fired units. The public comment period extends to May

23, 2011 and a final rule will occur sometime after that. OG&E is preparing comments

to the proposed rule and evaluating the appropriate course of action.

CONCLUSION

OG&E’s 2020 goal has allowed for flexibility in decision making for future expansi ons,

wind development, and emission control options. OG&E will continue to explore

demand side opportunities, monitor renewable resource markets, develop MI Plan and

prepare for the SPP Integrated Marketplace. As emission control requirements become

clea rer and technologies develop, OG&E will match technologies with the requirements

to provide the best value to customers.

### 201 1 Integrated Resource Plan

1



## Contents

I. INTRODUCTION

................................ ................................ ................................ .............. 5

II. IRP Objective and Process

................................ ................................ ............................. 6

III. Assumptions and Inputs

................................ ................................ ................................ . 7

A. Description of OG&E Service Territory

................................ ................................ ....... 7

B. Electric Demand and Energy Forecast

................................ ................................ ....... 7

1. Load Forecast Methodology

................................ ................................ ..................... 8

2. Forecast of Key Assumptions

................................ ................................ ................... 9

3. Energy Sales Forecast

................................ ................................ ............................ 10

4. Peak Demand Forecast

................................ ................................ .......................... 11

C. Resource Options

................................ ................................ ................................ ...... 11

1. Existing Supply Resources

................................ ................................ ..................... 11

2. S upply Side Resource Alternatives

................................ ................................ ........ 14

3. Demand Side Resources

................................ ................................ ........................ 15

D. Transmission

................................ ................................ ................................ ............. 18

1. Transmission Resources

................................ ................................ ........................ 19

2. Tra nsmission Needs for Generation Expansion Options

................................ ...... 20

3. SPP Transmission Expansion

................................ ................................ ................ 21

E. Needs Assessment

................................ ................................ ................................ .... 22

F. Scenarios

................................ ................................ ................................ ................... 23

G. Environmental Con siderations

................................ ................................ .................. 24

1. Renewable Energy Standards

................................ ................................ ................ 24

2. Carbon Dioxide Restrictions

................................ ................................ ................... 25

3. Regional Haze

................................ ................................ ................................ ......... 25

4. Utility MACT Rule

................................ ................................ ................................ .... 27

5. Other Environmental Requirements

................................ ................................ ....... 27

IV. Resource Planning Modeling and Analysis

................................ ................................ 28

A. Development of Models and Portfolios

................................ ................................ ..... 28

1. Model Development

................................ ................................ ................................ 28

2. Develop Distinct Portfolios

................................ ................................ ...................... 28

B. Portfolio Analysis

................................ ................................ ................................ ....... 32

1. Evaluation of Future Wind

................................ ................................ ...................... 32

2. S cenario Results

................................ ................................ ................................ ..... 33

### 201 1 Integrated Resource Plan

2

C. Risk Evaluation

................................ ................................ ................................ .......... 34

1. Sensitivity Results

................................ ................................ ................................ ... 34

2. Stochastic Analysis

................................ ................................ ................................ . 35

D. SPP Int egrated Marketplace

................................ ................................ ..................... 36

1. Integrated Marketplace Concepts

................................ ................................ .......... 37

2. Market Model

................................ ................................ ................................ ........... 38

3. Generation Expansion

................................ ................................ ............................ 38

4. Resul ts

................................ ................................ ................................ ..................... 38

V. SCHEDULES

................................ ................................ ................................ ................ 39

Schedule A

– Electric Demand and Energy Assumption

................................ .............. 39

Schedule B

– Existing Resources

................................ ................................ ................. 39

Schedule C

– Transmission Capability and Needs

................................ ....................... 41

Schedule D

– Needs Assessment

................................ ................................ ................. 41

Schedule E

– Resource Options

................................ ................................ .................... 42

Schedule F

– Fuel Procurement and Risk Management Plan

................................ ..... 43

Schedule G

– Action Plan

................................ ................................ .............................. 44

Schedule H

– Requests for Proposals

................................ ................................ .......... 46

Schedule I

– Modeling Methodology and Assumptions

................................ ................ 46

Schedule J

– Transmission System Adequacy

................................ ............................. 48

Schedule K

– Resource Plan Assessment

................................ ................................ .... 50

Schedule L

– Proposed Resource Plan Analysis

................................ .......................... 50

VI. Appendices

................................ ................................ ................................ .................. 51

### 201 1 Integrated Resource Plan

3



## List of Tables

T ABLE 

### 1: OG&E 2010 E NERGY 

### S ALES 

### F ORECAST



### ................................ ............................... 11

T ABLE 

### 2: OG&E 2010 P EAK 

### D EMAND 

### F ORECAST



### ................................ ................................ . 11

T ABLE 

### 3: OG&E P LANNED 

### G ENERATION 

### R ESOURCES



### ................................ .......................... 13

T ABLE 

### 4: N EW 

### S UPPLY 

### S IDE 

### R ESOURCES IN 

### 2010$

................................ .............................. 14

T ABLE 

### 5: E MISSION 

### C ONTROL 

### T ECHNOLOGIES IN 

### 2010$

................................ ....................... 15

T ABLE 

### 6: O KLAHOMA 

### EE P EAK 

### D EMAND AND 

### E NERGY 

### R EDUCTION



### ................................ ....... 16

T ABLE 

### 7: A RKANSAS 

### E NERGY 

### E FFICIENCY 

### P EAK 

### D EMAND AND 

### E NERGY 

### R EDUCTION



### ............. 17

T ABLE 

### 8: T OTAL 

### S YSTEM 

### E NERGY 

### E FFICIENC Y 

### P EAK 

### D EMAND AND 

### E NERGY 

### R EDUCTION



### ...... 17

T ABLE 

### 9: G EOTHERMAL 

### P ROGRAM 

### E XPANSION 

### P EAK 

### D EMAND AND 

### E NERGY 

### R EDUCTION



### ...... 17

T ABLE 

### 10: D EMAND 

### R ESPONSE 

### A SSUMPTION



### ................................ ................................ ....... 18

T ABLE 

### 11: OG&E T RANSMISSION 

### L INES



### ................................ ................................ ............... 19

T ABLE 

### 12: P ROXY 

### S ITES FOR 

### F UTURE 

### G ENERATION 

### R ESOURCES



### ................................ ......... 20

T ABLE 

### 13: SPP STEP P ROJECTS 

### C OST 

### S UMMARY



### ................................ .............................. 21

T ABLE 

### 14: P LANNING 

### C APACITY 

### M ARGIN



### ................................ ................................ .............. 23

T ABLE 

### 15: R EFERENCE 

### S CENARIO 

### P RICES



### ................................ ................................ ........... 24

T ABLE 

### 16: V ENTYX 

### S CENARIO 

### P RICES



### ................................ ................................ .................. 24

T ABLE 

### 17: NYMEX F UTURES 

### S CENARIO 

### P RICES



### ................................ ................................ .. 24

T ABLE 

### 18: O KLAHOMA 

### SIP SO 2 

### L IMITS



### ................................ ................................ ................. 27

T ABLE 

### 19: R EVENUE 

### R EQUIREMENT 

### C OMPONENTS



### ................................ ............................... 28

T ABLE 

### 20: N EW 

### S UPPLY 

### S IDE 

### R ESOURCE 

### O PTION 

### S CREENING 

### R EQUIREMENTS



### .................... 29

T ABLE 

### 21: W IND 

### S AVINGS 

### A NALYSIS



### , 30 Y EAR 

### NPVRR, ($B ILLIONS 

### )

................................ ... 33

T ABLE 

### 22: E MISSION 

### C ONTROL 

### O PTION 

### R ESULTS



### , 30 Y EAR 

### NPVRR, ($B ILLIONS 

### )

................ 34

T ABLE 

### 23: E MISSION 

### C ONTROL 

### O PTION 

### S ENSITIVITY 

### A NALYSIS



### , 30 Y EAR 

### NPVRR, ($B ILLIONS 

### )

................................ ................................ ................................ ................................ ..... 34

T ABLE 

### 24 : SPP G ENERATION 

### E XPANSION 

### (MW)

................................ ................................ .. 38

T ABLE 

### 25: SPP W IND 

### E XPANSION BY 

### S TATE



### ................................ ................................ ........ 38

T ABLE 

### 26: 30

- Y EAR 

### NPV W IND 

### A NALYSIS IN THE 

### IM ($M ILLIONS 

### )

................................ ........ 38



## List of Figures

F IGURE 

### 1: I NTEGRATED 

### R ESOURCE 

### P LANNING 

### S EVEN 

### S TEP 

### P ROCESS



### ................................ .... 6

F IGURE 

### 2: OG&E S ERVICE 

### A REA



### ................................ ................................ ........................... 7

F IGURE 

### 3: G ENERATOR 

### N AMEPLATE 

### C APACITY



### ................................ ................................ ..... 12

F IGURE 

### 4: P EAK 

### P LANNING 

### C APACITY



### ................................ ................................ .................. 12

F IGURE 

### 5: SPP B ALANCING 

### A UTHORITIES



### ................................ ................................ ............ 19

F IGURE 

### 6: SPP T RANSMISSION 

### E XPANSION 

### P LAN



### ................................ ................................ . 22

F IGURE 

### 7: A NALYSIS 

### T IMELINE



### ................................ ................................ ............................. 31

F IGURE 

### 8: P ORTFOLIO 

### D EVELOPMENT



### ................................ ................................ .................. 31

F IGURE 

### 9: 30 Y EAR 

### NPVRR W IND 

### E VALUATION WITH AND W ITHOUT 

### PTC

............................. 32

F IGURE 

### 10: W IND 

### T IMING 

### E VALUATION 

### (NPVRR)

................................ ................................ . 33

F IGURE 

### 11: S ENSITIVITY 

### A NALYSIS 

### R ESULTS



### , P ERCENT 

### I MPACT ON 

### R EFERENCE 

### NPVRR

..... 35

F IGURE 

### 12: S TOCHASTIC 

### A NALYSIS 

### R ESULTS



### , 30

- Y EAR 

### NPVRR, ($B ILLIONS 

### )

..................... 36

### 201 1 Integrated Resource Plan

4

### 201 1 Integrated Resource Plan

5



## I. INTRODUCTION

OG&E submits this interim Integrated Resource Plan ( “ IRP ” ) pursuant to the joint

stipulation and settlement agreement in Oklahoma Corporation Commission Cause No.

PUD 201000037.

This IRP presents a snapshot of challenges and opp ortunities for OG&E as of April

20 11 . OG&E's resource planning is the foundation for management decisions regarding

the appropriate methods and manner in which to meet the reliable future needs of its

retail customers. In reality, OG&E is continually evaluating resource alternatives in

response to constantly evolving conditions and opportunities.

Section II presents the IRP objective and process. Section III offers an overview of

OG&E, the demand and energy forecast

, and modeling assumptions and inputs used in

the analysis. Section IV explains the analysis methodology and results. Section V

concludes the report with a tabular summary of each section as described in Oklahoma

Corporation Commission rule OAC 165:35

- 37

- 4(c) and outlined below:

A. Electric demand and energy forecast

B. Forecast of capacity and energy contributions from existing and committed

sup ply

- and demand

- side resources

C. Description of transmission capabilities and needs covering the forecast period

D. Assessment of the need for additional resources

E. Description of the supply, demand

- side and transmission options available to the

utility to addre ss the identified needs

F. Fuel procurement plan, purchased power procurement plan, and risk

management plan

G. Action plan identifying the near

- term (i.e., across the first five (5) years) actions

H. Proposed RFP(s) documentation, and evaluation

I. Technical appendix for the data, assumptions and descriptions of models

J. Description and analysis of the adequacy of its existing transmission system

K. Assessment of the need for additional resources to meet reliability, cost and

price, environmental or other criteria

L. An an alysis of the utility’s proposed resource plan

In addition, this submittal contains several appendices that provide supporting materials,

including studies and reports that have been prepared by OG&E, by vendors retained

by OG&E, and stakeholders

.

### 201 1 Integrated Resource Plan

6



## II. IRP OB JECTIVE AND PROCESS

The objective of the OG&E’s IRP process is to analyze alternatives to meet customers’

demand and energy needs over a 30 year period given forward looking assumptions

based upon today’s circumstances

. OG&E continually monitors markets and inputs and

updates elements of the IRP . W hen material changes in assumptions occur , OG&E will

formally update its IRP submittal, as well. To accomplish the IRP objective, OG&E

utilizes a seven step process as outlined in Figure 1

.

Figure 1 : Integrated Resource Planning Seven Step Process

Define IRP

Objective Collect Assumptions

Develop Models

and Portfolios Computer

Simulations

Least Cost /

Least Risk

Analysis

Load

– OG&E and The Cadmus Group, Inc.

Resource Options

Existing Unit Characteristics

– OG&E

New Unit Characteristics

– EIA & NREL

Energy Efficiency

– OG&E & Frontier Associates, LLC

Demand Response

– OG&E and Structure Group

Transmission

– OG&E and SPP

Needs Assessment

– OG&E

Fuel

– OG&E, Ventyx and NYMEX

Environmental

– OG&E and Ventyx

Scenarios

– OG&E and Ventyx 

### Interpret Data

and

Draw

Conclusio ns

Develop

IRP Report

### 201 1 Integrated Resource Plan

7



## III. ASSUMPTIONS AND INPU TS

This section includes a description of major assumptions and inputs to the models used

to develop this IRP

.



## A. Description of OG&E Service Territory

OG&E serves more than 7 79 ,000 retail customers in Oklahoma and western Arkansas,

as well as several wholesale customers throughout the region. The service territory

covers approximately 30,000 square miles, includes 269 communities and surrounding

areas, and has a population of ap proximately 2 million. OG&E serves Oklahoma City,

which is the largest city in Oklahoma, as well as Ft. Smith, Arkansas. Of the 269

communities served by OG&E, 243 are in Oklahoma, and 26 are in Arkansas. OG&E’s

retail service area is shown in Figure 2

.

Figure 2 : OG&E Service Area

OG&E’s system control area peak demand for 20 10 , as reported by the OG&E system

dispatcher, was 6,626 MW on August 4 . The control area peak demand includes retail

, wholesale and other provider’s demands. OG&E’s load responsibility peak demand for

2010 was 6,171 MW on August 4.



## B. Electric Demand and En e rgy Forecast

OG&E retained the services of The Cadmus Group, In c. to assist OG&E’s Research

and Analysis department prepare the September 2010 load forecast that is presented in

Appendix A

– OG&E 2010 Load Forecast . Their report d escribes the data inputs,

### 201 1 Integrated Resource Plan

8

assumption methodologies, and models developed jointly with OG&E’s Research and

Analysis department with input from OG&E’s Interdepartmental Forecasting Task Force.

The 2010 retail sales fore cast utilized the revenue class based econometric modeling

framework that has been in place for over a decade. The 20 10 load responsibility peak

demand forecast is based on an hourly econometric model of weather and economic

effects on OG&E’s hourly load responsibility

, used since the 2000 forecast.

The load forecasting framework relies on independently produced forecasts of service

area economic and population growth, actual and normal weather data, and projections

of electricity prices for price

- sensitive customer cl asses. The final energy and demand

forecast includes Federal Energy Regulatory Commission ( “ FERC ” ) jurisdictional

wholesale contracts as post

- modeling adjustments.



## 1. Load Forecast M ethodology

Load forecasting includes projections of energy sales and peak dem and requirements.



### a) Energy Sales Forecast Methodology

The 2010 retail energy forecast is based on retail sector

- level econometric models

representing OG&E’s Oklahoma and Arkansas service territories. Historical and

forecast economic variables (drivers) are p rovided by the Center for Applied Economic

Research at Oklahoma State University ( “ OSU ” ).

In past forecasts, Moody’s Economy.com provided economic drivers that were used to

predict energy sales in OG&E’s Arkansas service territory. In 2010 , OG&E made the

decision to purchase forecasts of economic drivers for both Oklahoma and Arkansas

from OSU. The move from Moody’s Economy.com to OSU was made because

consolidating the sources for economic drivers would simplify the load forecasting

process. By using a s ingle source for economic drivers OG&E has eliminated the need

to adjust the Arkansas drivers to follow the same assumptions as the Oklahoma drivers.



### b) Peak Demand Forecast Methodology

The 2010 load responsibility forecast relies on an hourly econometric mod el

specification first used for the 2000 forecast and re flects the following:

1. Impact of different weekdays on hourly system load.

2. Impact of different summer months on hourly system load.

3. Influence of heat buildup during heat waves.

4. Impact of the combined effects of humidity and warm temperatures.

5. Non

- linearity in the load and temperature relationships at very high temperatures.

As has been the case for the past several years, weather

- adjusted retail energy sales

are the main driver for the peak model.

### 201 1 Integrated Resource Plan

9



## 2. Forecast of Key Assumptions

The following is an excerpt from the September 2010 load forecast.



### a) Economic Outlook

Over the last decade the Oklahoma economy has outperformed the nation during

recessions due to robust growth in the energy sector. Prudent lending practices and

limited direct erosion of the consumer balance sheet allowed Oklahoma to enter the

most recent recession later than the nation. The effects of the recession in Arkansas

have been dampened due to the limited influence of low energy pr ices and employers

delaying plans to outsource manufacturing operations. Both states have fared better

than the nation, and are poised to recover when energy prices increase and the rest of

the country returns to positive economic conditions.

1) Energy Secto r

The OSU forecast drivers anticipa te the price of oil hovering around $70/barrel, and

natural gas around $5 per MMBTU in 2010. These prices are close to the threshold

where energy switches from providing a net boost to restricting growth in the state

eco nomy. While the price of oil is beginning to increase, it has been around $70 a

barrel for most of the year, and the price of natural gas has been below $4.50 per

MMBTU . The Energy Information Administration ( “ EIA ” ) forecast 1 

### suggests that natural

gas w il l remain below $5 per MMBTU through the end of 2010 , with oil climbing to an

average of $80 a barrel in the fourth quarter of 2010

.

After experiencing considerable decline in activity in 2009, the energy sector in Oklahoma

is seeing a considerable recover y in 2010. Since hitting a low of $1.84 per MMBTU on September 4th, 2009, the price of natural gas has rebounded to nearly $4.50 per MMBTU

in 2010. This has allowed for the continued development of conventional oil and natural

gas wells in the Arkoma Bas in in western Oklahoma along with the Woodford Shale in

southeast Oklahoma and the Fayetteville Shale in central Arkansas.

The recovery of the energy sector in Oklahoma will play a vital role in the overall growth

of the Oklahoma economy. As energy pri ces increase so will revenue collections for

Oklahoma. The gross production tax on natural gas yielded $24 million in July of 2010

, which is $1.9 million or 8.4 percent above the prior year. Oklahoma has made efforts to

diversify its economy, but the ene rgy sector is still the foundation

.

2) Retail Electric Prices

The retail electric prices used in the forecast include the revised cost of operations

along with riders for various other projects. There are riders for OG&E’s Smart Grid and

the OU Spirit wind f arm included in the price forecast. Additionally, the price forecast

includes the cost of new transmission. Finally i n 2010 there was a fuel clause

adjustment refund paid to customers. The refund offset most of the rate increase in

1 The Energy Information Administration: Short

- term Energy Outlook,

http://www.eia.doe.gov/emeu/steo/pub/contents.html

### 201 1 Integrated Resource Plan

10

2010, so customers experienced a negligible increase in price during 2010. However,

the conclusion of the fuel clause adjustment refund at the end of 2010 will make the

effective price increase from 2010 to 2011 approximately 17 percent. A price increase

of this magnitude is responsible for the relatively low growth rate in 2011.

3) Price Elasticity of Demand

The own

- price elasticity of demand for the residential sector in O klahoma has been

restricted to

- 0.1 from 2010 to 2012 and

- 0.2 from 2013 to 2020. The unrestricted

est imate of own

- price elasticity of demand for the residential sector in Oklahoma is

- 0.24. This unrestricted estimate is relatively more elastic than the 2009 estimate of

- 0.05, but it remains highly inelastic when compared to other goods. The main cause o f

the disparity between the 2009 and 2010 estimates is the use of an all

- good price index

instead of an energy specific price index to adjust prices for inflation. The all

- good price

index more accurately reflects the effects of inflation on a consumer’s budget and their

energy consumption decisions. Own

- price elasticity of demand was restricted due to

the impacts it would have on the forecast when combined with unprecedented price

increases in the short

- run. The restrictions limit the effect of prices i n the near

- term and

allow for an increased long

- term response to changes in the retail price of electricity in

the Oklahoma residential sector. The elasticity estimates in other sectors were

relatively unchanged from the 2009 forecast, so there were no ot her restrictions

implemented.



### b) Weather Input

P eak demand and energy sales are highly sensitive to year

- to

- year weather variations.

Both can appear to decline even with positive economic growth when a hot year is

followed by an unusually cool year. Conversel y, if a hot year follows a cool year, energy

sales and peak demand can increase even though there may be little or no economic

growth.

Weather uncertainty is represented through a Monte Carlo modeling approach where

the more than 20 years of actual weath er are systematically input into the energy and

peak models to produce a possible outcome distribution.

OG&E’s weather

- year Monte Carlo approach runs weather years 1989 to 2009 through

weather

- sensitive energy models, along with the peak demand model, to develop a

probability distribution of possible outcomes.



## 3. Energy Sales Forecast

The 2010 energy sales forecast adds FERC wholesale sales contracts and line losses

to retail econometric model forecast projections. The forecast is based on normal

weather in b oth Oklahoma and Arkansas. In the fourth quarter of 2010, OG&E

extended the wholesale contract with Arkansas Valley Electric Cooperative Corporation

(AVECC) through June of 2015. The energy associated with this contract was added to

the 2010 energy sales forecast and is summarized in Table 1

### 201 1 Integrated Resource Plan

11 Table 1 : OG&E 2010 Energy Sales Forecast

GWH 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Wholesale 1,616 1,537 1,274 1,274

- - - - - -

Retail 27,501 27,865 28,364 28,828 29,224 29,682 30,013 30,501 30,950 31,369

Total 29,118 29,401 29,638 30,102 29,224 29,682 30,013 30,501 30,950 31,369

Retail Growth 1.9% 1.3% 1.8% 1.6% 1.4% 1.6% 1.1% 1.6% 1.5% 1.4%



## 4. Peak Demand Forecast

Table 2 shows the final load responsibility forecast, adjusted for wholesale loads 2 

### and

line losses. The forecast is based on normal weather conditions.

Table 2 : OG&E 2010 Peak Demand Forecast

MW 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Wholesale 281 274 239 243

- - - - - -

Retail 5,823 5,898 5,989 6,073 6,123 6,228 6,278 6,371 6,456 6,528

Total 6,104 6,172 6,228 6,317 6,123 6,228 6,278 6,371 6,456 6,528

Retail Growth 1.5% 1.3% 1.5% 1.4% 0.8% 1.7% 0.8% 1.5% 1.3% 1.1%



## C. Resource Options

OG&E plans for future generation needs by first understanding the resource options

available to the Company. The following sections identify the existing supply side

resources, supply side alternatives and the ongoing demand side resources.



## 1. Existing Supply Resources

OG&E's generation resources include coal fired units, gas fired steam units, gas fired

combined cycle ( “ CC ” ) uni ts, gas fired combustion turbine ( “ CT ” ) units, and wind

facilities. OG&E generates approximately 6 0 % of its electric energy from low

- sulfur

Wyoming coal and 37 % from natural gas and 3% wind. OG& E purchases 320 MW from

the qualifying facility AES plant at Shady Point that burns coal

, 120 MW from the natural

gas fired combined cycle PowerSmith plant and 6 MW of hydro generation from

Southwest Power Administration ( “ SPA ” ). The hydro generation is tr ansferred from

wholesale customers to OG&E as part of the wholesale customers ’ purchase power

agreements. OG&E currently has under contract three wind energy power agreements:

Sooner Wind at 50 MW, Keenan at 151.8 MW, and Taloga at 130 MW. For peak

plann ing purposes, these three wind farms contribute approximately 9 MW. Figure 3

depicts the composition of OG&E’s generation resources by nameplate capacity

.

2 This forecast reflects the termination of the AVECC contract in 2015 and the remaining contracts by 2014.

### 201 1 Integrated Resource Plan

12 Figure 3 : Generator Nameplate Capacity

OG&E’s net dependable rated capability for each unit is determined from unit testing

during the summer months in accordance with SPP Criteria 12. The latest Capability

Report was published on De cember 31, 2010 and reported a net dependable rated

capability of 6, 432 MW from OG&E’s ten power plants. The complete 2010 capability

report can be found in Appendix B

. Figure 4 depicts the composition of OG&E’s peak

planning capacity

.

Figure 4 : Peak Planning Capacity

OG&E’s current portfolio of electric generating facilities is presented in Table 3

. With the

exception of the McClain and Redbud plants, OG&E fully owns all of its plants. OG&E is

the operator of all of its plants, including McC lain and R edbud. For this IRP , a ll thermal

units are expected to operate beyond the 30

- year study

. 2,855 MW

33%

2,837 MW

33% 1,403 MW

16% 300 MW

4% 446 MW, 5% 332 MW, 4% 449 MW, 5%

Coal Fired Steam

Gas Fired Steam

Combined Cycle

Combustion Turbine

Purchase Power

- Thermal

Purchase Power

- Wind

Owned Wind

2,553 MW

37%

2,510 MW

36% 1,168 MW

17% 237 MW

3% 446 MW, 7%

9 MW, 0.1% 16 MW, 0.2%

Coal Fired Steam

Gas Fired Steam

Combined Cycle

Combustion Turbine

Purchase Power

- Thermal

Purchase Power

- Wind

Owned Wind

### 201 1 Integrated Resource Plan

13 Table 3 : OG&E Planned Generation Resources

UNIT TYPE

(PLANNING

CAPACITY) UNIT NAME FIRST

YEAR IN

SERVICE PEAK

PLANNING

CAPACITY

(MW) AVERAGE

HEAT RATE

(BTU/KWH)

Coal Fired

Steam

(2

, 553 MW) Muskogee 4 1977 505 10,935

Muskogee 5 1978 500 10,932

Muskogee 6 1984 502 10,948

Sooner 1 1979 522 10,223

Sooner 2 1980 524 10,232

Gas Fired

Steam

(2

, 510 MW) Horseshoe Lake 6 1958 159 11,253

Horseshoe Lake 8 1968 381 12,210

Mustang 1 1950 50 12,740

Mustang 2 1951 51 12,724

Mustang 3 1955 113 11,328

Mustang 4 1959 253 11,207

Seminole 1 1971 500 13,699

Seminole 2 1973 500 12,166

Seminole 3 1973 503 11,981

Combined

Cycle

(1

, 168 MW) Horseshoe Lake 7 1963 227 11,283

McClain* 2001 352 7,480

Redbud* 2004 589 7,187

Combustion

Turbine

(237 MW) Enid 1GT 1965 14 20,767

Enid 2GT 1965 14 20,767

Enid 3GT 1965 14 20,767

Enid 4GT 1965 14 20,767

Horseshoe Lake 9 2000 45 10,381

Horseshoe Lake 10 2000 45 10,381

Seminole 1GT 1971 17 N/A

Mustang 5A 1971 32 14,647

Mustang 5B 1971 32 14,647

Woodward 1963 10 19,082

Purchase

Power

-

Thermal

(446 MW) AES Shady Point 1991 320 11,817

PowerSmith 1998 120 8,583

SPA Hydro N/A 6 N/A

Purchase

Power

- Wind

(9 MW) FPL Wind 2003 2 N/A

Keenan 2010 4 N/A

Taloga 2011 3 N/A

Owned Wind

(16 MW) Centennial 2007 7 N/A

OU Spirit 2009 3 N/A

Crossroads 2012 6 N/A

Total Net Dependability Capability 6,939

* Represents OG&E owned interest

### 201 1 Integrated Resource Plan

14

The Mechani cal Integrity ( “ MI ” ) Plan is the formalization, development and

standardization of plant maintenance and reliability procedures, in an effort to mitigate

safety risks and improve reliability of the Company’s generation asse ts. Within t he plan

there is a p rogram on each piece of equipment or system to be inspected. This

includes an assessment of condition an d failure risk of the equipment and the

formulation of either repair or replacement plans based on the assessment.



