energy transmission expansion planning in the australian … · 2019. 8. 6. · south australia...
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1
Energy Transmission Expansion Planning in
the Australian Context: an integrated
solution for the gas and electricity markets
Sergio A. Díaz Pizarro, BSc. and MSc. in Electronics Eng.
Project submitted in partial fulfilment for the requirements for the degree of
MSc. Energy and Resources Management
UCL School of Energy and Resources Australia
I, Sergio A. Diaz confirm that the work presented in this report is my own. Where
information has been derived from other sources, I confirm that this has been indicated
in the report.
JULY 2014
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Acknowledgments
First I would like to thank my tutor and supervisor Dr. Ady James for his advices,
recommendations and conversations, it has been a pleasure to know him.
I also would like to thank Energy Exemplar, especially to Glenn and Louise for giving
the opportunity to perform my research in a friendly and amazing work environment.
Certainly without the support of my fellows, Felipe, Allen and Lummi would it be
impossible to develop the work presented in this thesis.
I would like to thank Maria and Pixie which perform a wonderful work supporting
students at UCL. You have a special place in our heart, thank you Maria and Pixie.
Finally to the love of my life, thank you Lorena.
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Abstract
Australia is in the verge of an energy planning dilemma. Most of its electricity
generation capacity is through coal resulting in enormous contribution of CO2 into the
atmosphere. In contrast, in 2007 Australia has acknowledged the climate change and
related to this fact a carbon fee was proposed and implemented in 2012 in order to
diminish the contribution of CO2 to global warming. Therefore the logical question
should it be which future energy path must Australia follows: business as usual, where
coal is the predominant fuel to generate electricity or a shift into a low carbon
economy?
The power generation and transmission expansion planning of the electricity sector
seeks an optimal answer to the following questions: when, what and where new
generation and transmission assets will be built over a specific period of time. The types
of answer that this plan will provide are influenced by the uncertainties mentioned
above: electricity demand, fossil fuel prices, new technologies and environmental
policies.
Policies making usually is a long process where the design at least in the energy sector
has powerful mathematical basis. It could be argued that any policy that overlaps with
engineering topics has powerful mathematical background in its design. For that reason
mathematical models especially in the energy sector are powerful tools for testing and
proving future path.
The motivation of this paper is related to a more efficiency approach to deal with the
expansion planning of the electricity sector. It is proposed for this thesis a co-optimized
expansion planning of the electricity and gas system which implicitly includes dynamics
of the consumption and production of the different basins connected to the gas network.
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This fact is important because points out possible constraints not only in the electrical
transmission lines, but also in gas transmission pipelines.
The integration of the gas network into the electricity planning has important outcomes
due to the projects developed in Curtis Island. In the forthcoming years, the Australian
gas market will evolve from an only domestic market to an international one where the
price of gas will be linked to its international value. Linked to this aspect, the period of
development projects coincides with major large-customer contract roll-off and the
resetting of prices considerably higher than historical levels.
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Table of Contents
Table of Contents ....................................................................................................... 5
List of Figures ............................................................................................................ 7
List of Tables.............................................................................................................. 9
1 Overview ........................................................................................................... 10
1.1 Introduction ...................................................................................................... 10
1.2 Motivation ........................................................................................................ 13
1.3 Objectives ......................................................................................................... 15
1.4 Thesis Structure ................................................................................................ 15
2 Background Information ................................................................................... 17
2.1 Introduction ...................................................................................................... 17
2.2 Electrical Power Expansion Planning .............................................................. 18
2.2.1 Strategies for solving optimization problem in electric planning ............. 20
2.2.2 Expansion planning uncertainties ............................................................. 21
2.3 Co-optimization of gas and electricity transmission expansion planning ........ 22
3 Gas and Electricity Modelling for the Expansion Co-Optimisation Planning.. 27
3.1 Introduction to Electricity and Gas System Modelling .................................... 27
3.2 Steps in a Power System Planning ................................................................... 29
3.3 A brief introduction to DC Load Flow ............................................................. 30
3.3.1 Transmission Line Modelling ................................................................... 31
3.3.2 Losses on a Line ........................................................................................ 32
3.4 A brief introduction to Optimisation ................................................................ 33
3.5 The National Electricity Market (NEM) .......................................................... 34
3.5.1 The Wholesale Market .............................................................................. 35
3.5.2 Transmission Congestion .......................................................................... 38
3.5.3 Electricity Demand.................................................................................... 40
3.5.4 Generators ................................................................................................. 41
3.6 The Australian South Eastern Gas Network ..................................................... 45
3.6.1 Overview of the South Eastern Gas System ............................................. 45
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3.6.2 Gas Basins ................................................................................................. 47
3.6.3 Pipelines .................................................................................................... 49
3.6.4 LNG committed Projects (Curtis Island) (Group, 2013) .......................... 51
3.6.5 Gas Demand Profile .................................................................................. 52
4 The formulation of the Long Term Planning Problem in PLEXOS® .............. 59
4.1 Introduction ...................................................................................................... 59
4.2 Energy Planning Tool ....................................................................................... 59
4.3 Formulation of the problem .............................................................................. 61
5 Results ............................................................................................................... 65
5.1 The Gas Model ................................................................................................. 65
5.1.1 Scenario 1 .................................................................................................. 66
5.1.2 Scenario 2 .................................................................................................. 68
5.2 The Co-optimized model .................................................................................. 70
5.2.1 Scenario 1, Co-optimisation of the Electricity and gas model including
Bowen-Surat Basin .................................................................................................. 73
5.2.2 Scenario 2, Co-optimisation of the Electricity and gas model: sensitive
analysis of the Bowen-Surat Basin .......................................................................... 76
6 Conclusions and Future Work .......................................................................... 80
7 Bibliography ..................................................................................................... 83
A. Annex 1- Lagrange Multiplier .......................................................................... 86
B. Annex 2 – Gas Demand Profile Algorithm ...................................................... 87
C. Annex 3 – Data used for the Gas Model (IES, 2013, SKM, 2013) .................. 91
D. Annex 4 – Gas projections (AEMO, 2013b) .................................................... 93
E. Annex 5 – Capacity built results Scenario 1: Co-optimization of the gas and
electricity system. (AEMO, 2013b) ......................................................................... 98
F. Annex 5 – Capacity built results Scenario 1: Co-optimization of the gas and
electricity system. ................................................................................................... 102
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List of Figures
Figure 3-1 Flows across the network (AEMC) ........................................................ 39
Figure 3-2 Input-output curve of a steam unit (Wood et al., 2014) ........................ 43
Figure 3-3: Locations of Australian’s gas resources and two potential gas basins.
