the economic impact of wind power on ercot regulation market

The Pennsylvania State University

The Graduate School

College of Earth and Mineral Sciences

THE ECONOMIC IMPACT OF WIND POWER

ON ERCOT REGULATION MARKET

A Thesis in

Energy and Mineral Engineering

by

Bin Zheng

2013 Bin Zheng

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

August 2013

The thesis of Bin Zheng was reviewed and approved* by the following:

Andrew N. Kleit

Professor of Energy and Environmental Economics

Thesis Advisor

Zhen Lei

Assistant Professor of Energy and Environmental Economics

Jeffrey R. S. Brownson

Associate Professor of Energy and Mineral Engineering

Luis F. Ayala H.

Associate Professor of Petroleum and Natural Gas Engineering

Associate Department Head for Graduate Education of Energy and

Mineral Engineering

*Signatures are on file in the Graduate School

iii

ABSTRACT

U.S. wind power generation has grown rapidly in the last decade due to

government policies designed to reduce pollution. Although wind power does not

contribute to environmental pollution, the inherent characteristics of intermittency of

wind power could increase the difficulty in regulating grid systems. In this case, more

ancillary services, particularly regulation, may be needed to guarantee grid reliability

and avoid power outage. Therefore, the integration of wind power may impose extra

heavy costs on system operation.

Our specific study focuses on the economic impacts of wind power integration on

Regulation Service in the ERCOT region of Texas. Our goal is to quantify the

amounts of additional ERCOT regulation services that will be required to support

increased wind power penetration. By accounting for time and market fixed effects,

autocorrelation, load and wind relevant variables, econometric models based on

hourly data in 2008-2011 are established to evaluate the overall and regional impacts

of wind integration on ERCOT regulation usage and requirement. In order to

determine the impacts of wind power integration in different scales, we estimate the

increases of ERCOT regulation services as wind generation changes from 0 to 300%

of the actual value.

The results show that wind power integration significantly impacts ERCOT

regulation services. Increases should be expected in the amount of regulation services

required as wind power becomes a more sizeable portion of ERCOT energy portfolio.

In addition, less regulation services are required in nodal market than in zonal market

with the same level of wind integration. The impact of wind power on Regulation

Down service is larger than that on Regulation Up service and the impact of wind

power on ERCOT regulation requirement is greater than that on ERCOT regulation

usage. Moreover, the impacts of wind power from different regions in ERCOT are not

alike.

iv

TABLE OF CONTENTS

LIST OF FIGURES ..................................................................................................... v

LIST OF TABLES ....................................................................................................... vi

ACKNOWLEDGEMENTS ......................................................................................... viii

Chapter 1 Introduction ................................................................................................. 1

1.1 Inherent Characteristics and Limit of Wind Power ........................................ 2

1.2 Financial Subsidies ......................................................................................... 5

1.3 Ancillary Service ............................................................................................ 6

Chapter 2 Literature Review ........................................................................................ 9

Chapter 3 RTO-level Data and Rules .......................................................................... 12

3.1 ERCOT ........................................................................................................... 12

3.2 Data Sources ................................................................................................... 18

3.3 Rules of ERCOT Ancillary Services .............................................................. 19

Chapter 4 Statistical Analysis ...................................................................................... 23

4.1 Data Description ............................................................................................. 23

4.2 Model Description .......................................................................................... 34

4.3 Methodology Description ............................................................................... 38

Chapter 5 Results ......................................................................................................... 43

Chapter 6 Conclusion and Future Work ...................................................................... 72

Bibliography ................................................................................................................ 74

Appendix A Non-technical Overview of RTO and CPS ............................................. 77

Appendix B Chi-Squared Distribution Table .............................................................. 78

Appendix C Durbin-Watson d-Statistic Table ............................................................. 79

v

LIST OF FIGURES

Figure 1-1. Generation Mix in ERCOT ....................................................................... 3

Figure 1-2. Typical Wind Turbine Power Curve ......................................................... 4

Figure 1-3. The Response Time Frame of Ancillary Services..................................... 8

Figure 3-1. ERCOT Service Area ................................................................................ 12

Figure 3-2. ERCOT 2011 Generation Capacity by Fuel Type .................................... 13

Figure 3-3. Percentage of ERCOT Energy Produced by Wind Power ........................ 13

Figure 3-4. ERCOT Market Transition ........................................................................ 15

Figure 4-1. ERCOT Wind Region Division ................................................................ 23

Figure 4-2. 02/01/2008 ERCOT Hourly Wind Generation Standard Deviation ......... 31

Figure 4-3. February 2008 ERCOT Hourly Wind Generation Standard Deviation .... 31

Figure 4-4. January 2010 ERCOT Load and Wind Trends ......................................... 32

Figure 4-5. 01/05/2010 ERCOT Load and Wind Trends ............................................ 33

Figure 4-6. Durbin-Watson d-Statistic ......................................................................... 38

Figure 5-1. 2008 ERCOT Regulation Usage Increase Trends ..................................... 60




Figure 5-5. 2008 ERCOT Regulation Requirement Increase Trends .......................... 66




vi

LIST OF TABLES

Table 1-1. Definitions and Characteristics of Key Ancillary Services ........................ 7

Table 3-1. ERCOT Transition to Nodal Market - Change ........................................... 17

Table 4-1. ERCOT Wind Farms in Culberson Region ................................................ 24

Table 4-2. ERCOT Wind Farms in Lubbock Region .................................................. 24

Table 4-3. ERCOT Wind Farms in Eastern Region .................................................... 24

Table 4-4. ERCOT Wind Farms in Gulf Region ......................................................... 24

Table 4-5. ERCOT Wind Farms in McCamey Region ................................................ 25

Table 4-6. ERCOT Wind Farms in Howard Region.................................................... 26

Table 4-7. ERCOT Wind Farms in Taylor Region ...................................................... 27

Table 4-8. Basic Information of Variables Studied ..................................................... 29

Table 4-9. Means and Standard Deviations of Wind Cross Terms Variables ............. 30

Table 5-1. Durbin-Watson Test Results....................................................................... 44

Table 5-2. Regression Results for Regulation Down Service ...................................... 45

Table 5-3. Regression Results of Wind Cross Terms for Regulation Down Service for

Model 4 ...................................................................................................... 46

Table 5-4. Regression Results for Regulation Up Service .......................................... 47

Table 5-5. Regression Results of Wind Cross Terms for Regulation Up Service for

Model 4 ...................................................................................................... 48

Table 5-6. Likelihood Ratio Tests Results for Regulation Down Service ................... 49

Table 5-7. Likelihood Ratio Tests Results for Regulation Up Service........................ 50

Table 5-8. Overall Impact of Wind Power on ERCOT Regulation Service ................ 50

Table 5-9. Regional Wind Impact on ERCOT Regulation Requirement .................... 53

vii

Table 5-10. 2008-2011 Regional Wind Impact on ERCOT Regulation

Requirement ............................................................................................. 55

Table 5-11. Weights of New Wind Capacity Allocation for McCamey, Lubbock,

Eastern and Gulf Region .......................................................................... 56

Table 5-12. Regional Wind Impact on ERCOT Regulation Usage ............................. 57

Table 5-13. 2008 ERCOT Regulation Usage Increase and Arc Elasticity .................. 60




Table 5-17. 2008 ERCOT Regulation Requirement Increase and Arc Elasticity........ 66




viii

ACKNOWLEDGEMENTS

I would like to show my sincerest gratitude to my advisor Dr. Andrew N. Kleit for

his continual support, inspiring ideas and encouragement that have allowed me to

pursue my goals. I am very grateful to Dr. Robert Michaels and my committee

members Dr. Zhen Lei and Dr. Jeffrey R. S. Brownson for their time and comments.

Thank you to my fellow colleagues in the Department of Energy and Mineral

Engineering for their friendship and useful information. I also would like to thank two

ERCOT contacts, Ms. Thuy Huynh and Ms. Zang Hailing for their help in patiently

answering my questions and kindly providing me with important data. In addition, I

am truly thankful to my parents and friends. Without their love and encouragement, I

would not have been fortitudinous to overcome each challenge in the unfamiliar

surroundings.

1

Chapter 1

Introduction

U.S. wind power generation has experienced rapid growth in the last 20 years. The

total installed wind capacity has increased from 1,500 megawatt (MW) in 1992 to

more than 50,000 MW in August of 2012 (Brown, 2012). The main reasons for the

fast-growing trend are the environmental benefits wind energy offers to the society

and government mandates.

Traditional sources of electricity are associated with air pollution, acid rain and

global warming, while wind is a renewable energy resource that produces no air or

water pollution and involves no hazardous substances. Hence, electricity generation

by wind power does not contribute to environmental pollution, climate change and

particulate-related health effects. Although wind power is environmentally benign, we

should keep its disadvantages in mind since they can influence the economic

competitiveness of wind power for integration into the electric utility systems.

Because of high capital costs of wind power plants and its inherent characteristics

of intermittency and variability, incorporation of wind power into the electric system

needs large amounts of financial subsidies from governments. This research aims to

evaluate the economic impacts of wind power integration on the electricity regulation

market. As the study is based on the Electric Reliability Council of Texas (ERCOT)

case, its main purpose is to determine the impacts of wind power on ERCOT

regulation services. Wind power has been continuously incorporated into ERCOT

system. Since wind power is intermittent, the wind integration makes it more difficult

for operators to regulate the grid. Therefore, we would like to know how many more

regulation services will be required if ERCOT continuously increases its wind

generation. In other words, we will quantify the amounts of additional ERCOT

regulation services that are required to support its wind integration in various degrees.

2

1.1 Inherent Characteristics and Limit of Wind Power

Wind power generation has technical characteristics which are inherently different

from those of conventional energy generation. Conventional electricity generation

through burning coal or gas can be controlled to a precise output level while wind

power generation is inherently intermittent and hard to be predicted. While wind

generation can be turned down or curtailed from its potential output, it cannot be

increased beyond the power level provided by the existing wind velocity (General

Electric, 2008). Wind velocity can change quickly, which means that short-term

fluctuations and intermittent intervals of wind generation output are likely to occur.

As a result, backup energy will be highly needed when the wind speed is too low.

Currently, gas turbines (particularly open-cycle gas turbines, OCGTs) are better suited

to back up wind turbines because OCGTs are designed to start and stop quickly

(Hughes, 2012). However, using gas-fired turbines to supply backup energy increases

a variety of of costs, such as fuel costs, capital costs, and operation and maintenance

(O&M) costs. Furthermore, the system regulators have to decide when to start backup

power and how long they should keep backup turbines running. Therefore, wind

power integration increases the difficulty and costs for electricity system regulation

and reliability.

It is necessary to balance power supply and load demand. In order to guarantee grid

reliability, avoid power outage and ensure backup energy for emergent events, the

stand-by output should be kept no less than the load demand. However, the power

supply should not exceed the load demand too much, because large power surplus

causes waste of energy and loss of profits to power systems. Although wind power is

intermittent, the balance could be achieved by a generation mix. Figure 1-1 displays

the generation mix in ERCOT from November 5 to November 11 in 2008 and reveals

the intermittency and variation of wind power. During high demand periods (typically

in the middle of the day), the nuclear and coal generations were maintained at the

same level, while the MWh of gas generation was changed based on the actual load

and wind power output as Figure 1-1 shows. Therefore, variation in wind power was

accommodated largely by gas generation. In contrast, during low-load periods, gas

3

generation was limited and in a stable output. The gap between the lower border of

wind generation and higher border of coal generation was almost unchanged, while

coal generation varied during low-load periods. This means that variation in wind

power was filled by coal generation. In addition, the times of peak availability of wind

resources in a given location may not coincide with the times of peak load demand for

electricity, which further affects the economy of wind power and makes it less

attractive to electric utilities than power sources that are available at all times (EIA,

1995).

Figure 1-1. Generation Mix in ERCOT (Kaffine et al., 2011)

In essence, wind is a form of solar energy. Wind energy is caused by the heating of

the atmosphere by the sun, the rotation of the earth, and the earth's surface

irregularities.1 Then the air flow over the blades of wind turbines causes them to

rotate, and finally the mechanical energy converts to electric energy. The amount of

electricity generated by a wind turbine depends largely on the wind speed, as the

potential wind energy varies with the cube of wind speed (Hughes, 2012). The

requirements for wind generation are strict. If the wind speed is too low, it is not

economic to keep the turbine running. In contrast, if the wind speed is too high, the

1 For more information about the definition of wind energy, refer to: www1.eere.energy.gov/

wind/wind_ad.html.

4

wind turbines must be closed down due to safety reasons. Modern wind turbines tend

to start up when the wind speed reaches 3-5 metres per second (6.7-11.2 mph, which

is called "cut-in" speed) and then turn off when the speed goes above about 25 metres

per second (56mph, which is called "cut-out" speed) as Figure 1-2 shows.2 Such strict

limit causes that a wind turbine cannot generate energy continuously as natural wind

speed is dynamic and uncontrollable.

Figure 1-2. Typical Wind Turbine Power Curve3

2 For more information about "cut-in" and "cut-out" speed of modern wind turbines, please

refer to: howtopowertheworld.com/disadvantages-of-wind-energy.shtml.

3 For more information about the wind turbine power curve, refer to: http://www.pfr.co.uk/

standfordhill/15/Wind-Power/119/Capacity-Factor.