## 2. Supply Side Re source Alternatives

The new supply side resources considered are those used by the EIA in developing

their Annual Energy Outlook 20 10 3



### . A summary is shown in Table 4

.

Table 4 : New Supply Side Resources in 2010$

TYPE TECHNOLOGY NOMINAL

CAPACITY

(MW) HEAT

RATE

(BTU/

KWH) OVERNIGHT

CAPITAL

COST

($/KW ) FIXED

O&M

COST

($/KW) VARIABLE

O &M

COST

($/MWH)

Coal Single Unit Advanced PC 650 8,800 3,167 35.97 4.25

Dual Unit Advanced PC 1,300 8,800 2,844 29.67 4.25

Single Unit Advanced PC w/ CCS 650 12,000 5,099 76.62 9.05

Dual Unit Advanced PC w/ CCS 1,300 12,000 4,579 63.21 9.05

Single Unit IGCC 600 8,700 3,565 59.23 6.87

Dual Unit IGCC 1,200 8,700 3,221 48.90 6.87

Single Unit IGCC with CCS 520 10,700 5,348 69.30 8.04

Natural

Gas Conventional NGCC 540 7,050 978 14.39 3.43

Advanced NGCC 400 6,430 1,003 14.62 3.11

Advanced NGCC with CCS 340 7,525 2,060 30.25 6.45

Conventional CT 85 10,850 974 6.98 14.70

Advanced CT 210 9,750 665 6.70 9.87

Fuel Cells 10 9,500 6,835 350.00

-

Uranium Dual Unit Nuclear 2,236 N/A 5,335 88.75 2.04

Biomass Biomass CC 20 12,350 7,894 338.79 16.64

Biomass BFB 50 13,500 3,860 100.50 5.00

Wind Onshore Wind 100 N/A 2,438 28.07

-

Offshore Wind 400 N/A 5,975 53.33

-

Solar Solar Thermal 100 N/A 4,692 64.00

-

Small Photovoltaic 7 N/A 6,050 26.04

- Large Photovoltaic 150 N/A 4,755 16.70

-

Geo

thermal Geothermal

- Dual Flash 50 N/A 5,578 84.27 9.64

Geothermal

- Binary 50 N/A 4,141 84.27 9.64

MSW Municipal Solid Waste 50 18,000 8,232 373.76 8.33

Hydro Hydro

- electric 500 N/A 3,076 13.44

-

Pumped Storage 250 N/A 5,595 13.03

-



### Assumptions for emission control technologies considered in this IRP were obtained

from Sargent & Lundy and Burns & McDonnell and are provided in Table 5

.

3 http://www.eia.doe.gov/oiaf/beck_plantcosts/pdf/updatedplantcosts.pdf

### 201 1 Integrated Resource Plan

15 Table 5 : Emission Control Technologies in 2010$

TECHNOLOGY UNIT

TYPE CAPITAL

COST ($M) FIXED O&M

COST ($M) VARIABLE O&M

COST ($/MWH)

Scrubber Coal 308.8 7.3 2.52

Low NO

x Burners Coal 14.3 0.9

-

Low NO

x Burners Gas 9.6 0.6

- Mercury Control

- Activated

Carbon Injection Coal 2.1 0.3 0.57



## 3. Demand Side Resources

Demand Side Management ( “ DSM ” ) is composed of two product areas that are focused

on the c ustomer end of the value chain. These areas are Energy Efficiency and

Demand Response.



### a) Energy Efficiency

Energy Efficiency ( “ EE ” ) programs are designed to en courage customers to

permanently improve how they use energy by using it differently and more efficiently.

Inducements are provided through programs and services designed to educate

customers to change energy usage behaviors and to more efficiently use ene rgy

. Several of OG&E’s programs have endured over the last few decades and their effect s

are captured in the econometric load forecast models and therefore in OG&E’s annual

load forecast. These continuing programs include :

 Positive Energy Home

– p romot ion of DOE Energy Star® Homes program

requirements targeting low

- income new construction projects

 Geot hermal Home

– p romotion of DOE Energy Star® Homes program

requirements targeting both the residential and commercial markets and focuses

on both new and r etrofit appliances

 Heat Pumps

– promotion of high efficiency heating and cooling options

encouraging the use of total electric HVAC units

 Rate Tamer®

– an energy information service targeting larger commercial and

industrial customers to provide on

- line access to their business’ energy

consumption patterns via the internet

 Power Factor Correction

– informs customer of power quality correction needs

and options to help customers become more efficient and reduce OG&E

generation needs

OG& E has recently introduced further comprehensive programs in effort to promote

energy efficiency opportunities for all classes of customers. Since these programs are

relatively new, the effects have not yet been realized in OG&E’s annual load forecast.

1) O klahoma Energy Efficiency Programs

In November 2007, OG&E contracted with Frontier Associates LLC to perform a

comprehensive two

- part potential study for its Oklahoma jurisdiction to identify potential

energy efficiency and demand programs for implementati on. The OG&E management

### 201 1 Integrated Resource Plan

16

team evaluated the Frontier Potential study programs and chose to implement eight

programs based upon customer benefit, market potential and budget criteria, as well as

the creation of an optimum portfolio to meet the goal of no ne w foss il fuel generation

until 2020. The following eight programs are described in Cause No. PUD 200900200 :

 Low Income Weatherization

– home thermal efficiency services

 Fixed Income Weatherization

– home thermal efficiency services

 Residential Thermal Ef ficiency

– home thermal efficiency services including

evaluation of HVAC equipment and duct work sealing

 Positive Energy : New Home Construction ( “ PE

- NHC ” )

– promotion of energy

efficient homes practices for construction of new homes

 Geothermal Heating, Coo ling and Water Heating

– offers incentives for

installation of geothermal heat pumps in new or existing homes

 Commercial Lighting

– promotion of the replacement of traditional fluorescent

lighting with more efficient florescent lighting

 Commercial/Industri al Standard Offer Program ( “ SOP ” )

– offers financial

incenti ves for the installation of a wide range of measures to reduce peak

demand and/or save energy

 Education

– offering of Custom Energy Report for customers and promotion

of LivingWise® school educati on program

The Oklahoma programs are designed to produce annual demand and energy savings

as shown in Table 6

.

Table 6 : Oklahoma EE Peak Demand and Energy Reduction

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Demand (MW) 36.6 36.6 36.6 34.6 32.7 30.7 30.7 30.1 27.6 25.2

Energy (MWh) 144,435 144,435 144,435 142,518 140,269 138,020 137,688 134,973 117,948 100,918



### 2) Arkansas Energy Efficiency Programs

The energy e fficiency programs in Arkansas continue as a result of the work started

through the Quick Start program as described in Docket No. 07

- 075

- TF . These five

programs are as follows :

 Weatherization

– home thermal efficiency services

 LivingWise®

– school educational program

 Custom Energy Report

– self

- administered energy survey for residential

customers to understand their energy usage

 Commercial Lighting

– education and incentives for replacing T

- 12 lamps in

commercial and industrial lighting systems

 Motor Replacement

– educates and offers incentives for replacing inefficient

motors with high efficiency motors

 Education

– provides information about energy efficiency and conservation

### 201 1 Integrated Resource Plan

17

The Arkansas programs realize an annual demand and energy savings as shown in

Table 7

.

Table 7 : Arkansas Energy Efficiency Peak Demand and Energy Reduction

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Demand (MW) 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.0 0.8

Energy (MWh) 4,738 4,738 4,738 4,738 4,738 4,738 4,738 4,431 3,307 2,759



### 3) OG&E Energy Efficiency Forecast

The combined forecasted peak demand reduction and energy savings from the

Oklahoma and Arkansas Energy Efficiency programs are found in Table 8

.

Table 8 : Total System Energy Efficiency Peak Demand and Energy Reduction

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Demand (MW) 38 .1 38 .1 38 .1 36 .2 34 .2 32 .2 32 .2 3 1.5 2 8.7 26 .0

Energy (MWh) 149,173 149,173 149,173 147,256 145,007 142,758 142,426 139,404 121,255 103,677



### 4) Additional Energy Efficiency Efforts

The current energy efficiency forecasts are based off of the 2009 plan to invest during

the years 2010, 2011, and 2012. The remainder of the forecast shows how those

efforts perform for the useful life of the measures implemented. OG&E continues to

inves tigate future opportunities to expand the energy efficiency efforts such as

geot hermal heating. At the time of analysis for this IRP, an expansion of the g eothermal

program is estimated to reduce peak demand by 27 MW in ten years and sav e up to

approximately 111,000 MWh. The se projections are provided in Table 9 . OG&E plans

to file a new comprehensive 3 year Demand Program offering during June 2012. It is

anticipated that some of the existing programs will remain and new measures will be

introduced utilizing technology to continue and expand the results of the present

portfolio.

Table 9 : Geot hermal Program Expansion Peak De mand and Energy Reduction

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Demand (MW) 0.2 0.4 1.2 2. 7 4.9 8. 1 11. 9 16.2 21.1 27.0

Energy (MWh) 853 1,693 5,134 10,860 20,276 33,096 48,358 66,117 87,855 110,737



### b) Demand Response Programs

Traditional d emand r esponse ( “ DR ” ) programs have been designed to encourage

customers to reduce their load during peak loading periods. Event driven programs are initiated by OG&E in response to varying external stimuli while p rice response programs

are t ariffs with predefined, recurring pricing. Since events are different from year to

year, they are not included in the Annual Load Forecast but existing price response

programs are included. Existing price response programs include:

### 201 1 Integrated Resource Plan

18

 Real Time Pricing [Day

- Ahead Pricing ( “ DAP ” )]

– hourly prices are provided for

the next day to allow customers the ability to shift their energy usage.

 Time

- of

- Use

– seasonally and time

- differentiated programs that communicate

varying prices to customers signaling them to shift their energy use habits

In Cause No. PUD 201000029, the Oklahoma Corporation Commission approved

OG&E’s plan to deploy Smart Grid technologies throughout the Oklahoma service

territory. The Distribution Automation Integrated Voltage and VAR control prog ram

allows reactive and voltage control elements on the circuit to be operated in a

coordinated fashion to reduce the voltage profile or reactive power requirements along

the feeder. This ability reduces on peak demand by 75 MW in within 10 years.

Other Smart Grid technologies included price response tools such as a Home Area

Network ( “ HAN ” ) system that provide s customers with near real time information on

their energy consumption, cost to date, current price, and assumed cost. The purpos e is

to allow com munication to in home devices; primarily Programmable Communicating

Thermostats ( “ PCT ” ) or In Home Displays ( “ IHD ” ) which provides information such as

price signals, historic usage as compared to other customers, or usage month to date.

Over the next ten y ears, OG&E is planning for 20% of the residential customers to

adopt the in home devices, each reducing their energy consumption during OG&E

system peak hours by an average of 1.3 kW. Likewise, commercial and industrial

customers will be able to take adva ntage of more price response programs with an

estimated peak demand reduction of 75 MW over the next 10 years.

In Cause No. PUD 200 800398, OG&E restructured the event based programs to offer

the Load Reduction Rider. This pricing schedule replaced prev ious event based tariffs

while lowering the customers’ annual on

- peak period maximum demand requirement

from 500 kW to 200 kW and above. More customers are eligible, however they needed

to subscribe to the new rate and declare their load curtailment level s. As a result, the

2010 subscription dropped to approximately 89MW. OG&E continues to educate

customers about the new tariff and expects the curtailment capabilities to return to the

same relative level as before the tariff change. Table 10 shows the forecasts for these

programs.

Table 10 : Demand Response Assumption

(MW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

D istribution A utomation 8 17 26 37 48 60 75 75 75 75

Res idential DR 71 143 216 218 219 221 223 225 227 228

C ommercial & I ndustrial DR

- - 23 47 70 71 72 72 73 73

Load Curtailment 105 120 135 150 151 154 155 157 159 161

Total Reduction 184 280 400 452 488 506 525 529 534 537



## D. Transmission

This section describes the OG&E’s transmission resources, SPP transmission

resources and any transmission upgrades related to OG&E generation expans ion

### 201 1 Integrated Resource Plan

19

options. Summary of network upgrades from the 2010 SPP Transmission Expansion

Plan is also discussed

.



## 1. Transmission Resources

OG&E operates approximately 4, 5 00 miles of transmission lines, 69 kV through 500 kV,

throughout its service territory. Table 11 provides details of OG&E’s transmission

system line mileage at variou s voltages. These electric transmission lines move large

amounts of power at high voltages from power plants. To increase reliability OG&E’s

transmission system is directly interconnected to seven other utilities ’ transmission

system s at over 50 interconn ection points. Indirectly OG&E is connected to the entire

eastern interconnection through the SPP regional transmission organization.

Table 11 : OG&E Transmission Lines

VOLTAGE 500KV 345KV 161KV 138KV 69KV TOTAL

MILES 47 911 205 1

, 864 1

, 465 4

, 492



### The SPP footprint covers 370,000 square miles and its 56 members serve over 5 million

customers. It covers all of Kansas and Oklahoma and parts of seven other states:

Arkansas, Louisiana, Mississippi, Missouri, New Mexico, Nebraska, an d Texas. A

representation of the balancing authorities 4 

### is provided in Figure 5

.

Figure 5 : SPP Balancing Authorities

4 http://www.spp.org/publications/SPP_Footprints.pdf 

### The SPP footprint is

comprised of 29

balancing authorities,

which are operated by

investor

- owned utilities,

cooperatives, and

municipal and state

agencies. A balancing

authority is responsible

for managing the

supply and demand for

electricity within its

borders to assure

reliability

### 201 1 Integrated Resource Plan

20



## 2. Transmission Needs for Generation Expansion Options

Supply side resource options may require transmission investments

. OG&E has

identified proxy power plant sites and estimated the transmission expansion cos ts for

each site

. The sites shown in Table 12 were chosen for analysis purposes only and no

determination has been made on future specific locations.

Table 12 : Proxy Sites for Future Generation Resources

SITE DESCRIPTION RESOURCE TYPE COUNTY

North Central OK Area Wind Kay , Oklahoma

South Central OK Area Wind Murray, Oklahoma

Seminole Power Plant NG CT Seminole, Oklahoma

Sooner Power Plant NG CC Noble, Oklahoma



### a) Wind

OG&E performed studies to estimate the transmission improvements necessary for

delivery of wind generation . Two proxy sites were used for new wind generation. The

first proxy site was interconnected on the Sooner to Rose Hill 345 k V transmission line

in Kay County, Oklahoma. The second proxy site was interconnected at the Arbuckle

Substation located in Murray County, Oklahoma. Both sites are in the service territory of

OG&E. Contingency Analysis was performed to determine if any overloads were

present due to the connection of the new wind generation. Power flow analysis

identified two network constraints. These network constraint s can be relieved by the

conversion of the Pric es Falls substation which is planned for pre

- summer of 2012 and

is already budgeted. Therefore there is no upgrade costs associated with the network

constraints identified.



### b) Seminole

Power flow analysis has indicated that connecting a new unit

, up to 1,000 MW

, at

Seminole will require transmission upgrades to correct overloads in the OG&E and AEP

control areas. The associated construction cost for a new unit at Seminole is estimated

to be $9 million . There may be additional stability cost that could be deter mined in the

SPP Study Process



### c) Sooner

Power flow analysis has indicated that connecting a new 500 MW unit at Sooner will

require transmission upgrades to correct overloads in the OG&E control area. The

associated transmission construction cost for a new 50 0 MW unit at Sooner is estimated

to be $ 7 million

. Power flow analysis has indicated that connecting a new 1,000 MW

unit at Sooner will require transmission upgrades to correct overloads in the OG&E and

Entergy control areas. The associated transmission c onstruction cost for a new 1,000

MW unit at Sooner is estimated to be $32 million

.

### 201 1 Integrated Resource Plan

21



## 3. SPP Transmission Expansion

In compliance with FERC Order 890 for transmission planning, the SPP does annual

expansion planning for the entire SPP footprint. Therefore OG&E provides input to the

SPP planning process, but is not ultimately responsible for the planning of the OG&E

system.

The main objective of SPP’s ten

- year regional reliability assessment is to create a

reliable long

- range transmission expansion plan for the SPP footprint. The assessment

identifies problems for normal conditions (no contingency) and single contingency

scenarios using NERC Reliability Standards, SPP Criteria, and local planning criteria. It

also coordinates appropriate mitigation plans to meet the SPP ’s region al reliability

needs.

The 20 10 SPP Transmission Expansion Plan 5 

### ( “ STEP ” ) summarizes transmission

planning efforts including regional reliability, local reliability, Generation Interconnection,

long

- term tariff studies due to transmission service requests , Balanced Portfolio, and

Priority Projects 6



### . Also identified are regional reliability projects in the 20 11

- 20 21

timeframe that do not need immediate action and will be considered in future plans.

Table 13 offers a cost summary

. Major transmission projects are illustrated in Figure 6

.

Table 13 : SPP STEP Projects C ost S ummary

UPGRADE

TYPE 2010 STEP

(NEAREST

10 MILLION)

2010 Priority Projects* $1,420

2009 Balanced Portfolio* $820

Transmission Service Request

and Generation Interconnection

Service Agreements* $650

Reliability

– Base Plan* $1,220

Reliability

– Other $540

Sponsored Upgrades

-



### SPP Subtotal $4.65B

Non

- OATT Upgrades $420



### STEP Appendix A Total** $5.1B

*Has filed Service Agreement or is Board Approved

* *SPP STEP Appendix A includes a breakdown of projects in the 10

- year horizon

5 20 10 STEP: http://www.spp.org/publications/2010_SPP_Transmission_Expansion_Plan_01

- 28

- 11.pdf

6 SPP Priorit y Projects Phase II Final Report : http://www.spp.org/publications /

Priority%20Projects%20Phase%20II%20Final%20Report%20

- %204

- 27

- 10.pdf

### 201 1 Integrated Resource Plan

22 Figure 6 : SPP Transmission Expansion Plan

Transmission improvements identified in the STEP and were included in the

transmission models for this IRP . Some of the benefits provided by these improvements

include reliability and the capacity for expansion of Oklahoma’s abundant wind energy.

A list of projects OG&E plans to construct can be found in Schedule J. Transmission

system expansion provides ben efits to members throughout the SPP; therefore, the

cost s of all projects constructed in the SPP are shared through various cost allocation

methods, depending on the type of project.



## E. Needs Assessment

Capacity needs are defined as the additional capacity r equired to meet the Company’s

customer requirement and to satisfy SPP's minimum 12% planning capacity margin

requirement. Section 4.3.5 of the SPP Criteria establish es the basis and define s the

required minimum capacity planning reserve margin for SPP memb ers as follows:

“The SPP performs generation reliability assessments to examine the regional

ability to maintain a North American Electric Reliability Council ( “ NERC ” ) based

target probabilistic Loss of Load Expectation ( “ LOLE ” ) standard of no more than

one day in ten years. Historical studies indicate that the LOLE of one day in ten

### 201 1 Integrated Resource Plan

23

years minimum can be maintained with a minimum capacity margin between 10

- 11%. Based on this, the SPP has established that each control area is req uired

to maintain a minimum planned capacity margin of 12% for steam

- based utilities

and minimum planned margin of 9% for hydro

- based utilities.”

Therefore, OG&E is required to maintain capacity levels that allow for a minimum of

12% margin between capaci ty and demand. This calculation is shown in the following

equation:

Capacity Margin % = (Total Net Dependable Capability)

- (Net On System Demand)

(Total Net Dependable Capability)



### Table 14 utilizes the above equation to provide a ten

- year capacity need forecast

. This

table includes all resources currently owned or under contract by OG&E. This table also

includes OG&E’s load res ponsibility and capacity margins. The resource gap , or

capacity needed to satisfy customer demand and the SPP minimum 12% margin, is

shown as Needed Capacity

.

Table 14 : Planning Capacity Margin

MW 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021



### Resources Owned Capacity 6,418 6,418 6,484 6,484 6,484 6,484 6,484 6,484 6,484 6,484

Purchase Contracts 455 455 455 455 455 455 453 453 333 333

Total Capability 6,873 6,873 6,939 6,939 6,939 6,939 6,937 6,937 6,817 6,817



### Demand Load Forecast 6,104 6,172 6,228 6,317 6,123 6,228 6,278 6,371 6,456 6,528

Energy Efficiency 38 38 39 39 39 40 44 47 50 53

Demand Response 184 280 400 452 488 506 525 529 534 537

System Demand 5,882 5,853 5,789 5,826 5,596 5,681 5,710 5,795 5,872 5,938



### Capacity

Needs Needed Capacity

- - - - - - - - - -

Capacity Margin 991 1,020 1,150 1,113 1,343 1,258 1,227 1,142 945 879

Capacity Margin (%) 14.4 14.8 16.6 16.0 19.4 18.1 17.7 16.5 13.9 12.9



## F. Scenarios

Scenarios are used to test each portfolio in a range of possible futures. This is done to

determine the best portfolio in the expected future, as well as how each plan performs

under changes in the expected assumptions. In this IRP OG&E is using three scenarios.



### a) Reference Scenario

OG&E develops internal forecasts for load, fuel and CO 2 

### prices. The economy

continues to slowly recover, shale gas keep s natural gas prices low and coal commodity

price growth remains slow.

### 201 1 Integrated Resource Plan

24 Table 15 : Reference Scenario Prices

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Coal ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

Natural Gas ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

CO 2 ($/tonne)

- - - - - - - - - -



### b) Ventyx Scenario

T he Ventyx Midwest Reference Case High Environmental Scenario assumes the

implementation of federal GHG legislation and national renewable energy standard

beginning in 2015 which also increases the demand and price of natural gas.

Table 16 : Ventyx Scenario Prices

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Coal ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

Natural Gas ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

CO 2 ($/tonne) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx



### c) NYMEX Futures Scenario

On February 22, 2011, OG&E conducted an IRP stakeholder meeting to review

assumptions for this IRP . One of the comments made in this meeting regarded the

Reference Scenario natural gas price as too high compared to current prices. In

response to this feed back, OG&E has included this scenario which applies the March 8,

2011 NYMEX forecast for natural gas.

Table 17 : NYMEX Futures Scenario Prices

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Coal ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

Natural Gas ($/ MMBTU ) 4.70 5.07 5.37 5.68 5.97 6.20 6.43 6.60 6.83 6.92

CO 2 ($/tonne)

- - - - - - - - - -



## G. Environmental Considerations

Uncertainty regarding environmental legislation and regulation is an important

consideration in resource planning. This section describes several environmental

considerations, which OG&E evaluated in connection with this IRP . OG&E continues to

study the p otential for additional new laws and regulations and to assess whether, and

to what extent, such laws and regulations will impact OG&E’s resource plan.



## 1. Renewable Energy Standards

In 2010, the Oklahoma Legislature passed HB 3028, the Oklahoma Energy Securit y Act

which reads as follows:

### 201 1 Integrated Resource Plan

25

“The renewable energy standard shall be a goal that fifteen percent (15%) of all

installed capacity of electricity generation within the state by the year 2015 be

generated from renewable energy sources...”

“(E)very electrici ty generating entity in Oklahoma may use energy efficiency and

demand side management measures to assist the state in meeting its renewable

energy standard. Provided, however, that demand side management may not be

used to meet more than twenty

- five perce nt (25%) of the overall fifteen percent

(15%) renewable energy standard for the state.”

Oklahoma is one of the leading states in wind generation development; therefore,

additional wind resources, as described in the Resource Options section on page 15

, are the primary renewable energy source for this IRP . With approximately 250 MW of

additional wind generation, along with the demand side management programs, 15% of

OG&E’s installed capacity will be from renewable resources on or before the 2015

deadline.



## 2. Carbon Dioxide Restrictions

The United States Congress has considered legislation for reducing greenhouse gas

emissions from mobile and stationary sources. One measure, passed by the House of

Representatives in 2009, would have included a cap

- and

- trade system for reducing

greenhouse gas output with a cap is set at graduated levels relative to 2005 CO 2 

### output.

Congress as a whole, however, did not pass this or an y similar legislation and, after the

November 2010 elections, the future of greenhouse gas legislation is uncertain.

In the absence of federal legislation, the EPA has taken action to begin regulating CO 2 

### and other greenhouse gases using its existing au thority under the Clean Air Act.

Specifically, EPA agreed in December 2010 to issue Emission Guidelines under Section

111(d) of the Clean Air Act that could give rise to greenhouse gas emission limits for

existing electrical generating units. Whether EPA has the authority to issue such

Guidelines or otherwise regulate greenhouse gas emissions is currently the subject of

several proposals being considered by Congress.



## 3. Regional Haze

In 2005, EPA, pursuant to the Clean Air Act, promulgated regulations to imp rove

visibility in national parks and wilderness areas (“Regional Haze Regulations”). These

Regional Haze Regulations require states, over approximately a 50

- year period, to

move toward the elimination of man

- made impacts on visibility in Class I areas. In

Oklahoma, the DEQ has developed rules that require certain resources to install Best

Available Retrofit Technology ( “ BART ” ). The OG&E units affected are: Seminole 1, 2,

and 3, Muskogee 4 and 5, and Sooner 1 and 2. In May 2008, OG&E submitted BART

evaluati ons for its affected generating units at Muskogee, Seminole and Sooner

Stations.

### 201 1 Integrated Resource Plan

26



### a) NO

x



### The BART evaluations were performed in accordance with EPA guidelines and address

two different types of emissions from these units that have the potential to affect

visib ility. The first type of emission addressed in the BART evaluation is nitrogen oxides

(“NO

x 

### ”). The BART evaluations demonstrate that OG&E should install low NO

x 

### combustion technology to minimize the creation of NO

x 

### during combustion. One of the

five fact ors considered in selecting BART is the cost effectiveness of available control

technologies. According to EPA’s cost

- effectiveness guidelines, the low NO

x 

### combustion techniques were shown to be cost effective. DEQ has agreed with the

proposed BART determi nation for NO

x 

### at the affected Seminole, Sooner and Muskogee

units. On March 7, 2011, the EPA proposed to accept the Oklahoma State

Implementation Plan (“SIP”) with respect to the NO

x 

### BART determination and proposed

the installation of low NO

x 

### combustion technology for reducing NO

x 

### emissions under the

Regional Haze Regulations.