Source: (Industry and Resources, 2013) .................................................................. 48
Figure 3-4 Contract Supply for LNG exports in Queensland for the (2013-2029):
Australia Pacific LNG (APLNG), Gladstone LNG (GLNG) and Queensland Curtis
LNG (QCLNG) ........................................................................................................ 52
Figure 3-5 Brisbane’s daily demand profile [TJ] for the year 2013 (Bulletin, 2013)54
Figure 3-6 Brisbane’s demand profile forecasted [TJ] (2013 -2031) ...................... 55
Figure 3-7 Gladstone’s demand profile forecasted [TJ] (2013 -2031) .................... 55
Figure 3-8 Mount Isa’s demand profile forecasted [TJ] (2013 -2031) .................... 56
Figure 3-9 New South Wales’ demand profile forecasted [TJ] (2013-2031) .......... 56
Figure 3-10 South Australia’s demand profile forecasted [TJ] (2103-2031) ........... 57
Figure 3-11 Tasmania’s demand profile forecasted [TJ] (2013-2031) .................... 57
Figure 3-12 Victoria’s demand profile forecasted [TJ] (2013-2031)....................... 58
Figure 5-1 Gas network modelled in PLEXOS®, the main demand zones included in
this study are: Mount Isa, Gladstone, Brisbane, Adelaide, Sydney, Melbourne and
Hobart ....................................................................................................................... 66
Figure 5-2 Gas cost at the demand nodes................................................................. 67
Figure 5-3 End volume basins (TJ) (Bowen-Surat is not included)......................... 67
Figure 5-4 End volume Basins (TJ) ......................................................................... 68
Figure 5-5 End volume basin (TJ) not including Bowen-Surat ............................... 69
Figure 5-6 End volume Basins (TJ) including Bowen-Surat ................................... 70
Figure 5-7 a) The NEM b) Model used for this thesis ............................................. 71
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Figure 5-8 Electricity Demand in the NEM (AEMO) ............................................. 72
Figure 5-9 Renewable requirements [MW] ............................................................. 73
Figure 5-10 Electricity Price over the period simulated ($/MWh) .......................... 74
Figure 5-11 Generation Capacity Built (MW) ......................................................... 75
Figure 5-12 Generation Capacity Retired (MW) ..................................................... 76
Figure 5-13 End volume basin (TJ) not including Bowen-Surat in the domestic
consumption ............................................................................................................. 77
Figure 5-14 Electricity Price over the period simulated ($/MWh) .......................... 78
Figure 5-15 Generation Capacity Built (MW) ......................................................... 78
Figure 5-16 Generation Capacity Retired (MW) ..................................................... 79
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List of Tables
Table 3-1 Major Gas Pipelines Summary (Bulletin, 2013)...................................... 51
Table 4-1, decision variables used for the expansion planning problem ................. 61
Table 4-2 parameters for the formulation of the expansion planning problem........ 62
Table C-1 Reserves by basin and type - PJ .............................................................. 91
Table C-2 Maximum production capacity – TJ/day ................................................ 91
Table C-3 Production costs by basin and type -$/GJ ............................................... 91
Table C-4 Pipeline capacities and tariff – TJ/day and $/GJ ..................................... 92
Table D-1. South Australia annual gas demand ....................................................... 93
Table D-2. 2013 Victorian annual gas demand ........................................................ 94
Table D-3. Queensland domestic annual gas ........................................................... 94
Table D-4. Tasmanian annual gas demand .............................................................. 95
Table D-5. New South Wales and Australian Capital Territory annual gas demand96
Table E-1. South Australia annual gas demand ....................................................... 98
Table E-2. 2013 Victorian annual gas demand ........................................................ 98
Table E-3. Queensland domestic annual gas............................................................ 99
Table E-4. Tasmanian annual gas demand ............................................................. 100
Table E-5. New South Wales and Australian Capital Territory annual gas demand101
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1 Overview
1.1 Introduction
Australia is in the verge of an energy planning dilemma. Most of its electricity
generation capacity is through coal resulting in enormous contribution of CO2 into the
atmosphere. In contrast, in 2007 Australia has acknowledged the climate change and
related to this fact a carbon fee was proposed and implemented in 2012 in order to
diminish the contribution of CO2 to global warming. Therefore the logical question
should it be which future energy path must Australia follows: business as usual, where
coal is the predominant fuel to generate electricity or a shift into a low carbon
economy? (Falk and Settle, 2011)
Electricity generation and transmission investment projects are highly influenced by
factors such as fossil fuel prices, electricity demand patterns and environmental taxes
(Owen and Berry, 2013). Decision making related to potential investment can be
hindered because of the risks associated with uncertainties related to the variables
mentioned above (Nagl et al., 2013).
The first critical issue that adds uncertainty to the future of electricity generation is the
price of fossil fuels, especially the gas price. The shale gas revolution has not only
impacted the United States but the international gas market as well (IEA, 2012a,
Stevens, 2012). A classic illustration of this impact is the role that the United States has
performed as a net importer of fossil fuel in the past years, and due to the shale
revolution this situation could change achieving self sufficiency of energy for over a
century (IEA, 2012b).
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Australia is endowed with important quantities of natural gas which allows to be an
important exporter of LNG (Goverment, 2010). Similarly to the United States,
breakthroughs in specific technologies such as fracturing and carbon capture & storage
will result in critical trade-off between domestic and international consumption adding
volatility to the gas price.
The importance of gas in the Australian energy context is mainly related to two factors:
CO2 content of its combustion and supporting role for intermittency of renewable
generation. Therefore, investment either in gas power generation or renewable energies
would be affected by the gas price especially in States for instance South Australia
where the participation of renewable is important (Goverment, 2010).
The National Electricity Market (NEM) is the longest worldwide interconnected system
with an extension of approximately 4500 KM. The system begins in Port Douglas in the
State of Queensland and it extends until Port Lincoln in South Australia. Huge
quantities of electrical energy are traded in the NEM (Owen, 2011). In an extensive
country as Australia, energy substructures are costly especially with low values of
population density (Falk and Settle, 2011).
According to the NEM electricity demand is the main driver for investment in this
sector (AEMO, 2012a) determining electricity forecasting and location of future
generation and transmission assets.
Last but not least, the Australian carbon tax has been on the table of discussion among
politicians, academics and stakeholders from the energy and industry sectors especially
in the last election period. A carbon tax of A$23/tonne CO2 was established in July
2012 under the government of the former Prime Minister Julia Guillard from the
Australian Labor Party (Goverment, 2011). However, the future of this fee has a
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deadline owing to the suspension of this tax because of the new government of the
Prime Minister Tony Abbot.
For the reasons mentioned above, Australia will face critical and unknown changes in
the electricity mix, driven by climate changes policies and energy demand patterns.
Indeed, this uncertain future will add risks to possible assets investment which will
affect the electricity price.
The power generation and transmission expansion planning of the electricity sector
seeks an optimal answer to the following questions: when, what and where new
generation and transmission assets will be built over a specific period of time
(Unsihuay-Vila et al., 2010). The types of answer that this plan will provide are
influenced by the uncertainties mentioned above: electricity demand, fossil fuel prices,
new technologies and environmental policies.
In order to address the demand of energy in Australia, the Australian Energy Market
operator (AEMO) uses PLEXOS (AEMO, 2012d) for their National Transmission
Network Development Plan (NTNDP) yearly report (AEMO, 2012c). NTNDP provides
several energy scenarios taking into account the least-cost generation approach where
the optimal expansion solution for electrical generation and high voltage transmission
system is obtained.
Similarly, the AEMO develops an expansion planning report related to the gas sector
called Gas Statement of Opportunities (GSOO) where the results take into consideration
transmission pipelines, wells and storage facilities (AEMO, 2012b).
Recently Australia has faced a number of challenges in the electric and gas networks
affecting planning reports and also commodity prices. Firstly, projected demand for
Liquefied Natural Gas (LNG) exports out of Queensland means that demand for gas in
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that part of the system is forecast to rise over the next 10 years, firstly to create storage
of LNG and then to supply the export market. At the same time gas supply in NSW is
becoming constrained, and the demand in Queensland will only add to the forecast
supply shortages.
1.2 Motivation
Australia is one of the main carbon polluters per capita in the world. Political and
environmental reasons have promoted that in 2007 Australia had signed and ratified the
Kyoto Protocol. Certainly, this agreement directly or indirectly way pushed the
implementation of a carbon tax to electricity generation. Nonetheless, in the last year
due to election period the future of this fee is uncertain. The critical aspect of the carbon
tax on Australia is related to the most influential electricity generation that has the
continent which is electricity generation through coal. Therefore a tax for carbon
emission could certainly influence future investment in fossil fuel power plant.
Besides the carbon tax there are others variables that create this big question mark over
electricity generation and transmission, for instance: electricity demand, new energy
technologies and fuel prices, especially gas price. As Owen and Berry (2013) have
observed the outcome of those aspects will result in an important variation in the
relative cost of new electricity generation.
Policies making usually is a long process where the design at least in the energy sector
has powerful mathematical basis. It could be argued that any policy that overlaps with
engineering topics has powerful mathematical background in its design. For that reason
mathematical models especially in the energy sector are powerful tools for testing and
proving future path.
14
Effective Energy Policies are based in mathematical models mainly because through
them it is possible to model behaviour or patterns from real life1. In fact, the gap
between the outcome of a mathematical model and the real pattern will depend of the
accuracy of the model and certainly the complexity of it.
Despite the fact that Energy policies are linked with the political approach that
government or stakeholders have, it is likely that behind any energy policy there is a
mathematical model which support the effectiveness of this policy. The complexity of
these mathematical models has increased in the last years due to the improvements on
computational management and an increase in the effectiveness of the algorithms used
for deals with complex models or systems.
