Interdependency of Electricity and Natural Gas
Markets in the United States: A Dynamic
I MASACUSETSILiii-i
Computational Model
MASSACHUSETTS IT
OF TECHNOLOGY
By
MAY 2 9 201
Sandra Elizabeth Jenkins
B.A. in Electrical Engineering
University of Massachusetts Amherst, 2012
LIBRARIES
SUBMITTED TO THE ENGINEERING SYSTEMS DIVISION IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN TECHNOLOGY AND POLICY
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2014
0 2014 Massachusetts Institute of Technology. All rights reserved.
Signature redacted
Signature of Author:
F gineering Systems Division
May 15, 2014
Certifiedby:Signature
redacted
Anura~dia M. Annaswamy
Director, Active-adaptive Control Laboratory
Senior Research Scientist, Department of Mechanical Engineering, MIT
Thesis Supervisor
Accepted by:
Accpteby:Signature
redacted_
I (Dava
J. Newman
Professor of Aeronautics and Astronautics and Engineering Systems
Director, Technology and Policy Program
Interdependency of Electricity and Natural Gas Markets in the United
States: A Dynamic Computational Model
by
Sandra Elizabeth Jenkins
Submitted to the Technology and Policy Program, Engineering Systems Division
in partial fulfillment of the requirements for the degree of
Master of Science in Technology and Policy
Abstract
Due to high storage costs and limited storage availability, natural gas is generally used as a justin-time resource that needs to be delivered as it is consumed. With the shale gas revolution, coal
retirements and environmental regulations, the interdependency of natural gas and electricity has
increased. These changes impact pipeline financing and power generation dispatch. Potential
solutions to gas-electricity interdependency challenges such as mismatched market schedules are
not too difficult to determine. However, a quantitative model is needed in order to evaluate these
solutions in order to provide insights into which solutions to interdependency concerns offer the
best outcomes. While it is clear that natural gas constraints will affect the cost of the electricity
system, there is a need for modeling to explore the relationship between fuel uncertainty and
system cost. In this thesis, a quantitative optimal flow model with a dynamic market mechanism
is used to measure the effects of natural gas-fired power producer's fuel uncertainty on the net
social benefit to consumers and producers. Modeling results indicate that fuel price uncertainty
negatively affects social welfare while demand response, information availability and
coordination improvements limit the impact of natural gas fuel uncertainty. To simulate improved
coordination, a second model is developed which includes natural gas network constraints. The
results of this model demonstrate how joint optimization of the networks could relax fuel
constraints on gas-fired generators and improve social welfare.
Thesis Supervisor: Anuradha M. Annaswamy
Title: Director, Active-adaptive Control Laboratory
Senior Research Scientist, Department of Mechanical Engineering, MIT
3
4
Acknowledgements
There are many people to thank for their help on my journey to complete this thesis. First and
foremost, I want to thank my wonderful advisor, Dr. Anuradha Annaswamy, for providing the
guidance and academic support I needed not just for this thesis, but for this chapter of my life.
You provided me with the opportunity to push the boundaries on my knowledge and refine what I
had already learned. I enjoyed our conversations and I hope to continue them in the future. Next,
thanks go to my research advisor during my first year at MIT, Melanie Kenderdine, for giving me
the opportunity to work on this research topic in the first place. Working with you was a privilege
and I enjoyed every minute of it.
Furthermore I would like to thank all the wonderful people I worked with at the MITei, the Active
Adaptive Control lab, and in the Technology and Policy Program, including the students, the
staff, and professors who provide me with both invaluable knowledge and companionship; you
know who you are and you have my eternal gratitude. This thesis could not have been completed
without you. Never have I been among such a concentrated group of extraordinarily smart and
welcoming people. Special thanks go to my close friends who have helped me enormously in the
past two years, from having conversations about energy systems to making sure I always got a
healthy meal.
Finally, for my family, thanks goes to my father for always helping me with my math homework
in grade school, and to my mother for always being there with endless encouragement and
fortitude, I wouldn't be here without you. Thank you Sally for helping me proof read at terrible
hours throughout my academic career, and thanks Heather and Kelly for being great little sisters.
I also want to thank my grandparents for always checking in with me, it really meant a lot.
5
6
Contents
1.
2.
3.
4.
Introduction ...............................................................................................................
11
1.1
M otivation for research ....................................................................................
12
1.3
Thesis Outline .................................................................................................
13
Background................................................................................................................
14
2.1
Shale G as Revolution......................................................................................
14
2.2
Coal Retirem ents and Em ission Regulations ..................................................
18
2.3
Renew able Energy Interm ittency....................................................................
19
2.4
Energy security................................................................................................
22
Overview of Electricity and N atural Gas M arkets ....................................................
24
3.1
Energy Regulation and Stakeholders .............................................................
24
3.2
Electricity Sector.............................................................................................
26
3.3
Natural gas sector...........................................................................................
31
3.3.1
Operations of Interstate Pipelines ...........................................................
33
3.3.2
Shipment Nominations and Contract Priorities ......................................
35
Interdependency Concerns ....................................................................................
37
4.1
Electricity sector reliance on natural gas.........................................................
37
4.2
N atural gas dem and changes...........................................................................
39
4.3
Pipeline financing concerns ...........................................................................
43
4.4
D ispatch concerns ..........................................................................................
46
4.6
Sm art Grid and D em and Response .................................................................
48
7
5.
6.
4.7
Concerns of focus for m odeling......................................................................
50
4.8
Conclusion.......................................................................................................
51
N atural Gas and Electricity Market M odeling ......................................................
53
5.1
Need for gas-electricity market m odels ........................................................
53
5.2
Electricity power m arket m odels.....................................................................
54
5.3
Natural gas market models.............................................................................
56
5.4
Prior Electricity and Natural gas Interdependency Models ............................
57
5.5
M odel Descriptions ........................................................................................
58
5.5.1
DM M model with natural gas uncertainty..................................................
58
5.5.2
Optimization Model with natural gas network constraints.....................
64
5.6
Data sources and assumptions.........................................................................
65
5.7
Results and conclusions .................................................................................
69
Conclusion.................................................................................................................
74
6.1
Summ ary of key findings ...............................................................................
74
6.2
Discussion ......................................................................................................
76
8
List of Figures and Tables
Figure 2-1: Henry Hub natural gas annual average spot prices..................................................
16
Figure 2-2: Shale Plays in the United States, 2011....................................................................
17
Figure 2-3: Projected Impacts of Proposed EPA Regulations..................................................
19
Figure 2-4: 60 MW wind farm generation profiles in CA ............................................................
20
Figure 2-5: A 2 MW Solar generation profile during 3 days in CA....................
21
Figure 3-1: NERC Regional Entities and Electricity Balancing Authorities.............................
27
Figure 3-2: Electricity restructuring by state .............................................................................
28
Figure 3-3: ISO-NE Wholesale Electricity Day-Ahead and Real-Time Markets...................... 30
Figure 3-4: Structure of the U.S. Gas Industry after 1992.........................................................
32
Figure 3-5: Natural Gas Transmission Capacity Market Timeline...........................................
35
Figure 4-1: Electricity Consumption by Primary Fuel in the US .............................................
38
Figure 4-2: Generation Mix by Fuel Type and Region in the United States ............................
39
Figure 4-3: Natural Gas Consumption by End Use .................................................................
40
Figure 4-4: Number of Forced Outages of Gas-Fired Generators due to Lack of Fuel............. 42
Figure 4-5: Changing Geography of Supply.............................................................................
44
Figure 4-6: Natural Gas and Electricity Market Schedules ......................................................
47
Figure 5-1: Natural Gas and Electricity Market Schedule Coordination..................................
61
Figure 5-2: Overall market mechanism timing ............................................................................
64
Figure 5-3: Forced Outages due to Lack of Fuel......................................................................
67
Figure 5-4: IEEE-4bus network...............................................................................................
68
Figure 5-5: A node pipeline network for the natural gas...........................................................
68
Figure 5-6: Awl Effects on Social Welfare...............................................................................
70
9
Figure 5-7: Awl Effects with Demand Response.......................................................................
71
Figure 5-8: Effects of Al Uncertainty ........................................................................................
72
Table 5-1: Coefficients for Consumers......................................................................................
66
Table 5-2: Coefficients for Generators......................................................................................
66
Table 5-3: Including pipeline network constraints.......................................................................
73
10
1.
Introduction
The electricity sector's reliance on natural gas-fired generation and therefore on the natural gas
sector has increased, and is going to increase in the foreseeable future. The Shale Gas Revolution
changed both the availability and prices for the fuel in the past decade. Currently, coal retirements
are creating a growing demand for new generation like gas-fired power plants. Due to concerns
over climate change and renewable energy goals, flexible generation, a characteristic of gas-fired
power plants, is needed to mitigate the intermittency of renewable resources in the future. Natural
gas also has the added benefit of being a resource within the United States.
Natural gas is a fuel that is not easily stored, and expensive to transport due to its relatively lower
energy density than other fossil fuels like oil. Because of the high storage costs and limited
storage availability, natural gas is generally used as a just-in-time resource, meaning that it needs
to be delivered when it is consumed. Storage of large quantities of natural gas is costly, so gasfired generation plants use gas as it is delivered to them. This leads to one just-in-time resource
(natural gas) being used by another just-in-time resource (electricity). The dependence between
these two sectors has led to concerns over scheduling, transportation, and communication.
In the United States, due to the increased demand from natural gas from gas-fired generators, the
transportation contracts these generators have are of increasing concern. Specifically, interruptible
contracts used by those generators are not as reliable as they once were due to increased risk of
pipeline constraints and interruptible contract curtailment. The use of gas-fired generators comes
with increased risk for curtailment due to fuel shortages at the same time as the use of natural gasfired generation is increasing in a number of regions in the United States. By understanding the
complexities of the natural gas and electricity systems, one can understand why the challenges
caused from the interdependency between the two energy sectors is a concern that has no easy
solution. Both sectors are subject to an array of regulatory authorities and have distinct markets
with their own set of rules which are not easy to change due to their intricacies.
11
With the growing need and push toward renewable energy integration, the ability of natural gas to
coordinate reliability to compensate for the intermittency of such resources is necessary. The
development of a more intelligent electricity system, or smart grid, can help with the changing
needs of the two sectors. However, modeling between the industries and combined planning
efforts are needed for fully realizing the benefits of natural gas, without incurring unnecessary
risk.
1.1
Motivation for research
The interdependency of the electricity and natural gas sectors is important to explore. As energy
is one of the driving forces for economic growth, the proper operation of the electricity and
natural gas systems is important to the economic stability and success of a country. In the United
States, a rising percentage of power production is being produced by natural gas-fired power
plants and it is a trend which is expected to continue in the coming decades. The historically low
natural gas prices, an aging coal fleet, and the need for increasingly flexible and fast acting power
generation plants due to the increased penetration of intermittent and uncertain renewable power
generation, are necessitating the increased reliance of the electricity sector on natural gas.
System operators need to know the availability of their generation plants in order to properly
dispatch them in a manner that both assures system reliability and minimizes the total system
cost. Without fully understanding how the natural gas and electricity system interact, it is very
difficult to rely on gas-fired generators which could potentially be curtailed. Understanding the
interactions between these two systems will help system operators to optimize their control in
such a way that could benefit both sectors.
Over the past decade, interest in the interdependency of natural gas and electricity has grown
significantly. There have been a number of reports from government and industry on the topic
[1][2][3][4]. The types of research being done on this topic are either qualitative or quantitative;
however, there is a lack of a mix of both in the field. This thesis focuses on qualitatively
exploring the interdependency of natural gas and electricity and then posing some potential
12
changes to policies and technologies that could help alleviate issues, and then modeling the
system to give a quantitative analysis.
An important problem facing regulators are system operators in the face of natural gas and
electricity interdependency is how these qualitative problems can be measured quantitatively.
While it is clear that natural gas constraints will affect the cost of the electricity system, there is a
need for modeling in the area to explore the relationship between fuel uncertainty and system
cost. Predicting when and where fuel transportation constraints will manifest in the future is
extremely difficult and sophisticated modeling tools are slowly being developed that model both
natural gas and electricity systems jointly, [5] [6] [7] [8] [9] [10] [11]. The models developed for this
thesis explore two issues with natural gas and electricity interdependency. The first model will
explore how changing the level of uncertainty in natural gas-fired generation costs, due to fuel
constraints and the cost of over and undertaking gas from pipelines, affects the overall system
cost for the electricity system. The second model will extend the Optimal Power Flow (OPF)
model outlined in Hansen et al. [12] to include natural gas constraints.
1.3
Thesis Outline
The outline of the thesis is as follows. Chapter 2 gives a background on the developments in the
United States, both technically and politically, which have brought about a sharp increase in
natural gas and electricity interdependency. Chapter 3 gives an outline of the natural gas and
electricity system. This includes information on the regulatory regimes and focuses on a
description of the energy markets and their relevance to natural gas and electricity
interdependency. Chapter 4 goes into detail on the scope of natural gas interdependency and the
challenges it is presenting to the reliability of both systems, and outlines potential changes to the
two energy markets which could alleviate some of the reliability risks. In order to further explore
some of the suggested changes to market design suggested in the previous chapter, Chapter 5
includes a quantitative analysis of selected suggestions. Finally, Chapter 6 presents a summary of
findings as well as a discussion and conclusion.
13
2.
Background
Natural gas has become an increasingly important energy resource in the United States. The
Shale Gas Revolution changed both the availability and prices for the fuel in the past decade.
Currently, coal retirements are creating a growing demand for new generation like gas-fired
power plants. Due to concerns over climate change and renewable energy goals flexible
generation, a characteristic of gas-fired power plants, is needed to mitigate the uncertainty and
intermittency of renewable resources in the future, and natural gas has the added benefit of being
a resource within the United States.
2.1
Shale Gas Revolution
Prices for natural gas have dropped dramatically over the last several years in what has been
dubbed the shale gas revolution, named for the rock which held previously inaccessible natural
gas reserves. This revolution resulted from two major occurrences. First, the recent recession in
2008 decreased demand for gas, and second, advances in the unconventional gas extraction, such
as horizontal hydraulic fracturing (horizontal high-volume slick water hydraulic fracturing)
increased supply [13].
Horizontal hydraulic fracturing is a method for fracturing rock using high pressure liquids
(typically water, sand, and a number of chemicals) in order to release trapped natural gas especially useful for rock formations like shale which have very low natural permeability. The
key innovation is the ability to drill horizontally which decreases the number of surface drilling
sites required. In addition to conventional reserves, the newly accessible unconventional reserves,
including those from shale formations, have contributed to the second largest natural gas resource
base in the world with 2,200 Trillion cubic feet (Tcf) of technically recoverable reserves in the
United States [14].
14
While this is generally thought of as beneficial to the environment due to the lower level of
greenhouse gases emitted when natural gas is burned for fuel in the place of coal, there is still a
significant controversy over the environmental effects of the extraction of the fuel using fracking.