### b) SO 2



### The second type of emission addressed in the BART evaluation is sulfur dioxide

(“SO 2 

### ”). EPA established a presumptive BART emission rate for each of the affected

coal

- fired uni ts at Muskogee and Sooner of 0.15 pounds of SO 2 

### emissions per MMBTU

of heat input. This emission rate can be achieved with the installation of dry flue gas

desulfurization, which also is known as a scrubber.

OG&E believes the presumptive BART emission rate for SO 2 

### does not apply if one

performs a complete analysis of the five factors used to establish BART and determines

a specific BART emission rate for each affected unit that considers that unit’s particular

characteristics and circumstances. The BAR T analysis performed by OG&E’s

consultant for the affected units at Muskogee and Sooner concludes that those units are

unique in that they burn low sulfur coal that dramatically changes the cost effectiveness

equation. As a result, the BART evaluation con cluded that scrubbers were not cost

effective under the Regional Haze Regulations and recommended emission limits that

require the units to continue burning low sulfur coal.

In the Oklahoma SIP, the estimated capital cost for scrubbers at the affected coa l units

was $1.219 billion. In addition to the capital costs, and as shown in the Oklahoma SIP,

OG&E expects to incur approximately $70 million annually to operate and maintain the

scrubbers. During the first quarter of 2010, the State of Oklahoma submit ted its SIP to

the EPA. The Oklahoma SIP provided for the continued use of low sulfur coal with

limited emission rates as shown in Table 18

.

### 201 1 Integrated Resource Plan

27 Table 18 : Oklahoma SIP SO 2 

### Limits

SO 2 CONTROL SOONER

UNIT 1 SOONER

UNIT 2 MUSKOGEE

UNIT 4 MUSKOGEE

UNIT 5

Hourly Emission Rate

(30

- day rolling average) 0.65 lb/ MMBTU 0.65 lb/ MMBTU 0.65 lb/ MMBTU 0.65 lb/ MMBTU

Emission Rate

(30

- day rolling average) 3,325 lb/hr 3,325 lb/hr 3,562 lb/hr 3,562 lb/hr

Annual Emission Rate

(12

- month rolling average) 0.55 lb/ MMBTU 0.55 lb/ MMBTU 0.55 lb/ MMBTU 0.55 lb MMBTU

Combined Annual Emission Rate 19,736 TPY 18,096 TPY



### On March 7, 2011, the EPA issued a proposed rule in which the Agency rejected the

Oklahoma SIP’s SO 2 

### BART determinations and instead proposed a Federal

Implementation Plan (“FIP”) with a SO 2 

### emission limit of 0.06 lb / MMBTU . The FIP

provides that this proposed emission limit can be achieved by either the installation of

four scrubbers on the four affected coal

- fired units or conversion of those four units to

natural gas

- fired units. The public comment period extends to May 23, 2011 and a final

rule will occur sometime after that. OG&E is preparing comments to the proposed rule

and evaluating the appropriate course of action. For the purposes of this IRP , OG&E

evaluated several emission control options including the continued use of low sulfur

coal, as well as the installation of s crubbers on all its coal

- fired units, the conversion or

replacement of those five coal

- fired units with natural gas

- fired units, the replacement of

those five coal fired units with new or existing natural gas fired units and several hybrid

scenarios.



## 4. Utili ty MACT Rule

On March 16, 2011, EPA issued proposed Maximum Achievable Control Technology

( “ MACT ” ) regulations governing emissions of certain hazardous air pollutants from utility

boilers. The proposal includes numerical standards for particulate matter, hydrogen

chloride and mercury emissions from coal

- fired boilers. In order to represent a

mitigation measure for mercury, activated carbon injection costs were included on all

portfolios that continued to use coal

- fired generation. OG&E is studying whethe r it

would be required to install any other control technologies like scrubbers or dry sorbent

injection technology to comply with the proposed rule. Initiatives needed to comply with

this proposed rule will be fully addressed in future IRPs. EPA is curr ently seeking public

comment on the proposed rule.



## 5. Other Environmental Requirements

There are several other requirements pending or proposed under the Clean Air Act that

could ultimately require some level of control at OG&E’s facilities. For example, rev ised

National Ambient Air Quality Standards (“NAAQS”) issued by EPA could result in

Oklahoma sources being required to reduce NO

x 

### and SO 2 

### emissions. OG&E has

assumed that none of these requirements will result in the installation of controls during

the pe riod covered by the IRP.

### 201 1 Integrated Resource Plan

28



## IV. RESOURCE PLAN NING MODELING AND AN ALYSIS

This section explains OG&E ’s analy sis methods and presents results

.



## A. Development of Models and Portfolios

The assumption data provided in Section III is the basis for developing models and

portfolios. The models and methodology for determining portfolios for analysis is

described in this section.



## 1. Model Development

Incorporation of assumptions and understanding how each variable impacts others is

the basis for model development. For this IRP , modeled data is analyzed on a n

incremental revenue requirement basis. The revenue requirement is composed of the

return on rate base, total expenses and production cost as shown in Table 19 . Return

on rate base and expenses are modeled in spreadsheets while production cost is

modeled in third party software.

Table 19 : Revenue Requirement Components

RETURN ON RATE BASE EXPENSES PRODUCTION COST

Capital I nvestment Depreciation Fuel Cost

Accumulated Depreciation Ad Valorem Variable O&M

Accumulated Tax Depreciation Fixed O&M Emission Cost



## 2. Develop Distinct Portfolios

The following section describes the three steps used in developing portfolios to analyze

in this IRP

.



### a) New Build Option Screening Process

The first step in developing portfolios is to decide which new resources should be

considered for analysis. OG&E utilized the Annual Energy Outlook 20 10 , prepared by

the EIA, for identifying proxy supply side resources. The proxy unit s are meant to

represent a generic type of unit and not the specific manufacture r or technology to be

placed into service. OG&E continually monitors the development of generation

technology and will determine which manufacture r a nd technology provides the best

value for its stakeholders when action needs to be initiated to meet OG&E’s obligations.

Three requirements were established for selecting supply side resource options to

analyze. These supply side resource options and sele ction requirements are illustrated

below in Table 20 and explained in the following subsections. Only resources that met

all requirements were selected.

### 201 1 Integrated Resource Plan

29

Table 20 : New Supply Side Resource Option Screening Requirements

TYPE TECHNOLOGY NOMINAL

CAPACITY

(MW) OVERNIGHT

CAPITAL

COST

(2010 $/KW) PROVEN

TECHNOLOGY COST/

SCALE PUBLIC

SENTIMENT

Coal Single Unit Advanced PC 650 3,167 Yes Yes

Dual Unit Advanced PC 1,300 2,844 Yes Yes

Single Unit Advanced PC w/ CCS 650 5,099

Dual Unit Advanced PC w / CCS 1,300 4,579

Single Unit IGCC 600 3,565

Dual Unit IGCC 1,200 3,221

Single Unit IGCC with CCS 520 5,348

Natural

Gas Conventional NGCC 540 978 Yes Yes Yes

Advanced NGCC 400 1,003 Yes Yes

Advanced NGCC with CCS 340 2,060 Yes Yes

Conventional CT 85 974 Yes Yes Yes

Advanced CT 210 665 Yes Yes Yes

Fuel Cells 10 6,835 Yes

Uranium Dual Unit Nuclear 2,236 5,335 Yes

Biomass Biomass CC 20 7,894 Yes Yes

Biomass BFB 50 3,860 Yes Yes

Wind Onshore Wind 100 2,438 Yes Yes Yes

Offshore Wind 400 5,975 Yes

Solar Solar Thermal 100 4,692 Yes Yes

Small Photovoltaic 7 6,050 Yes Yes

Large Photovoltaic 150 4,755 Yes Yes

Geo

thermal Geothermal

- Dual Flash 50 5,578 Yes Yes

Geothermal

- Binary 50 4,141 Yes Yes

MSW M unicipal Solid Waste 50 8,232 Yes

Hydro Hydro

- electric 500 3,076 Yes

Pumped Storage 250 5,595 Yes Yes

Yes = M eet s requirement



### (i) Proven Technology

In addition to providing construction and operating costs associated with the potential

supply side resources, the Annual Energy Outlook 20 10 also discusses how some

technologies are more developed than others. For example, while carbon capture and

sequestration is discussed as a solution to reduce CO 2 

### emissions , repeated utility scale

facilities have not been developed and operated . Therefor e this tec hnology is not

considered proven and is not included in a resource portfolio.

The advanced units in t he Annual Energy Outlook are usually not proven technologies

on a commercial scale. However, the Advanced CT is the same type of unit used in the

Conventional CC and is commercially available

. Furthermore, t he cost of the Advance d

### 201 1 Integrated Resource Plan

30

CT is also $300/kW less than the Conventional CT. Therefore, as a simplification, only

the Advanced CT is used in this analysis.

(ii) Cost/Scale

The second requirement co nsiders the cost and scale of the supply side option. The

Biomass CC option has a cost of $ 7,894 /kW

. This is significantly more expensive than

other renewable or baseload resource option; therefore it would not be a prudent

addition to a portfolio. Likew ise, the cost for a nuclear plant has greatly increase d since

the last resource plan and will not be considered in this plan. T he generating output for

Fuel Cells is less than what OG&E would need to meet system load growth and provide

baseload energy . W hile these technologies may provide business development

opportunities in the future, they will not be included in a 30 year plan until more

information is available.

(iii) Public Sentiment

Several proposed coal fired generating units have received considerable opposition

from environmental groups and the general public. Furthermore, the permitting of these

coal plants has experience d resistance and there is no evidence that these trends will

change in the future. For this reason , OG&E has decided not to consider coal fired

generatin g units in this IRP

.



### b) Emission Control Options

As mentioned in the Environmental C onsiderations section, OG&E’s coal fired steam

plants could be affected by proposed regulations. While the current Regional Haze

regulations only pertain to four of the five coal units in OG&E’s fleet, other regulations

could be proposed in the future that may include all coal unit s

. In this IRP a select

group of emission control options were analyzed to test alternatives for complying with

current or potential rules. OG&E continues to monitor evolving emission control options,

and no determination has been made as to which te chnologies might be used. The

opt ions included are:

 Benchmark

– OG&E’s use of Low Sulfur Coal is accepted as BART

 Scrub

– Installation of dry flue gas desulfurization with spray dryer absorb ers (dry

scrubbers) on all coal fired steam units.

 Hybrid Con vert

– Insta ll scrubbers on three coal fired units and convert two units

to burn natural gas

 Hybrid Replace

– Install scrubbers on three coal fired units and replace two units

with natural gas combined cycle units

 Convert 7



### – All coal fired steam units are retrofitted to burn natural gas

 Replace

– All coal fired steam units are retired and replaced with new natural

gas combined cycle units

7 Assum ptions for this option include approximately $6 million to convert each coal unit to burn gas and

approximately $110 million and $10 million to install gas pipelines to the Muskogee and Sooner Power Plants

(respectively). Other costs associated with conve rsion of the coal units to burn gas, including but not limited to

expansion of or improvements to existing transmission pipelines and gas storage arrangements, have not been

determined and are not included in the assessment for this option.

### 201 1 Integrated Resource Plan

31



### c) Portfolio Development

Considering the screening requirements for new supply side resources, three

technolo gies are available for analysis: conventional natural gas combined cycle,

advanced combustion turbine, and onshore wind

. As shown in the Needs Assessment

section, OG&E does not need to add capacity for the next ten years

. However, the

expansion of wind is based on energy

, not capacity needs. To value additional wind

resources, t his IRP analyzes the a ffect of adding 250 MW of wind generation

. The

installation date is varied to analyze the timing of additional wind generation

. The

emission control options are eval uated in 201 6 and t o simplify the analysis, portfolios

will either include CC units or CT units for future capacity needs starting in 2021. These

installations are represented in Figure 7

.

Figure 7 : Analysis Timeline

P ortfolio s are developed by combining 2 wind options, 6 emission control options and 2

future capacity options for a total of 24 portfolios. Each portfolio is analyzed under 3

scenarios for a total of 72 simulations

. Sensitivities are then performed for a n additional

24 simulations . These combinations are depicted in Figure 8

.

Figure 8 : Portfolio Development 2012

2014 2016

2018 2020

2022 2024

2026 2028

2030



### Emission

Control Option Wind

Evaluation New Resources to Meet

Capacity Needs

(2) Wind

Options

 With

 Without (6) Emission

Control Options

 Benchmark

 Scrub

 Hybrid Convert

 Hybrid Replace

 Convert

 Replace (2) New

Build

Options

 CC

 CT 24

Portfolios (3) Scenarios

 Reference

 Ventyx

 NYMEX Futures 72

Simulations

(4) Sensitivities

 Half Coal Price

 2x Coal Price

 Half Gas Price

 2x Gas Price (6) Emission

Control Options

 Benchmark

 Scrub

 Hybrid Convert

 Hybrid Replace

 Convert

 Replace 24

Simulations

### 201 1 Integrated Resource Plan

32



## B. Portfolio Analysis

The following section describes analysis results for wind timing

, emission control

options and future resource expansions

.



## 1. Evaluation of Future Wind

To analyze the economics of wind

, two 125 MW wind energy facilities are assumed to

be operational by 12/31/2012 . This date was selected in order to qualify for Federal

Prod uction Tax Credits ( “ PTC ” ). Figure 9 repre sents the costs and benefits of wind

energy using the assumptions in th e Benchmark portfolio. The 30 year NPV of revenue

requirements ( “ NPVRR ” ) of the wind is just under $800 million. Production cost savings

(fuel, variable O&M) reduces t his amount to approximately $ 1 5 0 million

. If the wind

energy facilities qualify, PTCs may offer customer savings of approximately $ 6 0 million

assuming PTCs are utilized starting in 2015

.

Figure 9 : 30 Year NPVRR Wind Evaluation with and without PTC

The installation date was then varied to analyze the timing of additional wind generation

. These analyses reflect that d elaying the construction of wind generation facilities

reduce s the NPVRR of the wind facilities yet also reduce s production cost savings. As

a result, absent the PTC savings assumed in 2012, adding two 125 MW wind energy

facilities increases customer costs

. This wind timing evaluation is shown in Figure 10

. $(200) $(100) $

- $100 $200 $300 $400 $500 $600 $700 $800 $900

Wind NPVRR Production Cost

Savings Customer Cost PTC Savings Customer Cost w/

PTCs Wind Costs Production Cost Savings PTC Savings

### 201 1 Integrated Resource Plan

33 Figure 10 : Wind Timing Evaluation (NPVRR)

This IRP assumes O G&E would not be able to execute a RFP through the competitive

bid process and have the wind faci lities operational before 12/31/2012. Therefore, the

remaining analysis does not include PTC savings.



## 2. Scenario Results

Scenario analysis involves changing multiple inputs and measures the impact of these

changes on the results. To study the economic benefit of wind with emission control

options , each portfolio was analyzed in each scenario with and without the addition of

250 MW of wi nd installed in 2013

. The savings under each of these combinations is

reflected as positive numbers belo w in Table 21

Table 21 : Wind Savings Analysis, 30 Year NPVRR, ( $Billions )

REFERENCE VENTYX NYMEX WEIGHTED

AVERAGE*

Benchmark

- CC

- 0.23 0.05

- 0.31

- 0.22

Benchmark

- CT

- 0.15 0.15

- 0.23

- 0.14

Scrub

- CC

- 0.22 0.04

- 0.30

- 0.22

Scrub

- CT

- 0.16 0.15

- 0.24

- 0.16

Hybrid Convert

- CC

- 0.16 0.17

- 0.23

- 0.15

Hybrid Convert

- CT

- 0.10 0.24

- 0.19

- 0.09

Hybrid Replace

- CC

- 0.22 0.05

- 0.29

- 0.21

Hybrid Replace

- CT

- 0.16 0.14

- 0.24

- 0.16

Convert

- CC

- 0.09 0.27

- 0.17

- 0.08

Convert

- CT

- 0.08 0.30

- 0.16

- 0.06

Replace

- CC

- 0.25 0.00

- 0.31

- 0.24

Replace

- CT

- 0.20 0.09

- 0.27

- 0.19

* Weightings are: Reference 60%, Ventyx 10%, NYMEX 30% $

- $100 $200 $300 $400 $500 $600 $700 $800 $900

2013 2015 2017 2019 2021 In Millions

Installation Year Production Cost Savings Customers Costs

### 201 1 Integrated Resource Plan

34

The 30 year NPVRR results for each of the three scenarios for port folios without

additional wind facilities are shown in Table 22

.

Table 22 : Emission Control Option Results , 30 Y ear NPVRR, ( $ Billions )

SCENARIO REFERENCE VENTYX NYMEX WEIGHTED

AVERAGE

Benchmark

- CC 21.4 31.3 20.4 22.1

Benchmark

- CT 21.5 32.1 20.3 22.2

Scrub

- CC 23.8 33.8 22.9 24.5

Scrub

- CT 23.8 34.4 22.7 24.5

Hybrid Convert

- CC 24.7 34.8 23.3 25.3

Hybrid Convert

- CT 25.1 36.0 23.5 25.7

Hybrid Replace

- CC 25.1 34.6 23.8 25.6

Hybrid Replace

- CT 24.9 35.1 23.5 25.5

Convert

- CC 27.0 37.5 24.9 27.5

Convert

- CT 27.6 39.2 25.3 28.1

Replace

- CC 27.3 36.1 25.4 27.6

Replace

- CT 27.0 36.3 25.1 27.3

* Weightings are: Reference 60%, Ventyx 10%, NYMEX 30%



## C. Risk Evaluation

S ensitivity analysis and stochastic analysis were used to evaluate the risk of select

portfolio s in this IRP

.



## 1. Sensitivity Results

Sensitivity analysis involves changing a single input variable and measures the i mpact

of that specific variable

. Table 23 shows the sensitivities of these variables in the

Reference portfolio.

Table 23 : Emission Control Option Sensitivity Analysis, 30 Year NPVRR,

( $Billions )

### 201 1 Integrated Resource Plan

35

Figure 11 shows which variable has the largest impact on each portfolio, and which

portfolio is the most sensitive to input changes.

Figure 11 : Sensitivity Analysis Results, Percent Impact on Reference NPVRR



## 2. Stochastic Analysis

Stochastic analysis is a probabilistic method of varying multiple inputs to measure the

impact on each portfolio. In general, lower revenue requirements and tighter bandwidths

around expected revenue requirements (e.g., lower risk) are preferred. The Reference

Scenario inputs varied in this analysis were natu ral gas, coal and capital cost

.

These stocha st ic comparisons show n in Figure 12 repre sent a range from the reference

scenario NPVRR value as shown in Table 22 to the 90 th 

### P ercentile value ( “ P90 ” ) . The

P90 represents t he point on the probability curve where , based upon the analysis, there

is a 90% certainty the revenue requirement will be less than that value

.

### 201 1 Integrated Resource Plan

36

Figure 12 : Stochastic Analysis Results, 30

- Year NPVRR, ( $B illions)



## D. SPP Integrated Market place

In 2009 the SPP Staff and stakeholders began developing the SPP Integrated

Marketplace ( “ IM ” ) for energy and operating reserves. Similar to other mature

RTO/ISOs, the IM will incorporate the purely financial Day

- Ahead and Transmission

Congestion Rights markets as well as a Re liability Unit Commitment process and Real

Time Balancing Market. Utilizing these markets and processes , SPP will commit and

dispatch r esources to minimize the cost of energy and operating reserves to serve the

SPP load.

Studies 8 

### have shown that impleme ntation of a commitment based market will provide a

more efficient operation of resources across the SPP footprint resulting in a net benefit

to OG&E customers. The IM will impact the daily operation of OG&E’s generation

resources, and therefore their oper ating costs. To capture the impact of the IM on the

8 http://www.sp p.org/publications/Economies_of_Scale_Market_Benefits.pdf

### 201 1 Integrated Resource Plan

37

portfolios, a market analysis was completed using the PROMOD IV® market simulation

tool. The Ventyx PROMOD IV® program is used to calculate production cost through a

security constrained economic dispatch for the SPP. The market analysis done for this

IRP assumes the IM will be fully operational on January 1, 2015, and will continue

through the study period which ends in 2041.



## 1. Integrated Marketplace Concepts

The IM is a significant change to current operat ions in the SPP footprint. An elementary

understanding of a few concepts is necess ary to understand this analysis . For more

information regarding the IM, the SPP provides ample educational material on their

website 9



### .

Unlike today, a company’s generation will no longer be committed to serve its native

load. In the IM, all r esources in the SPP are committed and dispatched to serve the

entire SPP load. Resources offer to sell energy and operating reserves into the IM and

loads bid to purchase energy from th e IM. Using simple supply and demand concepts,

SPP determines which resources are committed and dispatched in the energy and

operating reserve markets.

The IM will be a nodal market in which a Locational Marginal Price ( “ LMP ” ) is calculated

for every r esource and load. The LMP consists of the marginal energy cost, marginal

congestion cost, and marginal loss cost specific to the location that energy is being

injected or withdrawn. At a very high level, SPP pays resources the LMP for each MWh

injected an d load pays SPP for each MWh withdrawn from the SPP transmission

system. Therefore, the production cost seen by a company that has both generation

and load, such as OG&E, can be described as:

(Generator Variable Cost)

– (Generator Revenue) + (Load Cost)

= Production Cost

The generator variable cost consists of fuel, variable O&M, and any other variable

production costs associated with generating energy. The generator revenue is

determined by multiplying the generated MWh by the LMP, and the load cost is

determined by multiplying the hourly load by the associated LMP.

The SPP has 63GW of generating capacity, therefore it can be reasonably assumed

that any small change in the system, such as adding a new 500MW unit, will not

materially affect the energy cl earing price over the course of a year. Due to the long run

time of the market simulation, only the Reference S cenario was modeled which

produced an LMP forecast . This LMP forecast was then used in a spreadsheet model to

determine the generator variable co sts and revenues of resource options , as well as

OG&E’s load cost . Only the costs of the resource options were modeled in the

spreadsheet as costs for all other OG&E units were extracted from PROMOD IV®

.

9 http://www.spp.org/section.asp?pageID=138

### 201 1 Integrated Resource Plan

38



## 2. Market Model

The market model consists of load, gen erator, and transmission data for the entire

Eastern Interconnect, which Ventyx provides. The fuel forecasts were updated from the

Ventyx provided forecasts to match those used in this IRP. The NERC regions

simulated for this analysis were SPP, Entergy, MA PP, and MISO.



## 3. Generation Expansi o n

As part of the SPP Integrated Transmission Planning ( “ ITP ” ) process, a generation

expansion plan was developed by Black & Veatch for the SPP through 2030. For the

study period beyond 2030, the same capacity mix was contin ued. This expansion plan

is summarized in the table below.

Table 24 : SPP Generation Expansion (MW)

SPP NORTH SPP SOUTH TOTAL

Resource Type CC CT Coal CC CT Coal CC CT Coal

2015

– 2018 0 0 0 0 180 0 0 0 0

2019

– 2022 0 0 0 1

, 100 540 0 1

, 100 540 0

2023

– 2026 1

, 650 900 0 1

, 100 360 0 2,75 0 1,26 0 0

2027

– 2030 550 180 800 550 360 0 1, 1 00 5 4 0 800

2031

– 2041 1,470 360 0 1,470 540 0 2,940 900 0

Total 3,670 1,440 800 4

, 22 0 1, 98 0 0 7,890 3,420 800



### In addition to thermal unit expansion, wind generation expansion was modeled using

the results of a survey conducted by the SPP Cost Allocation Working Group ( “ CAWG ” ).

This wind was spread over the NREL wind profiles, and modeled in the same way as

discussed earlier. The total wind g eneration in the SPP is summarized below and

represents the installed wind capacity delivered to the state in the year 2022.

Table 25 : SPP Wind Expansion by State

T X OK KS MO NE LA N M AR TOTAL

MW 3,156 2,697 1,741 1,271 924 332 264 254 10,638



## 4. Results

The benefits provided by a wind generator consist of the revenue paid to that generator

for the energy it provides to the market. Based upon our current assumptions, the

analysis reflected the revenue from the additional 250 MW of wind would not cover the

associated incremental revenue requirement; therefore, it would not provide a benefit to

OG&E customers.

Table 26 : 30

- Year NP V Wind Analysis in the IM ($ Millions)

ADDITIONAL

RATE BASE EXPENSES REVENUE NET C OST

(SAVINGS)

414 3 81

- 472 112

### 201 1 Integrated Resource Plan

39



## V. SCHEDULES

This section is intended to provide a tabular summary of each section as described in

the OCC’s Electric Utility Rules , Subchapter 37 of Chapter 35, section 4 (c).



## Schedule A

– Electric Demand and Energy Assumption

This schedule is the electric demand and energy sales assumptions from the 2010 Load

Forecast . Details of these assumptions can be found in the Electric Demand and

En e rgy Forecast section on page 11 and also in Appendix A

– OG&E 2010 Load

Forecast

.

OG&E Peak Demand Forecast

MW 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Wholesale 281 274 239 243

- - - - - -

Retail 5,823 5,898 5,989 6,073 6,123 6,228 6,278 6,371 6,456 6,528

Total 6,104 6,172 6,228 6,317 6,123 6,228 6,278 6,371 6,456 6,528

Retail Growth 1.5% 1.3% 1.5% 1.4% 0.8% 1.7% 0.8% 1.5% 1.3% 1.1%



### OG&E Energy Sales Forecast

GWH 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Wholesale 1,616 1,537 1,274 1,274

- - - - - -

Retail 27,501 27,865 28,364 28,828 29,224 29,682 30,013 30,501 30,950 31,369

Total 29,118 29,401 29,638 30,102 29,224 29,682 30,013 30,501 30,950 31,369

Retail Growth 1.9% 1.3% 1.8% 1.6% 1.4% 1.6% 1.1% 1.6% 1.5% 1.4%



## Schedule B

– Existing Resources

This schedule provides a summary of existing supply side and demand side resources.

Details on this data can be found in the Resource Options section starting on page 11

.