The motivation and novelty of this paper is related to a more efficiency approach to deal
with the expansion planning of the electricity sector in Australia, adding complexity and
accuracy to the current approach performs by AEMO in the National Transmission
Network Development Report mentioned in the previous section. It is proposed for this
thesis a co-optimized expansion planning of the electricity and gas system which
implicitly includes dynamics of the consumption and production of the different basins
connected to the gas network. This fact is important because points out possible
constraints not only in the electrical transmission lines, but also in gas transmission
pipelines.
The integration of the gas network into the electricity planning has important outcomes
due to the projects developed in Curtis Island. In the forthcoming years, the Australian
1 For example the mathematics of classical mechanics it is possible to describe physical
events such as the movement of an apple following down from a tree. However, with
another perspective the same event can be explained with a more complex mathematical
approach.
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gas market will evolve from an only domestic market to an international one where the
price of gas will be linked to its international value. Linked to this aspect, the period of
development projects coincides with major large-customer contract roll-off and the
resetting of prices considerably higher than historical levels.
1.3 Objectives
Develop a single gas model in PLEXOS about the South Eastern gas Network
including LNG projects in Curtis Island. With the use of this model the
following objectives are proposed:
o Determine if gas reserves and production are sufficient to meet demand
in relation to LNG projects on Curtis Island.
o Determine if gas transmission pipelines and processing facilities are
sufficient to meet demand and deliver new gas production with the
interaction of the LNG projects on Curtis Island
Develop a co-optimized electricity and gas model that co-optimises the National
Electricity Market and the South Eastern gas network. The resulting model will
allow achieving a better result in relation to the expansion planning of the NEM.
Currently both models are simulated separately by AEMO iterating their
outcomes until the user decides.
1.4 Thesis Structure
The first chapter will introduce the reader with the problem and brief explanation about
the main objectives of the current work. The second chapter will provide with
background information about the problem of the expansion planning in electrical
system. It will also describe some strategies used in electrical expansion problem.
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Finally, it will describe the main works in relation to optimize both system electricity
and gas.
The third chapter is the introduction and explanations related to the methodology used
for modelling the National Electricity Market and the South Eastern Gas System. The
importance of this chapter is linked to provide the reader with information to understand
the procedure behind a co-optimize model for the electricity and gas market.
The formulation and structure of the optimisation problem is addressed in chapter 4. It
will provide the reader with information about PLEXOS and some assumption
performed for the model addressed in this paper.
Chapter 5 will provide results linked to the use of the gas single model and also the co-
optimised solution using the gas and the electricity model.
Chapter 6 will provide the conclusions of the report and proposal for future
development using the present work as basis to employ this new co-optimized tendency
in Energy expansion planning.
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2 Background Information
2.1 Introduction
Several changes have taken place in the electricity sector over the last two decades, and
these changes have been influenced by competition with the aim of bringing about the
achievement of an efficient allocation of resources (Griffin and Puller, 2005).
Undoubtedly, the most important change in this sector (at least in the developed world)
has been the transition from a regulated to a deregulated market, and this transition
implies the unbundling of electrical services (generation, transmission and distribution)
which were usually administrated and/or owned by state companies in a regulated
environment.
This adjustment to the market structure has definitely created a more complex
environment for investment, thus adding risk and uncertainties to the electricity sector
and it is clear to see where these uncertainties originate when reviewing the new market
structure. In a deregulated environment, the number of participants will be greater than
that of those in a regulated one, which is indeed the very idea of competition. However,
the fact that competition is the basis of the system implies that a lack of information is
inherent in this environment , as this lack of information and cooperative interaction
among participants (especially among generation companies) is likewise part of the very
essence of competition (Kirschen and Strbac, 2007).
Furthermore, global warming has been on the table among politicians, academics and
stakeholders from the energy sector, especially in the developed world, but the lack of
agreements with regards to climate change has decreased the effectiveness of any
energy policy associated with it (Helm, 2011). A good example of this uncertain
environment with regards to CO2 regulations is the current discussion on the future of
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carbon tax in Australia. Linked to environmental policies are the regulations related to
renewable targets that not only add complexity to the system but also increase risk due
to the variability of renewable resources. Unfortunately for energy planners and policy
makers, the electricity sector is a complex environment with several variables
determining its behaviour.
On the contrary, the transmission business is by its very nature a monopoly. In more
simple terms, there is no economical reason to have multiple companies providing the
same transportation service due to the infrastructure needed and the capital costs
associated with what is an economy of scale. Investment in this sector is thus
particularly regulated and associated assets are long-life, which create a more complex
investment environment than that of the generation sector.
2.2 Electrical Power Expansion Planning
The expansion planning for electrical power systems is a mathematical problem that
seeks the optimal combination of different electrical generators and transmission lines
according to a specific objective function. As with any optimization problem, its
formulation is linked to several constraints or requirements. These constraints are
associated with variables such as electricity demand, the limitations of generators,
transmission restrictions and the regulation of the country concerned as defined in the
study period.
Several authors agree on the complexity of the problem (Wu et al., 2006), as do de la
Torre et al. (2008) on its complexity associated with the non-lineal feature and the
uncertainties surrounding it. In the same work, it is argued that the manner in which the
problem has been addressed in the new environment has changed, mainly driven by the
fact that administration is in the hands of various different companies.
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The most common approach (or objective) applied to this type of optimization problem
is to minimize the investment and operation costs of different assets which integrate or
could be part of the power systems analysed (Wood and Wollenberg, 1984). This
approach was commonly taken in a regulated market where generation and transmission
of electricity were under the administration of the same company or organization. It
should be mentioned that the term optimization in this cost minimization approach
means focusing on the reduction of the outgoing cash flow (investment and operation
expenses) for a specific period of time where uncertainties should be taken into account
(Covarrubias, 1979).
It is certainly intuitive to try to formulate the objective function as a cost minimization
problem of which the elements of capital and operation costs are parts. However, de la
Torre et al. (2008) propose another approach where social welfare maximization is the
key objective. This work develops a mixed-integer linear programming for the long-
term transmission expansion problem in a pool-based market. One of the assumptions
made by the authors is that the transmission business is planned by an individual
organization. Nonetheless, as the electricity market is a competitive environment,
generators and loads are part of several companies. According to the definition used by
the authors, social welfare is equal to the surplus of demand plus the surplus of the
generator plus the merchandising surplus (total payments from the demand minus total
payments to the generators) minus the cost of investment in new lines.
Usually, the generation and transmission system are planned separately for different
reasons, and perhaps the main one is that different parties administrate theses
companies. However, in their proposal, Cedeño and Arora (2013) integrate both
systems into a model which expands the generation and transmission capacities over
three regions in deregulated power systems. Actually, the results from the model
20
suggest that generation expansion is associated with the addition of more renewables to
the system.
2.2.1 Strategies for solving optimization problem in electric planning
2.2.1.1 Linear Programming
This method has been popular through the years due to the simplicity of the
mathematics behind it. The objective function seeks to minimize the costs and the
constraints which are connected to the technical and economic aspects and system
reliability. For example, according toVillasana et al. (1985), in order to solve the linear
problem a combination of DC flow and a transport problem is proposed. The solution
identifies where there is a lack of power generation capabilities, so new power capacity
and transmission assets are added.
Another alternative is proposed by testing all the possible combinations (node by node)
in order to choose the combination that best allows to avoid overload in the transmission
lines and a combination with lower generation costs (Kaltenbach et al., 1970). At the
end of the period of study, an optimum network configuration is obtained. However, the
drawback of the proposed method is the high computational time linked to the number
of combinations tested. This is because the computational time increases exponentially
due to the quantity of nodes analysed.
Linear programming has a main advantage in the simplicity of the mathematical
formulation. Nonetheless, despite the fact they are not obsolete, they require that the
decision variables must be restricted in order to avoid extensive computation
calculation. It should be mentioned that in this type of model, uncertainty can be
incorporated.