5

1.2 Financial Subsidies

There are two primary policies providing market and financial incentives to support

the wind industry in the United States. One is the production tax credit (PTC), a

federal tax incentive of 2.2 cents ($0.22) for each kilowatt-hour (kWh) of electricity

produced or a federal production tax credit equal to a carbon dioxide price of about

$20/metric ton by a qualified wind project (Brown, 2012; Katzenstein et al., 2010).4

The other is renewable portfolio standards (RPS), also referred to as renewable

electricity standards (RES). RPS are state-level policies designed to encourage

generation of electricity from renewable power by requiring a certain percentage of

electricity be generated by renewable energy sources or a certain amount of qualified

renewable electricity capacity be installed. Specifically, the Texas renewable-energy

mandate was set as 5,880 MW by 2015 (about 5% of the state's electricity demand) in

2005. The 2005 legislation also set a goal of reaching 10,000 MW of renewable

energy capacity by 2025.5 Qualifying renewable energy sources include solar energy,

landfill gas, wind energy, biomass, hydroelectric, geothermal electric, tidal and wave

energy.

Based on the U.S. total wind capacity of over 50,000 MW (Brown, 2012) in 2012,

the U.S. government has to pay several billion dollars for subsidizing wind energy

generation. The amounts of such financial subsidies are expected to increase in the

future since the U.S. wind generation is growing. Hence, it is costly for governments

to provide financial supports to the wind industry.

4 For more information of the federal PTC for the U.S. wind industry, see Brown, P., 2012.

U.S. Renewable Electricity: How Does the Production Tax Credit (PTC) Impact Wind

Markets?

5 For more details about the RPS for Taxes, see Database of State Incentives for Renewables

& Efficiency: http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=TX03R&ee

=0.

6

1.3 Ancillary Service

Electric energy cannot be easily stored on a large-scale basis. Thus grid system

operators must be concerned with real-time system operations in order to ensure that

the amount of power generation is near-instantaneously matched to the amount of

customer load demand (General Electric, 2008; Brown, 2012). Instantaneous balance

of generation and load is always challenging because load and energy output are

constantly varying, where load variability results from random turning on and off of

individual loads, daily and seasonal load patterns, and different weather conditions.

As a general rule, sources of generation (typically coal and nuclear) with lower

marginal cost are utilized by the grid first, followed by higher marginal cost sources

(typically gas) as the load demand increases (Kaffine et al., 2011). As a source of

generation with a near-zero marginal cost, wind power generation is almost always

taken by the grid when available. As a result, the intermittency of wind power

requires rapid adjustment of fossil generation in response to increases or decreases in

wind generation. Therefore, large amounts of wind generation can potentially result in

operational reliability issues due to the intermittent and unpredictable nature of wind

power (Kaffine et al., 2011; Brown, 2012).

Ancillary services provide the resources that the system operator requires to

reliably maintain the balance between energy output and load demand, and mainly

include operating reserves (responsive reserve or spinning reserve, replacement

reserve and non- spinning reserve), regulation services (regulation down and up

services), voltage control and frequency control, and the first two items are seen as the

primary services. Specifically, the purpose of operating reserves is to protect the

system against unforeseen contingencies, such as unplanned generator outages, load

forecast error and wind forecast error. Regulation deployments are either increasing or

decreasing as necessary to fill the difference between real-time energy output and

actual system load (Potomac Economics, 2012). Different from the other ancillary

services, voltage control is not a power service. These services are briefly described in

Table 1-1, in which cycle time can be seen as how often the service will be set or

7

adjust. Intuitively, ancillary services with shorter response time need higher costs

(Kirby, 2004).

Table 1-1. Definitions and Characteristics of Key Ancillary Services (Kirby, 2004)

Service Responsive Speed Duration Cycle Time

Regulation

Service

Power sources online that can respond rapidly to system operator

requests for up and down movements

110 min Minutes Minutes

Spinning

Reserve

Power sources online that can increase output immediately in

response to a major generator or transmission outage

Seconds to <10 min 10120 min Days

Replacement

Reserve

Same as spinning reserve with 30-min responsive time; used to restore

spinning and supplemental reserves

<30 min 2 hours Days

Voltage

Control

The injection or absorption of reactive power to maintain transmission

system voltages within required ranges

Seconds Seconds Continuous

Note that regulation services and voltage control are supplied for normal operations

while replacement reserve and supplemental reserve are for contingency operations.

Table 1-1 indicates that they have different responsive time.

8

Figure 1-3. The Response Time Frame of Ancillary Services (Kirby, 2004)

9

Chapter 2

Literature Review

Although wind power produces no air or water pollution, some studies have

revealed its variable nature. In particular, given the inherent characteristics of wind

power, several studies have focused on how large-scale wind power integration would

affect regulation requirements of an electric system.

The intermittency of wind generation has been widely studied. Holttinen et al.

(2009a) indicated that the second and minute variability of large-scale wind power is

generally small, while wind generation over several hours will introduce additional

variability and uncertainty into the system operation. Therefore, more flexible

ancillary services in the system are needed, and how many additional ancillary

services are needed depends on how much wind power is integrated in the system.

Apt (2007) stated that intermittent wind generation must be matched with fill-in

power sources from storage or generation. The results showed that fast devices with

relatively low power would match the short-period fluctuations, while slower ramp

rate sources would match the longer period and higher amplitude fluctuations.

An overview of main results of integrating intermittent generation was presented in

Gross et al. (2006). The results showed that the primary impacts and costs introduced

through connecting increasing amounts of intermittent supply would arise because

additional system balancing actions will be needed to ensure reliability of supplies.

Specifically, for integration of intermittent renewables up to 20% of electricity supply,

extra power reserves for system balance due to short-term wind generation fluctuation

amount to about 510% of installed wind capacity. For example, if there is 10,000

MW of power supply and 2,000 MW of installed wind capacity, then the extra power

reserves or ancillary services due to wind integration will be approximately 200 MW.

Hughes (2012) stated that wind generation imposes heavy costs on other parts of

the electricity system. Although wind generation requires no fuel, its capital costs,

operating and maintenance (O&M) costs are relatively high. As a consequence, the

net saving in fuel can be less than the investment cost, let alone the additional costs of

backup capacity of other energy resources for wind power.

10

MacCormack et al. (2010) examined the effects of large-scale wind integration into

electric grid systems in deregulated energy-only markets on load demand and

dispachable conventional generation. It indicated that in the medium term, increased

penetration of wind generation can lead to lower electricity prices and increased

power supply for grid reliability. It also showed that in the long term, the deterioration

in reliability caused by large-scale wind integration can be addressed by a structural

optimization of the generation mix.

Holttinen et al. (2009), Holttinen et al. (2009a) and General Electric (2008) used

statistical models to estimate the increase in reserve requirements due to wind

integration. Usually, the increase in short-term regulation requirement was estimated

by including the variations of net load (load - wind power) in the model, or the

increased regulation services were evaluated by examining wind, together with wind

variation, load variation and actual historical utility load. We will adopt and modify

the latter method in our study.

The impacts of different levels of wind penetration have also been studied. Kirby

(2004) found that a large collection (penetration level of 50%) of wind plants could

increase the regulation requirement by 41% in the control area. Similarly, wind

integration with penetration levels of 10-20% of gross load demand has been studied

in Holttinen et al. (2009). The result showed that the estimated increase in short-term

reserve requirements was 1-15% of installed wind power capacity at 10% wind

penetration and 4-18% of installed wind power capacity at 20% wind penetration. For

example, if there is a power system with 10,000 MW total capacity and 1,000 MW

wind capacity, then the increase in short-term reserve will be 10-150 MW.

Other operational impacts of wind generation on the regulation requirements of an

electric utility grid system have been evaluated in Hudson et al. (2001). Here the

amount of capacity required to support the regulation services was largely determined

by the standard deviation of the 2-minute interval regulation data. The results based

on the standard deviation analysis of a 100 MW wind facility in Minnesota showed

that the regulation burden is inversely proportional to the number of wind turbines of

the wind facility, which means that wind-caused regulation burden could be reduced

11

by increasing the numbers of wind turbines in the system. Similarly in Holttinen et al.

(2009), it was demonstrated that wind facility aggregation of large areas can help in

reducing the variability and forecast errors of wind power.

12

Chapter 3

RTO-level Data and Rules

3.1 ERCOT

Texas is the leader among the United State in adding wind power to the state's

power generation portfolio (General Electric, 2008). ERCOT, managing the flow of

electric power to 85 percent of the state's electric load,6 has wind installed capacity of

more than 9,600 megawatts (MW) out of a total installed capacity of approximately

84,000 MW in 2011 (ERCOT, 2012). Figure 3-1 displays the service area of ERCOT.

Figure 3-1. ERCOT Service Area7

Figure 3-2 displays the ERCOT generation capacity by fuel type in 2011. It shows

that gas-fired and coal-fired power accounted for approximately 80 percent of

ERCOT total installed capacity. Wind was the third main generation source of

ERCOT, accounting for 13 percent of the total installed capacity in 2011. Figure 3-3

shows how the percentage of ERCOT total energy produced by wind power has

increased.

6

For detailed information of ERCOT's market share, see http://ercot.com/about.

7 The map of ERCOT regions derives from http://www.ercot.com/news/mediakit/maps.

13

Figure 3-2. ERCOT 2011 Generation Capacity by Fuel Type (ERCOT, 2012)

Figure 3-3. Percentage of ERCOT Energy Produced by Wind Power (Maggio, 2012)

As a Regional Transmission Organization (RTO), ERCOT has a responsibility to

ensures a reliable electric grid and efficient electricity markets. In order to control

system frequency and protect system reliability from imbalances between generation

and load and from unforeseen contingencies, ERCOT currently uses several ancillary

services which mainly consist of regulation services (regulation down and regulation

up), non-spinning reserve service and responsive reserve service.

14

Specifically, ERCOT Regulation Services consist of resources that can be deployed

by ERCOT to maintain the target system frequency within predetermined limits

according to the Operating Guides (ERCOT, 2012a). In other words, Regulation

Services are deployed in order to correct actual frequency to scheduled frequency,

where Regulation Up (RU) is the positive difference between actual load and

dispatched generation output, and is used when load exceeds the generation while

Regulation Down (RD) is used when load is less than the dispatched generation

output. ERCOT Responsive Reserve is the generation resources that ERCOT uses to

address loss of generation resources and unexpected large changes in generation

requirements or a significant deviation from the standard frequency, and ERCOT

Non-Spinning Reserve Service is generation resources that can come on line with

short notice to compensate for load forecast errors (General Electric, 2008).

In ERCOT, the energy output (the Generation to be Dispatched, GTBD) is

scheduled every five minutes. Since what we use in the research is hourly data, there

are twelve dispatch opportunities for any hour. Therefore, the case that both

Regulation Up and Down are positive in one hour is likely to occur. For example, if

the load forecast shows an expectation of 20,000 MW of load demand at 10:00, while

the actual load is 20,300 MW at that time, ERCOT has to deploy 300 MW (20,300

MW - 20,000 MW) Regulation Up service to correct the load forecast error.

Furthermore, if the dispatched generation is 20,500 MW at 10:05, while the actual

load is 20,300 MW at that time, 200 MW (20,500 MW - 20,300 MW) Regulation

Down service is needed to balance the energy output and load demand. In this case,

both Regulation Up and Down are required in one hour.

In ERCOT zonal market, the grid was divided into Congestion Management Zones

(CMZs), which were Houston, North, South and West Zones while in today's nodal

market, the grid consists of more than 4,000 nodes rather than 4 CMZs as Figure 3-4

shows. In September 2003, the Public Utility Commission of Texas (PUCT) ordered

ERCOT to develop a nodal wholesale market design, and the transition from zonal

market to nodal market occurred in December 2010.

15

Figure 3-4. ERCOT Market Transition (ERCOT, 2012)

The market transition was considered necessary because ERCOT zonal market

lacked sufficient price transparency, which resulted in less efficient power dispatch

and less efficient congestion management tools. Also, ERCOT operators had limited

options to resolve congestion, and participants who contributed to resolve local

congestion were not directly assigned the associated costs in the zonal market. In

other words, generators were possibly not compensated enough to cover their costs in

ERCOT zonal market. Nowadays in ERCOT nodal market, settlement prices are

based on locational marginal costs, so it ensures that a generator can be adequately

compensated for its costs (ERCOT, 2008).

One significant change under the nodal market is that deployments of energy occur

more frequently. Specifically, transactions in ERCOT zonal market were generally

limited to blocks of power where the quantity and price were the same for all hours

covered by the trade, while the nodal market enables both energy quantity and price to

vary every five minutes. The more frequent deployment of energy has meant that

ERCOT can correct the area control error (not matter caused by load forecast error or

wind generation variation) more quickly. Therefore, less regulation capacity needs to

be procured under the nodal market design (Potomac Economics, 2012). Additionally,

the wind generation changes were not incorporated into load forecast in ERCOT zonal

16

market, while in ERCOT nodal market, load change and wind change are considered

simultaneously. For example, if the load demand is forecasted to increase in 100 MW,

the actual load increase is 150 MW and wind change is +100 MW, 50 MW

Regulation Up for load increase and 100 MW Regulation Down for wind change are

required in zonal market, while 0 MW Regulation Up and 50 MW Regulation Down

for net load (load - wind) change are needed in nodal market. In other words, less

regulation services are required in nodal market.