### 201 1 Integrated Resource Plan

40 OG&E Planned Generation Resources

UNIT TYPE

(PLANNING

CAPACITY) UNIT NAME FIRST

YEAR IN

SERVICE PEAK

PLANNING

CAPACITY

(MW) AVERAGE

HEAT RATE

(BTU/KWH)

Coal Fired

Steam

(2

, 553 MW) Muskogee 4 1977 505 10,935

Muskogee 5 1978 500 10,932

Muskogee 6 1984 502 10,948

Sooner 1 1979 522 10,223

Sooner 2 1980 524 10,232

Gas Fired

Steam

(2

, 510 MW) Horseshoe Lake 6 1958 159 11,253

Horseshoe Lake 8 1968 381 12,210

Mustang 1 1950 50 12,740

Mustang 2 1951 51 12,724

Mustang 3 1955 113 11,328

Mustang 4 1959 253 11,207

Seminole 1 1971 500 13,699

Seminole 2 1973 500 12,166

Seminole 3 1973 503 11,981

Combined

Cycle

(1

, 168 MW) Horseshoe Lake 7 1963 227 11,283

McClain* 2001 352 7,480

Redbud* 2004 589 7,187

Combustion

Turbine

(237 MW) Enid 1GT 1965 14 20,767

Enid 2GT 1965 14 20,767

Enid 3GT 1965 14 20,767

Enid 4GT 1965 14 20,767

Horseshoe Lake 9 2000 45 10,381

Horseshoe Lake 10 2000 45 10,381

Seminole 1GT 1971 17 N/A

Mustang 5A 1971 32 14,647

Mustang 5B 1971 32 14,647

Woodward 1963 10 19,082

Purchase

Power

-

Thermal

(446 MW) AES Shady Point 1991 320 11,817

PowerSmith 1998 120 8,583

SPA Hydro N/A 6 N/A

Purchase

Power

- Wind

(9 MW) FPL Wind 2003 2 N/A

Keenan 2010 4 N/A

Taloga 2011 3 N/A

Owned Wind

(16 MW) Centennial 2007 7 N/A

OU Spirit 2009 3 N/A

Crossroads 2012 6 N/A

Total Net Dependability Capability 6,939

* Represents OG&E owned interest

### 201 1 Integrated Resource Plan

41 Oklahoma EE Peak Demand and Energy Reduction

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Demand (MW) 36.6 36.6 36.6 34.6 32.7 30.7 30.7 30.1 27.6 25.2

Energy (MWh) 144,435 144,435 144,435 142,518 140,269 138,020 137,688 134,973 117,948 100,918



### Arkansas Energy Efficiency Peak Demand and Energy Reduction

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Demand (MW) 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.0 0.8

Energy (MWh) 4,738 4,738 4,738 4,738 4,738 4,738 4,738 4,431 3,307 2,759



### Total System Energy Efficiency Peak Demand and Energy Reduction

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Demand (MW) 38.1 38.1 38.1 36.2 34.2 32.2 32.2 31.5 28.7 26.0

Energy (MWh) 149,173 149,173 149,173 147,256 145,007 142,758 142,426 139,404 121,255 103,677



### Demand Response Assumption

(MW) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

DA

- IVVC 8 17 26 37 48 60 75 75 75 75

Res DR 71 143 216 218 219 221 223 225 227 228

C & I DR

- - 23 47 70 71 72 72 73 73

Load Curtailment 105 120 135 150 151 154 155 157 159 161

Total Reduction 184 280 400 452 488 506 525 529 534 537



## Schedule C

– Transmission Capability and Needs

This schedule provides a description of the OG& E transmission system as described in

the Transmission Resources section on page 19

. A further description of transmission

adequacy and SPP projects are addressed in Schedule J.

OG&E Transmission Lines

VOLTAGE 500KV 345KV 161KV 138KV 69KV TOTAL

MILES 47 911 205 1

, 864 1

, 465 4

, 492



## Schedule D

– Needs Assessment

This schedule provides the needs assessment for new generating resources for the

next 10 years. A further description of these needs is found on page 22

.

### 201 1 Integrated Resource Plan

42 Planning Capacity Margin

MW 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021



### Resources Owned Capacity 6,418 6,418 6,484 6,484 6,484 6,484 6,484 6,484 6,484 6,484

Purchase Contracts 455 455 455 455 455 455 453 453 333 333

Total Capability 6,873 6,873 6,939 6,939 6,939 6,939 6,937 6,937 6,817 6,817



### Demand Load Forecast 6,104 6,172 6,228 6,317 6,123 6,228 6,278 6,371 6,456 6,528

Energy Efficiency 38 38 39 39 39 40 44 47 50 53

Demand Response 184 280 400 452 488 506 525 529 534 537

System Demand 5,882 5,853 5,789 5,826 5,596 5,681 5,710 5,795 5,872 5,938



### Capacity

Needs Needed Capacity

- - - - - - - - - -

Capacity Margin 991 1,020 1,150 1,113 1,343 1,258 1,227 1,142 945 879

Capacity Margin (%) 14.4 14.8 16.6 16.0 19.4 18.1 17.7 16.5 13.9 12.9



## Schedule E

– Resource Options

This schedule provides a description of the supply and demand side options available to

OG&E to address the needs identified in Schedule D and further explained starting on

page 14

.

New Supply Side Resources

TYPE TECHNOLOGY NOMINAL

CAPACITY

(MW) HEAT

RATE

(BTU/

KWH) OVERNIGHT

CAPITAL

COST

($/KW ) FIXED

O&M

COST

($/KW) VARIABLE

O&M

COST

($/MWH)

Coal Single Unit Advanced PC 650 8,800 3,167 35.97 4.25

Dual Unit Advanced PC 1,300 8,800 2,844 29.67 4.25

Single Unit Advanced PC w/ CCS 650 12,000 5,099 76.62 9.05

Dual Unit Advanced PC w/ CCS 1,300 12,000 4,579 63.21 9.05

Single Unit IGCC 600 8,700 3,565 59.23 6.87

Dual Unit IGCC 1,200 8,700 3,221 48.90 6.87

Single Unit IGCC with CCS 520 10,700 5,348 69.30 8.04

Natural

Gas Conventional NGCC 540 7,050 978 14.39 3.43

Advanced NGCC 400 6,430 1,003 14.62 3.11

Advanced NGCC with CCS 340 7,525 2,060 30.25 6.45

Conventional CT 85 10,850 974 6.98 14.70

Advanced CT 210 9,750 665 6.70 9.87

Fuel Cells 10 9,500 6,835 350.00

- Uranium Dual Unit Nuclear 2,236 N/A 5,335 88.75 2.04

Biomass Biomass CC 20 12,350 7,894 338.79 16.64

Biomass BFB 50 13,500 3,860 100.50 5.00

Wind Onshore Wind 100 N/A 2,438 28.07

-

Offshore Wind 400 N/A 5,975 53.33

-

Solar Solar Thermal 100 N/A 4,692 64.00

-

Small Photovoltaic 7 N/A 6,050 26.04

- Large Photovoltaic 150 N/A 4,755 16.70

-

Geo

thermal Geothermal

- Dual Flash 50 N/A 5,578 84.27 9.64

Geothermal

- Binary 50 N/A 4,141 84.27 9.64

MSW Municipal Solid Waste 50 18,000 8,232 373.76 8.33

Hydro Hydro

- electric 500 N/A 3,076 13.44

-

Pumped Storage 250 N/A 5,595 13.03

-

### 201 1 Integrated Resource Plan

43 Emission Control Technologies in 2010$

TECHNOLOGY UNIT

TYPE CAPITAL

COST ($M) FIXED O&M

COST ($M) VARIABLE O&M

COST ($/MWH)

Scrubber Coal 308.8 7.3 2.52

Low NO

x Burners Coal 14.3 0.9

-

Low NO

x Burners Gas 9.6 0.6

- Mercury Control

- Activated

Carbon Injection Coal 2.1 0.3 0.57



### Geo

- Thermal Program Expansion Peak Demand and Energy Reduction

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Demand (MW) 0.2 0.4 1.2 2. 7 4.9 8. 1 11. 9 16.2 21.1 27.0

Energy (MWh) 853 1,693 5,134 10,860 20,276 33,096 48,358 66,117 87,855 110,737



## Schedule F

– Fuel Procurement and Risk Management Plan

On May 15 , 20 10 , OG&E filed a Fuel Supply Portfolio and Risk Management Plan with

the OCC as part of Cause No. PUD 200100095. A summary of this plan is provided

below

.

Fuel Procurement and Risk Management Plan

OG&E files an annual Fuel Supply Portfolio and Risk Management Plan with the

Oklahoma Corporation Commission which details OG&E’s fuel procurement and risk

management plans. T he plan was filed on May 15, 20 10 under Cause No. PUD

20 0100095. This section is a summary of OG&E’s fuel procurement and risk

management plan.

Fuel Planning Process

In the spring of each year, OG&E develops a forecast of load responsibility for peak

demand and energy requirements on a weather normalized basis for each month of the

next calendar year. The Company then analyzes expected generation availability, fuel

price assumptions and contractual commitments and obligations. Additional factors

include such items as generation unit efficiencies, minimum loadin g requirements, ramp

rates, maintenance schedules, allowances for forced outages, and gas storage status.

OG&E develops a dispatch commitment plan based on these inputs. This plan provides

the Company with an estimate of annual usage by fuel type. The ann ual projection

incorporates OG&E’s assumed fuel requirements for the next year that is then broken

down into monthly requirements. The assumed fuel burn is subject to change due to the

potential purchase or sale of generation in the SPP hourly EIS market.

Resource Procurement Practices

1. Coal

Coal is procured under long

- term contracts utilizing the widely accepted risk

management laddering strategy. Contractual adjustments are made to the price of coal

### 201 1 Integrated Resource Plan

44

each quarter (up or down) due to quality variations. Cur rently, coal is purchased from

four (4) producers located in the Southern Powder River Basin of Wyoming. Rail

transportation is provided under long

- term contracts with the BNSF railroad for the

Sooner Plant. For the Muskogee plant, OG&E is currently operat ing under a Common

carrier tariff service from the Union Pacific Railroad pending a determination by the

Surface Transportation Board on a maximum jurisdictional threshold rated prescription.

2. Natural Gas

The Company acquire s approximately 60 % of its an nual natural gas burn through a

RFP process. OG&E obtains less than 0.1 % of its natural gas requirements from old er

contracts with fixed prices and the remaining requirements are bought on a monthly,

weekly, and daily basis. The key volumetric risk for nat ural gas procurement derives

from securing sufficient supplies during the high electricity demand periods of April

through October. OG&E currently transports it s gas through the OGT pipeline and

throug h the Enogex pipeline. OG&E has gas storage service un der contract with

Enogex 10 

### that allows OG&E’s gas units to swing load in response to customer

demands.

OG&E is currently considering alternatives such as long

- term physical supply contracts

to procure a portion of its forecasted natural gas requirements. These alternatives will

be intended to provide the Company with reliable base

- load supply. OG&E is also

considering alternate pricing structures to help mitigate the volatility associated with

natural gas prices.

3. Fuel Oil

Fuel oil is purchased through a competitive bidding process for delivery to the

consuming plant. Fuel oil is primarily used for startup fuel at the coal

- fired Sooner plant.

Fuel oil is transported to the plants via truck.



## Schedule G

– Action Plan

This schedule outlines the proposed actions for the next five years. These actions are in

accord with this IRP , and will position OG&E to complete the plan as described in this

report.

DEMAND RESPONSE AND ENERGY EFFICIENCY

OG&E last updated its IRP in J anuary 2010. This update builds on the conclusion s in

the January submittal that actions such as the timely termination of wholesale contracts,

encouraging energy efficiency and demand response programs and other programs

enabled by the smart grid offer b enefits to customers. These actions are expected to

reduce peak demand and, when combined with actions identified in the 2009 IRP, are

projected to defer the need for new fossil fuel generation beyond the year 2020

.

10 Oklahoma Gas & Electric Co. and Enogex LLC are subsidiaries of OGE Energy Corp.

### 201 1 Integrated Resource Plan

45 Actions to Reduce Peak Demand (MW)

2 012 2013 2014 2015 2016

Demand Retail Peak Forecast 5,823 5,898 5,989 6,073 6,123

Wholesale Peak Forecast 281 274 239 243

-

Total Peak Forecast 6,104 6,172 6,228 6,317 6,123

Energy

Efficiency

and

Demand

Response Energy Efficiency 38 38 39 39 39

Distribution Automation 8 17 26 37 48

Residential DR 71 143 216 218 219

Commercial & Industrial DR

- - 23 47 70

Load Curtailment 105 120 135 150 151

Total Peak Reduction 222 318 439 491 527

System Peak Demand 5,882 5,853 5,789 5,826 5,596



### RENEWABLE ENERGY

With the completion of the Crossroads Wind Farm, OG&E will have added 611 MW

since 2008

. OG& E has made no decision whether to issue a RFP for additional wind

resources but will continue to monitor the market for renewable projects that benefit

customers while contributing to the State ’s renewable energy goal.

TRANSMISSION

The 2010 STEP has ide ntified projects that will be constructed over the next five years

for reliability and economic purposes, including new generation additions such as wind.

OG&E plans t o participate in this expansion by constructing some of the projects that

have been appro ved for construction by the SPP Board of Directors as listed in

Schedule J

.

INTEGRATED MARKETPLACE

The SPP is still developing the rules for an Integrated Marketplace concept to provide

efficiencies and transparency to serving customers’ energy needs throughout the SPP.

This concept is expected to impact the way OG&E’s generation units operate.

LONG TERM GAS CONTRACT

OG&E is considering alternatives such as long

- term physical supply contracts to

procure a portion of its forecasted natural gas requirements. These alternatives will be

intended to provide the Company with reliable base

- load supply. OG&E is also

considering alternate pricing structures to help mitigate the volatility associated with

natural gas prices.

MECHANICAL INTEGRITY

The MI Plan is the formalization, development and standardization of plant maintenance

and reliability procedures, in an effort to mitigate safety risks and improve reliability of

the Company’s generation assets.

EMISSION CONTROL OPTIONS

On March 7, 2011, the EPA issued a proposed rule in which the Agency rejected the

Oklahoma State Implementation Plan (“ SIP ”) for S O 2 

### BART determinations and instead

### 201 1 Integrated Resource Plan

46

proposed a Federal Implementation Plan (“FIP”) with a SO 2 

### emission limit of 0.06

lb /MMBTU . The FIP provides that this proposed emission limit can be achieved by

either the installation of four scrubbers on the four affected coal

- fired units or conversion

of those four units to natural gas

- fired units. The public comment period extends to May

23, 2011 and a final rule will occur sometime after that. OG&E is preparing comments

to the proposed rule and evaluating the appropriate course of action.



## Schedule H

– Requests for Proposals

OG&E has made no decision whether to issue a RFP for additio nal wind resources but

will continue to monitor the market for renewable projects that benefit customers while

contributing to Oklahoma’s renewable energy goal



## Schedule I

– Modeling Methodology and Assumptions

This schedule is a technical appendix for the data, assumptions, and descriptions of

models needed to understand the derivation of the resource plan.

The table below explains who supplied each assumption and provides a reference for

where this information is found in the IRP. Since the load assumptio n was provided in

Schedule A, it has not been repeated here.

Assumption Source Page

Load OG&E and The Cadmus Group, Inc. 7

Natural Gas OG&E, Ventyx and NYMEX 24

Coal OG&E

, 24

CO 2 Ventyx 24



### Reference Scenario Prices

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Coal ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

Natural Gas ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

CO 2 ($/tonne)

- - - - - - - - - -



### Ventyx Scenario Prices

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Coal ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

Natural Gas ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

CO 2 ($/tonne) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx



### NYMEX Futures Scenario Prices

2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Coal ($/ MMBTU ) x x.xx x x .xx x x .xx x x.xx x x.xx x x .xx x x .xx x x .xx x x.xx x x.xx

Natural Gas ($/ MMBTU ) 4.70 5.07 5.37 5.68 5.97 6.20 6.43 6.60 6.83 6.92

CO 2 ($/tonne)

- - - - - - - - - -

### 201 1 Integrated Resource Plan

47

The table below explains who supplied the information for each resource option and

provides a reference for where this information is found in the IRP.

Resource Source Page

New Unit Characteristics EIA 14

Existing Unit Characteristics OG&E 13

Energy Efficiency Frontier Associates 17

Demand Response The Structure Group 18



### Descriptions of Software Tools

OG&E utilizes two software program s for production cost modeling.

GenTrader®

The GenTrader ® software provided by Power Costs, Inc. is designed to model complex

portfolios of power and fuel resources, including generators, contracts, options, and

ancillary services in great detail. Some of the functionalities include: multiple and

concurrent fuel and emission limits, multi

- stage combined

- cycle modeling, a ncillary

services like regulations and spinning reserve as well as energy limited contracts.

GenTrader® is used to simulate OG&E owned or contracted units serving OG&E’s load

PROMOD IV ®

The PROMOD IV® software provided by Ventyx is the industry

- leading F undamental

Electric Market Simulation software, incorporating extensive details in generating unit

operating characteristics, transmission grid topology and constraints, unit

commitment/operating conditions, and market system operations. PROMOD IV® is

use d to model the SPP Integrated Marketplace

The following figure describes when resources were put in service in each portfolio to

meet capacity needs.

Analysis Timeline

2012

2014 2016

2018 2020

2022 2024

2026 2028

2030



### Emission

Control Option Wind

Evaluation New Resources to Meet

Capacity Needs

### 201 1 Integrated Resource Plan

48



## Schedule J

– Transmission System Adequacy

This schedule is a description of the transmission system adequacy over the next 10

years

. SPP evaluates system adequacy and develops a transmission expansion plan to

determine what improvements are nec essary to ensure reliable transmission service

. The 20 10 STEP 11 

### describes improvements necessary for regional reliability, zonal

reliability, long

- term tariff studies due to transmission service requests and transmission

owner sponsored improvements. Inclu ded in below is a subset of the 2010 STEP that

OG&E has committed to construct.

Estimated Capital Expenditures for OG&E Committed Projects

YEAR DESCRIPTION TYPE OF UPGR A DE TYPE COST ($M)

2011 Muldrow to 3rd Street Substation Upgrade Zonal

- Sponsored 0.1

2011 Arkansas Conversion

Project Convert 69kV to 161kV Zonal

- Sponsored 26.7

2011 Rocky Point to Ardmore

69kV Reconductor Line Transmission Service /

Base Plan Funded 0.5

2011 Sunnyside to Uniroyal

138kV Substation Upgrade transmission service 0.1

2011 Dillard to Healdton Tap

138kV Substation Upgrade Transmission Service /

Base Plan Funded 0.3

2013 Crescent to Cottonwood

Creek Convert 69kV to 138kV Regional Reliability /

Base Plan Funded 4.5

2011 Bellcow Substation &

Transmission Line New Substation, New Line Zonal

- Sponsored 17.9

2011 Johnson County Project New Substation, New Lines,

New 345kV/138kV

Transformer Zonal

- Sponsored 27.6

2011 Gracemont 345kV New Substation, New

345kV/138kV Transformer Balanced Portfolio 14.7

2011 Alva 69kV Substation Upgrade Zonal

- Sponsored /

Byway Funded 0.1

2011 Wells 69kV New Capacitors Regional Reliability /

Byway Funded 0.4

2011 Little River Lake 69kV New Capacitors Regional Reliability /

Byway Funded 0.4

2011 Cushing Oil New Capacitors Base Plan 0.4

2011 Tiger Creek New Capacitors Base Plan 0.3

2012 Russett to WFEC

Russett 138kV Substation Upgrade Base Plan 0.3

2012 VBI to Adabell Substation Upgrade Base Plan 0.9

2012 Sooner to Rose Hill

345kV New Line Regional Reliability /

Base Plan Funded 57.8

11 20 10 STEP: http://www.spp.org/publications/2010_SPP_Transmission_Expansion_Plan_01

- 28

- 11.pdf

### 201 1 Integrated Resource Plan

49

YEAR DESCRIPTION TYPE OF UPGR A DE TYPE COST ($M)

2012 Sooner to Cleveland

345kV New Line Balanced Portfolio 64.8

2012 Hugo to Sunnyside

345kV New Line Transmission Service /

Base Plan Funded 187.0

2012 Sunnyside 345kV to

Sunnyside 138kV Install 2nd Bus tie

Transformer Transmission Service /

Base Plan Funded 6.8

2012 Oak Park to Johnson

161kV New Line Regional Reliability /

Base Plan Funded 3.2

2012 Johnson to Massard Convert 69kV to 161kV Regional Reliability /

Base Plan Funded 5.5

2012 Arcadia 345kV to Arcadia

138kV Install 3rd Bus tie

Transformer Transmission Service /

Byway Funded 8.5

2013 VBI Substation Upgrade Zonal

- Sponsored 0.0

2013 Fort Smith to Colony

161kv Reconductor Line Regional Reliability /

Base Plan Funded 2.5

2013 Seminole to Muskogee

345kV New Line Balanced Portfolio 179.1

2013 Canadian River 345kV New Substation Tapping

Pittsburg to Muskogee Line Regional Reliability /

Base Plan Funded 5.5

2013 Arcadia 345kV Convert to breaker and a half

configuration Highway 5.0

2014 Woodward EHV to

Stateline (Tuco) 345kV New Line and 2nd Bus tie

Transformer Balanced Portfolio 120.0

2014 Woodward EHV to

Hitchland 345kV New Double Circuit 345kV

Line High Priority / Highway

Funded 178.6

2014 Woodward EHV to

Medicine Lodge 345kV New Double Circuit 345kV

Line High Priority / Highway

Funded 134.4

2015 Cushing Project 69kV to

138kV Convert 69kV to 138kV Regional Reliability /

Byway Funded 16.0

2016 HSL East to HSL West

69kV Substation Upgrade Zonal

- Sponsored 0.3

2017 Fort Smith 500kV to

161kV Install 3rd Bus tie

Transformer Transmission Service /

Base Plan Funded 11.0

2017 VBI North 69kV Circuit 1,

Upgrade Current

Transformer Substation Upgrade Transmission Service /

Base Plan Funded 0.1

2019 Arcadia to Redbud

345kV Circuit 3 New Line Transmission Service /

Highway Funded 19.0

2019 Bryant to Memorial

138kV Substation Upgrade Transmission Service /

Byway Funded 0.3



### Transmission system expansion provides benefits to members throughout the SPP;

therefore, the cost s of all projects constructed in the SPP are shared through various

cost allocation methods, depending on the type of project.

### 201 1 Integrated Resource Plan

50



## Schedule K

– Resource Plan Assessment

This IRP assessed the need for additional resources to meet reliability, cost and price,

environmental, and other criteria established by the OCC , the State of Oklahoma, the

APSC, the Southwest Power Pool, North American Electric Reliability Council, and the

Feder al Energy Regulatory Commission. All criteria were met by all portfolios

considered in this IRP , in the base line condition. These criteria were also met in

scenarios and uncertainties which included variations in load growth, fuel prices,

emissions prices , environmental regulations, technology improvements, dema nd side

resources, and fuel supply, among others. This plan provides a comprehensive analysis

of the proposed options.



## Schedule L

– Proposed Resource Plan Analysis

This IRP demonstrates that all pro posed options meet all planning criteria as outlined in

Schedule K. The proposed action plan outlined in Schedule G best meets these criteria.

Documentation of the planning analysis and assumptions used in preparing this

analysis are described in Schedule I.

### 201 1 Integrated Resource Plan

51



## VI. APPENDI CES

Appendix A

– OG&E 2010 Load Forecast

Appendix B

– OG&E 2010 Capability Report

Appendix C

– Annual Portfolio NPVRR for Reference Scenario

Appendix D

– OGE 2011 IRP Oklahoma Collaborative Technical Conference

### 201 1 Integrated Resource Plan



# Appendix A

– OG&E 2010 Load Forecast

## Final Report



# 2010 OG&E Load Forecast

Prepared by:

OGE Research and An alysis Department

The Cadmus Group

September 14, 2010OGE Research and Analysis Department 

# Acknowledgements

Executive Sponsors

Bill Bullard

Scott Forbes

Craig Johnston Jesse Langston

Howard Motley

Max Myers



# Load Forecasters

Brian Eakin

Ken Seiden, The Cadmus Group

John Willey, The Cadmus Group



# Load Forecast Team Members

Jason Bailey

Bruce Breshears

Misty Garrett Leon Howell

Gary Marchbanks

Carri SullivanOGE Research and Analysis Department 

# Table of Contents

EXECUTIVE SUMMARY

............................................................................................................................... ........ 1

2010 E NERGY S ALES F ORECAST

............................................................................................................................... ........ 1

U NDERLYING F UNDAMENTALS

............................................................................................................................... ... 2

Energy Sector

............................................................................................................................... ........................ 2

Retail Electric Prices

............................................................................................................................... .............. 3

E NERGY S ALES F ORECAST

............................................................................................................................... ................. 4

2010 L OAD R ESPONSIBILITY P EAK D EMAND F ORECAST

......................................................................................................... 5

Weather Uncertainty....................................................................................................................

........................ 6

Historical Weather Normalized Retail Energy and Peak Demand

........................................................................ 8

ECONOMIC OUTLOOK

............................................................................................................................... ........ 11

E CONOMIC S UMMARY

............................................................................................................................... ................... 11

State of Oklahoma Economic Forecast............................................................................................................... 11

Oklahoma City Metropolitan Area

..................................................................................................................... 12

Fort Smith Metropolitan Area

............................................................................................................................ 12

E CONOMIC D RIVERS FOR E NERGY F ORECAST

..................................................................................................................... 13

LOAD RESPONSIBILITY PEAK DEMAND FORECASTING MODEL

............................................................................ 13

P EAK D EMAND F ORECASTING M ETHODOLOGY

.................................................................................................................. 13

Econometric Modeling Framework

.................................................................................................................... 13

F ORECASTING P EAK L OADS

............................................................................................................................... ............. 15

Expected Loads by Weather Probability

............................................................................................................. 16

FERC Wholesale Load Adjustments

.................................................................................................................... 17

RETAIL ENERGY MODELS

............................................................................................................................... .... 19

E CONOMETRIC M ODELING P ROCESS

............................................................................................................................... . 19

2010 E NERGY F ORECAST

............................................................................................................................... ............... 20

Retail Forecast

............................................................................................................................... ..................... 20

FERC Wholesale Load Adjustments

.................................................................................................................... 20

Energy Forecast Uncertainty

.............................................................................................................................. 23

RETAIL CUSTOMER FORECASTING MODELS

....................................................................................................... 24

R ETAIL C USTOMER M ODELING P ROCESS AND F ORECAST

..................................................................................................... 24

DATA SOURCES

............................................................................................................................... .................. 26

I NTERNAL I NFORMATION

............................................................................................................................... ................ 26

Sales and Prices

............................................................................................................................... ................... 26

Load Responsibility

............................................................................................................................... .............. 26

I NFORMATION O BTAINED FROM E XTERNAL S OURCES

.......................................................................................................... 27

Weather Data

............................................................................................................................... ...................... 27

Economic and Demographic Data

...................................................................................................................... 27

APPENDIX

............................................................................................................................... .......................... 28

C ARBON S CENARIO T ABLES

............................................................................................................................... ............. 28OGE Research and Analysis Department Page 1 

## EXECUTIVE SUMMARY

This report presents Oklahoma Gas & Electri c Services’ (OG&E) 2010 load forecasts. It

describes both peak demand and energy fo recasting models developed by OG&E and The

Cadmus Group with input from OG&E’s Load Forecasting Team.

The 2010 retail sales forecast utilized the reve nue class-based econometric modeling framework

that has been in place for over a decade. The 2010 load responsibility peak demand forecast is

based on an hourly econometric model of weat her and economic effect s on OG&E’s hourly load

responsibility series. The hourly modeling approa ch has been used since the 2000 forecast.

The load forecasting framework relies on indepe ndently produced forecasts of service area

economic and population growth, actual and normal weather data, and projections of electricity

prices for price-sensitive customer classes. The final energy and demand forecast includes

Federal Energy Regulatory Commission (FERC) jurisdictional wholesal e contracts as postmodeling adjustments.



### 2010 Energy Sales Forecast

The 2010 retail energy forecast is based on retail sector-level econometric models representing

OG&E’s Oklahoma and Arkansas service territories. Historical and forecast economic variables

(drivers) are provided by the Center for App lied Economic Research at Oklahoma State

University (OSU).