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2.2.1.2 Dynamic programming
A similar method to a finite sequential Markov process in time is described by
Dusonchet and El-Abiad (1973). The main idea is a dynamic discrete optimization
combined with a deterministic searching procedure linked to probabilistic dynamic
programming and the use of heuristic criteria. This method is designed to take
advantage of any information known about the problem while performing probabilistic
analysis of the occurrence of different events. The main drawback of this method is the
use of fixed probabilities for the events, which ideally should be variable.
2.2.1.3 Heuristic method
Heuristic methods are an alternative to the classical optimization approach. The term
heuristic is used to describe techniques that find local solutions to the optimization
problem. This means that they solve or evaluate step by step, evaluating and choosing
expansion options. In order to perform this process, they applied logic, empirical or
sensitive guidance. The use of this technique is attractive because of its lesser
computational effort in comparison with the classical approach. However, is not
possible to guarantee that a global solution can be found (Serrano et al., 2005).
2.2.2 Expansion planning uncertainties
In either a generation or transmission expansion problem, uncertainties are key aspects
in the evaluation and results of the models and how uncertainties in an optimization
problem are treated is not a trivial aspect. The method used to model uncertainty has
been approached with different techniques in the transmission planning problem. A
generation expansion problem tries to answer the questions of why, where, what and
when specific generators will be built or retired. Indeed, the evaluation of uncertainties
is an important aspect, and this is especially so with stochastic variables such as demand
or weather patterns. Jin et al. (2011) addressed uncertainties related to demand and fuel
22
prices with a two-stage stochastic mixed-integer program. Likewise, the objective
function uses the minimization approach. However, the expected cost and the risk of the
investment are the focus, while the stochastic approach is taken for treating
uncertainties in relation to gas prices and demand for electricity.
2.3 Co-optimization of gas and electricity transmission expansion planning
There is a growing tendency to bring together both systems and to obtain an optimal
solution from both an operation and planning perspective. Geidl and Andersson (2007)
developed a novel approach with the concept of energy carriers. The novelty of this
work was related to the bringing together of three types of energies-electricity, gas and
district heating- and optimally dispatching them. Through this approach, dispatch and
optimal flow problems are solved in order to obtain an optimal solution. Despite the fact
that it is a novel approach, the extension of the area addressed by the study is compact
with the proposed area in this study, the National Electricity Market (NEM).
With Bakken et al. (2007) a novel optimization approach takes also considers the
concept of energy carriers addressing the interaction between different energy
structures. The optimal problem is defined by taking multiple energy infrastructures and
capital cost of the different generation alternatives into account. However, the model
developed is again uncomplicated because it does not address an extensive area and thus
the computational time would be short.
In recent years, due to global warming and breakthroughs in generation and gas
extraction technologies, the relation between gas and electricity in transmission
planning has become stronger. This development requires new energy planning tools
that must consider the construction of new gas pipelines and compute natural gas prices
linked to the behaviour of the price in the entire sector.
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According to Chaudry et al. (2008), over the next decades it is predicted that natural gas
will be the fastest growing fossil fuel in use, with growth being driven mostly by
electricity demand from the generation sector. Of all fossil fuels, gas has the lowest
carbon content in its combustion (Shahidehpour et al., 2005). Furthermore, open cycle
gas power plants usually act as a back-up system for renewable generation due to their
high flexibility in turning on/off their electricity generation. In addition, close cycle gas
power plants boast the greatest efficiency among fossil fuel power plants. Rubio et al.
(2008) describe a detailed survey on the relation between gas and electricity systems,
highlighting the paramount importance of the integration of the gas system into the
power system operation.
Environmental and economic reasons have definitely encouraged these novel
approaches, especially for the advantages that electricity generation using gas has in
comparison with other fossil fuels. , and Several of the advantages that gas power plants
have over other plants make these types of plants good candidates in a low carbon
economy (Owen, 2011).
An integrated approach has been suggested by Unsihuay-Vila et al. (2010) where the
formulation of the expansion problem takes into account both systems. The authors’
approach is the use of a mixed integer linear model for the long-term planning. In order
to validate this model, a case study from Brazil is presented where the minimization of
capital and operation costs is used as the objective function. It is worth mentioning that
the model includes strong interaction between hydro and thermal plants. Another
interesting observation made by the authors is related to the link between both systems
(electric and gas) where they explicitly stated that this connection is made by the gas
power plants. They conclude that the model proposed, called the GP model (Long-term
multi-area expansion plan of natural gas systems), has been integrated into another
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model resulting from the GEP model (Long-term, multi-area expansion plan of
electricity and natural gas systems). In addition, the importance of natural gas when
hydro power is considered is linked to the complementary role of gas power plants in
order to mitigate the risks derived from water inflow uncertainties. The obvious result,
but no less important, is that the integral gas/electricity expansion planning results in
cheaper costs when compared to the disaggregated option. The authors propose the
resulting conclusion as an indicative valuating point to be taken into account in a
market-oriented environment. Integrated planning is able to strategically contemplate
both sectors in terms of operational and economical relations.
The interdependencies between electricity and gas market have been researched by
Lienert and Lochner (2012). The authors acknowledge the importance of the
relationship between gas and electricity in developing and evaluating a model for
electricity investment and dispatch integrated with the natural gas market dispatch. They
conclude that the competitiveness of gas-fired power stations has been determined by
seasonal gas prices. In order to support this argument, they performed simultaneous
analysis on other technologies. Accordingly, they point out that if seasonal gas prices
appear, gas-fired power plants should be built near the natural gas sources. In addition,
it highlights the importance of evaluating different paths for the transportation of
energy, either through transmission lines or gas transmission pipelines defining the
relocation of gas-fired power plants. According to the scenarios analysed in this paper,
it is better to transport electricity instead of gas. However, there is a contradiction with
the paradigm regarding the construction generator associated with renewable energy.
This is because the planner has the option of choosing where the gas power plant will be
located. On the contrary, in the case of renewable energies, the location of the
generation facilities is defined by the availability of the renewable source. Linked to this
25
argument is the fact that one of the main drawbacks of renewable energies facilities is
their location in relation to the demand zone.
The interrelation between gas and electric systems is also examined by Li et al. (2008).
It should be mentioned that the approach of this work is addressed from the operational
framework of a power system not taking into account the expansion planning in its
research proposal. The integrated model incorporates the natural gas network
constraints into the optimal solution of security-constrained unit commitment. The
outcome is a consolidated model which seeks to increase the system security.
Nonetheless, Li et al. (2008) have proved that the interrelation between both systems
could directly impact an electrical system in its economics and security. For instance the
gas price market would impact the cost of the electricity supply. Furthermore, a an
interruption to supply in pipelines could directly affect the power system if they are
connected to a gas-fired power plant. Although this is true, it is also true that pipeline
capacity can act, as a battery system which would smooth a sudden lack of supply from
the gas facility, similar to as in an electrical environment.
A qualitative and descriptive analysis is performed by Unsihuay et al. (2007). They
argued that most of the time, the generation expansion models consider a detailed
representation of the power system, but do not consider the integration with production,
storage and transportation of the natural gas industry. This paper proposes a method that
integrates the natural gas and electricity systems in which the objective is co-
optimisation expansion. A mathematical model of this problem is formulated as a
multistage mixed optimization problem where the objective function is to minimize the
integrated gas-electricity investment and operation costs. The referenced work provides
the reader with an accurate description of the equations associated with natural gas
wells and pipelines.
26
As previously mentioned, the integration of gas networks in the power expansion
planning would guide policies through a sustainable path. Unsihuay-Vila et al. (2011)
develop a model where three objectives are formulated: investment and operational
costs minimization which includes costs management on the demand side as well as the
cost of investment in carbon capture technology projects, minimization of greenhouse
emission from power generation plants and finally, maximization of supply security
based on the diversity of primary resources including energy imports. In the literature,
this approach is categorized as a multi-area, multi-objective and multi-stage model.
The evolution of computational systems has helped to address more complex problems
that include uncertainties due to stochastic variables. In Jin et al. (2011) the expansion
problem over the area of the Midwest USA is addressed. In this paper, both systems gas
and electricity are gathered in order to obtain the optimal solution of the planning cost
function. The problem is solved in two stages as a stochastic mixed-integer. The
importance of a stochastic approach is fundamental in treating uncertainties in fuel
prices and demand patterns. It should also be mentioned that uncertainties related to
environmental taxes are not taken into account.