During the extraction process natural gas is released as methane, which is a more powerful
greenhouse gas than carbon dioxide. For example, a Cornell University study concluded that shale
gas produced more emissions than coal; although the study's models have been called into
question for several reasons including the decision on the life cycle of the emissions in the
atmosphere and the levels of leakage in the extraction process [15]. The Environmental Defense
Fund released a study which examined the uncertainty of methane leakage and concluded that low
leakage levels are critical to realizing the benefits of natural gas for climate change [16]. There is
a lot of potential for natural gas to mitigate climate change, however due to the extreme levels of
uncertainty associated with both the emissions of natural gas and their effect on the environment,
it is necessary to closely monitor the industry and emission levels.
The change in demand and the dramatic increase in supply of natural gas have reduced the prices
for the fuel in the United States. Prices in natural gas have historically been volatile; however
until the shale gas revolution they had been increasing steadily with the average price in 2008 was
$8.86 per million British thermal units (MMBtu) with a high of $12.69/MMBtu in June. This
began decreasing dramatically in 2009 to a low of $2.75/MMBtu in 2012. However, with the
effects of the recession lessening and demand for gas increasing, the prices have risen slightly and
the average price in 2013 was $3.72/MMBtu with a high of $4.24/MMBtu in December [17]. This
is still significantly lower than the prices before the shale revolution and relatively stable, at least
for the moment, which makes usage of the fuel attractive.
The prices for natural gas are expected to change in the future, depending on the yield of the
projected wells and the cost of extraction (see figure 2-1). The technology used to recover
unconventional gas like that from the shale deposits is still fairly new and so price outcomes
could vary. The EIA 2012 Energy Outlook discusses uncertainties both with regard to the size of
economically recoverable shale resources and the cost of production. For example, the outlook
estimates that depending on well productivity, prices could vary by about $3/MMBtu in 2035
[18]. These prices are also expected to rise due to increased regulation of the production of natural
gas, such as fracking.
For example, the EPA recently announced a rule that requires new
15
unconventional gas wells to use emission reduction technologies by 2015 [19]. This rule is a step
in the right direction, and still gives the natural gas industry flexibility.
12.00 History
Projections
10.00 -
0
Lo
8.00 -
EUR
High ec nomnic growt
CL
6.00
-
4.00
-
Re
rence
0
daft
High EUR
Low economic growth
2.00 -
.
n~ nn
1990
1995
2000
2005
2010
2015
2020
2025
2030
2035
Figure 2-1: Henry Hub natural gas annual average spot prices in five cases, 1990-2035. Historical
data is represented for 1990 to 2010. From 2010 to 2035 are projections from the EIA Annual
Energy Outlook for five cases depending on economic growth and shale gas well as estimated
ultimate recovery (EUR) per well [18].
The prices for natural gas and estimates for future values are taken relative to Henry Hub, the
major natural gas distribution hub in Louisiana on the Gulf of Mexico. The introduction of shale
gas resources to the market has radically changed the geography of natural gas supply centers.
Conventional gas reserves in the United States have been primarily from the Midwest and Texas
near Henry Hub, while unconventional resources like shale gas have opened up access to reserves
in locations like the Northeast (see figure 2-2). In 2000, the Gulf of Mexico supplied a quarter of
the gas market. The same region provided just 6% in 2012 while the supply of gas from the
Rockies and the Northeast doubled from 14% to nearly 30% of total supply [20].
16
Umebe
pw
S~~m~sw.WyW'Yg. t
shaftwV Yang"
-
-
"O"MoIWt9
D""Wsww
0,hn
Ut
MN0 Sh.,
&Y
b4a"
09M
Figure 2-2: Shale Plays in the United States, 2011 [14]. A shale play is an accumulation of oil or
gas in which is found in shale rock, which was previously unable to be extracted. New advances
in unconventional drilling techniques have opened up previously unrecoverable reserves in many
parts of the US, most notably the Northeast.
While the shale gas revolution has greatly increased the available natural gas reserves, it is
important to note that many of the drilling developments are focused primarily on extracting oil
and liquid fuel, with natural gas extracted as a result of being in the same geological formations.
This is in part why natural gas prices are so low at the moment, when oil prices are high. It is
uncertain whether natural gas prices will remain as low as they have been in recent years;
however, US dependence on the resource is not going to fade with increasing prices, especially in
light of environmental regulations on emissions and energy security concerns.
17
2.2
Coal Retirements and Emission Regulations
As the outlook for natural gas reserves continues to improve, emission restrictions have made
other fossil fuel resources, such as coal, more costly. In the past decade, the power sector relied
on natural gas to fuel around 20% of electricity generation while coal provided about 50%. In
April of 2012, natural gas fired generation reached parity with coal generation, each providing
32% of the total electricity generated in the United States and, between 2010 and 2035, natural
gas use for power generation is forecasted to double [21].
The Environmental Protection Agency (EPA) has a number of new environmental regulations
which will affect coal retirements or curtailment. For example, Mercury and Air Toxics Standards
(MATS), which will be enforced in 2015, requires all coal and oil-fired generating units to meet
specified emission rates for mercury, acid gases, and other hazardous air pollutants. Many coal
generation plants do not meet these requirements. As of July 2012 roughly 10% of the total coalfired generation fleet of the United States, approximately 30 GW of coal plant capacity, had
announced plans to retire by 2016[22]. The three other major regulations that will affect coal
plants in coming years are the following: the Clean Water Act (CWA) Section 316(b) for Cooling
Water Intake Structures, which pertains to cooling water intake restrictions for power plants
drawing cooling water from lakes or rivers, Coal Combustion Residuals (CCR) proposed rule and
the Clean Air Interstate Rule (CAIR) as proposed in 2010 and finalized as the Cross State Air
Pollution Rule (CSAPR) in July 2011 [23].
In regions with traditionally large amounts of coal generation, like the Midcontinent ISO (MISO)
these regulations affect an even larger percentage of their footprint. For example, out of the 295
coal units in the MISO footprint, which includes parts of Iowa, Missouri, Illinois, Wisconsin,
Minnesota, Indiana, and Michigan, 247 are affected by the EPA regulations (see figure 2-3).
18
35 Units; 20,200
-MW
" Impacted by 1 Regulation
0 Impacted by 2 Regulations
* Impacted by 3 Regulations
" Impacted by 4 Regulations
Figure 2-3: Projected Impacts of Proposed EPA Regulations on Coal Units in the MISO Footprint
and
[23]. This study was conducted in 2010 and focuses on the Clean Water Act (CWA), Mercury
Air Toxics Standards (MATS), Coal Combustion Residuals (CCR) proposed rule, and the Clean
Air Interstate Rule (CAIR) as proposed in 2010.
For regions like MISO, natural gas is expected to fill much of the resulting base load gap from
don't
coal retirements. This takes a vast effort on the part of the electricity system operators who
own power generation and instead operate an energy market. These operators have to find ways to
properly incentivize investors build replacement power generation plants, such as natural gasmore
fired generation, so that they coincide with the coal requirements. This task becomes
new gas-fired
complicated if there is not enough pipeline capacity in the area to support
generation plants necessary to meet electricity demand.
2.3
Renewable Energy Intermittency
In the wake of climate change and other environmental concerns, another fast growing energy
secondresource in the United States is renewable energy. After natural gas, wind has been the
19
largest contributor of new capacity in many regions of the United States, especially in the last
seven years [3].
A number of problems are encountered when integrating renewable energy sources like wind and
solar onto the grid. The fast changes in power output from wind and solar generation requires
other generation plants on the electricity grid to adapt just as quickly to changes, however many
of the existing generation plants, like coal, are not able to change their power output at such time
scales without sacrificing their fuel efficiency or damaging their machines.
The rate of change for power (ramp) is particularly an issue for wind generation where there are
high rates of change in MW/min. Wind generation consists of large turbines which are spun by
wind currents to generate electricity. Because the power output of the turbines increases
exponentially with wind velocity, weather conditions dramatically affect the power output. In
order to compensate, fast ramping and power balancing is needed to make up for the change in
power output (see figure 2-4). For example, in Texas on July 23, 2005, the wind power output for
the region increased 646 MW in an hour and a half [24].
60
-
50-S40CL
420
0
10
-10
0
1
2
3
4
Time in Days
5
6
7
Figure 2-4: 60 MW wind farm generation profiles during a week in southern California [25].
Wind speed can increase or decrease dramatically over just a few minutes or hours and requires
grid operators to change the accompanying power generation within a similar timescale.
20
Solar energy from photovoltaic (PV) power production requires added attention from electricity
system operators as well. PV panels are made of semiconductors and generate electricity directly
from sunlight via an electronic process called the photoelectric effect. Electrons in the PV panels
are freed by solar energy and can be induced to travel through an electrical circuit into the
electricity grid [26]. Because the panels rely on sunlight, weather conditions such as cloud cover
and morning fog change the output of the solar resources in fast timescales (see figure 2-5).
2000
2 MW PV Plant Production
1800
1600
1400
0
1200
0
1000
800
0
600
400
200
0
8
-200
12
10
16
14
18
20
lime (Hour)
-
5-Mar-09
-
19-Mar-09
24-Jun-09
Figure 2-5: A 2 MW Solar generation profile during 3 days in southern California in 2009 [25].
Moreover, as solar production facilities are frequently planned in close proximity, they are often
connected to the same distribution feeder, which can cause high levels of voltage fluctuations and
complex power management problems for the system [25].
This is also especially true for
residential solar-PV installations.
Intermittent resources like wind and solar offer a growing challenge to electricity grid operators
because, like demand, they can fluctuate unpredictably. Short of major advances in storage
capabilities, balancing these resources requires power generation that is responsive and load
21
following. The ability to compensate for dramatic load shifts is one of the key characteristics of
many combined cycle natural gas power plants [27]. Right now, natural gas turbines are routinely
used to meet peak demand levels, showing that they can be essential for the integration of
renewables [28].
Natural gas power plants are already being used as power generation peaking plants to fill in the
gap of renewable generation throughout the day. The two forms of energy appear complementary
in many respects: natural gas electricity generation enjoys low capital costs with variable fuel
costs, while renewable energy generators have higher capital costs but zero fuel costs (besides
bioenergy). In some respects, natural gas has been thought of as a transition fuel to more
renewable energy because of this. However, more capital investment and reliance on natural gas
infrastructure will make an eventual transition away from the fuel to cleaner resources more
unlikely. Only the most favorable wind sites are able to compete with the low costs of natural gas.
Less favorable wind sites, solar, and other renewable energy technologies remain more expensive
because of the many difficulties due to the relative inexperience of the industry as a whole. There
is also higher costs associated with planning and permitting the new technologies [3].
2.4
Energy security
The Obama administration has been very vocal in its support for the natural gas industry for a
number of reasons including job creation, climate change mitigation potential, and energy
security [29]. While prices abroad for natural gas remaining high, low prices in the United States
has opened up important opportunities for trading liquid natural gas despite the high
transportation costs. Most importantly, it opens up opportunities to decrease US dependence on
foreign oil.
In 2012, the United States imported about 40% of the petroleum it consumed, and transportation
accounted for more than 70% of total U.S. petroleum consumption. With much of the world's
petroleum reserves located in politically volatile countries, the United States is vulnerable to
supply disruptions. However, because U.S. natural gas reserves are abundant, this alternative fuel
22
can be domestically produced and used to offset the petroleum currently being imported for
transportation use [30].
The combination of newly available large supplies of natural gas reserves, changing electricity
generation demands due to coal retirement, and energy security concerns, natural gas is a key
aspect of US energy production and policies. The next section will go into more depth on both the
electricity and natural gas markets and regulations.
23
3.
Overview of Electricity and Natural Gas Markets
By understanding the complexities of the natural gas and electricity systems, one can begin to
understand why the challenges caused from the interdependency between the two energy sectors
is a concern that has no simple solution. Both sectors are subject to an array of regulatory
authorities and have distinct markets with their own set of rules which are not easy to change due
to their intricacies.
3.1
Energy Regulation and Stakeholders
In the United States, there are several levels of regulatory agencies with different levels of
authority ranging from federal to local and from setting recommendations to requirements. While
the natural gas industry has the same basic structure throughout the country, the regulation of the
electricity sector varies by region and state. The system operation in the energy sector has as
much to do with policy and regulation as it does with the physical design and control. Decisions
to change the energy sector involve a wide number of stakeholders from the natural gas industry,
the electricity industry, as well as state, federal, and regional regulators. The relevant energy
regulatory authorities, as they relate to electricity and natural gas interdependency:
The Department of Energy (DOE) is a department of the US government which is headed by
the US Energy Secretary who is a part of the President's cabinet. The DOE is concerned with the
federal policies regarding energy, especially those involving foreign policies, and safety in
nuclear material. In relation to natural gas and electricity interdependency, most notably the DOE
permits liquefied natural gas (LNG) export sites. With prices of gas significantly higher abroad,
exporting large quantities of natural gas could change available supply and prices of gas
domestically.
The United States Congress is the legislative branch of the government and plays a significant
role in making laws related to energy. Recently Congress has begun to consider gas-electricity
24
interdependency issues. Specifically, the House Energy and Commerce Committee's Energy and
Power Subcommittee held two hearings in March of 2013 that considered the coordination
challenges of natural gas and electricity interdependency, as well as possible federal solutions.
The first was titled American Energy Security and Innovation: The Role of a Diverse Electricity
GenerationPortfolio and dealt with looking at how the coal regulations proposed by the EPA will
affect the diversity of generation and general concerns with the ability of the US to meet energy
demand with significantly fewer fossil fuels [31]. The second, titled American Energy Security
and Innovation: The Role of Regulators and Grid Operatorsin Meeting Natural Gas and Electric
Coordination Challenges, focused specifically on concerns surrounding electricity and gas
interdependency [32].
The Federal Energy Regulatory Commission (FERC) is a federal agency which is a subset of
the DOE which focuses on domestic regulation of energy at the federal level in the US and is
responsible for regional electricity market operations, regulating wholesale power transactions
and interstate pipelines, and setting cost-based transmission tariffs for gas transportation services.
FERC also permits the siting and construction of LNG facilities used for importing and exporting
natural gas, and certifies LNG facilities that are connected with interstate pipelines.
State Public Utility Commissions (PUCs) are state level governing bodies that regulate public
utilities including state owned, investor owned, or public owned utilities. Other names include
utilities commission, utility regulatory commission (URC), and public service commission (PSC).
Generally, PUCs have regulatory authority over the siting of power plants and transmission assets
within their state and authority over the generation mix that is employed under their jurisdiction.
However, the magnitude of the reach of each PUC varies greatly by state. In relation to gaselectricity interdependency, PUCs require local distribution companies (LDCs) to meet the
"human need" customers (which include hospitals, residential homes, nursing homes, etc.) before
other customers (such as natural gas-fired generation plants) [33] [34].
Independent System Operators and Regional Transmission Operators (ISO/RTO) were
created by FERC to administer the transmission grid on a regional basis and to operate bulk
electricity power systems and account for about 60% of the US electricity power supply [35]. If
the state has a deregulated market, ISO/RTOs promote wholesale competition in the electricity
system through defining market rules, which have particular importance in gas-electricity
25
interdependency. Changing these rules is difficult, and the ISO/RTO must go through a lengthy
stakeholder processes and receive approval from FERC to change or update market rules.