In addition, ERCOT ancillary services have been partly changed due to the market

transition. Specifically, the replacement reserve service (RPRS) in ERCOT zonal

market has been replaced by the Reliability Unit Commitment (RUC) in the nodal

market. The RUC is used to economically determine additional resource commitment

to match the forecasted load and ensure that there is sufficient generation capacity in

the proper locations to reliably meet operational need (ERCOT, 2008). In addition,

there was no day-ahead energy market in ERCOT zonal market, while ERCOT nodal

market runs both day-ahead energy and ancillary services. The main change of

ERCOT service due to the market transition to nodal market is briefly described in

Table 3-1.

17

Table 3-1. ERCOT Transition to Nodal Market - Change (ERCOT, 2008)

Zonal Market Nodal Market

No day-ahead energy market;

Day-ahead market for ancillary services

procured for capacity

Day-ahead energy and ancillary services

co-optimized market (DAM)

Local congestion cost uplifted Local congestion cost directly assigned

Replacement reserve service (RPRS) Day-ahead reliability unit commitment

(DRUC)

Portfolio-based offers by zone Resource-specific offers

Run Balancing Energy Service (BES)

every 15 minutes

Run Security Constrained Economic

Dispatch (SCED) every 5 minutes

Zonal market clearing prices for BES for

generation and loads

Nodal locational marginal pricing (LMP)

for generation;

Zonal weighted LMP for loads

18

3.2 Data Sources

The details of ERCOT wind generation are not disclosed to the public through the

official website. We obtained the one-minute wind power data from ERCOT directly.

The raw data covers the wind generation information of all the wind sites of ERCOT

from January 2008 to December 2011.

Similar to wind power data, the data of ERCOT regulation service is limited from

the official website, particularly the detailed information of regulation deployments.

Most of the relevant data (regulation usage, regulation requirement and market

clearing price for regulation services) from the website is for ERCOT zonal market.

As the time span we focus on is 2008-2011, and ERCOT experienced a market

transition in December 2010, additional data and information, particularly the hourly

raw data of ERCOT regulation deployments from December 2010 to December 2011

was needed. The raw data of regulation deployments was also provided by ERCOT

directly.

As the purpose of regulation service is to reliably and continuously maintain the

balance between energy output and load demand, load-relevant variables are included

in the model that is to be introduced in the next chapter. Historical information on

hourly loads by ERCOT control area for 2008-2011 is available in ERCOT official

website, http://www.ercot.com/gridinfo/load/load_hist/.

19

3.3 Rules of ERCOT Ancillary Services

ERCOT ancillary services consist of Regulation Service (RGS, Regulation Up and

Regulation Down), Non-Spinning Reserve Service (NSRS) and Responsive Reserve

(RRS). ERCOT has developed specific requirements and methods for its ancillary

services, determining the quantities of different services. In the ERCOT market,

particularly in the nodal market, wind generation has been directly considered in the

determination of the monthly Ancillary Service requirements, specifically Regulation

Up, Regulation Down and NSRS.

ERCOT Regulation Service is deployed in order to correct actual frequency to

scheduled frequency. ERCOT is also required to evaluate normal requirements for

Regulation Service Up and Regulation Service Down on an annual basis. In general,

ERCOT uses historical data to evaluate Regulation Service requirements. Specifically,

ERCOT calculates the 98.8 percentile of actual Regulation Up or Down Service that

was deployed for the 30 days prior to the time of the study and the same month of the

previous year. The 98.8 percentile of positive and negative 5 minute net load changes

are calculated for the same time period, where the net load is defined as the ERCOT

load minus the total wind output. ERCOT also considers the increased amount of

wind penetration. In order to get the increase in wind capacity, ERCOT first calculate

the total nameplate capacities of wind resources in the ERCOT network model at the

time of the study and at the same time in the previous year separately (ERCOT,

2012a). Then the difference between them is the increased amount.

For determining the base Regulation Up Service requirements, ERCOT will make a

comparison among the 98.8 percentile of the Regulation Up Service deployments over

the last 30 days, the 98.8 percentile of the Regulation Up Service deployments for the

same month of the previous year, the 98.8 percentile of the positive net load changes

over the last 30 days, and the 98.8 percentile of the positive net load changes for the

same month of the previous year (ERCOT, 2012a). Then ERCOT will take the largest

number of this group. The base Regulation Down requirements are determined

similarly.

20

Although ERCOT schedules regulation requirements in advance, schedule changes

are likely to occur during the 6 a.m.-10 p.m. periods. As a result, ERCOT sometimes

finds that its maximum deployment rate of Regulation Service is insufficient to

control system frequency. Therefore, ERCOT may need to use extra regulation

services to respond to such large energy swings.

In addition, ERCOT may also need additional Regulation Service deployments in

order to meet the Control Performance Standard (CPS) that is set by North American

Electric Reliability Council (NERC). Specifically, if it is determined that the ERCOT

average CPS18 score was less than 100% during the time of the study, then in the

upcoming month, ERCOT will procure an extra 10% of both Regulation Up and

Down deployments for hours of the day in which the CPS1 value was less than 100%.

This value will increase to 20% if the CPS1 score for the previous month was less

than 90% (ERCOT, 2012a).

The operating reserves in ERCOT ancillary service market consist of responsive

reserve and non-spinning reserve service (Wang et al., 2011). In ERCOT regulation

market, Responsive Reserve Service (RRS) is resource ERCOT uses to restore the

frequency of ERCOT System within the first few minutes of unexpected events that

cause significant deviations from the standard frequency (ERCOT, 2012a). The

ERCOT Operating Guides set the minimum RRS requirement at 2,300 MW for all

hours in normal operations. However, deployments of 500 MW RRS will be included

in the net load analysis for Non-Spinning Reserve Service (NSRS). This means that

an additional 500 MW will be added to the 2,300 MW minimum requirement for

NSRS, resulting in a total RRS minimum requirement of 2,800 MW.

ERCOT Non-Spinning Reserve Service (NSRS) is deployed to replace the loss of

generation capacity, and it is generally used to compensate for load forecast errors

and/or wind forecast uncertainty when large amounts of reserve are not available

online. ERCOT NSRS consists of generation resources that can be ramped to a

specified output level within 30 minutes, or load resources that are capable of being

8 CPS1 score measures the relationship between the system's area control error and the

system frequency every one minute. More information about CPS is available in Appendix A.

21

interrupted within 30 minutes (ERCOT, 2012a). Note that ERCOT NSRS supplies are

required to run or stop at a specified output level for at least one hour.

Similar to the time period of Regulation Service, the periods that are selected for

NSRS analysis are the last 30 days prior to the study and the days of the same month

in the previous year. For determining the quantity of NSRS, ERCOT generally

calculates 95 percentile of the net load uncertainty for the analyzed days for all hours.

The average Regulation Up requirements for the same time will also be calculated.

Then ERCOT will purchase NSRS such that the combination of NSRS, 500 MW of

RRS, and Regulation Up can cover 95% of the net load uncertainties observed in the

net load forecast accuracy calculation (ERCOT, 2012a). The method for determining

ERCOT RRS requirement can be mathematically represented by Equation 3-1.

In general, NSRS is not continuously in need. It is usually deployed in cold weather

days when the load rise in the early morning outpaced the ability of generation for

Regulation Up Service. It is also deployed in the afternoon during summer seasons

when high loads and unit outages outstrip the capability of base load units. In days

with unexpected changes in weather, NSRS is also needed. Since ERCOT NSRS is

not always needed as its Regulation Service, the quantitative analysis in this paper

does not evaluate the wind impacts on NSRS.

Not surprisingly, the intermittency of wind power will increase the instability of the

whole grid system. Consequently, increases in system variability and uncertainty will

lead to an increased need for reserves in the form of ancillary services. A rise in total

ancillary services deployments, along with a likely corresponding increase in the

market clearing price for these services, will result in greater total cost (Maggio,

2012).

In order to evaluate the economic impacts of wind-powered integration on the

ERCOT regulation market, in the next two chapters, this paper will first describe the

detailed information of all studied variables. Second, the impacts of wind power on

the ERCOT Regulation Service will be examined, and the specific increased amounts

22

will be provided. Third, the economic impacts (costs of extra Regulation Service for

wind generation) of wind power will be estimated by making use of the information

of market clearing price for ERCOT Regulation Service and the increase in the

amount of those services for wind power.

23

Chapter 4

Statistical Analysis

4.1 Data Description

The research dataset contains more than 40 variables, each of which consists of

more than 35,000 hourly observations from the ERCOT zonal and nodal markets

(2008-2011). Although there are theoretically 35,064 (8760 3 + 8784) hours in

2008-2011, the real number of the hourly data for each variable used in the study is

35,058 because some data is not available due to Daylight Saving Time or market

transition.

According to the detailed one-minute wind power data provided by the ERCOT

contact, there are more than 70 wind sites owned by ERCOT, and the number is still

increasing. These wind sites are divided into seven regions, Culberson, McCamey,

Howard, Lubbock, Taylor, Eastern and Gulf. The ERCOT wind regions can be briefly

mapped as Figure 4-1. The area of each circle shown in Figure 4-1 roughly represents

the number of wind sites in each wind region.

Figure 4-1. ERCOT Wind Region Division

24

The specific division of ERCOT wind farms in 2010 is shown in Table 4-1 to Table

4-7.9 There are not many wind farms in Culberson, Eastern, Lubbock and Gulf

Region as the following four tables show. As a result, the wind generations of these

four regions were less than those of Howard and Taylor Region, each of which owns

more than 20 wind sites.

Table 4-1. ERCOT Wind Farms in Culberson Region

Owner Site Name County Wind Capacity, MW

Nextera Kunitz Culberson 40.3

Nextera Delaware Mountain Culberson 28.5

Table 4-2. ERCOT Wind Farms in Lubbock Region


Invenergy McAdoo Dickens 150.0

Res-America Whirlwind Floyd 59.8

Table 4-3. ERCOT Wind Farms in Eastern Region


Iberdrola Barton Chapel Jack 120.0

BP Clipper Silver Star Erath 60.0

Nextera Wolf Ridge Cooke 112.5

Table 4-4. ERCOT Wind Farms in Gulf Region


Pattern/Blue Arc Gulf Wind 1 Kenedy 141.6

Pattern/Blue Arc Gulf Wind 2 Kenedy 141.6

Iberdrola Penascal 1 Kenedy 160.8



9 The ERCOT wind region division shown in Table 4-1 - Table 4-7 is based on the contents

in Windfarm-names2009.xls provided by ERCOT directly.

25

Table 4-5. ERCOT Wind Farms in McCamey Region


AEP Wind Desert Sky I Pecos 84.0

AEP Wind Desert Sky II Pecos 76.5

Nextera Indian Mesa Orion Pecos 82.5

Nextera King Mountain Wind NE Upton 79.3

Nextera King Mountain Wind SW Upton 79.3

Nextera King Mountain Wind NW Upton 79.3

Nextera King Mountain Wind SE Upton 43.3

BP Sherbino Mesa I Pecos 150.0

Nextera Southwest Mesa Wind Farm Crockett 74.2

Nextera Woodward Mountain I Pecos 82.5

Nextera Woodward Mountain II Pecos 77.2

Duke Notrees I Ector 152.6

26

Table 4-6. ERCOT Wind Farms in Howard Region

Owner Site Name County Wind Capacity,

MW

Shell Brazo Wind Ranch I Borden 99.0

Shell Brazo Wind Ranch II Borden 61.0

Eurus Bull Creek Borden 180.0

Eon Champion Scurry 126.5

Invenergy Camp Springs II Scurry 120.0

NRG Elbow Creek Wind Howard 121.9

Enel WKN Enel Snyder Wind Project Scurry 63.0

Eon Forest Creek Wind Farm I Glasscock 124.2

Eon Forest Creek Wind Farm II Glasscock 90.0

Edison Mission Goat Wind I Sterling 80.0

Edison Mission Goat Wind II Sterling 69.6

Edison Mission Cedro Hill Webb 150.0

Eon Inadale Scurry 197.0

Duke Ocotillo 1 Howard 58.8

Eon Panther Creek 1 Howard 142.5



Eon Pyron Scurry 249.0

Nextera Red Canyon Borden & Garza 84.0

TGP NYC Big Spring Howard 34.3

Invenergy Stanton Martin 120

Eon Papalote Creek 1 San Patricio 179.9

Eon Papalote Creek 2 San Patricio 200.1

3rd Planet Loraine I Martin 49.5

3rd Planet Loraine II Martin 51.0

Invenergy Scurry County Wind Scurry 130.5

27

Table 4-7. ERCOT Wind Farms in Taylor Region

Owner Site Name County Wind Capacity,

MW

AES Buffalo Gap 1 Taylor 120.6

AES Cirello (Buffalo Gap 2) Taylor 232.5

AES Louis (Buffalo Gap 3) Taylor 170.2

AES Callahan Divide Taylor 114.0

Nextera Capricorn Ridge 1 Coke 214.5




Nextera Horse Hollow 1 Nolan 213.0




Res-America Hackberry Shackelford 165.6

Horizon Lone Creek Post Oak Shackelford 100.0

Horizon Mesquite 1 Shackelford 200.0

Horizon Post Oak Shackelford 100.0

Blue Arc South Trent Taylor 101.2

Blue Arc Sweetwater 1 Nolan 37.5

Blue Arc Sweetwater 2A Nolan 97.5

Blue Arc Sweetwater 2B Nolan 16.0


Blue Arc Sweetwater 4A Nolan 119.0

Blue Arc Sweetwater 4B Nolan 105.8


Eon Roscoe Tonkawas Nolan/Fisher/Scurry 209.0

AEP Wind Trent Mesa Wind Farm Nolan & Taylor 150.0

Invenergy Turkey Track Nolan 169.5

NRG Langford Tom Green 150.0

28

Besides the ERCOT regional wind output, we also study some other individual and

cross-term variables. The dependent variables in our study are Regulation Down (RD)

and Regulation Up (RU) usages. We will estimate hourly RD and RU usages based on

the independent variables, constructed models and regression parameters. The annual

regulation down and up requirements will be further calculated based on the estimated

values. Regulation services are mainly used to balance the power supply and load

demand, so ERCOT Load and Load Change may largely influence regulation usages.