Also worthy of mention is the importance in including a stochastic approach that is not
only related to uncertainties associated with carbon tax, fuel prices or even electricity
demand. The importance of including a stochastic approach in an expansion planning
solution is related to the characteristic of certain renewable energies that are determined
by weather patterns such as hydro, wind and solar. For instance, in Unsihuay-Vila et al.
(2010) they analysed the planning in the Brazilian scenario with a stochastic approach
due to the influence of hydro and wind power plants.
27
3 Gas and Electricity Modelling for the Expansion Co-Optimisation
Planning
3.1 Introduction to Electricity and Gas System Modelling
The abundance of natural resources and their consumption define the economic growth
of a country and hence the well being of it. Energy is a key factor for the society’s
development, though in some countries can be limited due to natural or imposed
restrictions. It worth to mention that by definition, natural resources are limited which
implies the existence of regulations and policies behind the management of them.
Energy policies are designed and implemented in order to achieve a specific objective
which is usually aligned with the society’s welfare of a country. Certainly, the
elaboration of them is a long and complex process which includes several stages.
Among them and especially in the energy sector, mathematical modelling is a key
element in the efficient implementation of a specific policy. Through mathematical
modelling, several scenarios and strategies can be analysed in order to achieve a desired
simulated outcome.
The evolution of the energy markets has increased the necessity to use complex models
to achieve a more accurate and deep approach (Foley et al., 2010, Wallace and Fleten,
2003). There are two main factors that determine this new elaborate environment:
competition and uncertainties (Stoft, 2002). Liberalized markets are intrinsically riskier
than regulated, linked to the fact of lack of information and interaction among
participants. In relation to uncertainties, basically there are three main uncertain
elements that add complexities to an energy environment: fuel price, energy
28
consumption patterns and finally the introduction of intermittent2 renewables energies
such as wind and solar.
Among the diversity of models that can be modelled in the area of energy, especially in
the electricity sector there are two types of approach. The first one is closed to the
behaviour of the network from the point of view of dynamics of electrical systems. In
this context several studies are related, for example: short-circuit analysis, behaviour of
electrical harmonics in the system, transient patterns of power systems and finally
power flow analysis. Mathematical models linked to those studies are not included in
this report3 due to the complexities mathematics associated to it (Grainger and
Stevenson, 1994).
The second approach is associated with the operation and economics of power systems
(Wood et al., 2014, Kirschen and Strbac, 2004). That is to satisfy energy demand taking
into account aspects such as: reliability, infrastructure, renewable energies, and
stochastic elements. For the context of this chapter, the following models will be
described from the optimization approach mentioned where variables of heat rate,
starting cost maintenance scheduled and investment projects are important.
The aim of Chapter 3 is to provide the reader with the methodology behind the
construction of the model for the Australian Electricity and Gas system. The stages of
the power system planning are presented in section 3.2 which defines some of the
milestones described in this thesis. The importance of section 3.2 and 3.3 are linked to
the basic understanding of the mathematics behind a power system optimisation
2 The term intermittent is associated with the unpredictable characteristic of weather
conditions. 3 However a brief explanation of DC power flow is included due to the importance
associated with the dispatch and transmission process.
29
problem which is the case of the present work. Section 3.5 and 3.6 described the two
systems that will be modelled and coupled in this paper.
3.2 Steps in a Power System Planning
The tools and methodologies associated with power system planning have changed
dramatically over the years, influenced by uncertainties, competition and improvements
in computer calculation. Nonetheless, the basic steps for this process have been
maintained and can be summarized (Covarrubias, 1979) :
a) The use of demand forecasting with projections over periods of 5-30 years. In
these projections there are two key elements: peak and annual demand. Energy
planning must take both elements into account in order to maintain system
reliability and security.
b) Analysis of the different alternatives to supplying the energy demand in the
evaluation period. This analysis includes environmental, technical and economic
constraints.
c) Analysis of current generation units from a technical and economic framework.
The objective of this analysis is related to choosing which generating candidates
are appropriate from the expansion point of view. In addition, this analysis also
includes which unit will be retired. It should be noted that the economic analysis
must include long and short term cost and also construction times.
d) Compilation and determination of technical information regarding assets
involved in expansion planning.
e) Determination of the economic and technical parameters which affect
investment: discount rate, level or reliability required from the generating
systems.
30
f) Determination of the procedure which will be used for determining the optimal
expansion strategy within the imposed constraints over the period of analysis.
g) Qualitative revision of the results with the aim of confirming the viability of the
answer obtained.
The milestones mentioned are fundamental steps in order to build an energy expansion
planning. Some of them explicitly and implicitly are performed by the optimisation
software’s and even by energy planners.
3.3 A brief introduction to DC Load Flow
The present paper deals with power system analysis from the economic point of view of
the system. However, the physical constraints associated to assets of the network follow
physical laws such as the Kirchhoff’s Law. In addition, most of the elements of the
NEM are Alternating Current (AC) assets. However, to deal with optimisation problems
the most suitable and common approach used in the academia is to assume that the
power system is a Direct Current system (DC). For that reason the present section will
briefly discuss this simplistic approach which is used by the software utilized in this
report.
The DC load flow model provides approximate but simple relationships between
generation and demand levels at the buses and real power flows through the lines
(Wood et al., 2014). These relationships yield explicit formulas giving the marginal
impact on network losses or specific line flows from incremental changes in demand or
generation at some bus of the network.
The DC load flow provides an approximate solution for a network carrying AC
(alternating current) power. The term “DC” comes from an old method of computing a
31
solution using an “analog computer” built out of resistors and batteries where direct
currents were measured.
3.3.1 Transmission Line Modelling
Consider a balanced three-phase transmission line between two buses (or nodes) of a
network. Assume at Bus j and Bus k the “a” phase voltages are respectively
( ) ( 4 ) ( 3-1)
( ) ( ) ( 3-2)
with phases “b” and “c” shifted in place by 120° and 240°.
Let’s define Real power leaving Bus j and Real power leaving Bus j and flowing
towards Bus k (may be + or -). Then:
∑
( 3-3)
Where the summation is over all buses connected to Bus j. Qj and Qjk are defined
similarly representing the imaginary or reactive power.
Assume line i connects Bus j to Bus k. The equation that relates Pjk to the voltage
magnitudes Vj, Vk and voltages phases , and the characteristics of line i is
( ) ( )
( 3-4)
4 : Voltage phase angle at Bus j, has been introduced to denote the fact that the phases
of the voltage sine waves vary with location on the network. The voltage magnitude and
angles vary continuously between buses.
32
Ri: Resistance of Line i
Xi: Reactance of Line i
In order simplified the equation ( 3-5) the following assumptions need to be done. The
first assumption is related to the difference between ( ). The angle’s difference in
high-voltage systems is negligible which implies that
( )
( ) ( )
( 3-6)
In a per unit system (high voltage rating), Vj and Vk then equation ( 3-7)
reduces to:
( ) ( 3-8)
3.3.2 Losses on a Line
Define Li to be the real power losses on line i. By definition is
( 3-9)
Using equation ( 3-10)
(
( )) ( 3-11)
Keeping the assumption of high power systems where the difference ( ) is small
and using the second order term of the approximation in ( 3-4):
( ) ( )
( 3-12)
Assuming a per unit system:
33
( ) ( 3-13)
If Xi> Ri that yields with Li=Ri*PJK. This result provides an acceptable assumption for
high voltage systems which allows achieving a lower complexity in the optimization
formulation, hence the computational timing for solving the problem.
3.4 A brief introduction to Optimisation
The present paper deals with the problem of Energy Expansion Problem in the
Australian context. In the academic literature, capacity and transmission expansion
problems are grouped together as optimisation problems. Therefore, in order to
introduce the problem addressed in this paper, a brief introduction of mathematical
optimisation is included in this section.
An optimisation problem is mainly composed of two parts: an objective function and
restrictions (or constraints). The objective function can be easily identified because it is
a mathematical equation that can be minimized or maximized depending on the problem
which will be solved.
The canonical expression for an optimum problem is:
( 3-14)
( 3-15)
In order to keep this section brief, linear programming will be described. Linear
programming is a technique that deals with linear problems (objective function) and
which is subject to linear equality and linear inequality constraints.