In past forecasts, Moody’s Economy.com provided economic drivers that were used to predict

energy sales in OG&E’s Arkansas service terr itory. This year, OG&E made the decision to

purchase forecasts of economic drivers for both Oklahoma and Arkansas fr om OSU. The move

from Moody’s Economy.com to OSU was made b ecause consolidating the sources for economic

drivers would simplify the load forecasting proce ss. By using a single source for economic

drivers OG&E has eliminated the need to adju st the Arkansas drivers to follow the same

assumptions as the Oklahoma drivers.

In 2007 the Oklahoma economic driver series were ad justed for structural changes in the state’s

economy. OSU’s research had revealed a “billionaire” effect that inflates the real income and

gross state product series that ar e critically important in fore casting OG&E’s energy sales.

Table 1 below, compares the growth rates of 2010 and 2009 forecast drivers. The “ex-energy”

variables, where the “billionaire” effect is removed, are compared to their unadjusted

counterparts. The comparison reveals:

• That the difference in growth rates between the ex-energy seri es and their counterpart is

still a significant factor, and is in fact increasing for several of the series compared to the

forecasts from 2009.

• Most of the 2010 drivers exhib it lower growth rates as a re sult of the recent economic

downturn.OGE Research and Analysis Department Page 2 

### Table 1. Economic Driver Growth Rate Comparison

Economic Drivers 2010 Drivers

Average

Growth Rate

2010 to 2020 2009 Drivers

Average

Growth Rate

2010 to 2020 2010 Drivers

Average

Growth Rate

2010 to 2015 2009 Drivers

Average

Growth Rate

2010 to 2015 2010 Drivers

Average

Growth Rate

2016 to 2020 2009 Drivers

Average

Growth Rate

2016 to 2020

Real Personal Income OKC 4.04% 3.83% 3.93% 3.67% 4.17% 4.06%

Real Personal Income Ex

Energy OKC 3.54% 3.36% 3.50% 3.26% 3.59% 3.52%

Difference 0.50% 0.47% 0.43% 0.41% 0.58% 0.55%

Real GSP 2.28% 3.04% 1.78% 2.74% 2.88% 3.49%

Real GSP Ex Energy 2.21% 2.60% 2.05% 2.53% 2.40% 2.70%

Difference 0.07% 0.44% -0.27% 0.20% 0.48% 0.79%



### Underlying Fundamentals

Over the last decade the Oklahoma economy has outperformed the nation during recessions due

to robust growth in the energy se ctor. Prudent lending practices a nd limited direct erosion of the

consumer balance sheet allowed Oklahoma to en ter the most recent recession later than the

nation. The effects of the recession in Arkans as have been dampened due to the limited

influence of low energy prices and employe rs delaying plans to outsource manufacturing

operations. Both states have fared better than the nation, and are poised to recover when energy

prices increase and the rest of the country returns to positive economic conditions.

Energy Sector

The OSU forecast drivers anticipa te the price of oil hovering ar ound $70/barrel, and natural gas

around $5 per million btu’s in 2010. These prices are close to the threshold where energy

switches from providing a net boost to restricting growth in the state economy. While the price

of oil is beginning to increase, it has been around $70 a barrel for most of the year, and the price

of natural gas has been below $4.50 per million btu’s. The Energy Information Administration

(EIA) forecast 1 

### suggests that natural gas will remain below $5 per million btu’s through the end

of the year, with oil climbing to an averag e of $80 a barrel in the fourth quarter.

After experiencing considerable decline in activ ity in 2009, the energy sector in Oklahoma is

seeing a considerable recovery in 2010. Since hitting a low of $1.84 per mmBtu on September 4th,

2009, the price of natural gas has rebounded to ne arly $4.50 per mmBtu in 2010. This has allowed

for the continued development of conventional oil and natural gas wells in the Arkoma Basin in

western Oklahoma along with th e Woodford Shale in southeas t Oklahoma and the Fayetteville

Shale in central Arkansas.

The recovery of the energy sector in Oklahoma will play a vital role in the overall growth of the

Oklahoma economy. As energy prices increase so will revenue collections for Oklahoma. The

gross production tax on natural gas yielded $24 milli on in July, which is $1.9 million or 8.4 percent

above the prior year. Oklahoma has made efforts to diversify the economy, but the energy sector is

still the foundation for the Oklahoma economy.

1 The Energy Information Administration: Short-term Energy Outlook,

http://www.eia.doe.gov/emeu/steo/pub/contents.htmlOGE Research and Analysis Department Page 3 

### Retail Electric Prices

The retail electric prices used in the forecast in clude the revised cost of operations along with

riders for various other projects. There are ri ders for OG&E’s Smart Grid and the OU Spirit

wind farm included in the price forecast. Additi onally, the price forecast includes the cost of

new transmission. In 2010 there wa s a fuel clause adjustment paid to customers. The fuel

adjustment offset most of the rate increase in 2010, so customers experienced a negligible

increase in price during 2010. Ho wever, the conclusion of the fuel clause adjustment at the end

of 2010 will make the effective price increase from 2010 to 2011 approximately 17 percent. A

price increase of this magnitude is responsible for the relativ ely low growth rate in 2011.

Price Elasticity of Demand

The own-price elasticity of demand for the residential sector in Ok lahoma has been re stricted to

-0.1 from 2010 to 2012 and -0.2 from 2013 to 2020. The unrestricted estimate of own-price

elasticity of demand for the residential sector in Oklahoma is -0.24. This unrestricted estimate is

relatively more elastic than the 2009 estimate of -0.05, but it remains highly inelastic when

compared to other goods. The main cause of th e disparity between the 2009 and 2010 estimates

is the use of an all-good price index instead of an energy specific price index to adjust prices for

inflation. The all-good price index more accurately reflects the effects of inflation on a

consumer’s budget and their energy consumption decisions. Own-price el asticity of demand was

restricted due to the impacts it would have on the forecast when combined with unprecedented

price increases in the short-run. The restrictions limit the effect of prices in the near-term and

allow for an increased long-term response to changes in the retail price of electricity in the

Oklahoma residential sector. The elasticity estim ates in other sectors were relatively unchanged

from the 2009 forecast, so there were no other restrictions implemented.

CO 2 

### Emission Regulations

The potential for limits on carbon dioxide emissi ons has increased the de gree of uncertainty

relating to future operating expe nses. To understand the potential effects of carbon legislation on

future energy sales and peak demand an alternativ e scenario was developed for the load forecast.

The scenario is based on the assumption that there will be limits on carbon dioxide emissions

beginning in 2012. The projected emissions regul ations will put upward pressure on energy

prices and increase overall operating expenses. This will result in an increas ed price forecast and

lower overall energy sales and peak demand. S ee the appendix for the corresponding forecast

scenario output.

Table 2. Alternative Scen ario Cost of CO2 Emissions

Year Nominal CO2 Price ($/tonne)

2012 $15.69

2013 $17.67

2014 $19.72

2015 $21.81

2016 $23.46

2017 $25.22

2018 $27.11

2019 $29.16

2020 $31.34OGE Research and Analysis Department Page 4 

### Energy Sales Forecast

The 2010 retail energy sales forecast is summarized in Table 3 below. The table also contains the

energy sales forecast adjusted for wholesale sales contracts and line losses to wholesale and retail

sales. The forecast (and 2009 actual sales) is ba sed on normal weather in both Oklahoma and

Arkansas. The underlying retail forecast is anticipated to grow at an average annual rate of 1.3%.

The energy sales forecast adjusted for wholesale sales projects aver age growth at 0.86%, with the

difference relative to retail growth due to expi ring wholesale contracts. Table 4 and Table 5

provide the annual growth rates of the retail sa les forecasts for all sectors in Oklahoma and

Arkansas, respectively. Table 6 presents the fore casted annual growth rates of the different

wholesale sales contracts. No te that by 2015, all wholesal e contracts will have expired.

Table 3. 2010 Retail and Wh olesale Energy Sales Forecasts

Year Energy Forecast

(MWh) Including

Wholesale Sales and

Line Losses Energy Growth

Rates Including

Wholesale Sales

and Line Losses Retail Energy

Forecast

(MWh) Retail Energy

Growth Rates

2009 27,800,572 24,640,489

2010 28,228,196 1.54% 24,982,578 1.39%

2011 28,413,888 0.66% 25,218,899 0.95%

2012 27,908,465 -1.78% 25,704,426 1.93%

2013 28,169,061 0.93% 26,044,153 1.32%

2014 28,387,249 0.77% 26,510,774 1.79%

2015 28,828,465 1.55% 26,945,009 1.64%

2016 29,224,415 1.37% 27,315,090 1.37%

2017 29,681,602 1.56% 27,742,408 1.56%

2018 30,013,134 1.12% 28,052,280 1.12%

2019 30,501,277 1.63% 28,508,530 1.63%

2020 30,949,656 1.47% 28,927,616 1.47%



### Table 4. 2010 Oklahoma Retail Sales Forecast Growth Rates by Sector

Year Residential Commercial Public Authority Street lighting Industrial Petroleum

2010 1.21% 1.17% 0.26% 1.96% 0.50% 0.50%

2011 0.76% 0.82% 2.17% 1.40% 0.50% 0.50%

2012 1.80% 2.89% 2.61% 1.37% 0.50% 0.50%

2013 1.38% 1.79% 1.38% 1.37% 0.50% 0.50%

2014 2.33% 2.20% 1.73% 1.38% 0.50% 0.50%

2015 1.46% 2.51% 2.19% 1.37% 0.50% 0.50%

2016 1.11% 1.95% 2.08% 1.36% 0.50% 0.50%

2017 1.18% 2.41% 2.59% 1.36% 0.50% 0.50%

2018 0.53% 1.66% 2.16% 1.35% 0.50% 0.50%

2019 1.40% 2.31% 2.59% 1.34% 0.50% 0.50%

2020 1.15% 2.06% 2.21% 1.34% 0.50% 0.50%OGE Research and Analysis Department Page 5 

### Table 5. 2010 Arkansas Retail Sales Forecast Growth Rates by Sector

Year Residential Commercial Public

Authority Street

Lighting Industrial Petroleum

2010 0.91% 1.33% 5.69% 0.73% 12.02% 5.00%

2011 0.54% 3.05% 5.59% 0.52% 0.50% 0.50%

2012 3.03% 4.51% 3.82% 0.60% 0.50% 0.50%

2013 2.02% 2.76% 3.57% 0.65% 0.50% 0.50%

2014 2.32% 3.25% 3.28% 0.66% 0.50% 0.50%

2015 2.31% 3.22% 3.45% 0.65% 0.50% 0.50%

2016 2.08% 3.06% 3.83% 0.64% 0.50% 0.50%

2017 2.04% 2.91% 4.14% 0.62% 0.50% 0.50%

2018 1.87% 2.69% 4.12% 0.61% 0.50% 0.50%

2019 1.90% 3.10% 4.11% 0.60% 0.50% 0.50%

2020 2.06% 3.52% 4.41% 0.59% 0.50% 0.50%



### Table 6. 2010 Wholesale Sales Forecast Growth Rates

Year Municipal AVEC SPA OMPA MDEA Total

2010 -11.64% -0.91% 7.11% 4.20% -2.55% -0.16%

2011 -8.43% -6.41% 1.88% 0.00% 0.00% -4.36%

2012 -79.82% -100.00% -58.21% 0.00% 0.00% -70.33%

2013 -100.00% -100.00% 0.00% -46.56% -25.26%

2014 -100.00% -66.67% -92.32%

2015 -100.00% -100.00%



### 2010 Load Responsibility Peak Demand Forecast

The 2010 load responsibility forecast relies on an hourly econometric model specification first

used for the 2000 forecast. The modeli ng framework reflects the following:

• Impact of different weekdays on hourly system load.

• Impact of different summer months on hourly system load.

• Influence of heat buil dup during heat waves.

• Impact of the combined effects of humidity and warm temperatures.

• Non-linearity in the load and temperature relationships at very high temperatures.

As has been the case for the past several years, weather-adjusted retail energy sales are the main

economic driver for the peak model.OGE Research and Analysis Department Page 6 

### Table 7 shows the actual 2009 retail load, along w ith the final load responsibility forecast,

adjusted for wholesale loads and line losses , for 2010 and beyond. The forecast is based on

average weather conditions over the past 35 years. U nderlying retail peak lo ads are anticipated to

grow at an average annual rate of 1.2% over the next decade, wh ich is slightly less than the

growth rate for retail energy sales.

Table 7. 2010 Load Responsibility Peak Demand Forecast

Year Total Load

Responsibility

Peak Demand

(MW) Forecast*

(Average

Weather) Total Load

Responsibility

Growth Rates Retail Load

Responsibility

Peak Demand

(MW) Forecast

(Average

Weather) Retail Load

Responsibility

Growth Rates

2009 5,917 5,617

2010 6,012 1.62% 5,702 1.53%

2011 6,054 0.70% 5,736 0.60%

2012 5,874 -2.98% 5,823 1.51%

2013 5,937 1.07% 5,898 1.29%

2014 5,989 0.87% 5,989 1.54%

2015 6,073 1.41% 6,073 1.41%

2016 6,123 0.82% 6,123 0.82%

2017 6,228 1.71% 6,228 1.71%

2018 6,278 0.82% 6,278 0.82%

2019 6,371 1.47% 6,371 1.47%

2020 6,456 1.33% 6,456 1.33%



### Weather Uncertainty

As is well known within the electric industry, an d especially at OG&E, peak demand and energy

sales are highly sensitive to year-to-year weathe r variations. Both can appear to decline even

with positive economic growth when a hot y ear is followed by an unusually cool year.

Conversely, if a hot year follows a cool year , energy sales and peak demand can increase even

though there may be little or no economic growth. Weather uncertainty is represented through a

Monte Carlo modeling approach where the last 20 years of actual weather are systematically

input into the energy and peak models to produce a possible outcome distribution.

OG&E’s weather-year M onte Carlo approach runs weather years 1989 to 2009 through weathersensitive energy models, along with the peak demand model, to develop a probability

distribution of possible outcome s. Figure 1 shows the 95% c onfidence interval around the

expected energy sales forecast, including wholesal e adjustments, resulting from this modeling

process. Note that the declin e in sales of approximately 300,000 MWh from 2011 to 2012 is

principally the result of the assumption by OG&E that the current AVEC wholesale contract,

which is currently set to expire on November 30 th 

### , 2011, is not renewed.OGE Research and Analysis Department Page 7 

### The 1 out of 2 years forecast line shows energy sales, including w holesale adjustments, assuming

average weather years over the 20-year period. We note this “distribution average” is not the

same as normal weather. This thinking is consis tent with research findings by Chuck Doswell of

the National Severe Storms Laboratory in Norma n, Oklahoma. Mr. Doswell suggests: “...what is

considered ‘normal’ may not . . . correspond precis ely to the average. ‘Normality’ is a matter of

definition. In order to understand what ‘normal’ means, you have to understand what was done

to the data [in the normalization process].” 2



### The 1 out of 2 years average weather line indi cates there is a 50% probability that energy sales

will reach this level or higher. The normal weather forecast is actually closer to the lower end of

the distribution, with sales a pproximately 1.2% less (355,000 MWh on average per year) than the

50% probability line. Now, consider the 1 out of 30 years forecast. This line, which is

approximately 1,460,000 MWh higher than the normal weather forecast, shows energy sales

under more extreme weather events occurring just 3% of the time. Finally, the lower bound

forecast ( 29 out of 30 year case) shows sales may fall below the normal weather forecast by

approximately 140,000 MWh if w eather is milder than normal given expected economic

performance.

2 Doswell, Chuck, “Misconceptions About what is ‘Normal’ for the Atmosphere,” 1997. 24,000,000 25,000,000 26,000,000 27,000,000 28,000,000 29,000,000 30,000,000 31,000,000 32,000,000 33,000,000

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020MWh 

### Figure 1. Energy Model Foreca st Outcomes by Weather Probability

19 out of 20 Years

1 out of 2 Years

1 out of 20 Years

Normal WeatherOGE Research and Analysis Department Page 8 

### Figure 2 shows a similar graph for the load re sponsibility distribution. The weather modeling

indicates the 95% confidence in terval has a range of approxi mately 600 MW, with the upper

bound approximately 175 MW higher than the lo ad under expected weather conditions and a

lower bound over 420 MW lower than the expect ed load. Again, the decline in demand of

approximately 200 MW from 2011 to 2012 is principa lly the result of th e expiration of AVEC’s

wholesale contract.

Historical Weather Normalized Retail Energy and Peak Demand

To put the forecasted retail sales and native load responsibility in perspective, we show weather

normalized sales and peak demand data during th e model estimation period. Figure 3 shows the

retail and combined retail and wholesale forecasts of energy sales including losses as well as the

weather normalized historical sales, while Figur e 4 shows the weather normalized historical and

forecasted native load responsibility. Both the retail energy and native load responsibility

forecasts demonstrate growth similar to that in the historical model estimation period. 5,000 5,200 5,400 5,600 5,800 6,000 6,200 6,400 6,600 6,800

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 

### Figure 2. Load Responsibilit y Outcomes by Weather Probability

1 out of 30 Years

1 out of 2 Years

3 out of 4 Years

29 out of 30 YearsOGE Research and Analysis Department Page 9 22,000,000 23,000,000 24,000,000 25,000,000 26,000,000 27,000,000 28,000,000 29,000,000 30,000,000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020MWh 

### Figure 3. Retail and Wholes ale Energy - Weather Normalized

Historical and Forecast Sales

Historical Weather Normalized Retail

Sales

Forecast of Weather Normalized Retail

Sales

Historical Weather Normalized Total Sales

+ FERC

5,000 5,200 5,400 5,600 5,800 6,000 6,200 6,400 6,600

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020MW 

### Figure 4. Native Load Re sponsibility - Weather Normalized

Historical and Forecast Loads

Historical Weather Normalized Retail Load

Responsibility

Forecast of Weather Normalized Retail Load

Responsibility

Historical Weather Normalized Total Load

Responsibility

Forecast of Weather Normalized Total Load

ResponsibilityOGE Research and Analysis Department Page 10 

### This page intentionally left blank.OGE Research and Analysis Department Page 11 

## Economic Outlook

Key Takeaways:

• The Oklahoma and Arkansas economies are sh owing signs of recovery, but energy

prices remain a significant risk

• Violence in Mexico has delayed the re location of manufacturing operations

• The development of shale plays in Oklaho ma and Arkansas are vital to the economic

future of Oklahoma and Arkansas



### Economic Summary

Over the last decade the Oklahoma economy has outperformed the nation due to robust growth

in the energy sector. Prudent lending practices and limited direct erosion of the consumer

balance sheet allowed Oklahoma to enter the recession la ter than the nation. The effects of the

recession in Arkansas have been dampened due to the limited influence of energy prices and

employers delaying plans to outsource manufacturi ng operations. Both states have fared better

than the nation, but based on historical evidence ther e is no reason to believe that either state will

recover before the nation.

State of Oklahoma Economic Forecast

The economic recovery in Okla homa is expected to be mode st in 2010 and 2011. Economic

conditions are expected to return to more fa vorable conditions in 2012 and overall economic

growth should be at a sustainable level. The biggest unknown factor for the Oklahoma economy

is the energy sector. After experiencing cons iderable decline in ac tivity in 2009, the energy

sector in Oklahoma is seeing a considerable r ecovery in 2010. Since hitting a low of $1.84 per

mmBtu on September 4th, 2009, the price of natu ral gas has rebounded to nearly $5 per mmBtu

in 2010. This has allowed for the continued de velopment of conventional oil and natural gas

wells in the Arkoma Basin in western Oklaho ma along with the shale plays in the Woodford

Shale in southeast Oklahoma and the Fayetteville Sh ale in central Arkansas. The recovery of the

energy sector in Oklahoma will play a vital role in the overall growth of the Oklahoma economy.

As energy prices increase so will revenue collecti ons for Oklahoma. Oklahoma has made efforts

to diversify the economy, but the energy sector is still the foundation for the Oklahoma

economy.OGE Research and Analysis Department Page 12 

### Oklahoma City Metropolitan Area

The Oklahoma City Metropolitan Area fared better th an the rest of the state during the recession

due to government employment and a limit e xposure to manufacturing. The growth of

Professional and Business Services in the Oklaho ma City will continue to provide high paying

jobs that will allow real personal income in Okla homa City to grow faster than the state as a

whole. Another major factor in the economic future of Oklahoma C ity is the energy sector. The

only Fortune 500 companies 3 

### in Oklahoma City are in the Oil and Gas industry and are

dependent upon favorable energy prices to contin ue expanding. As energy prices rebound in the

coming years the Oklahoma City economy will experience sustainable growth.

Fort Smith Metropolitan Area

The future of manufacturing in the Ft. Smith Metropolitan Area is uncertain but better than

previous expectations. Violen ce in Mexico has caused major manufacturers in Ft. Smith to

cancel or postpone their pl ans to relocate their op erations to Mexico. Similar to the Oklahoma

economy, the economic recovery in Ft. Smith is expected to be modest in 2010 and 2011.

Economic conditions are expected to return to su stainable levels in 2012, but a decrease in the

violence in Mexico could prompt manufacturers to leave the Ft. Sm ith area. Further erosion of

the manufacturing base in Ft. Smith would offset any economic gains dur ing the recovery in

2010 and 2011.

3 “Fortune 500 2010: Oklahoma” (http://money.cnn.com/ma gazines/fortune/fortune500/2010/states/OK.html) $0.00 $2.00 $4.00 $6.00 $8.00 $10.00 $12.00

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 

### Figure 5. Natural Gas Price Forecasts

Market Risk Committee Forecast Ventyx Forecast

‐ With CarbonOGE Research and Analysis Department Page 13 

### Economic Drivers for Energy Forecast

The 2010 Economic Forecast calls for slowed economic growth in Oklahoma and Ft. Smith over

the next five years relative to the previous decade. The economic drivers for Ft. Smith show

higher growth rates over the next five years in comparison to the previous decade due to

relatively poor economic conditions during the pr evious decade. The growth rates for 2016 to

2020 are close to the long-term expectations for each economic driver.

Table 8. Economic Driv ers’ Growth Rates, 2010 Forecast

Economic Drivers and Models Average Economic Driver Annual Growth

Rates

1999-2009 2010-2015 2016-2020

Arkansas

Street lighting: Ft. Smith Population 0.9% 0.9% 0.8%

Residential: Real Personal Income 2.5% 2.6% 3.2%

Residential: Non-Ag Empl oyment 0.5% 1.5% 1.2%

Commercial: Real Personal Income 2.5% 2.6% 3.2%

Commercial: Retail and Business Services

Employment 0.4% 1.4% 0.6%

Public Authority: Real GMP 2.5% 2.9% 3.0%

Oklahoma

Street Lighting: OKC Population 1.1% 1.1% 1.1%

Residential: OKC Real Persona l Income* 3.6% 3.2% 3.6%

Commercial: OKC Real Personal Income* 3.6% 3.2% 3.6%

Public Authority: Real Okla homa GSP* 1.8% 1.7% 2.4%



## Load Responsibility Peak Demand Forecasting Model

Key Takeaways:

• Total peak load responsibility increase s by an average of 0.8% per year

• The expected peak load in 2020 is 6,456 MW

This section describes the 2010 load responsibilit y peak demand forecasting model. The forecast

follows a discussion of the basic methodology and related hourly econometric framework.



### Peak Demand Forecasting Methodology

Econometric Modeling Framework

The econometric modeling framework has been in place at OG&E since 2000. The modeling

structure consists of 24 separate hourly equatio ns, one for each hour of the day, with separate

intercept and slope coefficients in the various models. The hourly equations are estimated over

the May through September period.OGE Research and Analysis Department Page 14 

### The dependent variable is OG&E’s normalized load responsibility, less the fixed 25 MW

Oklahoma Municipal Power Authority (OMPA) Power Sales Agreement (PSA) load, and

includes line losses. Key inde pendent variables include:

• Cooling degree hours, base 76. This cooling degree hour variable is calculated in a

manner similar to cooling degree days and e ffectively represents temperature impacts

when temperatures exceed 76 degrees.

• A second temperature variable, defined as temperature—102 ° , which addresses the

“topping off” effect in which there is a reduction in the rate of load increases at very high

temperatures.

• National Oceanic and Atmospheric Administra tion’s (NOAA) misery index reflecting the

combined effects of humidity and warm temper atures. The misery bu ild-up or duration of

the misery index is captured th rough the weighted average of past hourly values of the

misery index. 4



### • Wind speed.

• School end date in May and start-up in August.

• Economic growth as reflected through weat her-adjusted retail energy sales, where

weather is effectively removed from the energy series such th at the resulting retail total

represents the aggregate impact of economic conditions on the OG&E system. The sales

are also normalized by the number of days in each month.

Other variables in the hourly models include bi nary (dummy) variables representing different

days of the week and different months within th e year, which interact with the weather variables

in most of the hourly equations.

Relevant weather stations are shown belo w in Table 9, along with the OG&E population

estimates from the 2000 census used to weigh data from each station:

Table 9. Weather Station Weights

Weather Station Population in OG&E

Territory Weight (% of OG&E

population)

Oklahoma City (Will Rogers) 1,215,619 63.4%

Fort Smith 285,644 14.9%

Guthrie 154,327 8.0%

Stillwater 153,029 8.0%

Muskogee 109,834 5.7%

4 The lag structure is designed to pick up the effects of a heat wave lasting a few days or more. More

electricity is demanded later (vs. earlier) in a heat wave, ev en when temperatures decline slightly. The implication is

that “design temperature” is not suffi cient for peak forecasting purposes. Th e temperature of the building is the

result of the accumulated outdoor temperatures, less the impact of the HVAC system . The weighted average is

capable of capturing the effects of both duration and nigh ttime cooling since high daytime temperatures and lower

nighttime temperatures are reflected in the average.OGE Research and Analysis Department Page 15 

### Forecasting Peak Loads

The peak demand forecast is generated via a probab ilistic approach by using the last 20 available

years of weather data, rather than a single year or an average of weather years. This Monte Carlo

approach essentially runs all weather years from 1989 to 2009 through the peak demand model,

while alternating the weather year “starting da y” seven times, so extreme weekday (weekend)

weather event probability is treated directly in the simulations. For exam ple, the most extreme

heat wave in the past might have begun on a Thursday and topped out on a Sunday. Since loads

are much lower on weekends, the heat wave would not have led to a system peak for the year.

However, the extreme weather did occur and might indeed have led to a peak event if it began on

a Monday and ended on a Thursday. This is why th e starting day for historic al weather from past

years must be translated into seven distinct possible outcomes.

This results in a matrix of 20 weather years by se ven days, or a total of 140 simulations given the

historical hourly weather data available to OG&E.

The process for constructing the pe ak demand forecast is as follows:

• Obtain a range of weather-feasible load forecasts for each year over the forecast horizon

(2010–2020) by multiplying the regression mode l coefficients by the corresponding

values of weather-related vari ables. As described above, th is step generates 140 weatherfeasible forecasts.

• Rank order these 140 annual load forecasts from highest to lowest, and assign

probabilities to the occurrence of each fo recast under the assumption of a uniform

distribution (i.e., each weather has an equal chance of occurrence).

All of the highest values (peaks) in the resu lting forecast distribution occur between 3:00 p.m.

and 7:00 p.m. (Central Daylight Time), w ith the majority occurring at 5:00 p.m.