The North American Electric Reliability Corporation (NERC) is a not-for-profit organization
which develops and enforces reliability standards at the regional level, assesses electricity
reliability, monitors the bulk power system and educates and certifies industry personnel. The
standards that NERC develops that directly affect the operation of electricity systems, but NERC
is prohibited by Section 215 of the Federal Power Act from setting reserve margin criteria or
ordering the construction of transmission to address inadequate resources [36].
The North American Energy Standards Board (NAESB) sets voluntary industry-wide
standards, though some are made mandatory by various regulatory bodies such as FERC. For
example, natural gas transmission capacity nomination deadlines have been set by the NAESB
and are used throughout the United States to standardize when and how interstate pipelines accept
requests to ship gas.
These regulatory bodies have differing levels of state, federal and regional authority, as well as
differing levels of priority and scope. There are times when it is unclear which agency takes
priority on issues, especially when it concerns state and federal authority. At times this is a benefit
because it allows issue to be examined by a number of different agencies; however it can also
slow down the processes for adapting to change.
3.2
Electricity Sector
The United States electricity system consists of three main interconnections: Eastern, Western,
and Electric Reliability Council of Texas (ERCOT). The physical infrastructure consists of
generators from which power is transferred via long distance, high-voltage transmission lines,
with the voltage gradually stepped down through distribution systems to the end-user. The United
States energy system has grown rapidly since it was first begun, and now includes over 3,200
electricity distribution utilities, over 10,000 generating units, tens of thousands of miles of
transmission and distribution lines, and millions of customers [37]. Since electricity demand is
26
largely treated as an uncontrolled exogenous input, the electricity utilities have an assumed the
"obligation to serve" in which generation needs to be operated to meet this exogenous load at all
times[38]. The demand for electricity from consumers, the load, changes with human activity
levels following daily, weekly, and monthly cycles [39]. For example, demand for electricity is
generally higher during the day when businesses are drawing power, and less at night when more
people are asleep and using less power. ISOs operate the electricity system by carefully balancing
supply and demand of power.
While FERC governs energy at the federal level, the regional level is broken up into regional
entities that group balancing authorities by NERC which develops and enforces reliability
standards (see figure 3-1). The system operations are controlled by the many different balancing
authorities within these regions. The balancing authorities are typically referred to as RTOs or as
ISOs.
Regions and
Balancing Authorities
'j
NPCC
J
RFC
SERC
FRCC
TRq
-- - -
DOmd
R0
.AW.t
.utwto
amhour,m,
v Ipj
.o.d
h-.N~ayk, w-w,W.d
*Bubble size is determined
by acronym width
As of July 25, 2012
Submit changas to balancdnenerc.com
Figure 3-1: NERC Regional Entities and Electricity Balancing Authorities[40]. Map also shows
DC connections between balancing authorities and regions.
27
The main difference between the two types of utilities is that RTOs tend to be state run vertically
integrated utilities, which own both the transmission and generation of power, while ISOs are
deregulated utilities that still own transmission, but have sold off their generation capacity.
Deregulated utilities, such as ISO New England (ISO-NE), buy wholesale electricity in the
markets where power and ancillary services (such as backup power capacity) are sold
competitively. Different states and regions have different levels of deregulation that range from
no deregulation (vertically integrated utilities) to those that have deregulated both power
generation and distribution, which is electricity transmission at the consumer level (see figure 32).
V'-
Figure 3-2: Electricity restructuring by state [13]. In deregulated states, individual distribution
companies work with an ISO to supply power to consumers. In other states, individual utilities or
utility holding companies operate the electricity system or are a part of an RTO [35].
28
The benefit of deregulation is that with the increased competition, the cost of electricity will be
lower and generation companies will have more incentive to be efficient and implement cost
saving measures. Another of the main benefits of deregulated markets is the opportunity to
promote market-based solutions that provide strong incentives for efficiency. However, while
their probably exist technological solutions for ensuring a completely adequate electricity system
dependent on natural gas, the incentives to develop and deploy them are not fully in place or
understood. Market operators face the challenge of how to enable those technical solutions in the
bounds of complicated regulatory and market structures. Research and development priorities
should be set in order to alleviate impending market constraints, and creating appropriate
economic incentives will allow the market to discover technical solutions without the vertically
integrated utilities method of picking winners which can lead to economic inefficiencies.
The downside of deregulation is that the market rules need to be carefully considered and
enforced to prevent market manipulation. A strong example of market manipulation in the
electricity sector occurred during the California electricity crisis in 2000, where a few generation
companies with large market shares were able to withhold generation capacity to artificially raise
prices. Many of the issues with natural gas and electricity interdependency happen in deregulated
regions where market planning is more difficult, if perhaps more cost effective. Vertically
integrated utilities, like Tennessee Valley Authority, have a great deal of freedom in how they
operate their electricity systems. Operators need to be concerned about sensitivities such as fuel
neutrality, reliance on economic incentives, and complicated market participant involvement
processes. This lack of constraints allows integrated utilities to address problems quickly and with
certainty. In regional electricity markets, much more thought and due diligence is given to every
market rule and regulatory change. In order to change a rule, ISOs have to go through a stake
holder process. The stakeholder process, through which market operators engage market
participants before submitting rule changes to the FERC for final approval, can be time
consuming and cumbersome due to the large number of stakeholders and regulatory bodies.
Due to the complexities of deregulated markets, problems with the interdependency between
electricity and natural gas are most dominant in regions with ISOs and their wholesale electricity
markets. Market planning and operation occurs on several time horizons. In the long term, ISOs
conduct transmission and interconnection planning and deregulated utilities often operate a
29
forward capacity market (FCM). The FCM creates incentives for future power generation
capacity build out to ensure an adequate level of installed generation in locations with high
electricity demand. In the medium term, ISOs coordinate planned power plant outages to make
sure that they do not all coincide in a manner that threatens system reliability. In the short term,
ISOs coordinate the dispatch of power generation units to meet the day's electricity demand.
Short term market operation and generator dispatch are where gas and electricity interdependence
concerns are the most pressing, although long-term solutions are often needed to prevent shortterm issues. There is a day-ahead market (DAM) which secures the next day's generation based
on a prediction of demand. The next day, the real-time market makes up for any short comings in
the generation dispatch schedule created the previous days (see figure 3-3).
Day-Ahead Market (DAM)
el
I
/
Real-Time Market (RTM)
Figure 3-3: ISO New England Wholesale Electricity Day-Ahead and Real-Time Markets in 2012
[41]. The generators are compensated based on the real-time locational marginal price (LMP).
In the day-ahead market, generators "bid" the price and quantity of the power they can produce as
well as their operating constraints, such as ramp up time and minimum output levels, to the ISO
30
which then computes the market clearing price. The dispatch is determined using a security
constrained unit commitment model (SCUC model), that ensures that supply equals forecasted
demand within a suitable reliability margin which limits the amount of non-served energy. During
the operating day, system operators manage the electricity system using the real-time market. The
ISO orders generators to change their output of electricity to make up for supply and demand
imbalances in five minute increments. The generators are compensated based on the real-time
locational marginal price (LMP) of their power[42]. LMPs are calculated based on how much
electricity is worth in a specific location, called a node, based on the amount of electricity
demand, the supply of electricity, and the level of transmission constraints. This method reflects
the costs of the transmission system far more accurately than zonal pricing, and provides price
signals for the additional costs of electricity caused by transmission congestion, line losses, and
generation. In day to day operations, electricity system operators schedule and dispatch natural
gas-fired generators without taking into account the adequacy of their fuel supply because they
assume generators include price uncertainty in their costs. The problem occurs when there is
quantity uncertainty and natural gas pipeline constraints manifest in real time that limit the
amount of natural gas available to the gas-fired generators, threatening the reliability of the power
they provide and subsequently the electricity system.
3.3
Natural gas sector
The natural gas infrastructure consists of transmission (pipelines), producers (wells), storage, and
consumers. Pipelines use compressors along the line to create the flow of the fuel from the
injection point on the line to the consumer of the natural gas.
Since the deregulation of the natural gas market in the United States in 1992 gas sales and
pipeline transportation are sold in separate markets (see figure 3-4), the structure of the market
has evolved more organically than regional electricity markets due to the lack of a central
controller (like an ISO). This is in part because natural gas pipeline companies and natural gas
marketers have fulfilled the role of a central controller after deregulation, albeit in a decentralized
manner. Natural gas has three distinct markets: the commodity gas market, the transmission
31
capacity market and the financial market. The commodity market is where contracts for physical
natural gas quantities are traded, while the transmission capacity market is where contracts for gas
delivery are traded [43]. The financial market is similar to that in electricity and is used for risk
hedging against price changes. An understanding of the commodity market and transmission
market is most relevant for gas-electricity interdependency.
r-Local
distribtin
r
-onnuercial
Sconpanies
e
Producers
ne
~
*.~et
rMarketers
-
Residential
Indutstrial
Eeti
spot
market
__
Gas transportaftok
Bypass Pipeline
-
-
-
-
-
-
-tlte
Gas sales
Figure 3-4: Structure of the U.S. Gas Industry after unbundling of sales from pipeline
transportation, after 1992 [43]
The physical commodity markets include participants from all over the natural gas industry
including producers, pipelines, marketers, local distribution companies, and large end users.
Transactions in the commodity market are conducted between the buyer and seller in spot markets
and hubs, often with marketers as intermediaries, to minimize the costs and risks of natural gas
supply[43]. When evaluating market coordination between gas and electricity systems based on
economic efficiency, it is important to consider more than just simply aligning transmission
deadlines of the two markets. For example, the gas market is most liquid between 8am and
9am[41], just before the start of the gas transmission capacity day, which begins at 10 am. If the
timing of the markets is such that gas-fired generators are trying to buy gas outside of these times,
it can be difficult or costly as there are fewer market participants.
32
Natural gas trade relies on long term contracts because of the high fixed costs for transportation
compared to other resources. For example, low pressure piped gas has 180,000 Btu per cubic feet,
while crude oil has on average 1,010,000 Btu per cubic feet, almost six times the value for the
same volume [44].
3.3.1
Operations of Interstate Pipelines
Pipeline operators are concerned with providing their customers with gas transportation capacity
while maintaining suitable pressure within the pipeline. There are definite opportunities for
pipeline system operators to fulfill their primary goals of gas transportation and maintaining
pipeline pressure and also offer additional services with increased flexibility for consumers.
NAESB established the following pipeline capacity contract classifications and their relative
levels of delivery priority [41][45].
Primary Firm Capacity is bought through long-term contracts with monthly service fees, and is
what most LDCs rely on for gas delivery. LDCs supply natural gas to residential consumers and
businesses within their distribution network. Primary firm customers have first priority to be
served during constraints. Once a primary firm customer nominates its shipments of gas, they
cannot be denied the delivery of that amount. If a primary firm customer needs to increase a
previously established nomination, a shipper with an interruptible contract can be curtailed in
order to satisfy this request. Any capacity that a customer like an LDC holds but does not use can
be sold as secondary firm capacity.
Secondary Firm Capacity guarantees delivery of the holder's initial nomination, but does not
guarantee delivery of additional nominations during times of constraints. As holders of secondary
firm capacity cannot be denied delivery in most cases, they can prevent primary firm capacity
holders from scheduling additional gas transport in later nomination cycles, which are explained
in the next section.
Interruptible Capacity: Interruptible contracts offer the most flexibility because they are only
paid for when used, but are limited to when excess system capacity is available after all primary
33
firm and secondary firm requests have been satisfied. As the name suggests, holders of
interruptible capacity contracts are susceptible to be denied delivery when primary firm contract
holders increase their nominations or when there are physical constraints on the interstate pipeline
system. These are the contracts that most gas-fired generators use due to their flexibility and
lower cost since gas-fired generators can submit the cost of these contracts into their electricity
bid as a variable cost, however they often cannot do the same for firm contracts.
Excess natural gas in the interstate pipeline that increases the pressure above the minimum that
the customers require is called line-pack. Line-pack consists of gas that is injected at the well
head, Liquid Natural Gas (LNG) storage, and pipeline interconnection points[46]. Line-pack
gives pipeline operators considerable flexibility in the volume and timing of withdrawals and
injections by their customers. As long as there is sufficient line-pack in the pipeline, contracts for
natural gas transportation often allow customers the flexibility to overtake and undertake from the
pipeline. Overtaking is taking more than the scheduled quantity and undertaking is leaving gas in
the system that was previously scheduled to be removed. Pipeline operators will often resell this
excess gas. The pipelines charge the consumer extra based on how much they over or
undertake[47].
The added flexibility of line pack also allows for non-ratable takes, in which the customer
consumes its contracted amount over any time period. For example, you can take all of your
scheduled gas quantity over a specific time frame, such as the morning or the evening rather than
in hourly increments. This is especially useful for gas-fired generators that have uncertain demand
profiles as a result of gas and electricity market misalignment and regular demand fluctuations.
Unfortunately, the sometimes excessive over and undertaking of gas has caused problems
between generators and pipeline operators. Since pipelines generally schedule transmission
assuming the gas is taken throughout the day in regular increments, when generators overtake gas
expectantly, this creates balancing problems for the pipeline system operators[41].
When there are difficulties in maintaining appropriate pressure in the system, operators may limit
the amount of gas allowed to be overtaken and undertaken, or only allow for ratable takes from
the pipeline, restricting customers to take gas in 1/24th increments throughout the subsequent
twenty-four hour period[46]. In anticipation of such restrictions, generators may over-nominate
gas so that they have more than they need, knowing that they will be restricted to 1/24t of their
34
total nomination each hour, and sell the gas that they do not consume. In addition to the potential
loss of value on the re-sold gas, power generators would also be faced with costly imbalance fees,
making ratable take scenarios quite expensive.
Shipment Nominations and Contract Priorities
3.3.2
Transmission capacity nomination cycles have been set by the NAESB (see figure 3-5).
Generators, marketers, and other participants contact pipeline operators to nominate capacity,
of
specifying the amount of gas they would like to ship, the point of injection and the point
receipt.
1230
TUIy
Nogmi62
CirdeDaiim
1900
IT g
NaoviueCyde
DhuiuCdkhm
1100
IIO-Dtyl
10.00
S#tofN&4atuIGosDa
D dreif hT 64
l iNEvoiu
Cyd Doi"g
iuab
18:00
Ddimv
o
1
-
htmuDa 2
22:00
DdofW yof
I
-DAT I
Sccuhl~iSSvd
Natural Gas Day
Figure 3-5: Natural Gas Transmission Capacity Market Timeline[41]. The vertical dotted line in
the timeline represents midnight. All times in EST.
During the nomination process, pipeline operators take nominations first from holders of firm
contracts. These holders of firm contracts nominate how much capacity they will be using the
following period, up to their total allotted firm capacity. Any excess is sold on a secondary market
as released capacity. This capacity is bought on the short term by power generators and industrial
users as interruptible capacity.
Pipeline operators schedule natural gas deliveries with successive rounds of capacity nominations,
where capacity subscribers tell the pipeline operators the quantity of gas they would like to ship.