The objective function for the canonical expression is described by the equation ( 3-14)
. The objective of the linear programme is to maximize (or minimize) this objective
function composed by the vector X determined by the transposed matrix C. The second
34
part of an optimization problem is described by the equation ( 3-15) which sets the
restrictions or constraints in relation to the problem to be solved5. In mathematical terms
equation ( 3-15) defines the feasible area where the optimisation problem will be
allocated.
3.5 The National Electricity Market (NEM)
The National Electricity Market (NEM) is one of the longest worldwide interconnected
system with an extension of approximately 4500 Km (AER, 2013b). Until 1997,
electricity in Australia was supply by state-owned companies with minimal interactions
between states (Weron, 2007). The system commenced its operation in 1998, working
as a wholesale market supplying electricity to the states of Queensland, South Australia,
Victoria, Australian Capital Territory and New South Wales. In 2005 the state of
Tasmania was included in the system.
The NEM begins in Port Douglas in the State of Queensland and it extends as far as
Port Lincoln in South Australia. According to Falk and Settle (2011), structures linked
with the Australian power system are expensive owing to the low levels of population
density. Electricity demand is the main driver for investments in this sector (AEMO,
2012c) determining electricity forecasting and location of future generation and
transmission assets. Currently, the market includes six jurisdictions which are
interconnected by transmission networks. Every year, electricity transactions in the
NEM exceeds $10 billion to meet electricity demand supplying electricity to eight
million customers (AEMO, 2010).
One of the main characteristic of the NEM is its extension, which is reflected by two
aspects: geographical location of electricity generator and load centres; and the
5 The region defined by the constraints is said to be the feasible region for the
independent variables. If the constraints
35
interconnection between the five regions of the NEM. The NEM depends on the
regional transmission interconnectors for transaction of the vast bulk of electricity to its
end use consumers (AEMO, 2010). Every state in the NEM is connected through
regional transmission interconnectors, which are characterized by high voltage and
power ratings. These interconnectors are the backbone of electricity trading in the NEM
due to their transportation role when one demand’s state requires energy from another
state. The transaction is performed if the price in the region lacking in electricity is
equal or greater than the price of production and transportation from the export region.
The presence of these interconnectors makes inter-regional electricity trade possible and
hence contributes to the increased supply reliability in the NEM.
3.5.1 The Wholesale Market
The introduction of competition in the electricity sector has created profound changes,
which have determined how electricity is traded today. In a deregulated system such as
the Australian one, electricity is pooled as it flows from generators and supplies to
loads. This means that suppliers and consumers trade electricity in a virtual market
managed for the Australian Market Operator. This virtual markets are known as
electricity pools (Kirschen and Strbac, 2004).
The NEM is an energy wholesale market having a gross pool model where transactions
are performed ex-ante. The predominant costumers are energy retailers which bundle
electricity to residential, commercial and industry energy users. Demand and supply are
matched immediately in real time through a centrally-coordinated dispatch process. The
market operator (AEMO) manages the dispatch6 process taking into account physical
6 An brief explanation of the Lagrange multiplier is done in Appendix A. This
coefficient is the basis to understand how the dispatch process is performed.
36
system restrictions (transmission and generation), of which the most important is to
match electricity demand with supply. In addition, not all generators have to trade in the
NEM, but it is mandatory for those with a capacity over 30 MW to trade in it. Biddings
are performed every five minutes by generators, offering a specific quantity of
electricity associated with its respective price.
The dispatch process is managed by the AEMO7 which is also the system operator (SO).
When the bidding stage has been performed by generators, the system operator ranks
them from the lowest to highest value and then it dispatches the generation units which
have the minimum cost to supply electricity to match demand. The dispatch price or
clearing price8 corresponds to the price of the last unit dispatched (Kirschen and Strbac,
2004). This price is the same for each unit dispatched which at first may seem odd, but
it has key implications in relation to the market efficiency. For example, if the clearing
price would not be the same for all the dispatched units, some generators would try to
bid high in order to obtain more revenues. The problem with this approach is linked to
an error in the price estimation, i.e. if these units have low marginal costs, but their bids
are highly mismatched with the real price, they could be out of the dispatch process
which implies that the real dispatch price would be higher due to the most economical
units being outside it. The spot price of electricity is generated every 30 minutes taking
into consideration the average price of the six clearing prices (Weron, 2007). AEMO
uses the spot price to define the settlements for transactions in relation to energy traded
in the NEM.
7 According to regulations provided in the National Energy Law
8 It is also called the System Marginal Price (SMP)
37
As previously mentioned, Australia operates an energy9 only market which yields
compensation for both variable and fixed costs. Actually, as the Australia experience
has shown, an energy market provides clear signals for investments through energy
spikes. One of the best examples among the cases in Australia is that if the state of
South Australia. Air-conditioning has determined and modified the peak demand of this
state which is why South Australia has a “peaky” shape of electricity demand. In the
beginning of the wholesale market (1999-2000), the NEM spot price for South Australia
repeatedly reached the 5000 AUD/MWh price cap during peak hours due to high
temperatures in summer. Because of this fact, the government decided to increase the
cap to 10000 AUD/MWh, which provides notorious signals to investors for new
generation project due to the new safe and reliable environment created with the raising
of the ceiling price. Thus, the installed capacity has grown by almost 50% in the period
1998-2003, of which an important part corresponded to open cycle gas turbines which
are used for peaking purposes (Weron, 2007).
The structure of the market certainly defines how much faster new generation
investment would be committed. The idea behind an energy-only market is to provide
9 There has a lot of discussion about which market design provides optimal signal for
investment. According to Weron (2007) the debate is between three main designs: to
establish capacity payments, organize markets and a energy only market. The basis
behind a capacity market, which was first introduced in Chile 1982, is to pay generator
a daily proportion which is proportional to the reliability that this generator provides to
the power system, for instance its availability. Despite of this interesting approach,
international experience has shown that this design gives little incentives to solve
capacity investment. For example, generators in order to receive more revenues in
relation to capacity payments would try to reduce the capacity instead to increase it.
38
compensation for both variable and fixed costs. Price spikes provide strong and direct
feedback to investors in relation to new electricity generation assets (Newham, 2008).
Those signals came from the spot market in periods of high short-term electricity
providing suppliers and retailers to possibly sign long-term contracts with the aim of
supporting new investment in power generation (Galetovic et al., 2013). Australia
differs from other countries in its lack of capacity payments instruments which certainly
provide softer signals to go ahead with investments (Galetovic et al., 2013).
3.5.2 Transmission Congestion
As it was mentioned previously, AEMO selects the bids and offers that optimally clear
the market whilst ensuring that security constrains imposed by the transmission network
are fulfilled. An important aspect of the electricity market is the transport of electricity
from the source to the demand zone through transmission networks. Congestion10
is
generated when elements of the network reach their limit and are not capable to
transport more energy (AEMC).
The price that consumers and producers pay or are paid is the same for all participants
connected to the same node. For example in Figure 3-1 Flows across the network
(AEMC)Figure 3-1 a simple network is depicted with three nodes. In node A, a generator
is connected in order to supply electricity to consumers connected in node C. In order to
explain the congestion phenomenon, an infinity capacity of the line is assumed and
losses are not included in the calculation. The price to generate electricity in node A is
equal to marginal cost of the last MWh produced by the generator. Because the network
depicted has infinite capacity, congestion is not present; hence the cost paid by the
10
Congestion is a phenomenon that could occur in any sector associated with to
transport.
39
consumer will be equal to the marginal cost of the generator. Therefore, the lack of
transmission congestion results in a unique market clearing price for the entire system.
As can be observed in Figure 3-1, electricity can go through node C by two paths. If the
shorter path is constrained and demand of electricity has not been supplied, another path
has to be used in order to deliver the commodity. When congestion is present, the price
of electricity for the consumer is composed by the value at the generator’s node and the
cost to transport electricity to the node C (Kirschen and Strbac, 2004).
Figure 3-1 Flows across the network (AEMC)
A congested element implies that electricity in this point has reached its maximum
transportation quantity. This definition is usually related to the security point of view.