Therefore, we will include these two items in our model. In order to comprehensively

examine the load effect, the variable Load Load will also be included. Because wind

generation is intermittent, we need to consider wind variation. Therefore, Hourly

Wind Standard Deviation (Std. Dev.) will be added to the models. It will be calculated

as the standard deviation of 60 one-minute raw wind data in each hour.

Note that we will introduce a indicator representing the ERCOT market type, where

0 represents the zonal market and 1 represents the nodal market. The reason is that the

data we study is in the period of 2008-2011, and ERCOT experienced a market

transition from the zonal market to the nodal market in December 2010 as discussed

in the previous chapter. So far, we have constructed the base model that includes five

independent variables, ERCOT Load, Load Load, Load Change, Market Indicator

and Hourly Wind Standard Deviation. Three complex models will be constructed

based on the base model and we will choose the best one among these four models to

estimate the impact of wind power.

In the complex models, we will consider the effects of Regional Wind Load or/

and Regional Wind cross terms. The reason is that wind generation in one region can

be partially correlated with or even negatively related to that in another region. In

addition, the load demand and wind generation in each region may affect each other.

In order to examine the possible cross effects, we will further test their significances.

If the test results show that they can largely influence the regulation usages, we will

retain them in the best model, otherwise they will be dropped.

In addition, we will use Market Clearing Price for Capacity for ERCOT Regulation

Down and Up services to roughly calculate the increased costs of regulation services

29

due to wind integration, but they will not be included in the models. We first examine

the basic information of all variables discussed above. Their means, standard

deviations, minimum values and maximum values are shown in Table 4-8 and 4-9.

Table 4-8. Basic Information of Variables Studied

Mean Std. Dev. Min Max

MCPC for RD, $/MWh 10.912 13.500 0.140 593.000

MCPC for RU, $/MWh 16.229 64.752 0.470 2584.940

ERCOT Load, GWh 36.323 9.092 19.664 68.392

Load Load 1402.030 745.319 386.681 4677.506

Load Change, GWh 0.000 1.752 -5.157 5.678

Hourly Wind Std. Dev., GWh 0.102 0.095 0.000 3.880

Culberson Wind, GWh 0.009 0.010 -0.020 0.047

McCamey Wind, GWh 0.285 0.219 -0.120 0.914

Howard Wind, GWh 0.728 0.728 -0.180 8.172

Lubbock Wind, GWh 0.068 0.056 -0.020 0.208

Taylor Wind, GWh 1.176 0.878 -0.280 3.717

Eastern Wind, GWh 0.083 0.080 -0.030 0.281

Gulf Wind, GWh 0.140 0.176 -0.060 0.676

Culberson Wind Load 0.328 0.339 -1.021 2.655

McCamey Wind Load 10.204 8.221 -6.125 47.626

Howard Wind Load 26.016 27.530 -9.188 388.518

Lubbock Wind Load 2.373 2.005 -1.021 11.297

Taylor Wind Load 41.346 31.655 -14.292 188.919

Eastern Wind Load 2.880 2.806 -1.531 14.634

Gulf Wind Load 5.439 7.440 -2.459 44.893

Note that though we have one-minute raw wind data, we focus on hourly wind data

rather than one-minute wind generation in this paper in order to keep it consistent

with other hourly variables. The means and standard deviations (in parenthesis) of

regional wind power cross terms are shown in Table 4-9. Specifically, the numbers in

30

the cell on the second row second column represent the information of the variable

Culberson Wind McCamey Wind, and so on. As Table 4-8 shows, the regional wind

variables are in GWh, so the wind cross terms are in (GWh)2. As there are seven wind

regions in the ERCOT market, we will examine 21 wind cross terms in this study.

Table 4-9. Means and Standard Deviations of Wind Cross Terms Variables

McCamey

Wind

Howard

Wind

Lubbock

Wind

Taylor

Wind

Eastern

Wind

Gulf

Wind

Culberson Wind 0.003

(0.004)

0.008

(0.013)

0.001

(0.001)

0.012

(0.017)

0.001

(0.001)

0.001

(0.003)

McCamey Wind

0.282

(0.427)

0.024

(0.030)

0.443

(0.511)

0.030

(0.042)

0.044

(0.077)

Howard Wind

0.069

(0.096)

1.247

(1.893)

0.090

(0.139)

0.146

(0.313)

Lubbock Wind

0.106

(0.129)

0.008

(0.011)

0.011

(0.020)

Taylor Wind

0.142

(0.196)

0.197

(0.354)

Eastern Wind

0.017

(0.031)

31

In order to show the inherent variability of wind power described in Chapter 1, we

present the standard deviation trends of ERCOT wind power on a daily basis (Figure

4-2) and a monthly basis (Figure 4-3). The hourly wind power is for ERCOT, which

is the sum of wind generation of all wind sites in each hour. The date and month were

randomly selected.

Figure 4-2. 02/01/2008 ERCOT Hourly Wind Generation Standard Deviation

Figure 4-3. February 2008 ERCOT Hourly Wind Generation Standard Deviation

0.00

50.00

100.00

150.00

200.00

250.00

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

Win

d S

td. D

ev. (

MW

h)

Hour on 02/01/2008

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

1

27

53

79

10

5

13

1

15

7

18

3

20

9

23

5

26

1

28

7

31

3

33

9

36

5

39

1

41

7

44

3

46

9

49

5

52

1

54

7

57

3

59

9

62

5

65

1

67

7

Win

d S

td. D

ev. (

MW

h)

Hours in February 2008

32

Figure 4-2 and 4-3 indicate that the ERCOT wind generation was variable in 2008.

In Chapter 1, we mentioned that both wind generation and load demand are hardly

accurately predicted. Their variation trends sometimes may coincide, but usually they

are different. This will make it more difficult for system operators to schedule

regulation services. We can roughly see the difference between the variation trends of

ERCOT load and wind power on a daily (Figure 4-5) and monthly (Figure 4-4) basis.

The studied date and month were randomly selected.

Figure 4-4. January 2010 ERCOT Load and Wind Trends

Figure 4-4 indicates that ERCOT wind generation did not follow the trends of

ERCOT load in January 2010. ERCOT load reached the monthly peak on 01/08/2010.

Then it declined steadily until the end of the month. In contrast, ERCOT wind power

changed greatly and randomly in January 2010.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

20

10

/1/1

20

10

/1/3

20

10

/1/5

20

10

/1/7

20

10

/1/9

20

10

/1/1

1

20

10

/1/1

3

20

10

/1/1

5

20

10

/1/1

7

20

10

/1/1

9

20

10

/1/2

1

20

10

/1/2

3

20

10

/1/2

5

20

10

/1/2

7

20

10

/1/2

9

20

10

/1/3

1

ERC

OT

Load

, GW

h

Day in January 2010

ERCOT Load

ERCOT Wind

33

Figure 4-5. 01/05/2010 ERCOT Load and Wind Trends

Figure 4-5 shows that the wind output variation coincided with the load variation in

the early morning and nighttime on 01/05/2010. For the rest of the day, however, the

wind generation tends to be relatively high during low-load hours. The periods when

load is increasing and wind output is decreasing require other generation resources to

increase output in order to compensate for the net load (load demand minus wind

generation) increase. This will largely increase the difficulty in providing regulation

services.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

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

Hour on 01/05/2010

ERCOT Load

ERCOT Wind

34

4.2 Model Description

In order to quantitatively test the impact of wind power on the ERCOT regulation

service and the significance of the explanatory variables, we construct four models

from simple to complex for further comparison and statistical analysis. All the

constructed models are linear regression models.

Model 1:

Model 1 is the simplest model we study, where represents the actual hourly

regulation up or down deployments, represents the hourly load, load change is

equal to minus , Market_Indicator indicates the market type,

Wind_Std. Dev. represents the regional wind power standard deviation and

Total_Wind is total ERCOT wind generation. We can obtain the regression

coefficients , the t-statistic of each variable, the standard error of each variable and

the log likelihood value of each model from the regression results by running the

regression model. Specifically, is the estimated parameter of each explanatory

variables. t-statistic is a ratio of the departure of an estimated parameter from its

notional value and its standard error and indicates the significance of each variable in

each model. Log likelihood is the natural logarithm of a likelihood function.

We need to consider the unobservable fixed effects when constructing models

because the potential correlation between the fixed effects and independent variables

will cause biases in the estimated coefficients. Such biases can be corrected by

creating panel variables when running regression. We also need to test and resolve

problems of autocorrelation because in the presence of autocorrelation the estimates

of coefficients of independent variables are inefficient. In other words, the standard

errors will be underestimated and the coefficient of multiple correlation (R2) will be

overestimated because of autocorrelation. The specific method will be discussed in

Section 4.3. All the acronyms, symbols and coefficients in Model 1 apply to Model 2,

Model 3 and Model 4.

35

Regulation services are mainly used to balance the power supply and load demand.

As a result, Load and Load Change may largely influence regulation deployment.

Therefore, we will include these two items in our model. In order to comprehensively

examine the load effect, the variable Load2 will also be included. Intuitively, more

regulation services are needed as load and wind generation increases. As a

consequence, the parameters of Load, Load2 and Total_Wind are expected to be

positive for both Regulation Up and Down services. Because wind generation is

intermittent and variable, we need to consider wind variation. Therefore,

Wind_Std.Dev. is included to the models. Since we generally require more regulation

reserves when we have larger short-term (one hour in our study) wind power change,

the coefficient of Wind_Std.Dev. is expected to be positive for both Regulation Up

and Down services. More Regulation Up services may be needed when load demand

increases and more Regulation Down services may be required when load decreases.

Therefore, we expect different signs of the coefficient of Loadt - Loadt-1 for

Regulation Up and Down services. In addition, the market type is indicated as 0 for

zonal market and 1 for nodal market in our study. We believe that the market

transition in 2010 improved the ERCOT market. In other words, less regulation

services should be needed in the nodal market than the zonal market with the same

scale of wind integration. Hence the coefficient of Market_Indicator is expected to be

negative for both Regulation Up and Down services.

Model 2:

New variables, Regional_Windit (i = 7 12) have been included in this model,

which represents the hourly wind generation in each region. Specifically,

Regional_Windit (i = 7 12) represent Culberson Wind, McCamey Wind, Howard

Wind, Lubbock Wind, Eastern Wind and Gulf Wind respectively. We consider not

only total ERCOT wind but also individual regional wind because the wind

36

generations and wind impacts of seven regions may be quite different. In order to

further quantify the increased regulation services due to wind integration in each

region, regional wind generation will be examined separately in Model 2, Model 3

and Model 4. All the acronyms, symbols and coefficients in Model 2 apply to Model

3 and Model 4. We do not expect that wind power in seven regions will have same

impacts. The relevant results could be a guidance for ERCOT to allocate the wind

capacity newly introduced to the system.

Model 3:

The total load demand in ERCOT and wind generation in each region may affect

each other. In order to examine the possible cross effects of them on regulation

services and test their significances, seven cross terms (Regional_Windit Loadt, i = 7

13) have been further included in this model. represent the coefficients of

the cross terms. All the acronyms, symbols and coefficients in Model 3 apply to

Model 4.

37

Model 4:

We further construct Model 4, the most complex model in this paper. Apart from

the individual regional wind and the cross terms of Load Regional_Wind, 21 wind

cross-term variables have been further included in this model. Wind generation in one

region can be partially correlated with or even negatively related to wind power

output in another region. Therefore, we need to examine the cross effects of regional

wind power and test how significant they are. represent the undetermined

coefficients of them.

38

4.3 Methodology Description

We have constructed four models for both ERCOT Regulation Up and Down

Service. The next step is to find out the best model that is most suitable for the study.

In order to achieve this, we first run Ordinary Least Squares (OLS) regression for all

the models described above by using the data for relevant variables in 2008-2011 and

the Stata10

programming language. Then in order to test potential autocorrelation

caused by misspecification, we will use Durbin-Watson Test statistic to obtain the

value of d for each model. In order to test for positive autocorrelation at significance

, we need to compare d to lower and upper critical values (dL, and dU,). According

to Figure 4-6, if d is between 0 and dL,, autocorrelation is clearly evident; if d is

between dL, and dU,, we neither accept nor reject the presence of autocorrelation; if

d is around 2, there is no statistical evidence that the error terms are autocorrelated.

Figure 4-6. Durbin-Watson d-Statistic11

We have to deal with autocorrelation if it does exist in our models. The problems of

autocorrelation can be resolved by adding lagged dependent variables to the models,

differencing the dependent variable or running Generalized Least Squares (GLS)

regression instead of OLS regression. We will adopt the first method in our study.

Specifically, lagged dependent variables (RDt-1 and RUt-1) will be included on the

right hand side of each model for both Regulation Up and Down services. Next, we

can obtain the coefficients (i = 0 40), the t-statistic of each variable, the standard

error of each variable and the log likelihood value of each model from the regression

results. Specifically, is the estimated parameter of each explanatory variables.

10

Stata is an integrated statistical software that provides data-management commands for

data analysis, data management and graphics. For more information about Stata, please refer

to website: http://www.stata.com/.