35
The priority of delivery is determined by the capacity contracts the subscribers purchase. There
are two main types of capacity contracts relevant to gas-electricity interdependency. Firm
capacity contracts, which are typically used by Local Distribution Companies (LDCs) as
mentioned earlier, provide first priority gas transportation and are the most expensive capacity
contract with monthly service fees. Natural gas-fired generators typically use interruptible
capacity contracts, which are only paid for when used and has the lowest priority dispatch on the
gas pipeline system. Gas-fired generators use interruptible contracts because not only are they the
least expensive and offer the most flexibility, generators are often unable to recover the cost of a
long-term firm contact in their electricity bid in energy markets. In wholesale electricity markets,
generators are only allowed to bid their variable cost for generation, which include fuel costs and
maintenance costs associated with the production of power, rather than their capital costs, which
should be recovered by other means, in order to prevent market manipulation.
If an LDC underestimates the amount of capacity they need to meet residential demand for the
following period, firm transportation contracts allow them to call the pipeline operator and
request more capacity, up to the total firm contract amount. Any capacity in the interruptible
market can be bumped without notice in order for the pipeline to fulfill its contractual obligations
to the LDC. This system is important to the operation of the pipeline, and allows pipeline
companies to price transportation based on reliability, although it is a major concern for electricity
system reliability in the face of natural gas interdependency, which will be the focus of the
following section.
36
4.
Interdependency Concerns
Due to high storage costs and limited availability, natural gas is generally used as a just-in-time
resource, meaning that it needs to be delivered as it is consumed. Storage of large quantities of
natural gas is costly, so gas-fired generation plants use gas as it is delivered to them. This leads to
one just-in-time resource (natural gas) being used by another just-in-time resource (electricity).
The dependence between these two sectors has led to concerns over scheduling, transportation,
and communication.
The growing reliance on a single fuel in the electricity sector as well as the changing demand in
the gas sector has profound effects on the operation of the two transportation systems. These
changes affect how pipelines are financed and how electricity system operators dispatch the
available generation. With the growing need for and push toward renewable energy integration, it
is imperative for natural gas to coordinate reliably with the power sector in order for gas-fired
generators to compensate for the intermittency of renewable resources. The development of a
more intelligent electricity system, or smart grid, can help with the changing needs of the two
sectors. However, modeling between the industries and combined planning efforts are needed to
fully realize the benefits of natural gas without incurring unnecessary risk.
4.1
Electricity sector reliance on natural gas
In the last decade, the power sector has relied on natural gas to fuel around 20% of electricity
generation while coal has provided about 50%; however, the percentage of natural gas-fired
generation in the electricity system has been increasing dramatically (see figure 4-1). In April
2012, natural gas fired generation finally reached parity with coal generation, each providing 32%
of the total electricity generated in the United States. Between 2010 and 2035, natural gas use for
power generation is forecasted to double[21]. New Environmental Protection Agency (EPA)
emissions regulations are expected to reduce roughly 10% of coal generation plants in the United
States, with more expected retirements in the next ten to fifteen years [48]. Most of this capacity
37
is being replaced with natural gas-fired generation as recent developments in the extraction of
unconventional gas have reduced gas prices dramatically. Natural gas prices directly impact unit
commitment and economic dispatch. For example, the change in prices for the fuel is at times the
difference between using a gas-fired generator and using other fossil fuel resources like oil.
o Natural Gas
* Coal
* Conventional Hydro
o Nuclear
UOil
U
Wind
U Other
4500
4000
3500
0
3000
0.
E
2500
4A
C 2000
0
Lm 1500
1000
500
0
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
Figure 4-1: Electricity Consumption by Primary Fuel in the US from 1990 to 2012. This
interdependency is growing mainly because there is an increased reliance on natural gas in the
power sector.
The growing reliance on natural gas for power production has led to the exacerbation of issues
with resource dependence and scheduling for the natural gas and power sectors. With this rise in
the use of natural gas for electricity production, problems with natural gas and electricity
coordination have gone from a nuisance to major reliability risks, especially in times of peak
demand or gas constraints. In some regions these problems are more prevalent than in others (see
figure 4-2).
38
100% 90% -
80% -
o Power Imports
70% 0 Renewables
60% 0 Nuclear
50% -
40%
MCoal
30% -
M Natural Gas
20% -MOil
10% 0%
S Atlantic W S
E S
W North E North Mountain
Central
Central
Central Central
Mid
Atlantic
New
England
Pacific
Figure 4-2: Generation Mix by Fuel Type and Region in the United States, 2012 [18].
In the United States, the various regions have vastly different levels of dependence on natural gas;
some regions have almost no natural gas on their system, while others rely on natural gas for over
half of their electricity production. Some regions, like the West Coast, are far from
unconventional natural gas resources, like shale gas reserves, while others, like the New York and
Pennsylvania areas, have enormous supplies in their backyard [13]. In some regions like New
England, where there is a particularly heavy dependency on natural gas, electricity prices follow
natural gas prices. This is because of the large percentage of natural gas fired generation on the
system.
4.2
Natural gas demand changes
Because the electricity sector is using more natural gas for power generation, the natural gas
sector has had a growing demand from the electricity sector (see figure 4-3). This has an impact
39
on for the transportation and flow of natural gas in the pipelines, as well as the market operations.
Natural gas consumers have to request to transport a specific quantity of gas over the day, called a
nomination. The natural gas marketers, who coordinate the fuel delivery for many natural gas
fired generators, as mentioned in the previous chapter, hold work hours typical to most of their
customers. Many natural gas marketers close on the weekends, holidays, and overnight, leading
many gas-fired generators to buy weekend packages. This can cause uncertainty in their supply
because they are not able to properly adjust their fuel nomination, which can be especially
necessary over holidays when consumer demand for electricity can be unpredictable. Increasing
demand from gas-fired generators also changes how gas is shipped; previously, natural gas was
not used by generators which demand large amounts of their nomination in only a few hours, but
was instead used over the course of the day by residential and commercial consumers who use it
for heating. In order to deal with the added level of uncertainty, some pipelines, like the
Algonquin pipeline that services New England, requires gas-fired generators to provide hourly
burn profiles of plants directly connected to the pipeline [49].
Natural Gas Consumption by End Use
10000000
- - Residential
-
- - - Commercial
9000000
-
8000000
-
7000000
-Industrial
Electric Power
6000000
5000000
.
.l.
4000000
3000000
--
2000000
1997
2000
2005
2002
2008
2011
2013
Figure 4-3: Natural Gas Consumption by End Use [13]. The major growth in natural gas
consumption in the US has been from the power sector. This change has important implications
for how gas is transported.
40
Natural gas transportation contracts dictate transportation fees, and thus their design is important
for investment in pipeline infrastructure. Power plants in many parts of the country rely on
interruptible contracts for gas supply in order to adapt to changing market conditions because they
are unable to recover the higher cost of long-term firm contracts through their electricity market
bids. These short-term interruptible contracts are subordinate to firm contracts in delivery priority
and do not contribute to the traditional pipeline financing and regulatory approval process.
Specifically, pipelines are currently not built without a significant portion of their capacity being
contracted out with firm contracts ahead of time to make sure there is sufficient demand to
warrant the investment.
For several years this system worked well: gas demand increases from LDCs that signed long
term firm contracts that encouraged pipeline investment and regulatory approval [46]. The ample
excess pipeline capacity that was not contracted, or capacity not used in the short-term, was used
by natural gas power generators that relied on interruptible contracts.
This system has been increasingly stressed in the last two decades. Now the growth in additional
natural gas demand is largely from natural gas power plants that are averse to buying long-term
firm contracts. The challenges from this demand shift are particularly pressing in NERC regions
like RFC and NPCC where a significant portion of forced outages of gas-fired generators arise
from a lack of fuel availability (see figure 4-4). A forced outage is when a generator that was
previously available to supply power to the electricity system becomes unavailable. An outage
can occur for a number of reasons, including fuel constraints and unscheduled maintenance.
41
SPP
FRCC
MRO
2%.
1%
0%
Figure 4-4: Number of Forced Outages of Gas-Fired Generators due to Lack of Fuel by
Region[50]. Florida Reliability Coordinating Council (FRCC), Midwest Reliability Organization
(MRO), Northeast Power Coordinating Council (NPCC), Reliability First Corporation (RFC),
SERC Reliability Corporation (SERC), Southwest Power Pool (SPP), Texas Reliability Entity
(TRE), Western Electricity Coordinating Council (WECC)
Though power generators are a large and growing customer segment for natural gas pipeline
companies, and the service that pipeline operators provide is immensely important to the
operation of the electricity system, the contracts on which many generators rely simply do not
encourage investment in pipeline infrastructure. Furthermore, the interruptible capacity contracts
on which power generators are so dependent can threaten reliability of the electricity system. If
LDCs underestimate the amount of capacity that will be required to meet demand in the shortterm, or if electricity system operators need an immediate influx of power from a natural gas
power plant that does not have fuel nominated for delivery, interruptible capacity contracts often
are unable to provide certainty that gas will be delivered for power generation. This is because of
the lower priority of delivery, and results in difficulties in nominating for additional capacity.
Worse, there is the threat of being "bumped", where a shipper with a higher priority contract
decides to increase its receipt of gas, causing the loss of capacity to a lower priority interruptible
contract holder [51].
42
When LDCs need to increase their shipment nominations, pipeline operators recall interruptible
shipment contracts and force generators to go without gas. Historically, there has been enough
excess capacity for LDCs to increase their shipment nominations without recalling interruptible
contracts, but with utilization rates approaching 100% in some areas, in order for an LDC to serve
its customers, some users of interruptible service will have to be curtailed.
4.3
Pipeline financing concerns
Some amount of new pipelines are needed to move gas from recently developed supply centers
resulting from the shale gas revolution to traditional demand centers is necessary (see figure 4-5).
While more expensive than dual fuel and demand response, pipeline expansion greatly increases
the reliability of fuel delivery for generators, assuring they will be available for electricity system
operators to call upon. Natural gas that is abundant in a certain region but lacks the infrastructure
to move to where it is in demand can lead to price anomalies that diminish the economic value of
the resource. For example, a substantial negative basis occurred for several years at the Opal Hub
in the Rocky Mountains before new pipelines were built: gas producers were selling their natural
gas at below market value at the Henry Hub because of lack of natural gas pipeline infrastructure.
A price basis is the difference between the price of gas at a given location and the price of gas at
the Henry Hub, the reference price for gas in the United States. A negative basis refers to the price
of gas at a given location being below the price at the Henry Hub [20]. Often, oil and natural gas
are in the same reserves, so when oil is produced natural gas can be a byproduct of that
production, called associated natural gas. In the Bakken oil fields of North Dakota, producers
flare associated natural gas that they are unable to move to market, which resulted in an estimated
$1 billion lost in fuel through 2012 although the value in the loss fuel is far exceeded by the value
of the extracted oil [52].
43
1W
I1VW
Vn
20
00
21
Figure 4-5: Changing Geography of Supply. The figure shows the change in production output
from 2000 to 2012. Regions like the Northeast show significant increase due to the shale gas
revolution [20].
Firm capacity contracts are used to finance the expansion of natural gas pipelines and as natural
gas demand in the power sector typically uses interruptible contracts, the build out of new
pipelines needs to be addressed. Revising the FERC permitting rules, which require firm capacity
as a primary way for investors to prove there is sufficient consumer demand to warrant the
infrastructure expansion as mentioned earlier, could allow investors to consider the interruptible
contracts as another way to prove sufficient demand. Encouraging gas-generators to sign firm
contracts by allowing generators to bid the cost of the firm contract in their costs, which they are
currently only allowed to do for their interruptible contracts, would increase the reliability of fuel
delivery to vital gas-fired generators and would incentivize pipeline expansion where necessary
without changing the FERC permitting rules. However, mandating purchasing of firm capacity
would add an extra cost to some generators, but not for power generators who rely on nuclear,
coal, or other fuels. Unique and flexible pipeline transportation contracts, beyond firm, secondary
and interruptible contracts, can offer the gas and electricity industries more flexibility. For
44
example, as gas generators are called to balance renewable resources, they will require a fuel
supply contract which offers greater flexibility for the timing of delivery, as mentioned in the
previous section, such as "bandwidth" or "swing" type fuel supply contracts [41].
Another problem with requiring firm contracts is that during times of constraints on the pipeline,
gas-fired generators could still be curtailed. There is a difference between generators behind the
city-gate, who obtain their gas from the city's LDCs and those directly connected to the pipeline
system. These generators manage imbalances with the LDC and are subject to any additional rules
and regulations the LDC may have. For example, LDCs are obligated to serve their human needs
customers first (which include hospitals, residential homes, nursing homes, and other critical
consumers). So even if a generator behind the city-gate had a firm contract, it runs the risk of the
LDC curtailing their gas during constraints [46]. It is clear that any solutions, mandates or
otherwise, need to consider that not all gas-fired generators are connected directly to pipelines.
While some new pipeline investment is necessary, the problem of underinvestment might be
overstated. The worst natural gas constraints happen for only a few hours a year, and there does
not need to be enough capacity to supply all gas generators with firm deliveries simultaneously
because the cost would far outweigh the benefit from avoiding the occasional pipeline constraint.
Although statistics from FERC suggest that few miles of new pipelines are under construction,
many recent pipeline additions have been small feeder pipes needed to connect new supply
sources to existing large interstate pipelines.
Changing pipeline financing is not the only way to solve problems with gas-electricity
interdependency. As regional power sectors become more reliant on natural gas as a main source
of energy, it is important to look at ways to diversify resources. This can come in the form of
diversifying the sources of natural gas and increasing fuel availability and flexibility. The
development of distributed natural gas storage could allow for increased supply reliability during
pipeline delivery constraints. LDCs also have fuel reserves that could be opened up to alleviate
demand on pipelines during constraints, although LDCs tend to be extremely conservative with
their reserves as they have a commitment to deliver to residential and human needs customers.
Some of these reserves could be made available during times when a gas-fired generator is vital to
system reliability to those same critical customers. Dual fuel capacity, demand response, and oilfired generation are all options that would be less expensive than building more pipeline capacity.
45
However, like mandating the purchase of firm capacity, mandating dual fuel capability would add
extra costs to some generators over others, and so the implementation of these measures
significantly affects their success and if done incorrectly, could lead to market distortions.
4.4
Dispatch concerns
In order to optimize gas-electricity dispatch, the alignment of the markets needs to be addressed.
The following figure is a simplified view of the New England ISO (ISO-NE) energy market and
natural gas capacity market (see figure 4-6) . Natural gas-fired generators need to coordinate their
dispatch and fuel delivery over two gas days (specifically, the gas intra-day market of the
previous day, and the gas intra-day market of the current day) in order to meet their day ahead
electricity obligations due to the timing of the markets and gas flow start times. Optimizing
dispatch schedules, diversifying power system resources and addressing pipeline financing are
important ways to address gas-electricity interdependency issues.
46
Electric Day E2Electric
fnitial Offers Due
Day E:
Reofferm
Cnuiafers Du~e
nda
M 0Nomn
Noon
12AM
Electric Day EO
It
12AM
NOOn
w IDay
2Gas
Noon
12AM
Figure 4-6: Natural Gas Capacity Market and Electricity Energy Market Schedule Coordination.