Congestion could affect the price of electricity supplied because another generator or
path should be used for satisfied demand, which implies a more expensive alternative to
the former. As a matter of fact, because of this aspect the price of electricity in
Australia varies from state to state11
.
11
This a very simplified explanation and other aspects must be taken into account.
40
The question about congestion is whether it is a good thing or bad thing for the efficient
performance of the market. Congestion affects economically generators and retailers
because creates risks. Moreover, it affects the efficiency of the network. However, from
an investment point of view, it provides a clear signal for transmission investment
where the opportunity cost of having a constrained network is evaluated against the
benefits of future transactions in unconstrained network.
The presence of congestion means that the locational marginal price or the zonal market
clearing price might be employed (Weron, 2007). The first method is the addition of
the generation marginal cost, transmission cost and cost of marginal losses. This amount
can vary for different nodes and even for busses within local area. Nodal price is the
best example of this method where the electricity prices are valued according to the
place where it is generated and supplied. Despite the simplicity of the description, this
method leads to higher transaction costs and complexity. On the contrary, zonal price
defines the value for nodes within the zone and obviously different price for different
zone. Certainly the latter system has fewer complexities than nodal prices, though
phenomenon like negatives prices can appear. The Australian system uses zonal prices
rather locational pricing.
3.5.3 Electricity Demand
An important part of the capacity and transmission expansion problem is the estimation
of the electricity demand over the period analysed. This is not a trivial task, due to the
fact that this equation is one of the restrictions of the optimization problem described in
equation (4-2), which can determined the answer to the optimisation problem.
Electricity supply must match with the electricity demand. However there are two
aspects that complicate this process. First, as mentioned, the dispatch process is
41
performed before the electricity has been consumed. Second, the electricity demand
usually grows in time which implies new generation assets will be built. Therefore, a
forecasting of the electricity demand must be made. In the case of the expansion
problem, as the consumption of electricity in forthcoming years is unknown, forecasting
is performed by AEMO in order to decide whether to support investment decisions in
relation to the quantities of generators have to be built or retired. Forecasting is key task
in energy planning.
For the purpose of this paper, the forecasting in shown Figure 5-8 will be used. It was
built with data informed by Australian Electricity Market Operator.
3.5.4 Generators
The cost of electricity generation is based on three aspects:
1. The fuel cost used for electricity generation.
2. The efficiency of the generation process, which is defined by the type of
technology used.
3. The maintenance cost of the plant.
3.5.4.1 Thermal Power Plants
Through the years the problem of economic dispatch for thermal systems has been
solved by numerous mathematical methods. However, it is still a growing area in
electrical planning due to computational calculations improvements (Wood and
Wollenberg, 1984). Nowadays, environmental regulations have defined new constraints
for thermal dispatch, where the objective function has been focused in minimizing the
quantity of pollutants into the environment and also the cost of dispatch.
Thermal Power Plants are supplied by fossil fuels: gas, oil and coal. Each of these units
has their own input-output curves which describe the amount of fuel required to produce
42
and give constant power output for one hour Figure 3-2. Because these curves represent
the consumption of the fuel used to generate electricity, they are a key element in order
to model the electricity market and its interaction with the gas supply chain.
It could be argued that the most popular thermal generation is the steam unit. Basically
this technology is composed by three elements: a boiler, a turbine and a generator. In
the boiler, steam is produced which is used to drive a turbine couple to an electrical
generator. Usually, these generators are categorized as synchronic induction machines
due to the rotor frequency are proportional to the electrical frequency of the grid. It
worth to mention that a steam unit supply electricity to the grid and also the auxiliary
system consuming between 2-6% of the electrical gross output (Wood et al., 2014). A
common figure for the efficiency of these units is around 30% and 35% which is
equivalent to heat rates of 11,4 Btu/kWh and 9,8 Btu/kWh12
.
12
A kWh has a thermal equivalence of approximately 3412 Btu.
43
13
Figure 3-2 Input-output curve of a steam unit (Wood et al., 2014)
The second types of thermal plants are the combustion units or also known as gas
turbines. The combustion process through hot gases drives an electrical generator (also
synchronic) to generate electricity. These units are grouped in open cycle and closed
cycle. The main difference of these two types of units is related to their efficiencies and
starting time.
Due to the intermittency of renewables, Open Cycle Power Plants (OCPP) are used as
back-up systems to supply electricity when for instance wind is not blowing. Because of
the high flexibility of their process, OCPPs are able to stop the electricity process
according to the demand requirements and renewable source availability. Mention
should also be made to the high energy efficiency process of Combined Cycle Power
Plants (CCPP). Among all the process of electricity generation using fossil fuel, CCPP
has the highest efficiency to transform mechanical energy into electricity. In addition, a
13
The resolution of optimisation problems are determined for the type of curve
modelled. From the point of view of complexity and computing timing, it is better to
have a smooth and convex curve.
44
key factor in favour of gas power plants is linked to its modular and compact which
enables them to increase their installed capacity in relation to electricity demand (Owen,
2011).
For the purpose of the model used in this paper, a common practice was used to model
combined and open-cycle plant as conventional steam units for new generation
candidates in the expansion planning process. Due to the features mentioned in the
above paragraph OCGT plants are used to shape the demand and are grouped as peaking
power plants.
The calculation of the generation costs is performed in $/MWh units, which is called in
the academic literature short-run marginal cost or SRMC (Kirschen and Strbac, 2004).
Using the value of the Heat Rate14
(GJ/MWh) and the cost of fuel, the electricity
generation costs is obtained.
Generation costs can be categorized as:
1. Operation and maintenance costs
2. Stating and Shut down costs
3. Cost of the auxiliary system
4. Cost of debt and equity
3.5.4.2 Renewables Energies (Wind and Solar Technologies)
Wind and solar generation share a common property: intermittency. This concept
involves two unrelated aspects: non-controllable variability and partial unpredictability
(Pérez-Arriaga & Batlle, To appear). Generated electricity has to be consumed owing to
it can be stored economically. Electricity consumption is variable through time.
Therefore, electricity generation is variable as well. However, in fossil fuel generation,
14
The heat rate is the inverse of efficiency of the process
45
it can be forecasted to a certain point. Linking to this argument is the pattern of
intermittency for renewable energies. Due to weather condition and complex
mathematic models, wind and solar generation would be more difficult to forecast in
comparison with fossil fuel generation. The key point to note is that the non-controllable
variability means that a generator could be unavailable when is demanded due to
consumption. Hence the non-controllable variability from green energies will be higher
than conventional fossil fuel generation.
Wind is generated indirectly by the action of the Sun which heats the surface of the
Earth. This process will produce flows of warm air which will rise resulting in vertical
and horizontal air currents. The wind velocity is determined by the sun, land surface and
season. Thereby, electricity generation through wind could be highly variable in a
specific area. Linking to this argument, forecasting generation is more complex than the
usual prediction made for conventional fossil fuel generators.
Electricity generation using directly the Sun is determined by sun exposition and
seasonal patterns. The lack of an electrical generator, hence inertia, makes solar
photovoltaic system highly sensitive to cloudy phenomenon, resulting in an important
diminishing of output power in a PV system. Despite this fact, the predictability of PV
solar systems is higher than wind energy generation due to certainty of weather
prediction.
3.6 The Australian South Eastern Gas Network
3.6.1 Overview of the South Eastern Gas System
Government reforms to the gas sector in the 1990s led to structural reform and
significant changes in ownership (Verikios and Zhang, 2011). In particular, vertically
integrated gas utilities were disaggregated and most government-owned transmission
46
pipelines were privatised. Additional modifications related to these changes have
improved competition in the Australian gas sector.
Among the most modified states has been Victoria, where the gas distribution and retail
business are managed by the private sector. In addition, the establishment of a spot
market has formally created an environment for transactions. Certainly, the
improvement of the infrastructure for gas transportation has supported the establishment
of an official market. For that reason, the interconnection with the Moomba to Sydney
pipelines (MSP) at Culcairn and the construction of the Iona Gas Plant and storage
facilities near Port Campbell have helped to increase the market efficiency.