11 For more information about Durbin-Watson test, see Greene, W., H., 2007. Econometric

Analysis. Sixth Edition. New York University.

39

t-statistic is a ratio of the departure of an estimated parameter from its notional value

and its standard error and indicates the significance of each variable in each model.

Log likelihood is the natural logarithm of a likelihood function. The next step is to

remove the problems of autocorrelation. In order to achieve this, we will create lagged

variables and add them to the right hand side of each model. Take Model 1 as an

example, we will run OLS regression for the following new Model1 instead of the

original Model 1.

New Model 1:

In addition, we further consider fixed effects. Apart from the market type indicator,

we further include hour and day indicators in the models. To avoid multicollinearity,

we will include 23 hour indicators and 6 day indicators in each model, representing 1

am to 11 pm and Monday to Saturday. For example, if a data is created at 1 am, the 1

am indicator is 1 for this data; if not, the indicator is 0.

Next we will compare two nested models among the constructed models. In order

to achieve this, we will introduce likelihood ratio tests (LRTs) that compare two

models provided the simpler model is a special case of the more complex model,

where the simpler model has fewer parameters than the general one. The LRTs test

statistic is asymptotically distributed as a chi-squared random variable with degrees of

freedom (n) equal to the difference in the number of parameters between two models.

Hence, we will also check 2 values at 95% confidence level in order to determine

whether the null hypothesis should be accepted or not. Specifically, there are seven

variables in Model 1 and thirteen in Model 2. After running the LRTs, a 2(13 - 7 = 6)

value will be returned. If the returned 2(6) is between

20.95(6) and

20.05(6), we

should accept the null hypothesis; otherwise we should reject it, which means that the

coefficients or parameters of the extra six variables in Model 2 cannot be set at

zero. In other words, the extra six variables in Model 2 cannot be disregarded. This

40

method also applies to the other sets of possible comparison among the constructed

models.

The next step is to find out the overall impact of wind power on the ERCOT

Regulation Service. In order to calculate the increased regulation usages and

requirements due to wind integration, we need to know the actual regulation services

and the estimated values without wind impact. In order to estimate the overall impact

of wind power, we need to use the regression model to calculate the increased

regulation usage and requirement due to wind integration. Specifically, we will use

the raw data and all the coefficients ( ) of the regression model to estimate the hourly

regulation usage in the case without (with 0%) wind power by applying the regression

model with setting all wind-relevant variables to zero. We can further calculate the

estimated regulation requirement based on the estimated regulation usage. Note that

we will evaluate the wind impacts on an annual basis. The hourly data in December

2010 will be categorized to 2011 because ERCOT experienced a market transition

from zonal market to nodal market in December 2010. In addition, a market type

indicator variable allows us to examine the effect of market type. In this way, the

results for 2011 will reflect the impacts of wind integration on ERCOT nodal market.

ERCOT generally determines regulation requirements on a monthly basis by

calculating the 98.8th percentile of the Regulation Service deployments (5-minute

data) over the last 30 days and the 98.8th percentile of the Regulation Service usage

over the same month in last year. Then the largest of these values will be set as the

regulation requirements as long as there is no emergent events (unexpected power

outage, clement weather, etc) in that period. The 5-minute data, however, is not

available to us and all the data we use in the study is hourly data. In this case, we will

adjust the methodology by calculating the 98.8th percentile of the regulation usage

and analyze the requirement on annual basis. Once we have the regulation usage and

requirement in the case of 0% wind and 100% wind, we can obtain the increased

regulation usage and requirement. If we do not consider the possible impact of wind

power on the market clearing price for capacity (MCPC) of ERCOT Regulation

41

Services, we can roughly calculate the extra or increased costs due to wind integration

based on the method used in Maggio (2012). This can be represented as:

Besides the overall impact of wind power, we will also estimate the individual

impact of regional wind generation on ERCOT Regulation Service. Similarly, we will

estimate the hourly regulation usage by running the selected model by keeping the

wind data of a certain region unchanged and setting all the other wind-relevant

variables at zero. In this way, we can observe the increased regulation deployment

and increased regulation requirement caused by wind power in each region. Note that

the hourly wind standard deviation is related to wind power in each region. In order to

obtain the hourly regulation usages without wind generation in a certain region, the

hourly wind standard deviation should be recalculated in this step when we estimate

individual wind impact in each region by using the selected model. The specific

method will be described in the next chapter. In addition, we will introduce an impact

factor (IF) that can be mathematically represented as:

where RG represents Regulation Up/Down usage or requirement. The increased (in

certain cases could be decreased) RG can be considered as the difference between RG

in the case of 100% (estimated wind power/actual wind power = 100%) wind power

and RG in the case without (estimated wind power = 0) wind power.

Finally, in order to determine how the increases of ERCOT regulation usage and

requirement change as ERCOT wind power increases in a wide range, we will

calculate the increases of regulation usage and requirement, and graph variation trends

as the ratio of estimated wind power / actual wind power varies from 0 to 300% with

20% interval. More detail will be introduced in the next chapter. Although we can

create the graph for the regulation growth trends as ERCOT wind power increases, the

actual functions of these curves are not available. In order to show the variation trends

in each 20% interval mathematically, we will introduce arc elasticity, which can be

calculated as:

42

12，

where x represents % wind (estimated wind generation / actual wind generation) and

y represents regulation usage or requirement increases in our study.

12

For more information about arc elasticity, refer to Allen, R., G., D., 1933. The concept of

arc elasticity of demand. Review of Economic Studies, 1(3).

43

Chapter 5

Results

In order to select the most suitable model, we will first run Ordinary Least Squares

(OLS) regression for all the constructed models by using the hourly data for relevant

variables. For comparison, we recall the constructed models.

Model 1:

Model 2:

Model 3:

Model 4:

44

First, we use Durbin-Watson statistic to detect the presence of autocorrelation in

our models. Specifically, we run OLS regression for all original models for both

Regulation Up and Down services. Then we run Durbin-Watson test and compare the

returned values of d to the lower critical values for each regression. If d is between 0

and dL,, we have problems of autocorrelation. The results for Durbin-Watson statistic

are shown in the Table 5-1. Relevant lower critical values are shown in parentheses.

Note that the lower critical value depends on the level of significance (), number of

observations (n) and number of independent variables (k). The values we include in

parentheses are based on the Durbin-Watson Table ( = 0.05) and the complete lower

and upper critical values of Durbin-Watson d-statistic are shown in Appendix C.

Generally, the extreme case shown in conventional Durbin-Watson tables is generally

(n = 200, k = 10). However, there are more than 30,000 observations in each model

and more than 10 explanatory variables in Model 2, Model 3, and Model 4 in out

study. In this case, we will compare the returned d to the value of the extreme case.

Since lower critical value increases and approaches to 2 as the number of observations

increases, the true lower critical value for each case must be greater than dL,0.05(n=200,

k=10). In other words, if the returned d is less than dL,0.05(n=200, k=10), it must be

less than the true lower critical value.

Table 5-1. Durbin-Watson Test Results

d-statistic Model 1 Model 2 Model 3 Model 4

RD Service 1.51 (1.67) 1.51 (1.67) 1.51 (1.67) 1.52 (1.67)

RU Service 1.50 (1.67) 1.50 (1.67) 1.51 (1.67) 1.51 (1.67)

The results show that autocorrelation is clearly evident in all constructed models for

both Regulation Up and Down services. After removing the problems of

autocorrelation and considering fixed effects in the models, we can obtain the

estimated coefficients by running the regression models. There are 35,058

observations for each variable. The results for Regulation Up and Down will be

shown separately, in which t-statistic value of each variable will be in parentheses,

45

and t-statistic absolute values greater than 1.96 will be in bold type. The regression

results for Regulation Down Service are shown in Table 5-2 and Table 5-3.

Table 5-2. Regression Results for Regulation Down Service

Regulation Down Model 1 Model 2 Model 3 Model 4

ERCOT Load 3.666

(6.21)

3.648

(6.13)

4.123

(5.70)

4.059

(5.56)

Load Change 1.482

(2.46)

1.500

(2.48)

0.844

(1.38)

0.811

(1.32)

Load Load -0.032

(-4.47)

-0.032

(-4.43)

-0.022

(-2.66)

-0.022

(-2.61)

Indicator Variable for

ERCOT Market Type

-68.370

(-40.91)

-68.843

(-38.70)

-68.839

(-38.65)

-70.024

(-38.41)

ERCOT Wind 1.832

(4.29)

0.393

(0.33)

0.097

(0.02)

-0.449

(-0.07)

Wind Std. Dev. 160.842

(21.49)

159.856

(21.24)

159.514

(21.21)

160.936

(20.71)

Lagged RD 0.260

(42.43)

0.259

(42.37)

0.257

(41.95)

0.255

(41.65)

Lagged RU 0.023

(3.75)

0.024

(3.77)

0.026

(4.15)

0.026

(4.19)

Culberson Wind

-9.663

(-0.14)

-347.450

(-1.07)

-508.464

(-1.39)

McCamey Wind

9.990

(2.30)

81.327

(4.27)

62.549

(2.71)

Howard Wind

-0.275

(-0.14)

8.358

(0.89)

3.288

(0.28)

Lubbock Wind

63.390

(4.13)

243.577

(3.51)

244.220

3.04

Eastern Wind

-18.181

(-1.39)

-105.631

(-1.85)

-79.164

(-1.13)

Gulf Wind

4.097

(0.87)

102.861

(5.62)

128.145

(5.44)

Culberson Wind Load

10.374

(1.12)

13.888

(1.44)

McCamey Wind Load

-1.987

(-4.25)

-1.774

(-3.59)

Howard Wind Load

-0.217

(-1.31)

-0.191

(-1.10)

46

Lubbock Wind Load

-5.101

(-2.62)

-5.617

(-2.77)

Taylor Wind Load

0.012

(0.08)

0.015

(0.10)

Eastern Wind Load

2.313

(1.48)

2.594

(1.57)

Gulf Wind Load

-2.581

(-5.48)

-2.738

(-5.44)

Constant -20.792

(-1.69)

-23.079

(-1.85)

-54.714

(-3.48)

-51.408

(-3.21)

R-squared 0.183 0.183 0.186 0.187

Table 5-3. Regression Results of Wind Cross Terms for Regulation Down Service for

Model 4

McCamey

Wind

Howard

Wind

Lubbock

Wind

Taylor

Wind

Eastern

Wind

Gulf

Wind

Culberson

Wind

554.787

(1.42)

296.787

(1.96)

1339.097

(0.98)

-310.085

(-2.61)

1002.291

(0.87)

-774.085

(-1.61)

McCamey

Wind

6.926

(1.03)

-85.984

(-1.05)

8.774

(1.45)

-13.000

(-0.19)

-19.392

(-0.80)

Howard

Wind

89.857

(2.94)

-1.700

(-0.88)

-37.807

(-1.53)

-7.983

(-1.27)

Lubbock

Wind

10.769

(0.40)

-712.661

(-2.79)

69.344

(0.73)

Taylor

Wind

30.880

(1.79)

-4.622

(-0.58)

Eastern

Wind

10.708

(0.14)

We can roughly see that all the base variables (ERCOT Load, Load Load, Load

Change, Indicator Variable and Wind Std. Dev.), McCamey Wind, Lubbock Wind,

Gulf Wind and the relevant Regional Wind Load variables are significant for

Regulation Down Service. We also examine the regression results for ERCOT

Regulation Up Service that is displayed in Table 5-4 and Table 5-5.

47

Table 5-4. Regression Results for Regulation Up Service

Regulation Up Model 1 Model 2 Model 3 Model 4

ERCOT Load -1.879

(-3.21)

-1.422

(-2.41)

-3.356

(-4.69)

-3.204

(-4.43)

Load Change 1.205

(2.01)

1.248

(2.09)

2.184

(3.60)

2.234

(3.67)

Load Load 0.024

(3.48)

0.019

(2.71)

0.027

(3.30)

0.026

(3.12)

Indicator Variable for

ERCOT Market Type

-17.890

(-10.80)

-20.433

(-11.59)

-20.956

(-11.88)

-19.401

(-10.74)

ERCOT Wind -1.775

(-4.20)

1.688

(1.43)

4.259

(0.80)

12.418

(1.93)

Wind Std. Dev. 120.173

(16.20)

122.509

(16.43)

121.792

(16.34)

118.478

(15.39)

Lagged RD -0.039

(-6.35)

-0.040

(-6.55)

-0.038

(-6.24)

-0.038

(-6.20)

Lagged RU 0.226

(36.62)

0.223

(36.09)

0.219

(35.47)

0.218

(35.25)

Culberson Wind

192.098

(2.74)

-64.013

(-0.20)

-154.449

(-0.43)

McCamey Wind

-16.026

(-3.73)

-79.260

(-4.20)

-78.104

(-3.42)

Howard Wind

-6.610

(-3.38)

-23.609

(-2.55)

-28.042

(-2.40)

Lubbock Wind

-65.508

(-4.31)

-168.672

(-2.45)

-217.774

(-2.73)

Eastern Wind

17.565

(1.35)

-83.187

(-1.47)

-104.273

(-1.50)

Gulf Wind

20.235

(4.33)

-22.053

(-1.22)

-67.311

(-2.89)

Culberson Wind Load

7.094

(0.78)

4.579

(0.48)

McCamey Wind Load

1.693

(3.66)

1.507

(3.08)

Howard Wind Load

0.383

(2.33)

0.375

(2.18)

Lubbock Wind Load

2.800

(1.45)

3.536

(1.76)

48

Taylor Wind Load

-0.077

(-0.52)

-0.129

(-0.83)

Eastern Wind Load

3.166

(2.04)

2.873

(1.76)

Gulf Wind Load

0.951

(2.04)

1.211

(2.43)

Constant 161.742

(13.28)

154.359

(12.50)

216.041

(13.87)

212.542

(13.40)

R-squared 0.104 0.106 0.109 0.110

Table 5-5. Regression Results of Wind Cross Terms for Regulation Up Service for

Model 4

McCamey

Wind

Howard

Wind

Lubbock

Wind

Taylor

Wind

Eastern

Wind

Gulf

Wind

Culberson

Wind

55.656

(0.14)

-63.984

(-0.43)

326.714

(0.24)

-42.393

(-0.36)

916.507

(0.81)

1004.209

(2.10)

McCamey

Wind

-3.328

(-0.50)

84.234

(1.03)

-12.155

(-2.03)

10.490

(0.16)

50.513

(2.10)

Howard

Wind

-67.650

(-2.23)

2.509

(1.31)

0.010

(0.00)

-0.050

(-0.01)

Lubbock

Wind

-16.796

(-0.63)

694.133

(2.74)

-46.003

(-0.49)

Taylor

Wind

-36.390

(-2.13)

3.060

(0.39)

Eastern

Wind

30.600

(0.39)

The results show that the constant parameter, all the base variables, most of the

Regional Wind variables and most of the Regional Wind Load variables are

significant to the Regulation Up Service, which is somewhat different from the results

for the Regulation Down Service. Since the significance of each variable is different

in each model, we still cannot determine the best model in this step.