Note that the previous day ahead electricity market (E-0) does not post the dispatch schedule until
well after both the Intra Day nomination (G-1) and the Day Ahead nominations (G-0) have
closed.
Market misalignment is not only a problem for ISO-NE. For example, in the New York ISO
(NYISO) there are similar problems with misalignment. Generators are required to submit their
electricity bid by 5am and the ISO posts the day-ahead schedule by 11:00am. Theoretically, this
means that generators have an hour and a half between when the ISO posts the day-ahead
schedule and the closing of the first day-ahead gas nomination cycle at 12:30pm. Since the gas
market is most liquid between 8:00am and 9:00am and the NY facility system (the two LDCs)
requires generators to announce the amount of capacity that they will use on the LDCs' systems
by 9:30am, generators nominate the gas that they anticipate needing before receiving a dispatch
schedule from the ISO. The timing constraints caused by the market misalignment has already led
to generators making economically rational decisions to postpone announcing availability to
avoid imbalance fees imposed by pipeline operators, even when they are most needed by the
system operators during the morning load ramp up.
47
Market misalignment, in relation to its negative effects on system reliability, makes incentivizing
flexible generation an important change in the operation of the electricity markets. Aligning
schedules between gas and electricity markets can facilitate the ability of market participants to
improve reliability; however, incentivizing flexibility can promote innovation and a more marketbased solution. If the right incentives are in place, innovations like virtual pipelines, which are
simply the transportation of gas over short distances with a series of trucks rather than an actual
pipeline, or dual fuel capability can add diversity to the fuel supply. In particular, there is a need
to make improvements in how reliability and flexibility are priced into the wholesale electricity
markets in the United States. Generator flexibility can provide opportunities for quickly shifting
load, adding resiliency to the electricity system to better handle current and future supply
diversity constraints and intermittency.
4.6
Smart Grid and Demand Response
The term smart grid has a lot of different meanings. In its most basic form, it is the
implementation of advanced electricity metering devices (smart meters) which allow consumers
to react to the real time price changes of electricity. Conventional meters simply record the
electricity consumption for a month. A profile is then used to estimate the time of use which is
used in conjunction with average prices to bill the consumer. Advanced metering, or smart
meters, generally refers to three types of meters: net metering, dual metering, and time of use
metering.
Net metering is often used in conjunction with residential solar installations and records the
amount of power consumed minus the amount of power produced for a given period of time
(generally a month) [53]. When the consumption is positive then the utility will price the power
based on some profile, similar to conventional meters. These meters are typically used for houses
with residential solar, and the profile used typically does not account for the intermittency of
generation. If distribution companies use volumetric charges to recover their costs, then with this
method consumers with net meters do not pay effectively for their use of the distribution system.
48
Since typically homeowners with higher incomes will adopt distributed generation first, the cost
of the distribution network will increase for lower income residents.
Dual metering is one attempt at preventing the distortions caused with net metering, as it records
the amount consumed and the amount produced separately [54]. This allows distribution
companies to price the consumer based on their overall consumption like a net meter. However,
dual metering does not fully take into account the benefit of having generation close to the load,
which significantly reduces electricity transmission losses. Also, the method still prices the
consumer based on an estimated profile like the conventional and net meters, which is hard to
predict and does not give the consumer any incentive to respond to the real time prices of power.
Time of use metering records the amount of electricity consumption, and the time it was
consumed. This allows for the use of a number of different pricing schemes including real time
pricing [55]. There is the concern that using time of use metering at the residential level might
cause an ineffective investment in distributed storage, but it also opens up the possibility for
independent companies to put in more efficient levels of storage to aggregate demand response
for groups of consumers.
Demand side signals, enabled through smart meters, are a way of allowing the consumers to react
based on their individual value of lost load, both for electricity and natural gas. The standard used
to assess the basis of new regulations for most PUCs is safe and reliable service at just and
reasonable prices [56]. While safety and reliability are worthwhile objectives, under this
assessment framework the value proposition of providing service is ignored because consumers
are unable to react efficiently to real time prices. With conventional meters, there is no way to
reflect the value of service of an individual customer, so PUCs instead assume a common value of
lost load across all their customers. With a smart grid system enabled through smart meters, the
increased ability for consumers to react to high electricity and natural gas prices caused by high
demand and pipeline constraints can mitigate supply scarcity problems.
Regulators and market participants need to fully internalize that the electricity and natural gas
systems are interconnected. In this regard, a smart grid with demand response for electricity can
be a way to mitigate not just electricity price volatility, but also natural gas price volatility since
the price spikes for gas and electricity often occur simultaneously. Specifically, it is worthwhile
49
to evaluate whether regulators should consider demand response or advanced metering with realtime pricing for natural gas at the residential consumer level as well. Giving consumers a realtime pricing option for electricity and looking into what demand response options for natural gas
would entail will prove beneficial to both energy sectors.
The role of demand response in electricity markets has been implemented in ISO-NE and NYISO
to some extent[57]. Large consumers of electricity bid into the demand response market and
system operators compensate them for reducing their consumption when electricity prices go
above their offer prices. Currently, this method does not allow the growing demand of residential
consumers the option to react to real-time prices [58]. Expanding demand response programs to
residential consumers could introduce much needed flexibility and opens up opportunities for
innovative uses of existing infrastructure. For example, the possibility of using electricity heating
as a type of storage since it currently causes major peaks in electricity prices during cold snaps.
Electric heating in both residential and office buildings, if dispatched during times when prices
are lower, could heat rooms before prices spike. With adequate insulation and consumer tolerance
for small temperature fluctuations in their home and work environments, this heat storage could
open up a flexible way to reduce some of the impact of peak electricity prices when the effect is
aggregated from numerous sources, although it is unclear how effective this would be.
4.7
Concerns of focus for modeling
An important problem facing regulators and system operators in the face of natural gas and
electricity interdependency is how these qualitative problems can be measured quantitatively.
Unfortunately, the data on the circumstances that have led to emergency situations for the gas and
electricity infrastructures is not widely accessible. While it is clear that natural gas constraints will
affect the cost of the electricity system, there is a need for modeling in the area to explore the
relationship between fuel uncertainty and system cost.
Predicting when and where fuel transportation constraints will manifest in the future is extremely
difficult and sophisticated modeling tools are slowly being developed that model both natural gas
50
and electricity systems jointly. These models are needed to address concerns such as investment
in natural gas generation, firm contracts for pipeline capacity, and dual fuel capability's benefit to
reliability.
The next section will pertain to the models developed for this thesis which explore two issues
with natural gas and electricity interdependency. The main focus will be on how changing the
level of uncertainty in natural gas-fired generation cost, due to fuel constraints and the cost of
over and undertaking gas from pipelines, will affect the overall system cost for the electricity
system. Next, a combined model for gas and electricity optimization is explored.
4.8
Conclusion
How the interdependency of the natural gas and electricity systems effects each sector is starkly
different. The natural gas sector has increasing demand for gas by power producers; however,
since these power producers mostly use interruptible contracts, the incentives to change their
system structure to improve reliability of supply to those generators is lower than it would be if
they had firm contracts. Firm consumers take priority, and provide the bulk of the investment
remuneration for the pipelines. The electricity system operators, on the other hand, have the
imperative to supply reliable power to electricity consumers, and their ability to do so is directly
affected by their growing dependence on natural gas-fired generation plants.
The policy recommendations from this chapter focus on mitigating market and fuel uncertainty
for natural gas-fired generators in order to improve overall system reliability and efficiency. A
wide range of considerations and solutions are necessary for addressing issues with natural gas
and electricity interdependency in areas with different resources and political regimes.
Coordinating market schedules, diversifying power system resources and addressing pipeline
financing are the three main solutions needed to address gas-electricity interdependency issues in
most regions.
Market timing differences cause many power generators to incur price uncertainty in the
electricity market and quantity uncertainty in the gas market. These uncertainties can lead to
51
inefficient markets and unavailability of generators which lead to reliability risks and price
increases for the electricity sector. Besides improving the coordination and alignments of the
natural gas and electricity markets, a way to decrease natural gas uncertainty to generators is for
electricity system operators to incentivize marketers, which coordinate the fuel nominations, to
stay open overnight and on the weekends.
The interstate pipeline system that was designed to serve LDCs and their relatively predictable
demands is increasingly being relied upon by power generators with highly variable demand
profiles. State regulatory requirements for what qualifies as a human need customer should be
revisited, considering that heating residential homes, which is the basis of the service obligation
on the gas side, often requires electricity as an input. As LDCs have an obligation to serve human
need first, generators are sometimes unable to secure affordable fuel supplies in times of most
extreme cold weather events.
It is unclear if the current pipeline regulation and financing structures is adequate to face
impending electricity system challenges. Allowing gas-fired generators to pass on the costs of
firm contracts into their electricity bids in the energy market or mandating firm contracts can be
quick ways to increase the amount of pipeline capacity; however, it might not be the most
economical solution in light of climate change concerns. Ultimately, system operators need to
understand the financial value of reliability of generators on their system, and ensure that
generator remuneration reflects this.
Finally, using demand response and other smart grid building blocks to increase both the
adaptability of the power system to changes, as well as to increase the amount of information
available, will allow for better operation of both the gas and electricity systems. Expanding
demand response programs to residential consumers could introduce much needed flexibility and
opens up opportunities for innovative uses of existing infrastructure.
52
5.
5.1
Natural Gas and Electricity Market Modeling
Need for gas-electricity market models
The interdependency between the natural gas and electricity system increases as more natural gas
generation plants are used for replacing coal and balancing intermittent and uncertain renewables.
Integrated modeling between the two systems, which includes both the physical infrastructure and
market structures, is needed to help predict how and when outages of gas delivery for generators
or natural gas price spikes could affect electricity system dispatch and reliability. Currently both
energy sectors model their systems separately and only use simple estimates for the actions of the
other energy system, rather than creating more informed models which could allow for both
systems, even if just market operations, to be optimized simultaneously.
There are numerous examples of situations where demand for natural gas and electricity peak at
the same time[59]. In July 2002, a pressure spike from efforts to repair a leak at the Collins
generating facility near Chicago, IL lead to four of five generators to go offline which totaled
2,019 MW of generation. In this case there was no load shedding, which is deliberate switching
off of electrical supply to parts of the electricity network due to lack of power supply. However,
the fact that such a relatively small gas disruption caused such a large loss of capability
demonstrates the significance of the risk to electricity system reliability [60]. There is especially
danger during cold snaps which leads to spikes in gas prices and curtailment of gas-fired
generators. In February 2006, record low temperatures in Colorado caused a high demand for gas
by residential consumers for heating. More than 1,000 MW of power producers could not obtain
natural gas, which resulted in load shedding of over 500 MW and causing more than 323,000
customers to be without power for several hours [61]. Electricity system operators guard against
such unscheduled outages of generation with operating reserves. The overcapacity need, as well
as the number of occurrences of coupled price spikes in gas and electricity, can be avoided by
better understanding of gas-electricity interdependency through computational modeling.
Gas-electricity models are also important for understanding how to integrate intermittent and
uncertain renewables into the electricity grid. Natural gas power plants are already being used as
53
power generation peaking plants to fill in the gap of renewable generation throughout the day.
Intermittent resources like wind and solar offer a growing challenge to electricity grid operators
because, like demand, they can fluctuate unpredictably and due to their introduce significant
levels of uncertainty into market models [62][63]. Short of major advances in storage capabilities,
balancing these resources requires system operators to utilize peaking plants, like gas-fired
generators, to compensate. Electricity models that do not include the natural gas market and
network constraints will not be able to assess the reliability risk of fuel unavailability to gas-fired
generators which will be used even more in the future.
Combined models of gas-electricity systems and markets are important building blocks for smart
grid research and development. Having integrated market modeling between the two sectors can
enable better understanding of how to efficiently operate the increasingly complex energy
systems with growing demand, changing supply, and progressive climate change goals. For
example, the authors of [64] develop a transactive control architecture that incorporates the
interaction between real-time pricing, physical constrains, and demand response based loads in
the presence of uncertainty of renewables. Demand response for electricity can be a way to
mitigate not just electricity price volatility, but also natural gas price volatility during times of
coupled price spikes in gas and electricity, as described earlier. Current models, as described in
the next section, do not include information that could enable new ways of using smart grid
building blocks like demand response to solve problems arising from gas-electricity
interdependency.
5.2
Electricity power market models
Researchers and planners of electricity power systems rely heavily on engineering and economic
models in order to test various designs for the network and market operations. Linear
programming and optimization models have been used to determine least-cost designs and
operation of the system while satisfying physical and economic constraints for generation and
transmission system assets, as well as regulatory requirements such as reliability and
environmental emission standards [65].
54
Since deregulation of electricity power systems in many areas of the United States, there has been
ample development of modeling in the electricity power system [66]. These models vary
depending on the time scale and include electricity power system management models, operation
planning models, unit commitment and dispatch models, and real-time operation models.
Electricity power system management models are for resource planning and production pricing
(10-40 years), long range fuel planning (10-20 years), transmission and distribution planning (515 years), and demand-side management implementation planning (3-15 years) [65]. These
models differ across the two power system management paradigm: centralized planning versus
decentralized market-based planning and investment. Market based planning relies on proposing
incentives for generation investment, but still has the same centralized transmission and
distribution planning.
Operation planning models focus on modeling the required generation to meet electricity load
demands in the power system. A key portion of these models involves calculating optimal
dispatch of generators. The idea is that because electricity demand throughout the day varies
significantly, if all the required generation units to meet peak load were committed or on reserves
throughout the entire day the system would be enormously expensive. Turning units off when
they are not needed saves a great deal of money. Besides physically supplying enough power,
system operators need to satisfy load demands while operating the power system economically
[66].
Unit commitment and dispatch models include maintenance and production scheduling (2-5
years), fuel scheduling (1 year), and unit commitment (8 hours to 1 week) [65]. Unit commitment
models determine the optimal schedule of generating units over a set time period for a given
system[66]. For example, Security Constrained Unit Commitment (SCUC) models are used by
system operators in organized electricity markets like ISO-NE in order to determine the dispatch
schedule of generators given a set level of system reliability.
Real-time operation models are used to make up for any discrepancies between the predictions
of the unit commitment and dispatch models and the actual real time load. This includes
dispatching models at the economic dispatch level (1 to 10 minutes) and automatic protection
55
(fractions of a second) [65]. These maintain voltage and frequencies while minimizing cost and
avoiding unnecessary equipment stress.
5.3
Natural gas market models
Unlike the electricity system where there is a significant body of research and information
available, the natural gas system is relatively lacking in independent study and publicly available
information. In part, this is because of the decentralized nature of the industry itself which is
largely deregulated. Some exceptions to this are academic papers that focus on the natural gas
industry and modeling [67][68][69]. The types of models being developed fall into three main
categories: investment models, value chain models, and transportation models.