Table 10 illustrates mapping between event o ccurrence and the occurre nce probability. The

median load projections come from the 50 th 

### percentile of the distributi on. This means that half of

the time, the peak load would be expected to exceed this level; and half of the time, the peak load

would be below this level. In other words we w ould expect to hit this level at least once over a

two-year period, so we call this the 1 out of 2 year case.

Table 10. Probability Assignments

Event Occurrence Occurrence Probability

1 out of 30 years 3%

1 out of 10 years 10%

1 out of 4 years 25%

1 out of 2 years 50%

3 out of 4 years 75%

9 out of 10 years 90%

29 out of 30 years 97%OGE Research and Analysis Department Page 16 

### Consider now the 10 th 

### percentile of the distribution. There is a 10% chance loads will be at this

level or higher in any future year, which we interpret as a 1 out of 10 year event. On the other

side of the distribution, cons ider row 126 out of 140 (or the 90 th 

### percentile). There is a 90%

chance loads will be at this level or higher in any future year, which we interpret as a 9 out of 10

year event.

Expected Loads by Weather Probability

Table 11 and Figure 6 summarize the peak load m odel forecasts with a 95% confidence interval

around potential weather events, assuming no cha nges in the expected economic outlook. These

estimates include wholesale loads and the assump tion of expiring wholesale contracts. Following

the probability assignments in Table 10, the interp retation of these results is as follows. The 1 out

of 2 years or “expected” forecast shows the peak demand level given the 50 th 

### percentile of the

load forecast distribution, using a ll available historical weather data . In this case, there is a 50%

probability the peak load will reach this load level or higher.

Considering the 1 out of 10 years forecast, which is approximately 130 MW higher than the 1 out

of 2 years case, shows the estimated peak demand under a more extreme weather event that

occurs just 10% of the time. Pu t differently, over a 10-year pl anning horizon, it is likely that

OG&E will hit a summer peak consistent with the 1 out of 10 years forecast at least once. The

key area of uncertainty is in which year this event will occur.

Table 11. Peak Demand Model Forecasts by Weather Probability

Year 1 out of

30 Years 1 out of

10 Years 1 out of

4 Years 1 out of

2 Years 3 out of

4 Years 9 out of

10 Years 29 out

of 30

Years

2010 6,176 6,142 6,099 6,012 5,811 5,674 5,623

2011 6,217 6,183 6,141 6,054 5,854 5,715 5,665

2012 6,039 6,005 5,963 5,874 5,676 5,535 5,486

2013 6,102 6,068 6,025 5,937 5,739 5,597 5,548

2014 6,156 6,121 6,079 5,989 5,794 5,649 5,601

2015 6,236 6,201 6,159 6,073 5,869 5,733 5,681

2016 6,289 6,255 6,213 6,123 5,927 5,783 5,735

2017 6,392 6,358 6,315 6,228 6,026 5,887 5,837

2018 6,443 6,409 6,366 6,278 6,078 5,938 5,887

2019 6,538 6,504 6,461 6,371 6,175 6,030 5,982

2020 6,623 6,589 6,546 6,456 6,259 6,115 6,067OGE Research and Analysis Department Page 17 

### It is possible—even likely— that weather conditi ons will vary markedly from one year to the

next. For example, the weather-year forecast simulations reveal 2004 weather was the fourth

coolest of the 20 weather years, 2007 was in the middle of the range, and 2006 weather was the

fourth hottest. Dramatic weather condition chan ges, not economic growth, are responsible for

year-to-year differences. Overall, the 95% c onfidence interval associated with weather

conditions represents a signifi cant source of risk responsible for approximately 630 MW of

potential peak load variability.

FERC Wholesale Load Adjustments

FERC wholesale load adjustments are conducted in two steps based on known and verifiable

events. First, the OMPA wholesale load Power Sa les Agreement (PSA) contract is added to the

normalized load responsibility forecast from the model. Second, expiring c ontracts are subtracted

to obtain final 2010 Load Responsibility forecasts. Table 12 includes the e xpected dates that all

existing wholesale sales contracts will end assuming customers find alternate suppliers. 5,000 5,200 5,400 5,600 5,800 6,000 6,200 6,400 6,600 6,800

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020MW 

### Figure 6. Peak Demand Mode l Forecasts by Weather Probability

1 out of 30

Years

1 out of 10

Years

1 out of 4

Years

1 out of 2

YearsOGEResearchandAnalysisDepartmentPage18Table 12. 2010 Load Responsibility ForecastDemand (MW)200920102011201220132014201520162017201820192020FERC Load (without losses)MUNICIPAL1211.4 6.7 8.8 1.6 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0COOPERATIVE13201.8 216.4 221.2 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0SPA1418.0 17.2 17.5 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0OMPA PSA1525.0 25.0 25.0 25.0 25.0 0.00.0 0.0 0.0 0.0 0.0 0.0MDEA1620.0 20.0 20.0 20.0 10.7 0.00.0 0.0 0.0 0.0 0.0 0.0Total FERC Load (w/o losses) 276.2 285.3 292.5 46.6 35.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0Losses (Loss factor = 0.0867)23.9 24.7 25.4 4.0 3.1 0.00.0 0.0 0.0 0.0 0.0 0.0Total FERC Load (lossesadded)300.1 310.0 317.9 50.7 38.8 0.00.0 0.0 0.0 0.0 0.0 0.0Percentage Change in TotalFERC Load3.3% 2.6% -84.1% -23.5% -100.0%0.7% 0.7% 0.7% 0.7% 0.7% -100.0%Total Retail Load (with losses) 5,617 5,702 5,736 5,823 5,898 5,989 6,073 6,123 6,228 6,278 6,371 6,456Percentage Change in Total RetailLoad1.5% 0.6% 1.5% 1.3% 1.5% 1.4% 0.8% 1.7% 0.8% 1.5% 1.3%Load Responsibility (withlosses)Load Responsibility = TotalRetail Load + FERC, LossesAdded (includes curtailable load)*5,917 6,012 6,054 5,874 5,937 5,9896,073 6,123 6,228 6,278 6,371 6,456Percentage Change in LoadResponsibility1.6% 0.7% -3.0% 1.1% 0.9% 1.4%0.8% 1.7% 0.8% 1.5% 1.3%Load FactorLoad Responsibility = TotalRetail Load + FERC, LossesAdded*5,917 6,012 6,054 5,874 5,937 5,9896,073 6,123 6,228 6,278 6,371 6,456Total Retail Sales + FERC,Losses Added27,800,572 28,228,196 28,413,88827,908,465 28,169,061 28,387,24928,828,465 29,224,415 29,681,60230,013,134 30,501,277 30,949,656Load Factor 53.64% 53.60% 53.57% 54.24% 54.16%54.11% 54.19% 54.48% 54.41% 54.57% 54.65% 54.73%12Watonga contract can expire on February 28, 2012, Paris contract can expire on May 31, 2012, Orlando contract can expire on October 31, 2012 and the Gearycontract expired on April 30, 201013AVEC contract can expire on November 30, 201114Paris contract can expire on May 31, 2012 and Vance contract can expire on May 31, 201215OMPA PSA contract terminates on December 31, 2013 and is removed from the forecast at that time due to the absence of an Evergreen clause in the contract16MDEA contract 2 can expire on December 31, 2012and MDEA contract 1 can expire on April 30, 2014OGE Research and Analysis Department Page 19 

## Retail Energy Models

This section describes the methodology and results associated with sales equations estimates by

state and revenue class.

Key Takeaways:

• Total retail energy increases by an average of 1.47% per year

• Total retail energy for 2020 is expected to be 28,927,616 MWh



### Econometric Modeling Process

The monthly energy consumption analysis for each market segment follows a four-step process:

Step 1. Review 2009 forecast results to determin e which segments require the most

attention to alternative model specificati ons and visual inspect ion of each sales

series to identify sudden changes in usag e that might require dummy variables.

Step 2. Generate initial estimates us ing 2009 model specification.

Step 3. Inspect goodness-of-fit and othe r important statistics (e.g., R-squared, t-statistics,

multicollinearity statistics); visual inspecti on of actual versus predicted values of

the dependent variable over the historical period.

Step 4. Repeat steps 1 through 3 as needed un til a final specification is generated.

Between 10 and 50 models were estimated for each segment. The final model was not always the

one with the “best fit.” The overriding selecti on criterion was the m odel providing the best

forecast. For example, if a model with an R-squa re of 0.95 had a larger er ror in the out-of-sample

period than an alternative mode l with an R-square of 0.93, the latter model was selected. Table

13 and Table 14 illustrate the final model variables used for Oklahoma and Arkansas, respectively.

Table 13. Oklahoma Energy Model Drivers, 2010

Economic Drivers

Other Drivers Oklahoma Economic Outlook

Residential OKC Real Personal Income^ Real Residential electric price,

Heating-Degree Days (HDD),

Cooling-Degree Days (CDD)

Commercial OKC Real Personal Income^ Real Commercial electric price,

HDD, CDD

Public Authority Real GSP^ Real Public Authority electric price,

HDD, CDD

Street lighting OKC Population Free Street Lighting Service Variable

* Some models also have monthly-spec ific intercept and interaction terms.

^ Adjusted using definitions from OSUOGE Research and Analysis Department Page 20 

### Table 14. Arkansas Energy Model Drivers, 2010

Economic Drivers

Other Drivers Oklahoma Economic

Outlook

Residential Ft. Smith Real Personal

Income Real Residential electric price, HDD,

CDD

Commercial Ft. Smith Real Personal

Income, Ft. Smith Retail and

Business Services

Employment Real Commercial electric price, HDD,

CDD

Public Authority Real GSP Real Public Authority electric price,

HDD, CDD

Street lighting Ft. Smith Population

* Some models also have monthly-spec ific intercept and interaction terms.



### 2010 Energy Forecast

Retail Forecast

Table 15 summarizes the 2010 retail energy forecast (excluding line losses) by state and for the

company as a whole. Weather-normalized annual retail sales are expected to grow from 24,640

GWh in 2009 to 28,927 GWh in 2020, which translates into a 17.4% increase over OG&E’s

planning horizon, or an aver age annual increase of 1.47%.

Projected growth rates associated with these data are comparable to thos e observed over the last

decade. Weather-normalized sales grew by a pproximately 1.8% annually from 1997 through

2007. Average annual growth is projected to be lower from 2010 to 2014 (1.47%), consistent

with economic growth rates noted in the Economic Outlook section of this report. Average

annual sales growth in the last half of the forecast, the 2015–2 020 period, will be higher (1.46%),

again consistent with economic dr iver growth rate projections.

FERC Wholesale Load Adjustments

In 2010 OG&E and Cadmus were jointly respons ible for producing the forecasts of FERC

wholesale sales. OG&E provided Cadmus with hist orical wholesale sales data and the expiration

dates for current FERC wholesale contracts. Using an econometric forecasting approach similar

to what was used for the retail energy forecast models, Cadmus produced separate forecasts of

wholesale sales for all of the w holesale contracts. Out of model adjustments were then made to

those forecasts to remove sales of expiring c ontracts from the overall wholesale forecast. Table

16 combines the forecasts of wholesale sales w ith the retail energy forecast from Table 15,

yielding the final 2010 tota l energy sales forecast.OGE Research and Analysis Department Page 21 

### Table 15. 2010 Retail Energy Forecast (MWh)

Year Residential Commercial Public

Authority Street

lighting Industrial Petroleum TotalArkansas 2009 722,794 715,816 135,578 8,891 981,796 10,304 2,575,178

2010 729,347 725,335 143,298 8,956 1,099,760 10,819 2,717,515

2011 733,282 747,491 151,308 9,003 1,105,259 10,873 2,757,216

2012 755,490 781,209 157,087 9,058 1,110,785 10,927 2,824,556

2013 770,757 802,772 162,693 9,116 1,116,339 10,982 2,872,660

2014 788,664 828,890 168,031 9,176 1,121,921 11,037 2,927,719

2015 806,894 855,544 173,834 9,236 1,127,531 11,092 2,984,130

2016 823,659 881,711 180,484 9,295 1,133,168 11,148 3,039,464

2017 840,466 907,350 187,948 9,353 1,138,834 11,203 3,095,154

2018 856,214 931,770 195,692 9,410 1,144,528 11,259 3,148,874

2019 872,521 960,657 203,729 9,466 1,150,251 11,316 3,207,940

2020 890,534 994,432 212,714 9,522 1,156,002 11,372 3,274,576Oklahoma 2009 8,060,751 5,727,776 2,735,363 53,892 2,574,462 2,913,067 22,065,310

2010 8,157,927 5,794,732 2,742,487 54,950 2,587,334 2,927,633 22,265,063

2011 8,219,551 5,841,987 2,801,885 55,718 2,600,271 2,942,271 22,461,683

2012 8,367,433 6,010,762 2,874,937 56,483 2,613,272 2,956,982 22,879,870

2013 8,483,288 6,118,123 2,914,718 57,258 2,626,339 2,971,767 23,171,493

2014 8,681,224 6,252,563 2,965,126 58,046 2,639,470 2,986,626 23,583,055

2015 8,808,362 6,409,518 3,029,932 58,841 2,652,668 3,001,559 23,960,879

2016 8,906,022 6,534,393 3,093,069 59,644 2,665,931 3,016,567 24,275,626

2017 9,010,788 6,691,919 3,173,185 60,452 2,679,261 3,031,650 24,647,254

2018 9,058,135 6,802,889 3,241,648 61,269 2,692,657 3,046,808 24,903,406

2019 9,184,894 6,959,772 3,325,669 62,093 2,706,120 3,062,042 25,300,590

2020 9,290,742 7,103,290 3,399,081 62,925 2,719,651 3,077,352 25,653,040Total OG&E 2009 8,783,545 6,443,591 2,870,941 62,783 3,556,258 2,923,371 24,640,489

2010 8,887,273 6,520,068 2,885,784 63,907 3,687,094 2,938,452 24,982,578

2011 8,952,833 6,589,478 2,953,193 64,721 3,705,530 2,953,144 25,218,899

2012 9,122,924 6,791,970 3,032,024 65,541 3,724,058 2,967,910 25,704,426

2013 9,254,045 6,920,895 3,077,411 66,374 3,742,678 2,982,749 26,044,153

2014 9,469,888 7,081,453 3,133,157 67,222 3,761,391 2,997,663 26,510,774

2015 9,615,256 7,265,062 3,203,766 68,076 3,780,198 3,012,651 26,945,009

2016 9,729,681 7,416,105 3,273,553 68,938 3,799,099 3,027,714 27,315,090

2017 9,851,253 7,599,269 3,361,133 69,805 3,818,095 3,042,853 27,742,408

2018 9,914,349 7,734,659 3,437,341 70,678 3,837,185 3,058,067 28,052,280

2019 10,057,415 7,920,430 3,529,398 71,559 3,856,371 3,073,358 28,508,530

2020 10,181,275 8,097,721 3,611,795 72,447 3,875,653 3,088,724 28,927,616OGEResearchandAnalysisDepartmentPage22Table 16. Energy Forecast Accounting for Changes in Wholesale LoadEnergy(MWH)200920102011201220132014201520162017201820192020FERC Sales (without losses)MUNICIPAL1740,183 35,505 32,511 6,562 0 0 0 0 0 0 0 0AVEC18896,336 947,936 886,737 0 0 0 0 0 0 0 0 0SPA1971,232 76,298 77,731 32,487 0 0 0 0 0 0 0 0OMPA20210,182 219,000 219,000 219,000 219,000 0 0 0 0 0 0 0MDEA21125,849 122,640 122,640 122,640 65,533 21,844 0 0 0 0 0 0Total FERC Sales1,343,783 1,401,380 1,338,619 380,689284,533 21,844 0 0 0 0 0 0Growth Rate in FERC sales 4.3% -4.5% -71.6% -25.3% -92.3% -100.0% 0.5%0.0% 0.0% 0.0% 0.0%Retail Sales (without losses)Residential 8,783,545 8,887,273 8,952,833 9,122,924 9,254,0459,469,888 9,615,256 9,729,681 9,851,253 9,914,349 10,057,415 10,181,275Commercial 6,443,591 6,520,068 6,589,478 6,791,970 6,920,8957,081,453 7,265,062 7,416,105 7,599,269 7,734,659 7,920,430 8,097,721Industrial 3,556,258 3,687,094 3,705,5303,724,058 3,742,678 3,761,3913,780,198 3,799,099 3,818,0953,837,185 3,856,371 3,875,653Industrial Petroleum 2,923,371 2,938,4522,953,144 2,967,910 2,982,7492,997,663 3,012,651 3,027,7143,042,853 3,058,067 3,073,358 3,088,724Total Industrial 6,479,629 6,625,546 6,658,674 6,691,967 6,725,427 6,759,054 6,792,849 6,826,8146,860,948 6,895,252 6,929,7296,964,377Public Authority and Street Lighting2,933,724 2,949,691 3,017,9143,097,565 3,143,785 3,200,3793,271,842 3,342,491 3,430,9383,508,019 3,600,956 3,684,242Total Retail Sales 24,640,489 24,982,57825,218,899 25,704,426 26,044,153 26,510,774 26,945,009 27,315,090 27,742,408 28,052,280 28,508,530 28,927,616Growth Rate in Retail Sales 1.4% 0.9% 1.9%1.3% 1.8% 1.6% 1.4% 1.6% 1.1% 1.6% 1.5%Total MWH Sales (with losses)Total Retail Sales + FERC 25,984,27126,383,958 26,557,518 26,085,11526,328,686 26,532,619 26,945,009 27,315,090 27,742,408 28,052,280 28,508,530 28,927,616Losses221,816,301 1,844,239 1,856,3701,823,350 1,840,375 1,854,6301,883,456 1,909,325 1,939,1941,960,854 1,992,746 2,022,040Total Retail Sales + FERC, LossesAdded27,800,572 28,228,196 28,413,88827,908,465 28,169,061 28,387,24928,828,465 29,224,415 29,681,60230,013,134 30,501,277 30,949,656Growth Rate in Total Sales 1.5% 0.7% -1.8% 0.9% 0.8% 1.6% 1.4% 1.6% 1.1% 1.6% 1.5%17Watonga contract can expire on February 28, 2012, Paris Contract can expire on May 31, 2012, Orlando contract can expire on October 31, 2012 and the Gearycontract expired on April 30, 201018AVEC contract can expire on November 30, 201119Paris contract can expire on May 31, 2012 and Vance contract can expire on May 31, 201220OMPA PSA contract terminates onDecember 31, 2013 and is removed from forecast atthat time due to the absence of an Evergreenclause in the contract.21MDEA contract 2 can expire on December 31, 2013and MDEA contract 1 can expire on April 30, 201422The energy loss factor is 0.0699 for 2009 and beyondOGE Research and Analysis Department Page 23 

### Energy Forecast Uncertainty

As with the load responsibility peak demand for ecast, weather uncertainty in the energy models

is represented through a Monte Ca rlo modeling approach where the last three decades of weather

are systematically inputted into the various energy models to produce a distribution of possible

sales outcomes.

The weather-year Monte Carlo approach essent ially runs all weather years from 1989 to 2009

through the weather-sensitive energy models and the peak demand model to develop a

probability distribution of possible outcomes. Fi gure 7 shows the results directly from this

modeling process for energy sales and includes FERC adjustments.

The 1 out of 2 years average weather line indi cates there is a 50% probability that energy sales

will reach this level or higher. The normal weather forecast is actually closer to the lower end of

the distribution, with sales approximately 1.2% less (355,000 MWh on av erage per year) the

50% probability line.

Now, consider the 1 out of 30 years forecast. This line, which is approximately 1,460,000 MWh

higher than the normal weather forecast, shows energy sales under more extreme weather events

occurring just 3% of the time. Finally, the lower bound forecast ( 29 out of 30 year case) shows

sales may fall below the normal weather forecas t by approximately 140,000 MWh if weather is

milder than normal given expected economic performance. 24,000,000 25,000,000 26,000,000 27,000,000 28,000,000 29,000,000 30,000,000 31,000,000 32,000,000 33,000,000

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020MWh 

### Figure 7. Energy Model Foreca st Outcomes by Weather Probability

19 out of 20 Years

1 out of 2 Years

1 out of 20 Years

Normal WeatherOGE Research and Analysis Department Page 24 

## Retail Customer Forecasting Models

This section describes the methodology and results associated with state and revenue class

customer forecasting. These models were first es timated in 2005 and follow the general approach

for energy sales outlined in th is report’s prev ious section.

Key Takeaways:

• Total retail customers increases by an average of 1% per year

• The forecasted total number of retail customers in 2020 is 863,022



### Retail Customer Modeling Process and Forecast

Approximately five to ten models were estimated for each segment, with 2009 data held as an

“out-of-sample” forecasting test period. During the initial model specifi cation phase, attempts

were made at specifying models with a variety of different economic drivers. Table 17 and

Table 18 illustrate the final model variables used for the Oklahoma and Arkansas retail customer

forecasts, respectively.

Table 17. Oklahoma Customer Model Drivers, 2010

Economic Drivers

Oklahoma Economic Outlook

Residential Population of Oklahoma City

Commercial Population of Oklahoma City

Industrial Employment in the Oklahoma City

Manufacturing Sector

Petroleum Stepped Nominal Natural Gas Price

Forecast

Public Authority Population of Oklahoma City

Street lighting Population of Oklahoma City and Free

Street Lighting Service Variable



### Table 18. Arkansas Customer Model Drivers, 2010

Economic Drivers

Oklahoma Economic Outlook

Residential Population of Ft. Smith

Commercial Population of Ft. Smith

Industrial Employment in the Ft. Smith

Manufacturing Sector

Petroleum Stepped Nominal Natural Gas Price

Forecast

Public Authority Population of Ft. Smith

Street lighting Population of Ft. Smith



### Table 19 summarizes the 2010 annual retail customer forecast by sector and state, and for the

company as a whole.OGE Research and Analysis Department Page 25 

### Table 19. 2010 Retail Customer Forecast

Residential Commercial Public

Authority Street

Lighting Industrial Petroleum TotalArkansas 2009 53,965 8,732 1,435 26 382 55 64,595

2010 54,130 8,844 1,462 26 367 57 64,886

2011 54,295 8,959 1,497 27 368 58 65,203

2012 54,524 9,067 1,532 27 372 59 65,580

2013 54,801 9,183 1,568 27 375 59 66,013

2014 55,100 9,302 1,604 27 377 59 66,469

2015 55,405 9,419 1,641 27 378 59 66,930

2016 55,712 9,536 1,679 27 378 59 67,391

2017 56,017 9,650 1,718 28 378 59 67,848

2018 56,319 9,762 1,757 28 378 59 68,303

2019 56,618 9,873 1,797 28 378 59 68,754

2020 56,916 9,984 1,838 28 378 59 69,203Oklahoma 2009 609,127 76,666 14,292 224 2,685 6,367 709,361

2010 614,905 77,620 14,634 225 2,693 6,349 716,425

2011 621,548 78,639 15,021 226 2,705 6,304 724,444

2012 627,811 79,487 15,396 227 2,709 6,262 731,892

2013 634,082 80,336 15,776 228 2,708 6,231 739,361

2014 640,428 81,199 16,161 228 2,704 6,212 746,933

2015 646,819 82,071 16,551 229 2,699 6,204 754,573

2016 653,269 82,950 16,944 230 2,694 6,200 762,287

2017 659,761 83,836 17,340 231 2,689 6,200 770,057

2018 666,319 84,731 17,741 231 2,684 6,200 777,906

2019 672,933 85,634 18,144 232 2,679 6,201 785,824

2020 679,614 86,546 18,552 233 2,674 6,201 793,819Total OG&E 2009 663,092 85,398 15,727 250 3,067 6,422 773,956

2010 669,035 86,464 16,095 252 3,059 6,406 781,311

2011 675,844 87,598 16,518 253 3,073 6,362 789,647

2012 682,335 88,554 16,928 254 3,081 6,321 797,472

2013 688,883 89,519 17,343 255 3,083 6,290 805,373

2014 695,528 90,501 17,766 255 3,081 6,271 813,402

2015 702,225 91,490 18,192 256 3,077 6,262 821,503

2016 708,981 92,486 18,624 257 3,072 6,258 829,678

2017 715,777 93,486 19,058 258 3,067 6,259 837,905

2018 722,637 94,494 19,498 259 3,062 6,259 846,209

2019 729,551 95,508 19,941 260 3,057 6,260 854,578

2020 736,529 96,530 20,390 261 3,052 6,259 863,022OGE Research and Analysis Department Page 26 

## Data Sources

OG&E’s service territory encompasses approximat ely half of Oklahoma and a small area in

western Arkansas, including and su rrounding Ft. Smith. Historical data sources used to estimate

the econometric equations and prep are the 2009 forecast are divided into the following categories:

• OG&E company data (energy sa les, revenue, and load res ponsibility peak demand);

• Constructed variables for the mode ls (usually binary variables);

• Weather information; and

• Economic and demographic data from the Center for Applied Economic Research at

Oklahoma State University

This section describes each of these categories a nd the types of variables used in the econometric

models.



### Internal Information

Sales and Prices

OG&E’s Accounting Department provides sales (M Wh), revenue, and customer data by revenue

class. This information is recorded in the monthly energy sales report for both Oklahoma and

Arkansas jurisdictions. The monthly energy sales re port contains information from the 1970s to the

present. The six revenue classes are:

• Residential • Commercial

• Industrial • Industrial-Petroleum

• Public Authority • Street Lighting

Monthly residential, commercial, industrial, industrial-petroleum, public authority, and street

lighting sales data are modeled se parately by state. In the econometric models with statistically

significant electric price variab les, these variables are defined as “average” prices (energy

revenues divided by energy sales).

Load Responsibility

The peak load forecasts are obtained based on hi storical “Normalized Load Responsibility” data

(defined as the System Load minus OMPA Total Load plus OMPA PSA 5 

### plus Load Curtailment

plus real-time pricing (RTP) induced self-generat ion). The normalized load responsibility series

was further adjusted for peak demand modeli ng purposes by subtracting variable OMPA PSA

loads and forecasting these direct ly as wholesale FERC loads.

3 OMPA PSA contract terminates 12/31/2013 and is removed from forecast at that time due to the absence of an

Evergreen clause in the contract.OGE Research and Analysis Department Page 27 

### Information Obtained from External Sources

Weather Data

OG&E obtained the following information from the Depart ment of Commerce, NOAA:

• Cooling-degree days (CDD).

• Heating-degree days (HDD).

• A variety of hourly weather indicators, in cluding temperature, humidity, dew point,

precipitation, wind sp eed, and cloud cover.

NOAA’s definition of HDD is 65° minus the averag e of the high and low temperatures of the

day (or zero if the average of the high and low temperatures is greater than 65°). The definition

of CDD is the average of the high and low temp eratures of the day minus 65° (or zero if the

average of the high and low temperatures of the day is less than 65°). HDD and CDD for

Ft. Smith and Oklahoma City have been used in weather-sensitive sales forecasting equations.

Hourly weather data from these stations, and from Guthrie, Stillwater, a nd Muskogee, were used

to model and forecast peak loads.