Next two nested models would be compared by running likelihood ratio tests

(LRTs) for both Regulation Up and Down. As we have constructed four models, the

possible comparisons are Model1 vs. Model 2, Model 1vs. Model 3, Model 1 vs.

Model 4, Model 2 vs. Model 3, Model 2 vs. Model 4 and Model 3 vs. Model 4, in

49

which the former models (the null models) are the special cases of the latter ones (the

alternative models with more variables). The test is based on the ratio of two

likelihood functions that can be mathematically represented as:

13

The LRTs results are shown in Table 5-6 and Table 5-7. We would compare the

results to the relevant chi-squared values with 0.95 confidence level that are shown in

parentheses. For instance, the LRTs results for Model 3 and Model 4 show that 2(21)

is equal to 50.50, greater than 32.67, the chi-squared value with 0.95 confidence level.

Therefore, these 21 wind cross terms cannot be ignored when we estimate Regulation

Down Service. The complete chi-squared distribution table is attached in Appendix B.

Table 5-6. Likelihood Ratio Tests Results for Regulation Down Service

LRTs for RD Model 1 Model 2 Model 3

Model 2 24.25

(12.59)

Model 3 138.88

(22.36)

114.63

(14.07)

Model 4 189.39

(48.60)

165.13

(41.34)

50.50

(32.67)

The results show that all the LRTs values are greater than the chi-squared values

with the same degrees of freedom and 0.95 confidence level for ERCOT Regulation

Down Service. This means that all the null hypotheses should be rejected. In LRT, a

null hypothesis states that coefficients of additional variables in the alternative models

are zero. This means that if we accept the null hypothesis, we do not have to replace

the simple model with the complex one.

13

For more LRTs detail, refer to http://warnercnr.colostate.edu/~gwhite/fw663/Likelihood

RatioTests.PDF.

50

Table 5-7. Likelihood Ratio Tests Results for Regulation Up Service

LRTs for RU Model 1 Model 2 Model 3

Model 2 83.39

(12.59)

Model 3 198.41

(22.36)

115.02

(14.07)

Model 4 238.67

(48.60)

155.28

(41.34)

40.25

(32.67)

Similarly, the results above indicate that all the LRTs values are greater than the

chi-squared values with the same degrees of freedom and 0.95 confidence level for

ERCOT Regulation Up Service. In this case, we should reject all the null hypotheses.

This means that apart from the base independent variables, all the Regional Wind

variables, Regional Wind Load variables and Wind Cross Terms significantly

impact ERCOT Regulation Services and should be retained in the model. Therefore,

Model 4 is most suitable for this study and will be used for further estimation. Now

we can use Model 4 to estimate the overall impacts of wind power on ERCOT

Regulation Services. The results are shown in Table 5-8 and the increased values (the

specific influences) are in bold type.

Table 5-8. Overall Impact of Wind Power on ERCOT Regulation Service

Hourly

Regulation

2008

(Jan 2008 -

Dec 2008)

2009

(Jan 2009 -

Dec 2009)

2010

(Jan 2010 -

Nov 2010)

2011

(Dec 2010 -

Dec 2011)

2008-2011

Regulation Down

(RD) Usage in

100% Wind, MWh

94.258 98.871 108.869 38.589 83.661

RD Usage w/o

Wind, MWh 78.195 77.570 81.416 13.421 61.217

RD Usage

Increase, MWh 16.063 21.301 27.453 25.168 22.444

Regulation Up (RU)

Usage in 100%

Wind, MWh

103.593 103.719 104.906 86.990 99.424

RU Usage w/o

Wind, MWh 97.480 97.907 95.965 75.683 91.332

51

RU Usage

Increase, MWh 6.113 5.812 8.941 11.307 8.092

RD Requirement in

100% Wind, MWh 240.278 251.983 271.539 123.476 242.393

RD Requirement

w/o Wind, MWh 219.725 224.363 232.089 98.397 213.812

RD Requirement

Increase, MWh 20.553 27.620 39.450 25.078 28.581

MCPC for RD,

$/MWh 19.521 7.252 8.574 8.304 10.912

Increased Cost for

RD, Million US$ 3.524 1.755 2.711 1.979 10.936

RU Requirement in

100% Wind, MWh 239.614 235.716 247.619 185.530 232.184

RU Requirement

w/o Wind, MWh 227.495 222.914 230.179 160.774 218.039

RU Requirement

Increase, MWh 12.119 12.802 17.440 24.756 14.145

MCPC for RU,

$/MWh 22.709 9.703 9.764 21.707 16.228

Increased Cost for

RU, Million US$ 2.417 1.088 1.365 5.107 8.049

RD+RU

Requirement

Increase, MWh

32.672 40.422 56.890 49.834 42.762

Average Wind

Generation, GWh 1.706 2.051 2.996 3.190 2.489

Average Wind

Generation /

Average Load

4.8% 5.8% 8.1% 8.4% 6.9%

Percentage

Regulation

Requirement

Increase (RD+RU

Requirement

Increase / Average

Wind Generation)

1.9% 2.0% 1.9% 1.6% 1.7%

The results show that the impact of wind integration on ERCOT Regulation Service

is substantial. The incorporation of wind power increases both Regulation Up and

Down usages and requirements in all five periods. We can see that the impact on

Regulation Down is larger than that on Regulation Up. We also observe that the

52

additional costs on ERCOT Regulation Services caused by wind integration are very

high. For example, in Table 5-8, 240.278 MWh and 219.725 MWh are the 98.8th

percentiles of estimated regulation requirements in the case of 100% wind and 0%

wind in 2008 respectively. $19.521/MWh is the average of all hourly MCPC in 2008.

There are 8,784 hours in 2008. According to Equation 4-1, the additional cost on

ERCOT RD Service for 2008 is calculated as:

Additionally, both regulation usage and requirement with or without wind impact in

2011 are much smaller than those in other three years. This means that ERCOT nodal

market improved the regulation deployment, as a result of which, less regulation

services are required in nodal market than in zonal market. In addition, the results

indicate that the Regulation Up and Down requirements are greater than the actual

Regulation Up and Down usages respectively in all five periods. Such results should

be reasonable because the actual regulation services in need are hardly accurately

forecasted. Only the regulation requirements exceed the actual usages and extra

regulation services are reserved, can the grid reliability be guaranteed.

In addition, the results in the last two rows of Table 5-8 show the wind penetration

levels and percentage regulation requirement increases in five periods. Take 2011 as

an example, the estimated increase in ERCOT regulation requirement is 1.6% of

installed wind power capacity at 8.4% wind penetration in 2011. Holttinen et al. (2009)

found that the estimated increase in short-term reserve requirement was 1-15% of

installed wind power capacity at 10% wind penetration. The results in Table 5-8 show

that the percentage regulation requirement increase is 1.9% for 2008, 2.0% for 2009,

1.9% for 2010, 1.6% for 2011 and 1.7% for 2008-2011. All of these percentage

increases are close to the lower bound of 1-15% and the range based on the results is

1.6-2.0%, much smaller than the range stated in Holttinen et al. (2009). This means

that we could narrow the range of the percentage regulation requirement increase to

1.6-2.0%. A better conclusion could be made as the estimated increase in short-term

53

regulation requirement was 1.6-2.0% of installed wind power capacity at 4.8-8.4%

wind penetration.

We would further apply Model 4 for each region to evaluate the individual wind

impact of each region on ERCOT Regulation Service. The individual wind impacts of

each region on ERCOT Regulation Service are listed in Table 5-9 and 5-12.

Table 5-9. Regional Wind Impact on ERCOT Regulation Requirement

Scenario 2008 2009 2010 2011 2008-2011

ERCOT

Wind

= 0

RD Requirement

Increase, MWh 20.553 27.620 39.450 25.078 28.581

RU Requirement

Increase, MWh 12.119 12.802 17.440 24.756 14.145

Culberson

Wind

= 0

RD Requirement

Increase, MWh -1.458 2.206 2.108 0.190 1.072

RU Requirement

Increase, MWh -0.350 2.089 3.165 4.409 1.325

Wind Proportion 0.62% 0.49% 0.30% 0.26% 0.38%

IF for RD -11.43 16.24 17.83 2.93 9.87

IF for RU -4.66 33.18 60.57 69.01 24.65

McCamey

Wind

= 0

RD Requirement

Increase, MWh 3.200 3.224 8.814 3.148 4.279

RU Requirement

Increase, MWh -5.106 -3.663 -0.445 -0.454 -4.354

Wind Proportion 13.58% 12.32% 11.18% 10.09% 11.45%

IF for RD 1.15 0.95 2.00 1.24 1.31

IF for RU -3.10 -2.32 -0.23 -0.18 -2.69

Howard

Wind

= 0

RD Requirement

Increase, MWh 4.214 12.066 16.897 11.876 9.687

RU Requirement

Increase, MWh 0.652 0.218 3.692 3.049 0.574

Wind Proportion 20.86% 28.30% 33.66% 30.47% 29.25%

IF for RD 0.98 1.54 1.27 1.55 1.16

IF for RU 0.26 0.06 0.63 0.40 0.14

Lubbock

Wind

= 0

RD Requirement

Increase, MWh 2.678 4.210 11.470 5.153 4.717

RU Requirement

Increase, MWh -4.028 -4.355 -7.386 -3.540 -5.163

Wind Proportion 2.18% 3.38% 2.55% 2.73% 2.72%

IF for RD 5.97 4.50 11.39 7.52 6.06

IF for RU -15.23 -10.05 -16.60 -5.23 -13.41

54

Table 5-9. Regional Wind Impact on ERCOT Regulation Requirement (Continued)

Scenario 2008 2009 2010 2011 2008-2011

Taylor

Wind

= 0

RD Requirement

Increase, MWh 11.659 16.926 19.387 23.979 14.942

RU Requirement

Increase, MWh 16.464 8.692 11.743 8.381 11.131

Wind Proportion 61.57% 46.05% 42.37% 44.70% 47.23%

IF for RD 0.92 1.33 1.16 2.14 1.11

IF for RU 2.21 1.47 1.59 0.76 1.67

Eastern

Wind

= 0

RD Requirement

Increase, MWh -0.650 -2.065 -6.107 -8.013 -2.609

RU Requirement

Increase, MWh 0.842 3.939 5.110 7.283 3.074

Wind Proportion 1.16% 4.51% 3.30% 3.70% 3.32%

IF for RD -2.73 -1.66 -4.69 -8.63 -2.75

IF for RU 5.99 6.83 8.89 7.94 6.54

Gulf

Wind

= 0

RD Requirement

Increase, MWh 0.000 -0.809 -0.499 -5.482 -0.772

RU Requirement

Increase, MWh 0.000 0.868 5.187 9.979 3.567

Wind Proportion 0.02% 4.95% 6.64% 8.05% 5.64%

IF for RD 0.00 -0.59 -0.19 -2.72 -0.48

IF for RU 0.00 1.37 4.48 5.01 4.47

The results for ERCOT Regulation Requirement indicate that the absolute wind

impact on Regulation Down requirement increases as the wind proportion (Regional

Wind / ERCOT Wind) rises. We observe that the integration of McCamey and

Lubbock wind may decrease Regulation Up requirement, and that the incorporation of

Eastern and Gulf wind may decrease Regulation Down requirement. 2008-2011

regional wind impacts on ERCOT regulation requirement are summarized in Table

5-10. The results also show that the impact factors (absolute values) of Culberson,

Lubbock, Eastern and Gulf Region are much greater than those of Howard and Taylor

Region for both Regulation Up and Down. This means that regions with less wind

power may have larger relative impacts on both Regulation Up and Down

requirements. Comparing the impact factors for Regulation Down to those for

Regulation Up of Howard and Taylor Region, we can see that both the absolute and

55

relative impacts of these two regions with most wind generation for Regulation Down

are greater than those for Regulation Up.