Investment models are used to inform decisions for field investments in oil and gas which can
involve large cost and risk, especially with offshore investments [67]. What the majority of
models share is a common function of allocating limited market opportunity in the sale of natural
gas or oil amongst a set of gas fields in order to maximize profit [70]. With changes to the
productivity of fields in light of the shale gas revolution, these types of models are very useful for
analyzing future investment decisions in the face of uncertainty.
Value chain models are used to help make decisions on the planning and operation of the natural
gas supply chain. These models can consist of components like production, transportation,
processing, contracts,
and markets [68]. Value chain models are important because they
incorporate the complete network and optimize it simultaneously, which is an important part of
liberalized natural gas markets [67]. Because of the added complexity in market operations since
liberalization took place, it is important for these models to capture the complexities of the
different natural gas markets including transportation, commodity and financial markets.
Transportation models model the flows in pipelines for a given system network and are
important for studying the natural gas industry in light of gas-electricity interdependency. It is
important that these models accurately describe the properties of the natural gas transportation
network while remaining analytically tractable. Some components that are important for modeling
56
include network descriptions, descriptions of transient flows and the interaction of the gas with
compressors.
5.4
Prior Electricity and Natural gas Interdependency Models
Despite the growing ties between natural gas and electricity systems, the academic literature
contains relatively few articles on hybrid electricity-natural gas models which optimize the
operation and flows of both networks simultaneously. Unlike many traditional electricity models
that maximize net social benefit [65], these hybrid models recognize the costs and benefits of all
agents in both systems when determining the optimal set of natural gas and power flows and
corresponding marginal prices. Even so, there are still a number of articles on the area of hybrid
electricity and natural gas models. The authors of [6] propose an optimal natural gas and power
flow model that looks at equality constraints and the transformation between gas and electricity
networks, which maximizes total Social Welfare by summing benefits for all electrical and
natural gas consumers and subtracting the cost of all operations. Similarly, [7] propose an optimal
natural gas and power flow model that minimizes costs of power and gas optimal dispatch. The
security analysis in these models focuses on the short-term power system operation of the
consequences of gas system failures on electricity market operation.
In models that incorporate some representation of pipeline transportation, it is necessary to have
the right amount of complexity to describe the system. There are a few examples of models in the
literature which focus on the constraints of both the natural gas and electricity system. In [8], the
scope is expanded to take into account natural gas flow constraints, both with active (with
compressors) and passive (without compressors) pipelines with evaluation using a 4-bus network.
The model proposed in [9] evaluates the maximum amount of electricity power generation
possible from all of the combined-cycle power plants in a power system, taking into consideration
natural gas demand by non-electricity customers, natural gas network, and availability.
Traditionally, reliability analysis set the maximum output of power plants to constant parameters.
However, this assumption does not hold under fuel uncertainty. In [10] the authors discuss the
impact on the power system of different contingencies in the natural gas network that cut off the
supply to gas-fired generation plants. This work is furthered with a unit commitment problem
57
subject to natural gas network constraints with the possibility of fuel switching solved in [71].
Finally, Correa-Posada and Sainchez-martin in [11] developed a stochastic contingency analysis
for the unit commitment problem and analyzed the effects of network uncertainties in the shortterm operation of the integrated natural gas and electricity system.
The development of a comprehensive electricity-natural gas model that accommodates the
implications of delivery of natural gas, dominant contingencies that occur both in the electricity
and natural gas networks, and poorly coordinated electricity and gas markets is currently not
available, and the body of research in the area needs to be further developed. In the meantime, the
focus of this thesis has been on optimal power flow models which determine the dispatch of
generation with some level of integration between the natural gas system and the electricity
system by extending the work currently available in the literature.
5.5
Model Descriptions
The models developed for this thesis explore two issues with natural gas and electricity
interdependency. The first focus will be on how changing the level of uncertainty in natural gasfired generation cost, due to fuel constraints and the cost of over and undertaking gas from
pipelines, will affect the overall system cost for the electricity system. The model used for this
method will be based on a Dynamic Market Mechanism (DMM) approach designed by Kiani and
Annaswamy [72], and extended in Hansen et al. [12].
The second model will extend the OPF outlined in Hansen et al. [12] to include natural gas
constraints. This model will be solved using the typical optimization approach [73], in order to
explore how pipeline constraints can affect the dispatch of generation.
5.5.1
DMM model with natural gas uncertainty
58
In this section, an optimal power flow model is presented that captures important aspects of gaselectricity interdependency. This network model is expanded from the dynamic market
mechanism which is an alternative way to solve the OPF, an approach designed by Kiani and
Annaswamy [72], and extended in Hansen et al. [12]. The wholesale electricity market functions
with each consumer demand company submitting the bidding stacks of its demand to the pool and
each generating company submitting its own bidding stacks to the pool, which the ISO then clears
though a negotiation process to produce prices, consumption and dispatch schedules based on the
network constraints. First, the description of the consumer modeling is given, and then the
description of the generator modeling and finally the network model is defined.
Consumer Modeling:
For each consumer company the consumption is divided into three classes: fixed, adjustable, and
shiftable, denoted PDfj , PoDa and PDsj as in [12]. The consumer companies are defined as
j
E Da = {1,2,...., ND). Each consumer company is assumed to consist of one unit of each class
of consumption, with P'Da1 = PDfj + PDa1
.
The value of using each class of electricity for the
consumer is represented by the associated utility functions in which the marginal utilities are
decreasing linear functions of power consumption as follows:
CDaUDaj(P'Daj)
bDaj PDaj +
p2 Da
CDs -ip
UDSJ(PDS)
UDsjPDsj) =bDsj PDsj +
P2Dsj
The coefficients,
bDaj
, bDs,
CDa
and CDsj are consumer utility coefficients. In order to limit the
amount of adjustable demand so that the effects of natural gas uncertainty can be studied better,
the incremental cost coefficient CDaj is set at a high level.
Generator Modeling:
Generators are separated into two different categories, conventional dispatchable and nondispatchable. The difference between these two types is that non-dispatchable generators have
59
uncertainty in their fuel availability. The dispatchable generators are defined as i E G =
{1,2,....,NGcJ
and the non-dispatchable generators are defined as 1 E Gw = {1,2., NGw
Ramp constraints, startup and shutdown costs are not included in this model. Operating costs of
dispatchable generators is given by the following:
CGcL(PGcL) = bGc i
Gci
+
2Gci
Coefficients bGc, and CGc 1 are generator cost coefficients and power generation (PGc 1) is
constrained between a maximum and minimum value.
The operating costs of non-dispatchable generators, such as those with higher levels of
uncertainty include a mechanism to account for this variability. These cost functions were
developed in [72] and used to model the uncertainty of renewables like wind. In this thesis we
modify the equations slightly to represent uncertainty in natural gas generation, which in times of
constraints due to cold snaps, or from uncertainty due to scheduling mismatches between the two
energy sectors, can act much like an intermittent and uncertain wind generator as discussed in the
previous chapter of this thesis. This mechanism is to include the cost of supplying power from a
reserve generator. The costs of operations are given by the following.
CGj(PGwI)
CGWI(PGWL)
CGW 1 (PGWI)
bGw PGw +
C (Awl) = bw1 Awl +
+
w)
2 p2 Gw
A2 W
Coefficients bGwl and CGwl are generator cost coefficients, bw, and Cw, are reserve cost
coefficients. The coefficients bw, and cw, incorporate the cost of over and under-taking fuel. The
coefficient Awl
denotes the percentage of power uncertainty of the generator, given by the
following.
Awl= PGwlAGwI
The value for AGwi varies between 0 and 1 when the power is overestimated, and is negative if
power is underestimated. Fundamentally, the greater the uncertainty, the more the cost of
60
generation for the gas-fired generator approaches the reserve generator costs. For natural gas
generators, fuel uncertainty can result from pipeline constraints, scheduling uncertainty due to
market timing mismatches, or unexpected power generation needs as discussed in the previous
chapter of this thesis. For example, due to market timing, gas-fired generators do not receive their
dispatch schedule from the power system operators until after the pipeline capacity market has
already closed, as seen in figure 5-1.
2
3 *1E
Electric DayE
Electric Da yE2
Iitial Offers Due i
*4
Initial
leofers
I nitial Offer
Electric Day EO
I'sDu
u
Intra Day 2 Nomination
1ntra DaV
1
Timel Nominations
EveninNomnati on s
I as Flow from
CEvening Schedule Pos
12AM
12AM
Gas Day GO
Gas Day G1
Gas Day G2
12AM
Noon
Ahea
Noon
12AM
Figure 5-1: Natural Gas Capacity Market and Electricity Energy Market Schedule Coordination.
The previous day ahead electricity market (E-0) does not post the dispatch schedule (*1) until
well after both the Intra Day nomination (G-1), denoted as (*2), and the Day Ahead nominations
(G-0), denoted as (*3), have closed. Even the new day ahead market enacted by ISO NE, (*4),
does not post a schedule until after the Intra Day 1 Nomination (*2). The ISO proposed day ahead
schedule would have solved this problem; however, it was rejected by the FERC during the
stakeholder process in favor of the proposal of the New England Power Pool (NEPOOL) [74].
61
Market-Clearing:
The proposed market-clearing method maximizes the utility for consumers while minimizing the
cost of generation. This is done through optimizing a cost function subject to network constraints.
The electricity network in this model includes the capacity through the lines in the network that
are constrained and is congested when they approach their maximum limit.
The proposed model clears the electricity market optimizing a cost function subject to network
constraints, namely congestion due to line capacity limits and power balance. The ISO works
through a negotiation process with the generators and consumers, using the gradient play
described in Kiani et al. [72] , until a stable outcome is reached. A detailed derivation of this
process can be found in Hansen et al. [75]. The cost function for the market clearing process is
termed Social Welfare and it is defined as the following where Dq , G, , and G,
are the set of
indices of consuming units, dispatchable generating units, and non-dispatchable generating units,
respectively.
SW
= {
UDj(PDJ)
CGci(PGC)
-
jEDq
G
~
iEGc
(PGw)
1EGw
The optimization problem is to then to minimize -SW, and is subject to the following electricity
constraints where (5, is the voltage angle of node n, and Bmn is the susceptance of line n to m.
And where O, , t9,
On
, , and fln are the set of indices of dispatchable generating units, non-
dispatchable generating units, consumer demand units, and connected nodes, respectively,
connected to node n.
The nodal power balance Vn E N =1,
PGc
iEOn
(PGwI
+
..., B} where B is the number of buses:
AwI)
Y
~
Bmn[1n - Sm]= 0;
Dj -Z
jE0n
LEt9n
Inn
The transmission line limit, Vn E N, Vm E fn:
Bmn(5n
-
Sm] < Pnmmx
62
The Demand constraints, where pref
Dsi and pref
Dfj are found through the process described in Hansen
et al. [12] and dictate the desired demand profile, for Vj E Dq = {1, ..., ND} where ND is the
number of consuming units:
PDs,
-
=
r
pref
pf
pref
Dfj
'Daj
The generation limits the natural gas non-dispatchable generator V1 E G" = {I, ..., NGw} where
NGW is the number of non-dispatchable generating units:
PGw-
I'GW
In the DMM used for this portion of the modeling in this thesis, the LMP at each node is solved
through an iterative approach through an exchange of price, power production and consumption
between the ISO, generators, and consumers. The subgradient algorithm used was proposed in
Kiani et al. [72]. Each market participant has an associated time constant r which correspond to
the reaction time of different participants to the negotiation process. The coefficients rda and rds
correspond to the adjustable and shiftable consumption time constants. The coefficients -r. and
Tq
correspond to the dispatchable generation and non-dispatchable generation. The ISO
proposes an initial price of electricity, and generators give the power they can produce based on
that price. The ISO then takes this power information and proposes a new price for electricity to
the generators and consumers, which respond again with power quantities. Figure 5-2 gives a
graphical representation of the market mechanism timing where T specifies the time period of
interest and TdpC is time taken to calculate the desired demand profile given the percentage of
demand response in the system, and Tneg is the time for the overall negotiation process as it
converges toward equilibrium.
63
I
T
T
~neg
Td pc
time
t
t-T
t+T
Figure 5-2: Overall market mechanism timing where T specifies the time period of interest, TdPC
is time taken to calculate the desired demand profile and Teg is the time for the overall
negotiation process[12].
After a number of iterations, the quantity depends on the scale of the problem [76], the price that
the ISO gives does not change the action of the generators and consumers, and a solution has been
found. For the purposes of this thesis, the action of the generators and consumers are modeled
through the cost ftnctions defined above.
5.5.2
Optimization Model with natural gas network constraints
The following model has the same cost functions for the consumers and generators as outline in
the previous section and follows the same optimization constraints with the following additional
natural gas network constraints and changes to the constraint on PGwl.
The natural gas nodal balance Vi E U = {t,
... ,
Y} where Y is the number of nodes in the pipeline
network, Fij is the flow of gas into node i from node j, Fj is the flow of gas out node i to node j,
and Fgi is the flow of gas to the gas-fired generator Gi. And a is the set of indices of gas nodes
connected to node i.
I
jEati
Fi -
Fgj =
-
IFj
gE01
jEati
64
0
The flow pressure constraints Vi E U = {1, ..., Y}
where the constant C? depends on the
composition and length, diameter, and absolute rigidity of the pipeline [69] [67].
sign(Fi) -Fzi = Cj(p?
-
However, this constraint is non-linear and so the substitution 7rc = pis
made as presented in
Urbina et al. [8] and the left hand side is represented as a simple linear approximation of the
Weymouth equation F9 i = m - rwi + b, a simplification of what was presented in Correa-Posada et
al. [5].
The pressure limits the natural gas generators for each line from node i to j:
0 s! Ir, :5 Apmax
The generation limits the natural gas generators V1 E Dq where Eg is the efficiency coefficient
for the natural gas generator, which is currently the same for all generators for simplicity:
PGw 1 5 Eg - Fgi
The OPF for this portion of the modeling for this thesis is found through a standard optimization
approach modeled with the MatLab YALMIP tool box for prototyping optimization
problems [77]. For our purposes, the action of the generators and consumers are estimated
through the cost functions defined above.
5.6
Data sources and assumptions
The coefficients bDa, and bDS are the base price the consumer utility function and cDa, and cDjS
are the coefficients for the incremental utility price, which is the benefit the consumer gets from
changing their consumption. The incremental and base price coefficients determine the behavior
of the adjustable portion of the demand. The values for the consumer utility coefficients are as
listed in table 5-1. The constants for the shiftable demand, UDpS, were used in Hansen et al. [12] as
65
well as the time constant for UDaj . In order to limit the amount of adjustable demand so that the
effects of natural gas uncertainty can be studied better, the values in bold for UDaj have been
modified from what was used in [12]. The incremental cost coefficient CDa is set at a larger
negative value than the shiftable demand so that consumers have very little willingness to adjust
consumption and have a higher base utility of using electricity, much like a data center which
would not change their consumption of electricity even if prices rise to very high levels.
Constants
UDa.
UDs.
TdTime constant
bDBase cost
CD Incremental cost
0.8
75
-0.9
0.8
60
-0.21
Table 5-1: Coefficients for Consumers. Items in bold indicate changes made compared to [12].