Economic and Demographic Data

OG&E purchases economic and demographic data from Oklahoma State University. The data

include historical and forecasted time series used in the econometric models; these data include

population, real income, wages and sa laries, price deflators, various production and output series,

including industrial production, gross state pr oduct, natural gas prices, and employment.OGE Research and Analysis Department Page 28 

## Appendix

Carbon Scenario Tables

Table 3b. 2010 Retail and Wholesal e Energy Sales Forecasts with Carbon

Year Energy

Forecast

(MWh)

Including

Wholesale

Sales and Line

Losses Energy

Growth Rates

Including

Wholesale

Sales and Line

Losses Retail

Energy

Forecast

(MWh) Retail

Energy

Growth

Rates

2009 27,800,572 24,640,489

2010 28,228,196 1.54% 24,982,578 1.39%

2011 28,413,888 0.66% 25,218,899 0.95%

2012 27,775,312 -2.25% 25,579,973 1.43%

2013 27,935,414 0.58% 25,825,770 0.96%

2014 28,003,275 0.24% 26,151,887 1.26%

2015 28,379,067 1.34% 26,524,972 1.43%

2016 28,712,321 1.17% 26,836,453 1.17%

2017 29,129,122 1.45% 27,226,023 1.45%

2018 29,426,641 1.02% 27,504,105 1.02%

2019 29,880,548 1.54% 27,928,356 1.54%

2020 30,328,332 1.50% 28,346,884 1.50%



### Table 4b. 2010 Oklahoma Retail Sales Forecast Growth Rates by Sector with Carbon

Year Residential Commercial Public

Authority Street

Lighting Industrial Petroleum

2010 1.21% 1.17% 0.26% 1.96% 0.50% 0.50%

2011 0.76% 0.82% 2.17% 1.40% 0.50% 0.50%

2012 1.32% 1.89% 1.94% 1.37% 0.50% 0.50%

2013 0.29% 1.79% 1.39% 1.37% 0.50% 0.50%

2014 0.97% 1.95% 1.56% 1.38% 0.50% 0.50%

2015 0.99% 2.36% 2.08% 1.37% 0.50% 0.50%

2016 0.55% 1.89% 2.05% 1.36% 0.50% 0.50%

2017 0.85% 2.38% 2.57% 1.36% 0.50% 0.50%

2018 0.28% 1.59% 2.12% 1.35% 0.50% 0.50%

2019 1.19% 2.26% 2.56% 1.34% 0.50% 0.50%

2020 1.11% 2.18% 2.29% 1.34% 0.50% 0.50%OGE Research and Analysis Department Page 29 Table 5b. 2010 Arkansas Retail Sales Fo recast Growth Rates by Sector with Carbon

Year Residential Commercial Public

Authority Street

lighting Industrial Petroleum

2010 0.91% 1.33% 5.69% 0.73% 12.02% 5.00%

2011 0.54% 3.05% 5.59% 0.52% 0.50% 0.50%

2012 2.56% 3.96% 3.65% 0.60% 0.50% 0.50%

2013 1.95% 2.68% 3.54% 0.65% 0.50% 0.50%

2014 2.22% 3.14% 3.25% 0.66% 0.50% 0.50%

2015 2.20% 3.09% 3.42% 0.65% 0.50% 0.50%

2016 2.05% 3.03% 3.82% 0.64% 0.50% 0.50%

2017 2.02% 2.89% 4.13% 0.62% 0.50% 0.50%

2018 1.84% 2.66% 4.11% 0.61% 0.50% 0.50%

2019 1.87% 3.08% 4.10% 0.60% 0.50% 0.50%

2020 2.12% 3.59% 4.44% 0.59% 0.50% 0.50%



### Table 7b. 2010 Load Responsibility Peak Demand Forecast with Carbon

Year Total Load

Responsibility Peak

Demand (MW)

Forecast* (Average

Weather) Total Load

Responsibility

Growth Rates Retail Load

Responsibility Peak

Demand (MW)

Forecast (Average

Weather) Retail Load

Responsibility

Growth Rates

2009 5,917 5,617

2010 6,012 1.61% 5,702 1.52%

2011 6,056 0.73% 5,738 0.63%

2012 5,848 -3.43% 5,797 1.04%

2013 5,896 0.81% 5,857 1.03%

2014 5,918 0.38% 5,918 1.04%

2015 5,993 1.27% 5,993 1.27%

2016 6,031 0.64% 6,031 0.64%

2017 6,129 1.62% 6,129 1.62%

2018 6,174 0.73% 6,174 0.73%

2019 6,260 1.40% 6,260 1.40%

2020 6,338 1.25% 6,338 1.25%OGE Research and Analysis Department Page 30 

### Table 11b. Peak Demand Model Forecasts by Weather Probability with Carbon

Year 1 out of 30

Years 1 out of 10

Years 1 out of 4

Years 1 out of 2

Years 3 out of 4

Years 9 out of 10

Years 29 out of

30 Years

2010 6,176 6,142 6,099 6,012 5,811 5,673 5,623

2011 6,217 6,183 6,141 6,056 5,852 5,717 5,664

2012 6,012 5,977 5,935 5,848 5,647 5,509 5,459

2013 6,058 6,024 5,982 5,896 5,693 5,556 5,505

2014 6,083 6,049 6,007 5,918 5,720 5,579 5,529

2015 6,155 6,121 6,078 5,993 5,788 5,653 5,601

2016 6,197 6,162 6,120 6,031 5,834 5,691 5,642

2017 6,293 6,259 6,216 6,129 5,927 5,789 5,738

2018 6,337 6,303 6,261 6,174 5,972 5,833 5,783

2019 6,426 6,392 6,350 6,260 6,063 5,919 5,871

2020 6,505 6,471 6,428 6,338 6,142 5,997 5,949OGEResearchandAnalysisDepartmentPage31Table 12b. 2010 Load Responsibility Forecast with CarbonDemand (MW)200920102011201220132014201520162017201820192020FERC Load (withoutlosses)MUNICIPAL1211.4 6.7 8.8 1.6 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0COOPERATIVE13201.8 216.4 221.2 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0SPA1418.0 17.2 17.5 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0OMPA PSA1525.0 25.0 25.0 25.0 25.0 0.00.0 0.0 0.0 0.0 0.0 0.0MDEA1620.0 20.0 20.0 20.0 10.7 0.00.0 0.0 0.0 0.0 0.0 0.0Total FERC Load (w/olosses)276.2 285.3 292.5 46.6 35.7 0.00.0 0.0 0.0 0.0 0.0 0.0Losses (Loss factor =0.0867)23.9 24.7 25.4 4.0 3.1 0.00.0 0.0 0.0 0.0 0.0 0.0Total FERC Load (lossesadded)300.1 310.0 317.9 50.7 38.8 0.00.0 0.0 0.0 0.0 0.0 0.0Percentage Change in TotalFERC Load3.3% 2.6% -84.1% -23.5% -100.0%0.7% 0.7% 0.7% 0.7% 0.7% -100.0%Total Retail Load (withlosses)5,6175,7025,7385,7975,8575,9185,9936,0316,1296,1746,2606,338Percentage Change in TotalRetail Load1.5% 0.6% 1.0% 1.0% 1.0% 1.3% 0.6% 1.6% 0.7% 1.4% 1.2%Load Responsibility (withlosses)Load Responsibility =Total Retail Load + FERC,Losses Added (includescurtailable load)*5,9176,0126,0565,8485,8965,9185,9936,0316,1296,1746,2606,338Percentage Change in LoadResponsibility1.6% 0.7% -3.4% 0.8% 0.4% 1.3% 0.6% 1.6% 0.7% 1.4% 1.2%Load FactorLoad Responsibility =Total Retail Load +FERC, Losses Added*5,9176,0126,0565,8485,8965,9185,9936,0316,1296,1746,2606,338Total Retail Sales + FERC,Losses Added27,800,57228,228,19628,413,88827,775,31227,935,41428,003,27528,379,06728,712,32129,129,12229,426,64129,880,54830,328,332Load Factor 53.63% 53.60% 53.56% 54.22% 54.09%54.02% 54.06% 54.34% 54.26% 54.41% 54.49% 54.63%12Watonga contract can expire on February28, 2012, Paris contract can expire onMay 31, 2012, Orlando contract can expire on October 31, 2012 and the Geary contract expired on April 30, 201013AVEC contract can expire on November 30, 201114Paris contract can expire on May 31, 2012 and Vance contract can expire on May 31, 201215OMPA PSA contract terminates on December 31, 2013 and is removed from the forecast at that time due to the absence of an Evergreen clause in the contract16MDEA contract 2 can expire on December 31, 2012and MDEA contract 1 can expire on April 30, 2014OGE Research and Analysis Department Page 32 

### Table 15b. 2010 Retail Energy Forecast (MWh) with Carbon

Year Residential Commercial Public

Authority Street

lighting Industrial Petroleum TotalArkansas 2009 722,794 715,816 135,578 8,891 981,796 10,304 2,575,178

2010 729,347 725,335 143,298 8,956 1,099,760 10,819 2,717,515

2011 733,282 747,491 151,308 9,003 1,105,259 10,873 2,757,216

2012 752,026 777,057 156,834 9,058 1,110,785 10,927 2,816,686

2013 766,666 797,870 162,393 9,116 1,116,339 10,982 2,863,367

2014 783,666 822,900 167,665 9,176 1,121,921 11,037 2,916,365

2015 800,889 848,349 173,395 9,236 1,127,531 11,092 2,970,493

2016 817,279 874,066 180,019 9,295 1,133,168 11,148 3,024,975

2017 833,785 899,344 187,461 9,353 1,138,834 11,203 3,079,980

2018 849,114 923,260 195,175 9,410 1,144,528 11,259 3,132,746

2019 865,012 951,658 203,182 9,466 1,150,251 11,316 3,190,884

2020 883,388 985,868 212,194 9,522 1,156,002 11,372 3,258,346Oklahoma 2009 8,060,751 5,727,776 2,735,363 53,892 2,574,462 2,913,067 22,065,310

2010 8,157,927 5,794,732 2,742,487 54,950 2,587,334 2,927,633 22,265,063

2011 8,219,551 5,841,987 2,801,885 55,718 2,600,271 2,942,271 22,461,683

2012 8,328,204 5,952,202 2,856,143 56,483 2,613,272 2,956,982 22,763,287

2013 8,352,483 6,058,848 2,895,709 57,258 2,626,339 2,971,767 22,962,404

2014 8,433,563 6,176,924 2,940,894 58,046 2,639,470 2,986,626 23,235,522

2015 8,516,789 6,322,535 3,002,087 58,841 2,652,668 3,001,559 23,554,479

2016 8,563,586 6,442,195 3,063,555 59,644 2,665,931 3,016,567 23,811,478

2017 8,636,584 6,595,717 3,142,380 60,452 2,679,261 3,031,650 24,146,044

2018 8,660,752 6,700,902 3,208,971 61,269 2,692,657 3,046,808 24,371,358

2019 8,763,791 6,852,221 3,291,204 62,093 2,706,120 3,062,042 24,737,471

2020 8,860,799 7,001,367 3,366,444 62,925 2,719,651 3,077,352 25,088,538Total OG&E 2009 8,783,545 6,443,591 2,870,941 62,783 3,556,258 2,923,371 24,640,489

2010 8,887,273 6,520,068 2,885,784 63,907 3,687,094 2,938,452 24,982,578

2011 8,952,833 6,589,478 2,953,193 64,721 3,705,530 2,953,144 25,218,899

2012 9,080,230 6,729,259 3,012,977 65,541 3,724,058 2,967,910 25,579,973

2013 9,119,150 6,856,718 3,058,102 66,374 3,742,678 2,982,749 25,825,770

2014 9,217,228 6,999,823 3,108,560 67,222 3,761,391 2,997,663 26,151,887

2015 9,317,679 7,170,884 3,175,483 68,076 3,780,198 3,012,651 26,524,972

2016 9,380,865 7,316,261 3,243,574 68,938 3,799,099 3,027,714 26,836,453

2017 9,470,369 7,495,061 3,329,841 69,805 3,818,095 3,042,853 27,226,023

2018 9,509,865 7,624,162 3,404,146 70,678 3,837,185 3,058,067 27,504,105

2019 9,628,802 7,803,879 3,494,387 71,559 3,856,371 3,073,358 27,928,356

2020 9,744,187 7,987,235 3,578,638 72,447 3,875,653 3,088,724 28,346,884OGEResearchandAnalysisDepartmentPage33Table 16b. Energy Forecast Accounting for Changes in Wholesale Load with CarbonEnergy(MWH)200920102011201220132014201520162017201820192020FERC Sales (without losses)MUNICIPAL1740,183 35,505 32,511 6,562 0 0 0 0 0 0 0 0AVEC18896,336 947,936 886,737 0 0 0 0 0 0 0 0 0SPA1971,232 76,298 77,731 32,487 0 0 0 0 0 0 0 0OMPA20210,182 219,000 219,000 219,000 219,000 0 0 0 0 0 0 0MDEA21125,849 122,640 122,640 122,640 65,533 21,844 0 0 0 0 0 0Total FERC Sales1,343,783 1,401,380 1,338,619 380,689284,533 21,844 0 0 0 0 0 0Growth Rate in FERC sales 4.3% -4.5% -71.6% -25.3% -92.3% -100.0% 0.5%0.0% 0.0% 0.0% 0.0%Retail Sales (without losses)Residential 8,783,545 8,887,273 8,952,833 9,080,230 9,119,1509,217,228 9,317,679 9,380,865 9,470,369 9,509,865 9,628,802 9,744,187Commercial 6,443,591 6,520,068 6,589,478 6,729,259 6,856,7186,999,823 7,170,884 7,316,261 7,495,061 7,624,162 7,803,879 7,987,235Industrial 3,556,258 3,687,094 3,705,5303,724,058 3,742,678 3,761,3913,780,198 3,799,099 3,818,0953,837,185 3,856,371 3,875,653Industrial Petroleum 2,923,371 2,938,4522,953,144 2,967,910 2,982,7492,997,663 3,012,651 3,027,7143,042,853 3,058,067 3,073,358 3,088,724Total Industrial 6,479,629 6,625,546 6,658,674 6,691,967 6,725,427 6,759,054 6,792,849 6,826,8146,860,948 6,895,252 6,929,7296,964,377Public Authority and Street Lighting2,933,724 2,949,691 3,017,9143,078,517 3,124,476 3,175,7813,243,559 3,312,513 3,399,6453,474,825 3,565,945 3,651,085Total Retail Sales24,640,489 24,982,578 25,218,89925,579,973 25,825,770 26,151,88726,524,972 26,836,453 27,226,02327,504,105 27,928,356 28,346,884Growth Rate in Retail Sales 1.4% 0.9% 1.4%1.0% 1.3% 1.4% 1.2% 1.5% 1.0% 1.5% 1.5%Total MWH Sales (with losses)Total Retail Sales + FERC 25,984,27126,383,958 26,557,518 25,960,66226,110,304 26,173,732 26,524,972 26,836,453 27,226,023 27,504,105 27,928,356 28,346,884Losses221,816,301 1,844,239 1,856,3701,814,650 1,825,110 1,829,5441,854,096 1,875,868 1,903,0991,922,537 1,952,192 1,981,447Total Retail Sales + FERC, LossesAdded27,800,572 28,228,196 28,413,88827,775,312 27,935,414 28,003,27528,379,067 28,712,321 29,129,12229,426,641 29,880,548 30,328,332Growth Rate in Total Sales 1.5% 0.7% -2.2% 0.6% 0.2% 1.3% 1.2% 1.5% 1.0% 1.5% 1.5%17Watonga contract can expire on February 28, 2012, Paris Contract can expire on May 31, 2012, Orlando contract can expire on October 31, 2012 and the Gearycontract expired on April 30, 201018AVEC contract can expire on November 30, 201119Paris contract can expire on May 31, 2012 and Vance contract can expire on May 31, 201220OMPA PSA contract terminates onDecember 31, 2013 and is removed from forecast atthat time due to the absence of an Evergreenclause in the contract.21MDEA contract 2 can expire on December 31, 2013and MDEA contract 1 can expire on April 30, 201422The energy loss factor is 0.0699 for 2009 and beyondOGE Research and Analysis Department Page 34 25,000,000 26,000,000 27,000,000 28,000,000 29,000,000 30,000,000 31,000,000 32,000,000

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020MWh 

### Figure 1b. Energy Model Foreca st Outcomes by Weather Probability

with Carbon

19 out of 20 Years

1 out of 2 Years

1 out of 20 Years

Normal Weather

5,000 5,200 5,400 5,600 5,800 6,000 6,200 6,400 6,600

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 

### Figure 2b. Load Responsib ility Outcomes by Weather Probability

with Carbon

1 out of 30 Years

1 out of 2 Years

3 out of 4 Years

29 out of 30 YearsOGE Research and Analysis Department Page 35 22,000,000 23,000,000 24,000,000 25,000,000 26,000,000 27,000,000 28,000,000 29,000,000 30,000,000

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020MWh 

### Figure 3b. Retail and Whol esale Energy - Weather Normalized

Historical and Forecast Sales with Carbon

Historical Weather Normalized Retail

Sales

Forecast of Weather Normalized Retail

Sales

Historical Weather Normalized Total

Sales + FERC

5,000 5,200 5,400 5,600 5,800 6,000 6,200 6,400 6,600

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020MW 

### Figure 4b. Native Load Re sponsibility - Weather Normalized

Historical and Forecast Loads with Carbon

Historical Weather Normalized Retail

Load Responsibility

Forecast of Weather Normalized Retail

Load Responsibility

Historical Weather Normalized Total Load

Responsibility

Forecast of Weather Normalized Total

Load ResponsibilityOGE Research and Analysis Department Page 36 5,000 5,200 5,400 5,600 5,800 6,000 6,200 6,400 6,600

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020MW Figure 6b. Peak Demand Model Forecasts by Weather Probability with

Carbon with Carbon

1 out of 30

Years

1 out of 10

Years

1 out of 4

Years

1 out of 2

Years

### 201 1 Integrated Resource Plan



# Appendix B

– OG&E 2010 Capability Report

### 201 1 Integrated Resource Plan



# Appendix C

– Annual Portfolio NPVRR for Reference

Scenario

### 201 1 Integrated Resource Plan

Annual Revenue Requirement * , Reference Scenario ($ B illions)

NO WIND 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 30 YEAR

NPVRR

Benchmark

- CC 1.21 1.31 1.32 1.44 1.37 1.51 1.59 1.71 1.83 1.88 21.4

Benchmark

- CT 1.21 1.31 1.32 1.44 1.37 1.51 1.55 1.62 1.72 1.75 21.5

Scrub

- CC 1.21 1.33 1.46 1.68 1.78 1.89 1.96 2.06 2.17 2.21 23.8

Scrub

- CT 1.21 1.33 1.46 1.68 1.78 1.88 1.92 1.97 2.06 2.07 23. 8

Hybrid Convert

- CC 1.21 1.32 1.40 1.58 1.78 1.91 1.98 2.12 2.22 2.28 24.7

Hybrid Convert

- CT 1.21 1.32 1.40 1.58 1.78 1.90 1.94 2.03 2.11 2.17 25. 1

Hybrid Replace

- CC 1.22 1.39 1.55 1.74 1.91 2.01 2.07 2.16 2.27 2.29 25. 1

Hybrid Replace

- CT 1.22 1.39 1.55 1.74 1.91 2.00 2.03 2.08 2.16 2.15 24.9

Convert

- CC 1.21 1.31 1.32 1.46 1.93 2.05 2.13 2.32 2.41 2.51 27. 0

Convert

- CT 1.21 1.31 1.32 1.46 1.93 2.05 2.09 2.23 2.30 2.41 27.6

Replace

- CC 1.23 1.48 1.70 1.84 2.16 2.23 2.29 2.36 2.47 2.45 27. 3

Replace

- CT 1.23 1.48 1.70 1.84 2.16 2.22 2.25 2.28 2.36 2.30 27.0

WIND WITHOUT PTCS 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 30 YEAR

NPVRR

Benchmark

- CC 1.29 1.38 1.38 1.48 1.42 1.55 1.63 1.74 1.84 1.91 21.6

Benchmark

- CT 1.29 1.38 1.38 1.48 1.42 1.54 1.59 1.65 1.73 1.76 21.7

Scrub

- CC 1.29 1.40 1.51 1.73 1.82 1.92 1.99 2.09 2.19 2.23 24.0

Scrub

- CT 1.29 1.40 1.51 1.73 1.82 1.92 1.95 2.00 2.08 2.09 23.9

Hybrid Convert

- CC 1.29 1.39 1.46 1.63 1.81 1.93 2.01 2.14 2.23 2.29 24.9

Hybrid Convert

- CT 1.29 1.39 1.46 1.63 1.81 1.93 1.97 2.05 2.12 2.17 25.2

Hybrid Replace

- CC 1.30 1.46 1.61 1.78 1.94 2.04 2.11 2.19 2.29 2.31 25.3

Hybrid Replace

- CT 1.30 1.46 1.61 1.78 1.94 2.03 2.07 2.10 2.18 2.16 25.1

Convert

- CC 1.29 1.38 1.38 1.50 1.95 2.08 2.14 2.33 2.42 2.51 27.1

Convert

- CT 1.29 1.38 1.38 1.50 1.95 2.08 2.10 2.24 2.31 2.42 27.7

Replace

- CC 1.31 1.56 1.76 1.89 2.21 2.27 2.33 2.39 2.50 2.47 27.5

Replace

- CT 1.31 1.56 1.76 1.89 2.21 2.26 2.29 2.31 2.39 2.33 27.2



### * The Revenue Requirement in th ese table s includes a recovery of return on rate base,

expenses, and production costs

. The calculation assumes OG&E recovers

expenditures in the year they are made and recoveries based on the capital structure,

cost of capital and tax rate authorized by the Oklahom a Corporation Commission in

OG&E’s July, 2009 Rate Case order.

### 201 1 Integrated Resource Plan



# Appendix D

– OGE 2011 IRP Oklahoma Collaborative

Technical Conference1 

## OGE 2011 IRP Oklahoma Collaborative Technical Conference

February 22, 2011

Meeting Documentation

Introduction

The 2011 IRP submittal and associated Oklahoma Stakeholder Meeting are being performed pursuant to

an agreement reached in the Commission

‐ approved joint stipulation and settlement agreement in the

Crossroads docket (Cause No. PUD 201000037). The stipulation reads in relevant part:

M. The Stipulating Parties agree that on or before May 1, 2011, OG&E will submit an

interim, updated Integrated Resource Plan (“IRP”) as contemplated by Subsection 37 of Chapter

35 of the Commission’s Rules, provided that:

1) The updated IRP analysis will specifically address the need and timing for additional

wind resources in OG&E’s system, including but not limited to various amounts of

wind and timing of additional wind including assessments of the benefits based on

consideration of the operation of the SPP day

‐ ahead market, transmission

limitations/requirements for expanded wind resource development, the added costs

for fossil

‐ fuel

‐ fired power plants when those fossil fuel plants are used to

accommodate variable wind generation, current expectation of the impacts of

regional haze rules on OG&E’s coal generation and a range of scenarios for natural

gas prices and climate legislation and other factors which may impact the amounts

and timing of wind resource additions over the next ten (10 years.

2) No less than sixty (60) days prior to the filing of the updated integrated resource

plan, the Stipulating Parties further agree that OG&E will hold a collaborative

technical conference for all stakeholders in order to allow all stakeholders the

opportunity to provide input regarding utility objectives, assumptions, and planning

scenarios to be contained in the updated IRP analysis.

The Collaborative Technical Conference was held on February 22, 2009 in OGE’s offices. Several

stakeholders participated by teleconference. The participants were:2 OGE: Leon Howell, Kimber Shoop, Jesse Langston, Zac Hager, Michael Collins, Rhonda Redden,

Bill Wilkerson

Commission Staff: Karen Forbes, Joel Rodriguez, Tanya Hinex

‐ Ford, Trent Campbell

Attorney General: Bill Humes, Dan Peaco

OIEC: Tom Schroedter, Scott Norwood

Devon Energy: A.J. Ferate

Chesapeake Energy: Jamie Maddy

Oklahoma Sustainability Network/Chermac/CPV/Sierra Club: Jon Laasch, Cheryl Vaught

OGE Shareholders: Ron Stakem

AES Shady Point: Kendall Parish

Oklahoma Sustainability Network: Montel Clark

Sierra Club and Novus Wind Power: Bud Scott

The meeting was divided into two segments. The first part focused on a presentation that was made by

Leon Howell, OGE’s Director, Resource Planning. Stakeholders were encouraged to ask clarifying

questions throughout the presentation. The second part of the meeting was devoted to stakeholder

feedback on OGE’s IRP.

The meeting began with an introduction by OGE, introduction of the participating stakeholders and brief

comments on the conduct of the meeting by the facilitator. 1 The facilitator indicated that he would

prepare and distribute these meeting notes.



### Part 1: OGE Presentation and Questions

The meeting notes that follow incorporate presentation materials, comments by OG&E supplementing

the presentation slides, followed by a summary of questions by stakeholders and the OGE response.

1 The meeting was facilitated by Robert C. Yardley, Jr. a former regulator and Executive Advisor to

Concentric Energy Advisors.3 POSITIVE

ENERGY

T OGE THER ® 

### Purpose of Meeting

3 

## • Gather Stakeholder input to OG&E’s IRP

• Crossroads Joint Stipulation and Settlement

Agreement – June 29, 2010

– On or before May 1, 2011, OG&E will submit an interim, updated IRP

– No less than sixty (60) days before filing of the updated [IRP],...OG&E

will hold a collaborative technical conference for all stakeholders

– Opportunity to provide input regarding utility objectives, assumptions,

and planning scenarios to be contained in updated IRP analysis



## OG&E Comments:

• OG&E is in the process of developing an IRP to comply with the Crossroads Settlement

agreement.

• In today’s meeting we will present and seek feedback on the Process, Objectives, Assumptions

and Planning portfolio or scenarios.

• Next scheduled IRP under the OCC Electric Rules is due to be submitted in October 2012, so we

will be going through this process again next year.4 POSITIVE

ENERGY

T OGE THER ® 

### Some key elements of the 2011 IRP Update



### • The objective of the IRP is to determine the lowest

reasonable cost option, including risk, to meet customers’

demand and energy needs of the future

• Analysis from the Crossroads Settlement Agreement

– Need and timing of additional wind resources

– Assessment of the benefits of additional wind based on consideration of:

• SPP Day

‐ Ahead Market

• Transmission limitations/requirements for expanded wind resource development

• Additional costs for fossil fuel fired power plants when used to accommodate

variable wind generation

• Impacts of Regional Haze rules on OG&E’s coal generation

• Range of scenarios of natural gas prices, climate legislation, and other factors

4



## OG&E Comments:

• All of the assumptions from the 2010 IRP will be updated.

• We will identify the cost/benefits of wind additions that would allow OG&E to meet the State

Renewable Portfolio goal of 15% by 2015

• In addition, we will model an array of portfolios to get a broad understanding of the options

available to comply with possible emission regulations. These regulations include Regional Haze

and Maximum Available Control Technology (MACT) for Hazardous Air Pollutants (HAPs)

• Each portfolio will be analyzed in scenarios that are defined to consider climate change

legislation.