Table 5-10. 2008-2011 Regional Wind Impact on ERCOT Regulation Requirement

Region IF for RD IF for RU Wind Proportion

Culberson 9.87 24.65 0.38%

McCamey 1.31 -2.69 11.45%

Howard 1.16 0.14 29.25%

Lubbock 6.06 -13.41 2.72%

Taylor 1.11 1.67 47.23%

Eastern -2.75 6.54 3.32%

Gulf -0.48 4.47 5.64%

From Table 5-10, we can see that the integration of Howard and Taylor Wind

would increase both Regulation Up and Down requirements. However, Howard and

Taylor are currently the regions with most wind generation. We also observe that not

all integration of regional wind would increase both Regulation Up and Down

services. Because the impacts of McCamey / Lubbock Wind on ERCOT regulation

services are opposite to the impacts of Eastern / Gulf Wind, it is possible for us to

minimize the impacts of wind power on ERCOT regulation services if we reasonably

allocate new wind capacity in these regions. Specifically, we set WM, WL, WE and WG

as the weights of new wind capacity allocation for McCamey, Lubbock, Eastern and

Gulf respectively. Since we would allocate wind capacity only in these four regions,

the first condition is:

In order to minimize the impacts of wind power on ERCOT regulation services, we

need the following two conditions:

where

represent the impact factors of McCamey, Lubbock,

Eastern and Gulf for ERCOT RD requirement respectively.

56

where

represent the impact factors of McCamey, Lubbock,

Eastern and Gulf for ERCOT Regulation Up requirement respectively. Since one

more condition is needed to solve for WM, WL, WE and WG, we assume that two of

these weights are equal. In this case, there are six possible combinations: WM = WL,

WM = WE, WM = WG, WL = WE, WL = WG and WE = WG. The weights of new wind

capacity allocation for McCamey, Lubbock, Eastern and Gulf are listed in Table 5-11.

Table 5-11. Weights of New Wind Capacity Allocation for McCamey, Lubbock,

Eastern and Gulf Region

Assumption WM WL WE WG WM2+WL

2+WE

2+WG

2

WM = WL 23.12% 23.12% 63.68% -9.91% 0.52

WM = WE 49.32% 10.96% 49.32% -9.60% 0.51

WM = WG -10.31% 38.62% 81.99% -10.31% 0.84

WL = WE 505.79% -200.79% -200.79% -4.21% 33.65

WL = WG 92.55% -9.09% 25.64% -9.09% 0.94

WE = WG 154.59% -37.87% -8.36% -8.36% 2.55

From Table 5-11, we observe that there are six different combinations of weights of

new wind capacity allocation for McCamey, Lubbock, Eastern and Gulf Region for us

to minimize the impacts of wind integration on ERCOT regulation services. We also

see that negative weight exists in each combination. In this case, we would choose the

combination with the smallest WM2+WL

2+WE

2+WG

2. Therefore, WM = WE should be

the best choice. For example, with the assumption WM = WE, if 1,000 MW new wind

capacity is planned to be integrated to ERCOT system, we should theoretically

allocate 493 MW in both McCamey and Eastern, 110 MW in Lubbock Region and

decrease 96 MW in Gulf Region.

57

Table 5-12. Regional Wind Impact on ERCOT Regulation Usage

Scenario 2008 2009 2010 2011 2008-2011

ERCOT

Wind

= 0

RD Usage

Increase, MWh 16.064 21.302 27.453 25.169 22.444

RU Usage

Increase, MWh 6.113 5.812 8.941 11.307 8.092

Culberson

Wind

= 0

RD Usage

Increase, MWh -0.829 0.976 1.140 0.319 0.383

RU Usage

Increase, MWh -0.145 1.696 2.323 2.774 1.671

Wind Proportion 0.62% 0.49% 0.30% 0.26% 0.38%

IF for RD -8.31 9.32 13.86 4.91 4.50

IF for RU -3.81 59.35 86.71 95.09 54.35

McCamey

Wind

= 0

RD Usage

Increase, MWh 5.011 4.280 5.605 3.708 4.611

RU Usage

Increase, MWh -5.612 -3.721 -4.435 -3.013 -4.166

Wind Proportion 13.58% 12.32% 11.18% 10.09% 11.45%

IF for RD 2.30 1.63 1.83 1.46 1.79

IF for RU -6.76 -5.20 -4.44 -2.64 -4.50

Howard

Wind

= 0

RD Usage

Increase, MWh 2.753 5.036 7.063 4.576 4.803

RU Usage

Increase, MWh 0.268 2.068 1.731 2.326 1.610

Wind Proportion 20.86% 28.30% 33.66% 30.47% 29.25%

IF for RD 0.82 0.84 0.76 0.60 0.73

IF for RU 0.21 1.26 0.58 0.68 0.68

Lubbock

Wind

= 0

RD Usage

Increase, MWh 2.956 3.449 6.323 5.391 4.509

RU Usage

Increase, MWh -3.165 -3.345 -5.908 -5.408 -4.445

Wind Proportion 2.18% 3.38% 2.55% 2.73% 2.72%

IF for RD 8.43 4.78 9.03 7.84 7.38

IF for RU -23.71 -17.00 -25.90 -17.51 -20.18

Taylor

Wind

= 0

RD Usage

Increase, MWh 7.195 8.817 11.301 12.797 10.058

RU Usage

Increase, MWh 8.854 4.625 5.377 3.608 5.580

Wind Proportion 61.57% 46.05% 42.37% 44.70% 47.23%

IF for RD 0.73 0.90 0.97 1.14 0.95

IF for RU 2.35 1.73 1.42 0.71 1.46

58

Table 5-12. Regional Wind Impact on ERCOT Regulation Usage (Continued)

Eastern

Wind

= 0

RD Usage

Increase, MWh -0.362 -3.302 -4.230 -4.407 -3.077

RU Usage

Increase, MWh 0.286 3.475 3.374 4.590 2.955

Wind Proportion 1.16% 4.51% 3.30% 3.70% 3.32%

IF for RD -1.94 -3.44 -4.67 -4.73 -4.13

IF for RU 4.03 13.26 11.44 10.96 11.00

Gulf

Wind

= 0

RD Usage

Increase, MWh -0.012 -0.669 -0.263 -0.199 -0.284

RU Usage

Increase, MWh 0.010 1.827 4.943 6.076 3.236

Wind Proportion 0.02% 4.95% 6.64% 8.05% 5.64%

IF for RD -3.69 -0.63 -0.14 -0.10 -0.22

IF for RU 7.64 6.35 8.33 6.68 7.09

Similar to the results for ERCOT regulation requirement, Table 5-12 shows that the

regional wind impact on ERCOT Regulation Down usage increases as the value of

Regional Wind / ERCOT Wind increases. We observe that regions with more wind

power (Howard and Taylor Region) generally have smaller relative impacts on both

ERCOT Regulation Up and Down usage than those with less wind generation

(Culberson, Lubbock and Eastern Region). We can also see that Culberson, Taylor,

Eastern and Gulf have larger relative impacts (greater IFs) on Regulation Up usage

than on Regulation Down usage. We note that the absolute increases in regulation

requirement are generally greater than the increases in regulation usage in the same

period of time. This means that ERCOT may have to increase more regulation

capacity than the estimated increased regulation usage caused by wind integration.

The reason is that ERCOT deploys regulation services mainly based on their

estimated requirements. In addition, we observe that the integration of Eastern and

Gulf Wind would decrease ERCOT Regulation Down usage and the incorporation of

McCamey and Lubbock Wind would decrease ERCOT Regulation Up usage. Such

impacts on ERCOT regulation usage are consistent with those on regulation

requirement.

So far, we have estimated both overall wind impact and individual regional wind

impact on ERCOT Regulation Service. We were concerned about two cases before,

59

that is "100% Wind" and "0% Wind". As we have constructed the specific model and

obtained the coefficients of all independent variables, we can further determine how

the increases of ERCOT regulation usage and requirement change as ERCOT wind

power increases in a wider range. In order to achieve this, we can first calculate the

increases of regulation usage and requirement in different cases.

Specifically, if we examine the relevant increases in the case of 20% Wind, we

have to adjust all the raw wind data by multiplying them by 0.2 and recalculate the

hourly wind standard deviation. The hourly data of other wind-relevant variables,

such as Regional Wind Cross Terms and Regional Wind Load, will be changed

accordingly. Then we can rerun Model 4 and compare the returned values to those in

the case of 0% Wind. In this way, we can obtain the increases of regulation usage and

requirement, and graph variation trends as the ratio of estimated wind power / actual

wind power varies from 0 to 300% with 20% interval. The actual functions of these

variation trends are not available. In order to examine their change rates in different

intervals, we will calculate the arc elasticity by using Equation 4-3. Here we review

the equation:

where x represents % wind (estimated wind generation / actual wind generation) and

y represents the increases of regulation usage or requirement. For example, in the case

of 60% Wind in Table 5-12, the RD increase arc elasticity is calculated as:

The results for ERCOT regulation usage will be listed and graphed on an annual basis

and shown in Table 5-13, 5-14, 5-15 and 5-16, corresponding to Figure 5-1, 5-2, 5-3

and 5-4 respectively.

60

Table 5-13. 2008 ERCOT Regulation Usage Increase and Arc Elasticity

x RD Increase ( ), MWh RU Increase ( ), MWh

0 0.000 0.000

20% Wind 2.750 1.946 1.00 1.00

40% Wind 5.731 3.530 0.95 1.15

60% Wind 8.944 4.753 0.91 1.35

80% Wind 12.388 5.614 0.88 1.72

100% Wind 16.064 6.113 0.86 2.61

120% Wind 19.971 6.251 0.84 8.15

140% Wind 24.109 6.028 0.82 -4.23

160% Wind 28.479 5.442 0.80 -1.31

180% Wind 33.080 4.496 0.79 -0.62

200% Wind 37.913 3.188 0.77 -0.31

220% Wind 42.977 1.518 0.76 -0.13

240% Wind 48.273 -0.513 0.75 -0.02

260% Wind 53.800 -2.906 0.74 0.06

280% Wind 59.558 -5.660 0.73 0.12

300% Wind 65.548 -8.776 0.72 0.16

Figure 5-1. 2008 ERCOT Regulation Usage Increase Trends

-10

0

10

20

30

40

50

60

70

Incr

eas

ed

Re

gula

tio

n U

sage

(M

Wh

)

Estimated Wind/Actual Wind

RD Increase

RU Increase

61



0 0.000 0.000

20% Wind 3.961 1.311 1.00 1.00

40% Wind 8.071 2.548 0.98 1.04

60% Wind 12.332 3.711 0.96 1.08

80% Wind 16.742 4.799 0.94 1.12

100% Wind 21.302 5.812 0.93 1.16

120% Wind 26.011 6.751 0.91 1.22

140% Wind 30.871 7.616 0.90 1.28

160% Wind 35.880 8.406 0.89 1.35

180% Wind 41.039 9.122 0.88 1.44

200% Wind 46.348 9.763 0.87 1.55

220% Wind 51.807 10.330 0.86 1.69

240% Wind 57.415 10.822 0.85 1.87

260% Wind 63.173 11.240 0.84 2.11

280% Wind 69.081 11.584 0.83 2.46

300% Wind 75.139 11.853 0.82 3.00


0

10

20

30

40

50

60

70

80

Incr

eas

ed

Re

gula

tio

n U

sage

(M

Wh

)


RD Increase

RU Increase

62



0 0.000 0.000

20% Wind 4.941 2.185 1.00 1.00

40% Wind 10.157 4.171 0.96 1.07

60% Wind 15.647 5.959 0.94 1.13

80% Wind 21.413 7.549 0.92 1.21

100% Wind 27.453 8.941 0.90 1.32

120% Wind 33.769 10.135 0.88 1.45

140% Wind 40.359 11.130 0.87 1.64

160% Wind 47.224 11.927 0.85 1.93

180% Wind 54.364 12.526 0.84 2.40

200% Wind 61.778 12.926 0.82 3.34

220% Wind 69.468 13.128 0.81 6.13

240% Wind 77.433 13.133 0.80 281.56

260% Wind 85.672 12.938 0.79 -5.37

280% Wind 94.186 12.546 0.78 -2.41

300% Wind 102.975 11.955 0.77 -1.43


0

20

40

60

80

100

120

Incr

eas

ed

Re

gula

tio

n U

sage

(M

Wh

)


RD Increase

RU Increase

63



0 0.000 0.000

20% Wind 4.774 2.566 1.00 1.00

40% Wind 9.678 4.980 0.98 1.04

60% Wind 14.712 7.242 0.97 1.08

80% Wind 19.875 9.350 0.96 1.12

100% Wind 25.169 11.307 0.95 1.17

120% Wind 30.592 13.111 0.93 1.23

140% Wind 36.145 14.762 0.92 1.30

160% Wind 41.828 16.261 0.91 1.38

180% Wind 47.640 17.607 0.91 1.48

200% Wind 53.583 18.801 0.90 1.61

220% Wind 59.655 19.842 0.89 1.77

240% Wind 65.857 20.731 0.88 1.98

260% Wind 72.189 21.467 0.87 2.29

280% Wind 78.650 22.051 0.86 2.76

300% Wind 85.242 22.482 0.86 3.56


0

10

20

30

40

50

60

70

80

90

Incr

eas

ed

Re

gula

tio

n U

sage

(M

Wh

)


RD Increase

RU Increase

64

The results for ERCOT regulation usage show that all arc elasticity values of

Regulation Down increases are greater between 0 and 1 while the relevant values of

Regulation Up increases are all greater than 1 or less than 0 in 2008-2011. This

implies that all the variation trends of Regulation Down usage increase curves are

convex in 2008-2011 while all Regulation Up usage increase curves are concave. In

other words, the increases of Regulation Down usage grows faster than wind

integration, while the increases of Regulation Up usage grows slower than wind

integration or even decreases as wind generation increases in 2008-2011. We can also

see that all Regulation Down increases are much faster than Regulation Up increases

in 2008-2011. This means that the impact of wind power on Regulation Down usage

is larger than that on Regulation Up usage in 2008-2011.