Constants
G1
Ig Time constant
bG Base cost
2.8
31.89
0.7
35.67
G3
0.7
35.67
Reserve Base cost
--
--
50.9
Incremental cost
0.25
0.53
0.53
--
--
--
--
0.7
0 to 0.25
b,
cg
cgu Reserve incremental cost
AGW1 Uncertainty %
G2
Table 5-2: Coefficients for Generators. Items in bold indicate changes made compared to [12].
The conventional dispatchable generators with no fuel uncertainty and are labeled as G1 and G2 The non-dispatchable generation unit, G3 , is natural gas-fired generator with fuel uncertainty.
The coefficients for the generators are also based off of values used in Hansen et al. [12], however
the base cost prices for the three types of generators modeled were changed to reflect current
energy prices from the EIA's Electricity Power Annual report [78] and can be seen in table 5-2.
Also, the value for the reserve base cost for the natural gas generator has been revised. The base
66
cost for is calculated by taking into account the penalties pipelines imposes on generators for
taking fuel off of the Algonquin natural gas pipeline which services the New England area [47].
It was assumed that generators above 25 percent uncertainty will have difficulty competing
economically, and so the range of fuel uncertainty for the model is from 0 to 25 percent.
Determining the level of uncertainty that a particular natural gas generator faces can be difficult.
The level of uncertainty for natural gas generators also varies throughout the United States due to
market differences. For the purposes of this thesis, the values of uncertainty for the generator in
this model are derived from data from NERC [50] on the natural gas capacity outages in 2010.
For example, a value of 0.14 for AGw 1 is an average estimate for the uncertainty being faced in the
United States by taking the forced outages due to lack of fuel (see figure 5-3) and dividing it by
the natural gas-fired generation capacity for the electricity sector in the year 2010 [78].
Al I Forced Outages Due to Lack of Fuel
16000
14000
12000
0
10000
8000
(U
6000
-W
4000
0
2000
n
A. all
ill i
ILJ Is
Lm
-
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
MFRCC *MRO
MNPCC *RFC
*SERC
*SPP
ETRE *WECC
Figure 5-3: Forced Outages due to Lack of Fuel from data from NERC [50]
Through changing the various generator coefficients, as described earlier (see tables 5-1 and 5-2),
an analysis of the system can be conducted. The model for this thesis is demonstrated on a 4-bus
67
network which consists of three generators, two conventional (base unit and peaking unit) and one
non-dispatchable, and two consumers who demand power from the system (see Figure 5-4). The
electricity network data was chosen as it resulted in a typical LMP profile seen in ISO New
England [79] and uses demand data from 07/19/13 NEMASSBOST and CT.
Non-Dispatchable unit
Base unit
Peaking unit
2
3
1-2
3-.
D1
D2
Figure 5-4: IEEE-4bus network with G1 and G2 being conventional generators and G3 being a
natural gas-fired generator with fuel uncertainty from interruptible contracts and market
misalignment.
4
Gas
Supply
1 }
G3
2
3
Gi
G2
Figure 5-5: A node pipeline network for the natural gas constraint modeling. For simplicity
natural gas compressors are not used.
68
For the last part of the modeling for this thesis a simple four node natural gas delivery system,
also used by Urbina et al. [8] is used to model the natural gas constraints (see figure 5-5). The
natural gas pipelines have a value of 0.15 for each C?. This value corresponds roughly to
pipelines with a diameter of 625 mm and with a length of 20km [8]. The value for Ap!J" is set at
300k and the maximum and minimum node pressure squared are 100k and 400KPa 2 respectively.
Finally, 0.16 is used for the coefficient Eg, which is the fuel efficiency of the gas-fired generator.
This value is calculated using the 2012 heat rate for advanced combined cycle natural gas-fired
plants reported by the EIA [80].
5.7
Results and conclusions
The results for the electricity without gas optimization, the first optimal flow model explored,
show that small levels of uncertainty have little effect on the operation of the electricity system.
However as this uncertainty grows, as it has in recent years, the Social Welfare cost increases
dramatically (see figure 5-6). The figure shows two different points which give incite to how
natural gas un[81]certainty due to market misalignment and pipeline constraints affects Social
Welfare. By aligning the markets for gas and electricity and improving the reliability of gas fired
generators the uncertainty will diminish and the overall Social Welfare will increase. However,
there are diminishing returns for the market structure to eliminate all natural gas uncertainty, and
so it is likely that the cost of completely eliminating uncertainty will outweigh the marginal
benefit of further reducing the uncertainty. The US average, as calculated earlier, stands to make
significant gains by addressing gas-electricity uncertainty. Certain regions, the Northeast Power
Coordinating Council (NPCC), Reliability First Corporation (RFC) are regions of the United
States which have the highest percentage of forced outages of gas-fired generators due to lack of
fuel, as mentioned in the previous chapter and have significantly more to gain from improving
market coordination between the gas and electricity sectors.
69
AGW,
Effects on Social Welfare
No uncertainty
1.92E+04
1.90E+04
1.88E+04
S1.86E+04
-
1.84E+04
US average
0 1.82E+04
1.80E+04
1.78E+04
0
0.05
0.1
0.15
awl
0.2
0.25
0.3
(%)
Figure 5-6: The Social Welfare at each point Involves looking at a 24 hour period. The results
show that as uncertainty in the cost of fuel increases, the effect on Social Welfare grows
dramatically.
Another method of addressing the level of uncertainty associated with natural gas fired generators
is to adopt more demand response in the electricity system. Increasing the level of demand
response through the shiftable demand response method outlined in Hansen et al. [12], even if
only by 5 percent, dramatically increases the Social Welfare of the system, particularly for low
levels of natural gas uncertainty (see figure 5-7). This shows that small problems with natural gas
uncertainty do not need to be solved by changing the natural gas market structure, but can instead
be solved through incentivizing demand response in electricity markets.
70
AGWI
Effects with Demand Response
- * - &- - 5% demand response
-
-
0% demand response
2.15E+04
2.10E+04
2.05E+04
2.00E+04
-
+..
1.95E+04
1.90E+04
"MM
01.85E+04
1.80E+04
1.75E+04
1.70E+04
0
0.05
0.15
Aw (%)
0.1
0.2
0.25
0.3
Figure 5-7: The addition of just 5% shiftable demand response dramatically increases Social
Welfare, regardless of natural gas uncertainty levels.
One of the main benefits of models that combine natural gas and electricity systems is that the
uncertainty for generator fuel can become known to the electricity system operators ahead of
time. To explore this in the model, the maximum output for the gas-fired generation was fixed
and progressively reduced by the percentage uncertainty to make the graph in figure 5-8. This
graph shows the comparison between having the generators with uncertainty fuel costs (maximum
output with price uncertainty) which was introduced in figure 5-6, and having the uncertainty
portion of the fuel removed from the optimization (recalibrated maximum output) so that other
generation on the system meets demand instead. This is done through keeping AGwl at zero while
instead reducing the maximum power output PGax by the percentage of uncertainty. Reducing
the maximum power for the generator by the level of uncertainty effectively removes the need for
the gas figured generators to pay for more expensive reserve fuel, however this requires that there
is ample generators on the system to meet demand and that their costs are less than the cost of the
gas-fired generator with fuel uncertainty. Due to the lack of information sharing between
generators, pipeline operators, and electricity system operators, there are times when generators
71
buy more expensive fuel to increase their power output even though it is not economically
optimal and there are other generators on the system.
Effects of
AGwj
Uncertainty
------ Maximum output with price uncertainty
-
-
Recalibrated maximum output
1.92E+04
1.90E+04
-
1.88E+04
1.86E+04
-
1.84E+04
1.82E+04
1.80E+04
1.78E+04
0
0.05
0.1
0.15
Al
0.2
0.25
0.3
(%)
Figure 5-8: This figure shows the comparison between allowing fuel uncertainty (maximum
output with price uncertainty), and removing the uncertain fuel from the optimization by lowering
the maximum output of the gas-fired generator by the percentage of fuel that is uncertain
(recalibrated maximum output).
The next portion of the modeling was to create an optimization problem which included natural
gas constraints. The results from the power flow optimization model, rather than the proposed
DMM model, shows that by optimizing both networks at once, the Social Welfare can increase,
which is in line with the results from figure 5-8. For this model the three generators, G1 , G2 , and
G3 , are all gas generators hooked up to the natural gas network in the configuration shown in
figure 5-4. First to test the functionality of the optimization problem, two test runs were
preformed: one where the optimization problem did not include natural gas constraints and
another where the constraints for the gas network were added in. With very high limits on the
maximum generation of the power plants in the first run, which did not have natural gas
constraints, the two models had the same solution. When limits were imposed on the generation
plants, the model with natural gas constraints added in performed better (see table 5-3). This is
because it relaxes the constraint for the maximum output for the generators.
72
Power flow optimization
Power flow optimization
without gas constraints
with gas constraints
G,
60
69.0873
G2
30
25.4564
G3
30
25.4564
SW
779.4
800.6645
Table 5-3: Including pipeline network constraints can allow more flexibility for generators to be
dispatched optimally for Social Welfare maximization.
In conclusion, natural gas uncertainty can significantly affect Social Welfare, especially at higher
levels (figure 5-6). Addressing information availability can reduce this dramatically (figure 5-8),
but only if there is sufficient other generation to make up any lack of natural gas capacity, which
is not the case in many regions.
Demand response, even at small levels of penetration, can
improve Social Welfare dramatically and make up the Social Welfare losses due to uncertainty
(figure 5-7).
Further work on this model would include research the effects of including more adjustable
demand, and improving the linearization of the Weymouth equation for the flow pressure
constraints closer to what was presented in Correa-Posada et al. [5] by making a piecewise linear
approximation. Also, adding renewable generation into the model could allow for an analysis of
how natural gas fuel uncertainty affects the dispatch of renewable generation and the cost to
consumers and generators.
73
6.
Conclusion
First main contribution of this thesis is a qualitative analysis of natural gas and electricity
interdependency in the United States and recommendations to address issues as well as take
advantage of opportunities. The second main contribution is a quantitative analysis of select
issues and recommendations previously discussed.
6.1
Summary of key findings
In the United States, a rising percentage of power production is being produced by natural gasfired power plants and it is a trend which is expected to continue in the coming decades. The
Shale Gas Revolution changed both the availability of natural gas and the prices so that it became
an attractive fuel for investment. Coal retirements are creating a growing demand for new, cleaner
generation methods, and renewable energy goals require the flexible generation offered by many
gas-fired generators.
The natural gas sector has increasing demand from power producers, a segment of consumers
which they have not organized to supply in large quantities. Pipeline expansion procedures do not
fully account for power producers which prefer to use interruptible contracts. Out of the limited
types of contracts, gas-fired generators choose to use interruptible contracts which offer the most
flexibility, something required for participants in the electricity market, but these contracts do not
contribute to pipeline expansion. Changing the FERC permitting rules for pipeline expansion to
include interruptible contracts is one way to solve this problem, but so would pipeline operators
offering innovative capacity contracts, and electricity system operators allowing firm contracts
costs to be included in generators electricity bids. Each of these solutions has reservations.
Changing the FERC permitting rules might increase the build out of capacity, but there is still the
question of whether excess capacity is really needed, especially in the long term with the goal of
an eventual shift away from fossil fuels. Pipeline operators have a variety of customers besides
74
power producers, and need to consider their needs as well as the needs of gas-fired generators.
Changing capacity contracts would complicate their current methods of operation, and it is
unclear if all of their customers would benefit. Allowing power producers to include the cost of
firm capacity contracts into their electricity bids would incentivize capacity expansion, but the
added expense might be unjustified to the electricity consumers the cost is passed on to.
Requiring firm contracts without allowing power producers to recover the costs through their bids
would place an additional burden on gas-fired generators, but not other power producers like
nuclear and coal.
Marketers, which facilitate the movement of natural gas from producer to end user, often close on
weekends and holidays. This is a definite problem for gas-fired generators, which are forced to
buy gas in bundles for those days, but it is not necessarily a problem for the marketers, which do
not wish to incur the cost of working on those additional days. In an age where technological
solutions abound, it seems a simple task to find some way to work around this. Deciding how to
incentivize marketers to remain open on additional days requires more consideration.
Better market coordination between the two sectors could also improve the reliability of natural
gas delivery to power producers. Market timing differences cause many power generators to incur
price uncertainty in the electricity market and quantity uncertainty in the gas market. These
uncertainties can lead to inefficient markets and unavailability of generators, which can in turn
lead to reliability risks and price increases for the electricity sector. Electricity utilities are
working to change their market schedules, but it is easier said than done with the tradeoffs of
moving the day-ahead market earlier and a question of how and to what extent these changes will
improve coordination and availability of gas-fired generators. Also, changing the electricity
market timing could give an additional advantage to gas-fired generators over other types of
generation, which could improve resource diversity for regions becoming more heavily dependent
on a single fuel.
Technical evaluation of these problems and possible solutions is necessary and constitutes the
focus of the modeling section of this thesis. A quantitative OPF model was used to measure the
effects of natural gas-fired power producer's fuel cost uncertainty on Social Welfare. The model
was extended from Kiani and Annaswamy [72] and Hansen and Knudsen [12] by changing the
75
coefficients for the consumers and the non-dispatchable generator to reflect the constraints of
natural gas-fired generators operating in an electricity system without adjustable demand
response. These changes can be seen in tables 5-1 and 5-2. The results the simulations can be
found in figures 5-6, 5-7, and 5-8 and show that fuel price uncertainty negatively affects Social
Welfare, and demand response and information availability and coordination improvements can
limit these effects. To simulate improved coordination, a second model is developed which
includes natural gas network constraints. The results can be found in table 5-3 and show how joint
optimization of the networks can relax fuel constraints on gas-fired generators and improve Social
Welfare.
6.2
Discussion
The electricity and natural gas sectors are becoming more interdependent during a time where the
electricity industry is undergoing major changes due to technological advancements like smart
meters and improved communications. This shift towards a smarter grid with demand response
can dramatically change the extent of natural gas and electricity interdependency problems as
well as open up numerous opportunities for combined optimization of the networks.
Modeling improvements such as creating multiple time scales where reserve generation for
natural gas uncertainty runs out over time could give better insight into some of the problems
happening in New England, where successive cold weather snaps deplete oil reserves. Adding
renewable generation into the model could allow for an analysis of how natural gas fuel
uncertainty affects the dispatch of renewable generation and the cost to consumers and generators.
While there has been tremendous enthusiasm for the development of natural gas, there are a
number of concerns about the safety and health effects from extracting natural gas, specifically
with hydraulic fracturing, and whether current regulation is sufficient. How natural gas can
promote the integration of intermittent and uncertain renewables like wind and solar while also
competing with them in the energy market is unclear, especially if the uncertainties associated
with natural gas power generation prevent the fuel from being used to reliability balance supply of
76
power. Research into the benefits of having a more flexible demand, with demand response
penetration, verses having more flexible supply, with more natural gas-fired generation would
provide insight into how electricity system operators should organize their markets.