• Sensitivity analyses will performed on the top portfolios to examine alternative fuel and

emissions prices

• Planned SPP transmission additions including the resulting incremental costs to OG&E’s

customers will identified5 POSITIVE

ENERGY

T OGE THER ® 

### OG&E follows a seven step process to develop

the interim IRP

5 • Load Forecast

• Energy Efficiency

• Smart Grid

• Demand Response

• Generating Unit Data

• Emissions Constraints

• Transmission

• Fuel Prices

• Scenarios 2

Collect Assumptions

7

Develop Report

• Five year action plan

• Schedules of results 6

Interpret Data, Draw Conclusions

• Develop tables

comparing results

• Determine “best” plan 5

Least Cost/Least Risk Analysis

• Deterministic cost comparisons

• Scenario analysis

• Sensitivity analysis

• Stochastic analysis

• SPP Market analysis

4

Computer Simulation

• Computer processing

• Extrapolate data 3

Develop Models and Portfolios

• Develop models utilizing

assumptions

• Group options into portfolios for

evaluation 1

IRP Objective

• Future capacity needs and

timing

• Assess resource options that

respond to potential

environmental regulation

• Stakeholder Input for

steps 1, 2 and 3

• Public Meeting after

draft report is prepared



## OG&E Comments:

• Main objective of IRP is the needs and timing of future capacity additions

• Myriad assumptions go into the IRP

– the IRP team relies on several other groups within OGE to

provide assumptions relevant to their areas

• Stakeholder input is being sought for the first three steps of the process: objectives,

assumptions and the portfolios to be examined

• Computer simulation is a large piece of the process

– very time consuming6 POSITIVE

ENERGY

T OGE THER ® 

### Key inputs and assumptions



### • Load forecast

• System characteristics

– DSM Programs, PPA/Cogen Contracts, Generators

• New generation options

– Capital and O&M Costs, Operating Characteristics

• Wind additions

• Environmental requirements

• Transmission expansion

• Price forecasts

– Natural Gas and Coal

– CO

2

6



## OG&E Comments:

• We will review each of these areas in the following slides

POSITIVE

ENERGY

T OGE THER ® 

### Load Forecast

7 2012 2013 2014 2015 2016 2017 2018 2019 2020 Avg Growth

Rate (%)

Demand, MW 6,104 6,172 6,228 6,317 6,123 6,228 6,278 6,371 6,456 0.73

Energy, GWh 29,118 29,401 29,638 30,102 29,224 29,682 30,013 30,501 30,950 0.96 4,000 4,500 5,000 5,500 6,000 6,500 7,000

1995 2000 2005 2010 2015 2020PeakDemand,MW

Historical Forecast7 

## OG&E Comments:

• OG&E does an econometric

‐ based load forecast annually.

• We will use the most recent forecast that was complete in September 2010.

• The forecast will be included in the IRP report as schedule C.

• OG&E’s demand forecast does not reflect new demand side supply options such as demand

response due to smart grid or energy efficiency programs.

• The demand forecast reflects historical energy efficiency programs that are reflected in the data

used to develop the econometric forecast equations. It also includes the likely future demand

response such as load curtailments based on experience during recent peak periods.

• Wholesale load contract termination is reflected in the forecast most notably in the 2016 as

shown by the dip in the forecast.

• As with the demand forecast the energy forecast does not reflect future energy reduction due

to demand side supply options including demand response due to smart grid or energy

efficiency programs.

• The energy forecast does reflect historical energy efficiency programs.

• As noted the total load includes wholesale load along with transmission and distribution loses.

• The retail demand growth rate is projected to be 1.32% with an energy growth rate of 1.49%



## Questions:

• Does the forecast assume that wholesale load contracts expire 2015/2016, causing the dip?

o RESPONSE: That is correct

POSITIVE

ENERGY

T OGE THER ®

Wholesale Load 

### OG&E includes sales agreements in the IRP

Peak Load, MW 2011 2012 2013 2014 2015 2016

AVECC 221 224 228 232 236 0

SPA 18 0 0 0 0 0

Municipal 9 2 38 0 0 0

OMPA 25 25 25 0 0 0

MDEA 20 20 11 0 0 0

88 

## OG&E Comments:

• OG&E has recently terminated wholesale load agreements as contracts come to term.

Additional agreements will expire through 2015.



## Questions:

• Has the AVECC termination been pushed back?

o RESPONSE: Yes, the agreement was recently extended through 2015. The downturn in

the economy provided reduced capacity responsibility which allowed us to continue to

serve AVECC and avoid reallocating those costs to retail customers.

POSITIVE

ENERGY

T OGE THER ® 

### Demand Side Resources are included in OG&E’s

resource plan

Program Peak Load Reduction (MW)

2012 2013 2014 2015 2016 2017 2018 2019 2020

SmartGrid

‐ Residential DR 71 143 216 218 219 221 223 225 227

SmartGrid

‐ Commercial DR 0 0 23 47 70 71 72 72 73

SmartGrid

‐ Distribution Automation 8 17 26 37 48 60 75 75 75

Energy Efficiency 26 38 38 38 36 34 32 32 32

Load Curtailment 46 47 48 49 49 50 50 51 52

Total Peak Reduction 151 245 351 389 422 436 452 455 459

9 • SmartGrid forecast reflects plan approved in Cause No. PUD 201000029

• Energy Efficiency forecast reflects programs approved in Cause No. PUD 200900200

• Load Curtailment forecast reflects 2010 performance levels



## OG&E Comments:

• OG&E plans to reduce its peak demand requirements by 459 MW or 7% by 2020 through

Demand Response, Distribution automation, energy efficiency and load curtailment.

• Smart Grid and Energy efficiency programs have both been approved by the OCC and Load

Curtailment is an approved tariff (Load Reduction Rider).



## Questions:

• Is there any industrial demand response?

o RESPONSE: There are industrial DR programs, but they were not part of the benefits

included in the SmartGrid program and are therefore not included in this slide.

• Why is projected load curtailment about 100MW less than the last IRP?9 o RESPONSE: This projection reflects actual performance results from 2010 curtailments.

Load curtailment remains a priority for OG&E and we plan on being more aggressive

with our load curtailment program in the future, so it is possible that these numbers will

increase.

POSITIVE

ENERGY

T OGE THER ®

GWh

2010 Energy Mix 

### Supply Side Resources

Type Unit Capability

(MW)

Coal Muskogee 4, 5 & 6 1,499

Sooner 1 & 2 1,030

AES Shady Point (PPA) 320

CC McClain 360

Redbud 585

Horseshoe Lake 7 220

PowerSmith (PPA) 120

ST

Gas Seminole 1

‐ 3 1,468

Horseshoe Lake 6 & 8 539

Mustang 1

‐ 4 451

CT Horseshoe Lake 9 & 10 88

Tinker 5A & 5B 64

Enid & Woodward 50

Seminole GT 17

Wind OG&E Owned 448

PPA 333 OG&E Capacity Mix

MW

Coal

2,849

37%

CT

219, 3% ST Gas

2,476

33% CC

1,285

17% Wind

781

10%

10 Coal

16,156

58% Wind,

940, 3%

ST Gas

4,401

16%

CC

6,283

23%

CT, 53

<1%



## OG&E Comments:

• Today OG&E operates a diverse generation fleet that includes coal generation, gas generation

and wind generation.

• The capacity of wind reflected on this slide represents nameplate capacity and not the amount

of capacity that counts towards OG&E’s planning capacity margin requirements. We use 5%

capacity credit for the wind farms we don’t have a sufficient track record to claim higher

capacity credits for purposes of meeting the SPP reserve margin criteria.

• The wind generation includes the Crossroads facility that will come on line in 2012.

• OG&E will not assume any retirements through the study period. This assumption reflects the

results of the retirement study OG&E performed in the past.

• OG&E has purchase power agreements with both PowerSmith and AES Shady Point that come

to term during the study period.

• For modeling purposes, it will be assumed that each contract will be terminated and replaced

with new build capacity, effectively pricing that capacity based on a new build cost.



## Questions:

• When do the AES and Smith contracts expire?

o RESPONSE: Smith in 2019, AES in 2023.

• Are there purchase options on those contracts at the end of the period?10 o RESPONSE: I don’t believe there is a provision in the existing contracts for that.

POSITIVE

ENERGY

T OGE THER ®

6,000 6,500 7,000 7,500 8,000

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023MW 

### Load Forecast and Capacity Needs

11 2014

Forecasted load

exceeds capacity 2018

SmartGrid defers

capacity needs

2020

First capacity expansion is needed

after SmartGrid and Termination

of Wholesale Contracts

OG&E Generating Capacity

Smart Grid OG&E Capacity Responsibility

Wholesale Load Termination



## OG&E Comments:

• This chart indicates how OG&E plans on getting to the year 2020 with no new fossil fuel

generation.

o The capacity of OG&E’s existing generation is shown as the dotted line and reflect

termination assumption of both PowerSmith and AES purchase power contracts

o Next I’ll put on the graph our load forecast. From this chart you can see the capacity and

load lines cross in 2013 indicating that OG&E will need new capacity by 2014 if no other

actions are taken.

o The effect of demand response resulting in pushing capacity needs to 2018.

o Finally with the termination of wholesale load the need for new capacity will be delayed

until 2020.



## Questions:

• Does the termination of the wholesale contracts extend the need for capacity beyond 2020

o RESPONSE: We are right at 2020, within about 10 MW for capacity need assuming

wholesale contracts terminate.11 POSITIVE

ENERGY

T OGE THER ®

New Generating Unit Options 

### Three supply side options met screening

criteria to meet future capacity needs

Unit Overnight Capital Cost

($/kW) Capacity (MW)

AEO

2011 AEO

2010 %

Change AEO

2011 AEO

2010

NG CC 978 1,005

‐ 3 540 250

Adv CT 665 662 < 1 210 230

Wind 2,438 2,007 21 100 50

* EIA Annual Energy Outlook 2011

will be used in this IRP Update 18

EIA AEO 2011 Resource Options

Resource

• Adv PC w/o CCS

• IGCC w/o CCs

• IGCC CCS

• Conv NGCC

• Adv NGCC

• Adv NGCC CCS

• Conv CT

• Adv CT

• Fuel Cell

• Nuclear

• Biomass

• Geothermal

• Landfill Gas

• Conv Hydro

• Wind

• Wind Offshore

• Solar Thermal

• Solar PV $/kW

$ 2,844

$ 3,221

$ 5,348

$ 978

$ 1,003

$ 2,060

$ 974

$ 665

$ 6,835

$ 5,339

$ 3,860

$ 4,141

$ 8,232

$ 3,078

$ 2,438

$ 5,975

$ 4,692

$ 4,755

12



## OG&E Comments:

• Department of Energy, Energy Information Administration is referenced as EIA

• OG&E will base construction costs of new generation facilities on the recently released 2011

Annual Energy Outlook. A full list of resource options along with costs is given in the table on

the left side of this slide.

• OG&E has screened facilities using the same process as outlined in the 2010 IRP. The screening

criteria include proven technology, cost, and public acceptance. The units that will be

considered for future generation needs are listed in the table on the right. Coal plants are not

included do to the last criterion.

• In this interim IRP we do not consider nuclear because it was not one of the best options in the

last IRP, and its costs have increased about 30% since the last estimate. It will be analyzed in the

next IRP.12 POSITIVE

ENERGY

T OGE THER ® 

### Some factors effect the need and timing of

potential additional wind resources



### • Oklahoma HB 3028 – Oklahoma Energy Security Act

– Effective November 1, 2010

– Establishes renewable energy standard to set a goal that by 2015, 15%

of all installed capacity within the state shall be from renewable

sources

– With 255 MW of additional wind generation, 15% of OG&E’s capacity

will be renewable



### • Production Tax Credits

– Extended through December 31, 2012 by American Recovery and

Reinvestment Act of 2009

– Could be extended but no legislation is currently proposed

13



## OG&E Comments:

• HB 3028 establishes a goal of 15% of installed capacity come from renewable resources.

• 25% of renewable resources can come from demand side resources.

• Relying on expected DSM OG&E would need approximately 1035MW of wind generation to

meet the goal. With the addition of Crossroads in 2012 we have 781MW

• Production tax credits and accelerated depreciation offer a significant reduction in wind

generation costs



## Questions:

• Has OGE broken out whether the 255 MW of wind needed could be PPA or owned or both?

o RESPONSE: In the IRP, we are assuming owned resources because we do not want to

forecast what PPA prices could be. We would go through the RFP process to acquire any

additional wind.

• Is OGE trying to meet the 15% goal, the HB establishes a statewide goal?

o RESPONSE: Yes, OGE will try to meet that goal. We are assuming everyone will install

15% of their capacity to help meet the goal. As in the past, if we don’t show benefits

from a resource, we won’t add it, and wind may not show benefits after PTC’s expire.

However, OG&E will only add wind resources if they provide benefits to our customers.

• How did you get the 255MW?

o RESPONSE: The goal is based on 15% of installed capacity, but the bill is vague. We

assumed 15% of installed capacity. OG&E anticipates being able to take advantage of

the 25% of the goal allowed to be provided by DSM, based on OGE demand response

and energy efficiency programs.

• After the 255, have we estimate what % of total energy wind would be?13 o RESPONSE: We would be around 12

‐ 13% of energy production.

• Is that a mandate or goal?

o RESPONSE: It’s a goal, which is why we will still determine if meeting that goal is

beneficial to customers.

• What are your considerations when looking at wind resources?

o RESPONSE: We look at the wind profile, energy provided, what thermal generation it

displaces, etc. This is the approach we have taken to Crossroads and other wind benefit

analyses.

• Is it an economic analysis?

o RESPONSE: Yes, we will add any amount that shows benefit. It may be more or less than

the 255 MW.

• When you calculate your wind % do you include PPA and owned?

o RESPONSE: Yes.

• Is the wind capacity added to the denominator of the calculation?

o RESPONSE: Yes.

• Comment offered that 15% intended to include utility

‐ owned wind to avoid double counting of

PPA wind sourced from wind farms located in Oklahoma.

• Wind producers are reporting their own per OCC direction.

o RESPONSE: OGE will clarify with producers and OCC, and may revisit calculation for IRP.

• By 2015, the goal is for 15% of actual generation to be from wind?

o RESPONSE: It’s actually 15% renewable, which 25% of the 15% can be DSM. Thus wind

accounts for approximately 12

‐ 13% of the goal.

• What resources will be displaced by that wind?

o RESPONSE: It’s a mix of coal and gas. About 50% of wind production is at night, when

we generally run coal plants. We run gas plants to follow load during the day. Fuel prices

and other assumptions vary the percentages of each. The SPP market will affect this as

well.

• Is OGE or SPP doing a wind integration study?

o RESPONSE: The SPP retained Charles River to perform a wind integration study that is

available on the SPP website.14 POSITIVE

ENERGY

T OGE THER ® 

### Combining each option provides an array of

portfolios to be analyzed under each scenario

14 2

Scenarios

• Reference – No CO

2 , OG&E Gas

• Alternative – Third party CO

2 & Gas

3

Additional Wind Options

• No Wind – No Additional Wind

• With Wind – 255 MW in 2012 (eligible for PTC’s)

• With Wind – 255 MW in 2013 (not eligible for PTC’s)

5+

Environmental Options

• Benchmark – Low sulfur coal is BART

• Scrub – Scrub Coal Units

• Replace – Replace Coal Units with New CC

• Convert – Convert Coal Units to burn NG

• Hybrid(s) –Combinations of Scrub, Replace, and

Convert

2

New Resources

• CC’s –NG CC to meet future capacity needs

• CT’s –NG CT to meet future capacity needs Wind Scenario

Environmental 2012

60+

Modeling simulations 2 x 3 x 5+

x 2

= New

Resources

to meet

capacity

needs

30+

Portfolios developed =

30+ 2018

2020

2041



## OG&E Comments:

• We are going to look at the addition of wind options in 3 ways: no wind, wind in 2012 before the

PTC expires and wind in 2013 after the PTC expires.

• We are going to consider a number of environmental options as indicated in the slide.



## Questions:

• Are the natural gas price scenarios an OGE forecast and from a third party?

o RESPONSE: Yes, these will be discussed on the next slide.

• Why are you only looking at 2012 and 2013 for wind additions?

o RESPONSE: We are looking at no wind, wind just before termination of PTCs (end of

2012), and after termination of PTCs (start of 2013).

• Other than that, you won’t be looking at any other wind?

o RESPONSE: That’s correct.

• Is the 2018 the deadline for environmental compliance?

o RESPONSE: Because we don’t know now what the deadline will be, we have assumed

for modeling purposes that 2018 is the deadline for compliance. We do not expect that

the results would vary much if that date is moved forward or backward a year or two.

• The last IRP had about 1200MW of wind beyond 2015, now we’re at 255. What has changed?

o RESPONSE: One significant change is the increased uncertainty regarding the prospects

for environmental legislation. In the last IRP we assumed that we had to hit hard CO2

caps that wind helped OG&E to meet. Also, feedback from interveners after our last IRP

led us to evaluate only the next increment of wind.

• Have you thought about plugging in wind as an option and letting the model select?15 o RESPONSE: We do not have a modeling tool to do that and we do not believe that

existing available tools handle wind appropriately. They don’t model wind very well as

they use typical week analysis, not hourly. We use the PCI GenTrader tool that is an

hourly analysis. We believe that our approach is superior.

• Are you saying that the model will not optimize timing and quantity of wind selection?

o RESPONSE: Yes, it does not do that. We believe our method is the correct way to do

this.

• Under the SIP, is BART low sulfur coal, or low sulfur coal with a combination of wind and

conversion to NG?

o RESPONSE: It’s the addition of Low NOx Burners on coal plants and Seminole, which we

plan to do. The SIP was Low sulfur coal, and a cap on the production from the units to

limit production.

• Does one of the hybrids represent the State Implementation Plan (“SIP”)?

o RESPONSE: No, the benchmark represents the SIP, the hybrids are planned for future

regulations such as the MACT HAPS rule.

• What are the regional haze caps?

o RESPONSE: They are presented in the SIP.

• In the new resource options, are you using all CC or all CT?

o RESPONSE: Yes, initially. We may look at a mix later on, but not in the first pass.

• Is your benchmark consistent with the SIP?

o RESPONSE: Yes, those caps are in place.

• The scrub option in 2018? 4 units?

o RESPONSE: Yes, 2018, and all 5 units. Only 4 units fall under regional haze, but we are

assuming that new regulations would require scrubbing all 5 of our units.

• None of these options will be the alternative options from the SIP (options 2 or 3)? The option

to have reduced output and scrub later on around 2026 seems like a reasonable option.

o RESPONSE: No, we believe those are covered by our portfolios. We don’t believe the

answers are materially different if you look at scrubbing 4 or 5 units.

• With respect to the potential of scrubbers or MACT being required/implemented

– has OG&E

analyzed the effects of the construction of all that equipment on reliability or reserve margin

(specifically the amount of time they might be offline)?

o RESPONSE: Yes, the equipment can be built alongside an online unit, requiring the units

to be offline for a short period of time.

• If EPA imposes a Federal Implementation Plan (“FIP”), can OG&E still get the units built in time?

o RESPONSE: We assume EPA will allow a reasonable amount of time to get the

equipment installed.16 POSITIVE

ENERGY

T OGE THER ®

3 2 

### Scenario and sensitivity analyses evaluate portfolio

risk by changing input variables

15 Reference (No CO

2 Cost) Alternative (With CO

2 ) Scenarios are used to analyze all portfolios

Sensitivities are used to analyze best performing portfolios

• Natural gas price sensitivities from

“Reference” forecast

– High of 2x, Low of 1⁄2x • CO

2 price sensitivity from

“Alternative” forecast

– High of 2x 

### CONFIDENTIAL PRICE FORECASTS REDACTED



## OG&E Comments:

• Risk will be considered through both scenario analysis and sensitivity analysis. May include

stochastic analysis.

• Two separate futures or scenarios will be considered

o Reference case is what we believe NG prices will be, and has $0 CO2 cost for the entire

study period

o The Alternative scenario assumes a CO2 tax

o Our coal costs are from an internal forecast. Coal cost increases are primarily driven by

coal transportation cost increases.

• Sensitivity analysis will be performed changing 1 variable while hold others constant

• Stochastic analysis allows for a change of a number of variables at the same time. While we

perform the stochastic analyses as an extra step, they are generally not the primary factor in

identifying the best portfolio(s) as they depend on assumptions that are subject to considerable

uncertainty including correlation relationships among key inputs.



## Questions:

• How realistic do you think carbon legislation is passing in the next few years?

o RESPONSE: We certainly monitor that situation closely. We believe it’s a plausible

future that should be analyzed in the Alternative scenario.

• Any idea what other utilities are now doing for planning for carbon costs in IRPs?

o RESPONSE: We do participate in meetings with other utilities where this issue is

discussed in the planning context. Many other utilities use Ventyx as well. Many others

still evaluate CO2 legislation, and I believe it would be negligent to not analyze that

impact.17 • The gas forecast seems high, why is that?

o RESPONSE: Ventyx uses comprehensive models to determine their gas forecast. We

can’t speak to how they got that number.

• We can assume these are the prices in the scenarios?

o RESPONSE: Yes

POSITIVE

ENERGY

T OGE THER ® 

### Regional Transmission Expansion costs will be

reflected in the interim IRP

New Line (Double Cct) New Line

Reconductor / Conversion Benefits of EHV Expansion

• Production Cost savings through IMP as least cost

resources are deliverable throughout region

• Increased West

‐ to

‐ East transfer of wind resources

• Increased reliability

16 2010 STEP Projects

Project Type Cost,

$ in Millions

Balanced Portfolio 825

Priority Projects 1,418

Regional Reliability 1,463

Transmission Service 549

Other 605

Total E&C Cost 4,860



## OG&E Comments:

• SPP is responsible for transmission planning

• OG&E is responsible for roughly 13% of SPP’s total revenue requirement



## Questions:

• The last wind addition about tapped out the transmission capacity, is the 255 going to be

deliverable?

o RESPONSE: That is correct. Limitations will be relieved in 2014 as transmission projects

are complete. There are other suitable wind locations in Oklahoma that can provide the

255 MW and are not transmission constrained.

• What will you assume about transmission for them?

o RESPONSE: For a proxy, we are going to pick two locations and do transmission studies

to determine interconnection feasibility and cost.

• Will you estimate OG&E’s share of the $4.8B?

o RESPONSE: Yes, we will include in the plan an estimated annual revenue requirement of

SPP transmission expansion.18 POSITIVE

ENERGY

T OGE THER ® 

### The impact of the SPP market on the best

options will be analyzed



## • The SPP is transitioning to an Integrated Marketplace

(IMP)

– Consolidated Balancing Authority (CBA)

– Day

‐ Ahead energy and ancillary services markets

• Best plans in IRP will be evaluated in this new

marketplace

17



## OG&E Comments:

• OGE has been participating in the development of the SPP market, assumed to start in 2015.

• PROMOD is used to model the SPP market, which requires us to make assumptions regarding

the actions of other SPP members.

• OGE does not believe that introduction of the SPP market will have a drastic impact due to the

fact that OGE units are representative of the average in the SPP.



## Questions:

• Is it a security constrained economic dispatch?

o RESPONSE: Yes, transmission limits are considered. The significant EHV expansion will

greatly reduce transmission limitations.

• You said they’re not huge impacts, but do they justify transmission projects?

o RESPONSE: Yes, they have been justified by SPP on a regional level.

• Will only the energy market be modeled?

o RESPONSE: Yes, there are no plans for a capacity market in the SPP.

• Are you relying on SPP or OG&E studies to conclude that OG&E’s benefits will not be significant?

o RESPONSE: Both. The SPP showed a net savings to OGE of $20M over the next 10 years.

• Do those SPP studies show any of your units not running?

o RESPONSE: No, all our units will be utilized, but SPP studies project that some of our

units will run less. However, all our units will still be needed to meet capacity

requirements.

o Would you consider retiring these units if they are not going to be run for the next 6 or 7

years?19 o RESPONSE: Even though units may not provide energy, they still provide capacity which

has a value.

• Have you looked at making improvements so they would run?

o RESPONSE: Yes, we are looking at that.

POSITIVE

ENERGY

T OGE THER ® 

## Next Steps

March 15 – Meeting notes provided to participants

April 1 – Submit Draft IRP to OCC

Early April – Public Meeting

May 1 – Submit final IRP to OCC

18



### Part 2: Stakeholder Feedback

Stakeholders provided the following feedback to OGE:

1. Wind

– The two wind options (2012 and 2013) may be too limiting. It would be informative to

know how an additional wind option in a later year (2015 or toward the end of the decade)

would be evaluated. OG&E should employ competitive bidding for new wind resources.

2. Environmental Impact Modeling

– Appear to be modeling only a cap and trade approach. Might

consider evaluating control approaches to environmental regulation. Should expand horizon of

potential environmental regulation beyond the current decade.20 3. Portfolios

– OG&E is specifying that the model select either CCs or CTs to meet capacity needs.

Combinations of CCs and CTs should also be considered given the amount of wind in OG&E’s

portfolio. Consideration of natural gas plants should reflect both capital costs and operational

characteristics. OG&E should use its IRP modeling capability to perform studies of real

operational issues that are on the table from time to time (between required IRP submittals)

including actual Regional Haze options that are being considered.

4. Natural Gas Prices

– Gas price forecasts appear to be high. The IRP should expand on how these

forecasts were derived and consider incorporating other sources for natural gas price forecasts

as well.

5. SPP Day 2 Market

– Performing the SPP market analysis based only on the portfolios that

perform best in the initial analysis may mean that certain portfolios that are best may not make

it to the SPP Day 2 market analysis. Should consider performing analysis on all portfolios

reflecting Day 2 Market.

6. Presentation of Results

– It would be informative to see the average annual revenue

requirement impacts and not only the 30

‐ year Net Present Value revenue requirements. In

general, greater clarity on impacts to customers would be beneficial.

7. SPP Transmission Expansion Plan (“STEP")

– Concern expressed that it is difficult to determine if

elements of the STEP provide economic benefits to OG&E customers. Question raised as to

whether OG&E can provide insights using its IRP analyses.

8. Unit Retirement Analysis

– OG&E should evaluate retiring units that are not expected to run for

several years after introduction of the SPP Day 2 energy market.

9. Long Term Natural Gas Contracting

– IRP should identify impacts on OG&E’s natural gas

requirements under various portfolios to be considered in developing its long

‐ term gas

contracting plans.

10. Coal Plant Operations

– Concern expressed that coal plant performance has declined recently

and whether these issues are attributable to environmental constraints and are likely to

continue and be reflected in the IRP modeling. Preference expressed for a discussion of this

issue in the IRP submittal.

In closing, OG&E thanked the participants for their questions and feedback and indicated that the IRP

team would get together and review each element of feedback and determine what modifications to its

approach should be made. While not commenting extensively on the feedback, OG&E did note that it

had spent a fair amount of time discussing the best approach to modeling of the SPP impacts and

arrived at its current approach based on several factors including the need for extensive assumptions21 regarding other SPP market participants and complexity of running the hourly PROMOD model. OG&E

also indicated that the IRP will provide estimates of cost impacts of various planning portfolios but also

noted actual cost impacts will not be identified until OG&E makes a request for approval of a specific

resource decision at the OCC.

### 201 1 Integrated Resource Plan