We note that the tails of 2009 and 2011 Regulation Up increase curves go up

slowly, and 2008 and 2010 Regulation Up increase curves even go downward in the

end. This means that the Regulation Up usage in 2008 and 2010 could have decreased

as wind power increases in certain cases. This is because there are some negative

wind data in certain regions (Eastern and Gulf Region) in 2008 and 2010. When we

increase these negative wind data to a certain extent, they can largely influence the

estimated values. Let us take the Regulation Up increase curve in Figure 5-1 as an

example. We observe that the Regulation Up usage increase in the case of 120% wind

is greater than that in the case of 300% wind. If the wind outputs in Gulf and

Culberson are -50 MWh and 50 MWh in the case of 120% wind respectively, then the

values of Gulf Wind Howard Wind in the case of 120% and 300% wind should be

-3,600 and -22,500. Plus, the parameter of Gulf Wind Culberson Wind for

Regulation Up is 1,004.209, large and positive. Therefore, the item of "parameter

Culberson Wind Gulf Wind" would largely influence the estimated regulation value.

In this case, the increase of Regulation Up usage is likely to decrease as the ratio of

estimated wind and actual wind changes from 120% to 300%.

We may be curious about the negative wind data. Some newly built wind farms

could have negative wind generation in the beginning because some turbines may be

tested and need external power input rather than supply power output. In addition,

65

negative wind output could occur when wind speed is slow (not very slow). If wind

speed is around the cut-in speed, wind turbines will be kept running, though they may

not provide power output or even need extra power input (negative wind output) in

this case. This is because if we turn down the turbines and restart them, we are likely

to need more extra power input than keeping them running when the wind is blowing

at the "cut-in" speed. The relevant results for ERCOT regulation requirement will be

listed and graphed on an annual basis and shown in Table 5-17, 5-18, 5-19 and 5-20,

corresponding to Figure 5-5, 5-6, 5-7 and 5-8 respectively.

66

Table 5-17. 2008 ERCOT Regulation Requirement Increase and Arc Elasticity


0 0.000 0.000

20% Wind 3.041 2.332 1.00 1.00

40% Wind 7.538 4.955 0.78 0.93

60% Wind 11.790 7.127 0.91 1.11

80% Wind 15.479 9.706 1.06 0.93

100% Wind 20.553 12.119 0.79 1.01

120% Wind 26.393 14.614 0.73 0.97

140% Wind 33.595 15.136 0.64 4.39

160% Wind 41.504 16.860 0.63 1.24

180% Wind 50.902 18.617 0.58 1.19

200% Wind 60.443 19.645 0.61 1.96

220% Wind 72.541 22.797 0.52 0.64

240% Wind 85.795 23.371 0.52 3.50

260% Wind 99.704 24.428 0.53 1.81

280% Wind 115.033 26.289 0.52 1.01

300% Wind 131.783 28.734 0.51 0.78

Figure 5-5. 2008 ERCOT Regulation Requirement Increase Trends

0

20

40

60

80

100

120

140

Incr

eas

ed

Re

gula

tio

n R

eq

uir

em

en

t (M

Wh

)


RD Increase

RU Increase

67



0 0.000 0.000

20% Wind 3.041 2.270 1.00 1.00

40% Wind 7.127 5.023 0.83 0.88

60% Wind 13.481 7.629 0.65 0.97

80% Wind 19.794 12.261 0.75 0.61

100% Wind 27.620 12.802 0.67 5.15

120% Wind 35.853 15.302 0.70 1.02

140% Wind 45.486 20.055 0.65 0.57

160% Wind 55.379 24.357 0.68 0.69

180% Wind 66.950 31.355 0.62 0.47

200% Wind 80.737 37.589 0.56 0.58

220% Wind 95.989 43.062 0.55 0.70

240% Wind 110.664 50.743 0.61 0.53

260% Wind 130.535 61.243 0.49 0.43

280% Wind 152.291 74.647 0.48 0.38

300% Wind 172.282 87.965 0.56 0.42


0

20

40

60

80

100

120

140

160

180

200

Incr

eas

ed

Re

gula

tio

n R

eq

uir

em

en

t (M

Wh

)


RD Increase

RU Increase

68



0 0.000 0.000

20% Wind 5.247 1.561 1.00 1.00

40% Wind 10.578 4.751 0.99 0.66

60% Wind 18.278 8.128 0.75 0.76

80% Wind 27.296 11.767 0.72 0.78

100% Wind 39.450 17.440 0.61 0.57

120% Wind 54.140 23.629 0.58 0.60

140% Wind 74.749 30.389 0.48 0.61

160% Wind 93.740 39.131 0.59 0.53

180% Wind 116.288 51.605 0.55 0.43

200% Wind 147.083 59.921 0.45 0.71

220% Wind 176.160 71.775 0.53 0.53

240% Wind 217.118 86.809 0.42 0.46

260% Wind 265.597 102.860 0.40 0.47

280% Wind 316.588 122.522 0.42 0.42

300% Wind 367.946 147.293 0.46 0.38


0

50

100

150

200

250

300

350

400

Incr

eas

ed

Re

gula

tio

n R

eq

uir

em

en

t (M

Wh

)


RD Increase

RU Increase

69



0 0.000 0.000

20% Wind -1.023 2.851 1.00 1.00

40% Wind 0.238 6.772 -0.21 0.82

60% Wind 6.971 11.470 0.21 0.78

80% Wind 13.833 16.930 0.43 0.74

100% Wind 25.078 24.757 0.38 0.59

120% Wind 40.697 34.836 0.38 0.54

140% Wind 58.427 44.838 0.43 0.61

160% Wind 76.738 58.601 0.49 0.50

180% Wind 95.960 75.573 0.53 0.47

200% Wind 117.350 92.364 0.52 0.53

220% Wind 139.449 112.394 0.55 0.49

240% Wind 165.023 133.130 0.52 0.51

260% Wind 190.107 155.042 0.57 0.53

280% Wind 219.551 178.199 0.52 0.53

300% Wind 247.283 203.623 0.58 0.52


0

50

100

150

200

250

300

Incr

eas

ed

Re

gula

tio

n R

eq

uir

em

en

t (M

Wh

)


RD Increase

RU Increase

70

Unlike the results for ERCOT regulation usage, most of the arc elasticity values of

Regulation Up and Down increases for ERCOT regulation requirement are between 0

and 1 in 2008-2011 except for some cases in 2008. This implies that all the variation

trends of Regulation Up and Down requirement increases in 2008-2011 are concave

curves except for 2008 Regulation Up requirement increase. In other words, increases

of Regulation Up and Down requirements grow faster than wind power in 2008-2011.

For example, if we double the wind power output, we may have to triple or even

quadruple the increase of regulation requirement accordingly. We also observe that

two increase curves are much closer in 2011 (nodal market) than in 2008-2010

(zonal market) as Figure 5-5, 5-6, 5-7 and 5-8 show. This means that the difference

between Regulation Down and Up increases due to wind integration is smaller in

nodal market than in zonal market. In addition, we can see that the increase of

Regulation Down requirement grows faster than that of Regulation Up requirement as

wind power increases in all four figures. This indicates that more additional

Regulation Down services are required than additional Regulation Up services as

wind generation increases in 2008-2011. In other words, the impact of wind power on

Regulation Down requirement is larger than that on Regulation Up requirement in

2008-2011.

Comparing Figure 5-1, 5-2, 5-3 and 5-4 to Figure 5-5, 5-6, 5-7 and 5-8 respectively,

we observe that the curves for Regulation Down and Up requirements are closer than

those for Regulation Down and Up usages. We also see that the increases of

Regulation Up and Down requirements and Regulation Down usage rise quickly,

while the increase of Regulation Up usage goes up slowly as wind generation

increases in 2008-2011. This means that wind power integration largely influences

ERCOT Regulation Down and Up requirements and Regulation Down usage, while

the impact of wind power on ERCOT Regulation Up usage is relatively small.

In addition, from Figure 5-5, 5-6, 5-7 and 5-8 we note that there is no negative

increase on any curve of regulation requirement increase, while Figure 5-1 and 5-3

shows that the Regulation Up usage growth curves for 2008 and 2010 go downward

in the end. This implies that the impacts of wind power on both Regulation Up and

71

Down requirements continuously increase as ERCOT incorporates more and more

wind capacity into the system in 2008-2011. In contrast, the impacts of wind power

on Regulation Up usage first increase and then decrease as Estimated Wind / Actual

Wind changes from 0 to 300%. The reason for the partial downward trends on the

Regulation Up usage growth curves for 2008 and 2010 is that regulation usage is

calculated as the average of all estimated values in the period studied and some

negative regional wind data in 2008 and 2010 may influence relevant estimated values

of Regulation Up usage in these periods of time as discussed before.

72

Chapter 6

Conclusion and Future Work

Overall it can be seen that wind power integration into the electric grid does

significantly impact the ERCOT Regulation Service market. A rise should be expected

in the total amount of Regulation Service that is required as wind power becomes a

more sizeable portion of ERCOT energy portfolio. As a result, the money that is being

paid out for these services will be increased accordingly. The quantitative analysis

presented in this paper specifically shows this to be the case for both Regulation Up

and Down in ERCOT ancillary service market. Comparing the increases of regulation

services due to wind integration in 2008-2010 to those in 2011, we find that less

regulation services are required in nodal market than in zonal market with the same

scale of wind integration. Comparing the results in our study to the finding of

Holttinen et al. (2009), we may make a more accurate conclusion by narrowing the

range of the percentage regulation requirement increase.

From the results of regional wind impacts on ERCOT Regulation Requirement, we

can see that regions with more wind power (Howard and Taylor Region) generally

have smaller relative impacts on both ERCOT Regulation Up and Down services than

those with less wind generation (Culberson, Lubbock and Eastern Region). In

addition, not all integration of regional wind would increase both Regulation Up and

Down services. Therefore, it is theoretically possible for us to minimize the increase

of regulation services by allocating new wind capacity in McCamey / Lubbock

Region and Eastern / Gulf Region.

By examining the increases of regulation services in different scales of wind power

integration, we find that the impact of wind power on Regulation Down service is

larger than that on RU service in 2008-2011 as the scale of wind incorporation

increases. We can also see that the impact of wind power on ERCOT regulation

requirement is greater than that on ERCOT regulation usage in 2008-2011 as wind

integration increases. In addition, wind integration largely influences ERCOT

Regulation Up and Down requirements and Regulation Down usage, while the impact

of wind power on ERCOT Regulation Up usage is relatively small.

73

So far, we have evaluated the impacts of wind integration on ERCOT regulation

deployment. To comprehensively analyze the wind power impacts, we need to further

evaluate the impacts of wind incorporation on the market clearing prices for ERCOT

regulation service. We may further consider how individual regional wind generation

influences the prices and how the prices change as the levels of wind integration in

the future.

74

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Appendix A

Non-technical Overview of RTO and CPS

In order to help non-specialist readers without relevant technical background, this

appendix mainly presents basic concepts and information of Regional Transmission

Operators and Control Performance Standard that are discussed in the thesis.

Regional Transmission Operators (RTOs) /Independent System Operators (ISOs)

organizations are generally wholesale power markets which are operated by an

independent third party entity. The RTO/ISO controls the wholesale power and

transmission system within a defined area. In addition, RTO/ISO is responsible for

balancing electricity supply with demand and dispatching generation through market

price mechanisms (Brown, 2012). There are seven RTOs/ISOs in the United States,

California ISO, ERCOT RTO, Southwest Power Pool (SPP) RTO, New York (NY)

ISO, New England ISO, PJM Interconnection and Midwest ISO, and they serve about

60 percent of U.S. electric power customers.14

Although it is important to balance generation and customer demand, grid operators

are not required to perfectly match generation and load. In other words, reasonable

imbalance is allowed. The North American Electric Reliability Council (NERC) has

established the Control Performance Standard (CPS) to determine the amount of

imbalance which is permissible under reliable and safe circumstances. Specifically,

CPS1 measures the relationship between the system's area control error (ACE) and

the system frequency every one minute and CPS2 sets specific limits for control areas

on maximum ACE every 10 minutes.

14

For more information about RTOs/ISOs, see http://www.eia.gov/todayinenergy/detail.cfm?

id=790.

78

Appendix B

Chi-Squared Distribution Table

The chi-squared values with different degrees of freedom and confidence levels are

listed in the following table.15

15

For more details about chi-squared distribution, refer to http://sites.stat.psu.edu/~mga/401/

tables/Chi-square-table.pdf.

79

Appendix C

Durbin-Watson d-Statistic Table

The lower and upper critical values of Durbin-Watson d-statistic16

at 0.05 (=0.05) level

of significance are shown in the following table, where dL represents the lower critical value,

dU represents the upper critical value, k' represents the number of explanatory variables and n

represents the number of observations.

16

For more details about the lower and upper critical values of Durbin-Watson d-statistic, see

http://khishigt.stat.ses.edu.mn/a/images/upload/Statistical%20tables.pdf.

the economic impact of wind power on ercot regulation market

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