77
Bibliography
[1]
Federal Energy Regulatory Commission, "Coordination Between Natural Gas and
Electricity Markets," vol. 2. Department of Energy, pp. 0-9, 2012.
[2]
P. J. Hibbard and T. Schatzki, "The Interdependence of Electricity and Natural Gas:
Current Factors and Future Prospects," Electr. J., vol. 25, no. 4, pp. 6-17, May 2012.
[3]
A. Lee, 0. Zinaman, and J. Logan, "Opportunities for Synergy Between Natural Gas and
Renewable Energy in the Electric Power and Transportation Sectors Opportunities for
Synergy Between Natural Gas and Renewable Energy in the Electric Power and
Transportation Sectors," no. December, 2012.
[4]
A. Lee, 0. Zinaman, J. Logan, and M. Bazilian, "Interactions, Complementarities and
Tensions at the Nexus of Natural Gas and Renewable Energy," Electr. ..., 2012.
[5]
C. M. Correa-posada and P. Sanchez-martin, "Security-Constrained Optimal Power and
Natural-Gas Flow," pp. 1-8.
[6]
S. An, Q. Li, and T. Gedra, "Natural gas and electricity optimal power flow," 2003 IEEE
PES Transm. Distrib. Conf Expo. (IEEECat. No.03CH3 7495), no. 1, pp. 13 8-143.
[7]
C. Unsihuay, J. W. M. L. Sm, and A. C. Z. D. S. Sm, "Modeling the Integrated Natural Gas
and Electricity Optimal Power Flow," pp. 1-7, 2007.
[8]
M. Urbina and Z. Li, "A Combined Model for Analyzing the Interdependency of Electrical
and Gas Systems," 2007 39th North Am. Power Symp., pp. 468-472, Sep. 2007.
[9]
J. Muno, S. Member, N. Jimenez-redondo, E. Iee, and J. Perez-re, "Natural Gas Network
Modeling for Power Systems Reliability Studies," 2003.
[10]
M. Shahidehpour and T. Wiedman, "Impact of Natural Gas Infrastructure on Electric
Power Systems," Proc. IEEE, vol. 93, no. 5, pp. 1042-1056, May 2005.
[11]
C. M. Correa-posada and P. Sanchez-martin, "Stochastic Contingency Analysis for the
Unit Commitment with Natural Gas Constraints," pp. 1-6.
[12]
J. Hansen, J. Knudsen, and A. Kiani, "A Dynamic Market Mechanism for Markets with
Shiftable Demand Response," 2012.
[13]
EIA, "U.S. Energy infromation Administration." [Online]. Available: http://www.eia.gov/.
[14]
U.S. Energy Information Administration, "Natural Gas," US Department of Energy.
[Online]. Available: http://www.eia.gov/tools/faqs/faq.cfm?id=58&t=8. [Accessed: 19Sep-2013].
78
[15]
P. Stevens, "briefing paper The ' Shale Gas Revolution ': Developments and Changes,"
Chatham House Brief Pap., no. August, 2012.
[16]
R. a Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg,
"Greater focus needed on methane leakage from natural gas infrastructure.," Proc. Natl.
Acad. Sci. U. S. A., vol. 109, no. 17, pp. 6435-40, Apr. 2012.
[17]
U.S. Energy Information Administration, "Henry Hub Gulf Coast Natural Gas Spot Price."
[Online]. Available: http://www.eia.gov/dnav/ng/hist/rngwhhdM.htm. [Accessed: 05-Sep2014].
[18]
U S Energy Information Administration, "Annual Energy Outlook 2012," 2012.
[19]
T. Stecker, "New U.S. Fracking Emission Rules Unclear on Climate Impacts," Scientific
American, Apr-2012.
[20]
C. Davies and A. Goller, "Ensuring Future Natural Gas Availability," Present. to MITEI
Symp. Interdepend Nat. Gas Electr. Syst., pp. 1-40, 2013.
[21]
The INGAA Foundation Inc., "North American Natural Gas Midstream Infrastructure
Through 2035: A Secure Energy Future Updated," 2011.
[22]
B. M. Celebi, F. Graves, and C. Russell, "Potential Coal Plant Retirements: 2012 Update
Executive Summary," 2012.
[23]
L. Franks, J. Lawhorn, J. Smith, E. Thoms, and T. Haagenson, "Electric and Natural Gas
Coordination Case Study - The MISO Proactive Approach: A Collaborative Effort between
the Natural Gas Industry, the RTO and its Stakeholders.," 2013.
[24]
Y. Wan, "Analysis of Wind Power Ramping Behavior in ERCOT Analysis of Wind Power
Ramping Behavior in ERCOT," no. March, 2011.
[25]
J. H. R. Enslin, "Dynamic reactive power and energy storage for integrating intermittent
renewable energy," IEEE PES Gen. Meet., pp. 1-4, Jul. 2010.
[26]
Solar Energy Industries Association (SEIA), "Photovoltaic (Solar Electric)." [Online].
http://www.seia.org/policy/solar-technology/photovoltaic-solar-electric.
Available:
[Accessed: 05-Sep-2014].
[27]
E. Schmid, "Operational Flexibility Enhancements of Combined Cycle Power Plants,"
Siemens AG, no. October, 2008.
[28]
MIT, "The Future of Natural Gas: An Interdisciplinary MIT Study," 2011.
[29]
B. Plumer, "Obama says fracking can be a 'bridge' to a clean-energy future. It's not that
Available:
[Online].
2014.
Post,
Washington
The
simple.,"
79
http://www.washingtonpost.com/blogs/wonkblog/wp/2o14/01/29/obama-says-frackingoffers-a-bridge-to-a-clean-energy-future-its-not-that-simple/. [Accessed: 05-Sep-2014].
[30]
Alternative Fuels Data Center (AFDC), "Natural Gas Benefits and Considerations," US
Departmentof Energy. [Online]. Available: energy.gov /eere. [Accessed: 04-Jul-2014].
[31]
US House, "American Energy Security and Innovation: The Role of a Diverse Electricity
Generation Portfolio," Energy & Commerce Committee, Energy and Power Subcommittee.,
2013. [Online]. Available: http://energycommerce.house.gov/hearing/AESI-role-diverseelectricity-generation-portfolio.
[32]
US House, "American Energy Security and Innovation: The Role of Regulators and Grid
Operators in Meeting Natural Gas and Electric Coordination Challenges," Energy &
Commerce Committee, Energy and Power Subcommittee., 2013. [Online]. Available:
http://energycommerce.house.gov/hearing/AESI-role-regulators-and-grid-operatorsmeeting-natural-gas-and-electric-coordination-challenges.
[33]
N. Y. James A. Lahtinen, (Vice President Rates and Regulatory Economics, Binghamton,
"PSC NO: 88 Gas. General Information," no. effective date: 2/19/04, 2003.
[34]
Columbia Gas of Maryland, "Glossary of Terms: Natural Gas Glossary," 2014. [Online].
Available: https://www.columbiagasmd.com/about-us/glossary-of-terms.
[35]
U.S. Energy Information Administration, "Today In Energy," 2011. [Online]. Available:
http://www.eia.gov/todayinenergy/detail.cfim?id=790.
[36]
North American Electric Reliability Corporation, "2011 Special Reliability Assessment: A
Primer of the Natural Gas and Electric Power Interdependency in the United States," no.
December, p. 104, 2011.
[37]
U.S. Energy Information Administration, "Energy in Brief." U.S. Department of Energy,
2012.
[38]
M. Kezunovic, "Energy Cyber-Physical Systems: Research Challenges and Opportunites,"
in NSF Workshop on Energy Cyber PhysicalSystems, 2013.
[39]
H. Y. Yamin, "Review on methods of generation scheduling in electric power systems,"
Electr.Power Syst. Res., vol. 69, no. 2-3, pp. 227-248, May 2004.
[40]
North American Electric Reliability Corporation, "Regions and Balancing Authorities,"
2012. [Online].
Available: http://www.ferc.gov/market-oversight/mkt-electric/nercbalancing-authorities.pdf.
[41]
ISO New England - Strategic Planning Initiative, "Addressing Gas Dependence," 2012.
80
[42]
S. Adamson and R. D. Tabors, "Pricing Short-term Gas Availability in Power Markets,"
2013.
[43]
A. Juris, "Development of Natural Gas and Pipeline Capacity Markets in the United
States," no. March, Mar. 1998.
[44]
P. Stevens, "The 'Shale Gas Revolution': Hype and Reality," Chatham House Rep., no.
September, 2010.
[45]
ICF International, "Assessment of New England ' s Natural Gas Pipeline Capacity to
Satisfy Short and Near- Term Electric Generation Needs," 2012.
[46]
K. Joseph, "Gas & Electric Coordination: The need for information sharing," 2013.
[47]
Algonquin Gas Transmission LLC, "Algonquin Gas Transmission Tariff," IRE Trans.
Broadcast Transm. Syst., vol. PGBTS-1, no. 1, Mar. 1955.
[48]
B. M. Celebi, F. Graves, and C. Russell, "Potential Coal Plant Retirements: 2012 Update,"
no. October, 2012.
[49]
Spectra Energy, "Algonquin Gas Transmission, LLC: Critical Notices," 2014. [Online].
Available: https://infopost.spectraenergy.com/infopost/default.asp?pipe=AG.
[50]
North American Electric Reliability Corporation, "Accommodating
Dependence on Natural Gas for Electric Power," no. May, 2013.
[51]
[Online].
2014.
Glossary,"
Gas
"Natural
Association,
gas
American
05-May-2014].
[Accessed:
Available: http://www.aga.org/Kc/glossary/Pages/N.aspx.
[52]
R. Salmon and A. Logan, "Flaring Up: North Dakota Natural Gas Flaring More Than
Doubles in Two Years," 2013.
[53]
Solar Energy Industries Association (SEIA), "Net Metering." [Online]. Available:
http://www.seia.org/policy/distributed-solar/net-metering.
[54]
State Government of Victoria, "About smart meters: Glossary," 2013. [Online]. Available:
http://www.smartmeters.vic.gov.au/about-smart-meters/reports-andconsultations/advanced-metering-infrastructure-customer-impacts-study-volume1/glossary.
[55]
J. K. Yura, "Pacific Gas and Electric Company: Electric Schedule E-7 Residential time-ofuse service," no. January, 2011.
[56]
Public
"Louisiana
LPSC,
http://www.lpsc.louisiana.gov/.
Service
81
Commission."
[Online].
an Increased
Available:
[57]
ENERNOC, "Northeast Businesses Save with DemandSMART," 2013. [Online].
Available: http://www.enernoc.com/for-businesses/demandsmart/in-us. [Accessed: 13-Sep2013].
[58]
U.S. Energy Information Administration, "Retail Sales and Direct Use of Electricity to
Ultimate
Customers."
[Online].
Available:
http://www.eia.gov/electricity/annual/html/epa_02_02.html. [Accessed: 24-Sep-2013].
[59]
M. Urbina and Z. Li, "Modeling and analyzing the impact of interdependency between
natural gas and electricity infrastructures," 2008 IEEE Power Energy Soc. Gen. Meet. Convers. Deliv. Electr. Energy 21st Century, pp. 1-6, Jul. 2008.
[60]
Disturbance Analysis Working Group, "2002 System Disturbances," North American
Electric Reliability Corporation(NERC), 2004. [Online]. Available: http://www.nerc.com/.
[61]
Disturbance Analysis Working Group, "2006 System Distrubances," North American
Electric Reliability Corporation(NERC), 2007. [Online]. Available: http://www.nerc.com/.
[62]
A. Kiani and A. Annaswamy, "Equilibrium in Wholesale Energy Markets: Perturbation
Analysis in the Presence of Renewables," IEEE Trans. Smart Grid,vol. 5, no. 1, pp. 177187, Jan. 2014.
[63]
A. Kiani and A. Annaswamy, "Perturbation Analysis of the Wholesale Energy Market
Equilibrium in the Presence of Renewables."
[64]
A. Kiani and A. Annaswamy, "A hierarchical transactive control architecture for
renewables integration in Smart Grids," 2012 IEEE 51st IEEE Conf Decis. Control, pp.
4985-4990, Dec. 2012.
[65]
B. F. Hobbs, "Optimization methods for electric utility resource planning," Eur. J.Oper.
Res., vol. 83, no. 1, pp. 1-20, May 1995.
[66]
H. Y. Yamin, "Review on methods of generation scheduling in electric power systems,"
Electr.Power Syst. Res., vol. 69, no. 2-3, pp. 227-248, May 2004.
[67]
K. T. Midthun, Optimization models for liberalized natural gas markets, no. October.
2007, pp. 1-6.
[68]
A. Tomasgard, M. Fodstad, and K. Midthun, "Optimization Models for the Natural Gas
Value Chain," 2007, pp. 521-558.
[69]
D. De Wolf and Y. Smeers, "The Gas Transmission Problem Solved by an Extension of the
Simplex Algorithm," no. February 2014, 2000.
[70]
J. Sullivan, "The application of mathematical programming methods to oiland gas field
development planning," Math. Program.,vol. 42, pp. 189-200, 1988.
82
[71]
T. Li, M. Eremia, and M. Shahidehpour, "Interdependency of natural gas network and
power system security," Power Syst. IEEE ..., vol. 23, no. 4, pp. 1817-1824, 2008.
[72]
A. Kiani and A. Annaswamy, "Wholesale energy market in a smart grid: Dynamic
modeling and stability," IEEE Conf Decis. Control Eur. Control Conf, pp. 2202-2207,
Dec. 2011.
[73]
E. Litvinov, "Presentation on Locational Marginal Pricing." 2011.
[74]
R. Marsh, "Changes in ISO-NE's Day-Ahead Energy Market Schedule To Take Effect
Next Week," Bracewell & Giuliani, Energy Legal Blog, 2013. [Online]. Available:
http://www.energylegalblog.com/archives/2013/05/16/4571.
[75]
J. Hansen, J. Knudsen, and A. M. Annaswamy, "Demand Response in Smart Grids:
Participants , Challenges , and a Taxonomy," 2014.
[76]
A. Kiani, A. M. Annaswamy, and T. Samad, "A Hierarchical Transactive Control
Architecture for Renewables Integration in Smart Grids: Analytical modeling and
stability," IEEE Trans. Smart Grid,Spec. Issue Control Theory Technol. (to Appear., 2014.
[77]
J. L~fberg, "YALMIP: A free MATLAB Toolbox for rapid prototyping of optimization
Available:
[Online].
2004.
problems,"
http://www.mathworks.com/matlabcentral/newsreader/viewthread/59636.
[78]
U.S. Energy Information Administration, "Electric Power Annual 2012," no. December,
2013.
[79]
ISO New England, "Markets." [Online]. Available: http://www.iso-ne.com/markets/.
[80]
U.S. Energy Information Administration, "Updated Capital Cost Estimates for Utility Scale
Electricity Generating Plants," no. April, 2013.
[81]
A. Kiani, A. M. Annaswamy, and T. Samad, "Hierarchical Transactive Control
Architecture for Renewables Integration in Smart Grids: Analytical modeling and
stability," IEEE Trans. Smart Grid, no. Special Issue on Control Theory and Technology
(to appear), 2014.
83