Several infrastructures have helped to interconnect and increase the number of
consumers (and competition), including the construction of the Eastern Gas pipeline to
provide natural gas to Tasmania in 2002, the development of the Otway and Bass basins
and finally, the construction of the Sea Gas Pipeline (SEA Gas) connection with the
Iona Gas Plant in Victoria to Adelaide in 2004.
Not only appropriate infrastructures necessary are to increase competition, so too are
higher numbers of gas suppliers and reserves. The development of unconventional
reserves such as coal-seam and shale gas has helped to bring about more alternatives for
supplying gas in the forthcoming years. The Queensland Gas Scheme in 2005 has had
an important part to play in stimulating the development of CSM reserves. This scheme
has been replaced by the carbon tax.
An abundance of coal seam gas reserves and the prospect of higher margins selling
LNG into the Asian market have led to the establishment of an LNG export industry
comprising of 3 committed projects on Curtis Island near Gladstone.
47
This section will describe the gas model developed by the South Eastern Gas systems in
PLEXOS. It also will provide a description of the Southern Eastern Gas System. This
description will include the main pipelines, basins and the new LNG project on Curtis
Island. Moreover, as any energy system model such as the gas one needs a consumption
profile, this section will describe how a gas demand profile was built for the demand
zones described in this model.
To clarify for the reader, the described model is not a model of the dynamics of the gas
process. That is, the present model will describe the supply chain process where the
final product is gas delivered to a demand point (gas node). In fact, the final price
depicted in the results section has been generated by the cost of all stages in the gas
supply chain.
3.6.2 Gas Basins
A basin is defined as a depression, usually of considerable size, which may be erosional
or structural in origin (Allaby, 2008). It should be mentioned that the modelling
addressed in this report is not from the perspective of basic dynamics, known commonly
as basin modelling.
48
Figure 3-3: Locations of Australian’s gas resources and two potential gas basins. Source: (Industry
and Resources, 2013)
The main features modelled are:
Production per day
Initial volume
End volume
For the context of the paper, the following basins were taken into account: Bass,
Bowen/Surat, Cooper-Eromanga, Gippsland, Gunnedah, Otway, Galilee, Moranbah,
Clarence-Moreton, Gloucester, Sydney and Cooper (shale gas). Gas reserves are
reported under the Petroleum Resource Management System as proven (1P-90%
certainty of an economic resource), proven plus probable (2P – 50% certainty of an
economic resource) and proven plus probable plus possible (3P-10% certainly of an
49
economic resource). For the purpose of this paper 2P reserves were considered as 100%
of certainty in the evaluation of the basins mentioned. Moreover, 3p reserves were also
included in the analysis, however due to the high price associated with this type of
reserves; the optimal answer with the respective scenarios modelled did not include the
use of these types of reserves.
In Annex 3 – Data used for the Gas Model (IES, 2013, SKM, 2013)the data used for the gas
basin modelling can be found. In addition, it was assumed for the purposes of this report
that gas fields are named using the associated basin where they are located. The only
category used is related to the type of reserve: conventional or non-conventional.
3.6.3 Pipelines
The gas transmission pipeline’s structure is similar to the electricity’s. The gas
transmission sector is characterised by features associated with a monopoly due to its
capital intensity and a decrease in the marginal cost as the quantity of gas transmitted
increases.
The extension of the Australian gas transmission network is about 20,000 Km (AER,
2013a). These types of structures have wide diameters and operate at high pressure
values with the aim of optimising shipping capacity. Gas is transported from upstream
producers to energy consumers located in major demand centres or hubs through
pipelines. For the purpose of the present report, only transmission gas pipelines are
covered. The model developed in this paper assumed that the major demand zones are
located in: Gladstone, Brisbane, Mt. Isa, Adelaide, Melbourne, Sydney and Tasmania.
Regulatory changes in the market structure have allowed Australia’s gas pipelines to be
privately- owned (Table 3-1 Major Gas Pipelines Summary (Bulletin, 2013)). The main
owner of these structures is the APA Group which has stakes in the distribution and
50
transmission sector. In addition, State Grid Corporation of China and Singapore Power
International also have stakes through the companies Jemena and SP AusNet.
Competition has been supported by investment over the last years, creating an
interconnected gas network in the south-eastern part of Australia. Most of the
investment in the transmission network has been driven by two factors: new supply
sources and the increment of supply security. The result is an interconnected pipeline
network covering Queensland, New South Wales, Victoria, South Australia, Tasmania
and the Australian Capital Territory. Gas is obtained from the closest point of
distribution. Nonetheless, interconnection of the large pipelines has allowed the increase
of the number of transaction, thus facilitating competition in the market. The
development of unconventional reserves and the integration of them into the network
have diversified the options for gas supply. Certainly, this diversification would benefit
the final consumer by allowing a competitive price for the commodity, driven by a
competitive environment and appropriate structures for its transportation.
The pipeline sector is under the jurisdiction of the Australian Energy Regulator where
the regulatory structure is defined by the National Gas Law and Rules. There are two
elements that determine the regulation in the gas sector: competition and significance
criteria (AER, 2013a). The type of regulation applied to different pipelines can be
categorized as: full and light regulation.
In full regulation, the regulator has to approve an access arrangement submitted by the
pipeline provider. The function of an access arrangement is to set out terms and
conditions under which third parties can use a pipeline. This agreement has to stipulate
one reference service that a significant part of the market is likely to seek and a
reference tariff for the service.
51
On the other hand light regulation means that the pipeline provider must publish access
prices and other terms and conditions on its website. Among the gas pipelines in eastern
Australia that are covered by light regulation are: the Carpentaria Gas Pipeline in
Queensland, the covered portions of the Moomba to Sydney Pipeline and the Central
West Pipeline in New South Wales. No distribution network is currently subject to light
regulation.
Table 4 in Appendix 3 details the characteristic of the pipelines addressed in this study.
Table 3-1 Major Gas Pipelines Summary (Bulletin, 2013)
Pipeline name Owner Regulation Capacity factor
(2011-2013)
Capacity (TJ/day)
Queensland Gas
Pipeline
Jemena None 83% 142
Carpentaria APA Light 84% 119
Roma – Brisbane
Pipeline
APA Full 73% 240
South West
Queensland Pipeline
APA None 32% 385
Moomba to Sydney
Pipeline System
APA Light 39% 439
Moomba to
Adelaide Pipeline
System
QIC None 51% 253
SEA Gas Pipeline APA (50%) None 61% 314
Eastern Gas Pipeline Jemena None 73% 268
NSW –Victoria
Interconnector
APA Full 37% 90/73
Longford to
Melbourne
APA Full 48% 1030
Tasmania Gas
Pipeline
TGP None 35% 129
3.6.4 LNG committed Projects (Curtis Island) (Group, 2013)
According to the projections of the LNG projects in Queensland, the following demand
requirements will be requested in the following years. These requirements are modelled
in PLEXOS® which is illustrated in Figure 3-4 Figure 3-4. There are three projects
located in Curtis Island which are confirmed: Australia Pacific LNG (APLNG),
Gladstone LNG (GLNG) and Queensland Curtis LNG (QCLNG). The Arrow’s project
52
was not included in this study due to lack of information, nonetheless with PLEXOS®
different scenarios can be modelled, for instance: project start date, demand request,
pipelines maintenance schedule, etc.
Figure 3-4 Contract Supply for LNG exports in Queensland for the (2013-2029): Australia Pacific
LNG (APLNG), Gladstone LNG (GLNG) and Queensland Curtis LNG (QCLNG)
3.6.5 Gas Demand Profile
A fundamental part of an energy expansion study is an appropriate projection of
demand in order to plan the supply. The case of gas demand is not the exception. For
the purpose of the model and study presented in this paper, a demand profile was
developed using data from the 2013 Gas Forecast developed by AEMO (AEMO,
2013b) and the National Gas Bulletin Board (Bulletin, 2013).
In order to develop the gas profile used for this study, it is necessary to introduce the
reader to some mathematical background information related to analysis of linear
systems. This is useful for explaining the methodology used to build the gas demand
profile.
APLNG QCLNG
GLNG
53
Any function in the domain of time can be represented by the addition of several
sinusoidal terms. Depending of the accuracy of the representation, the number of terms
could be infinite. Equation (3-16) describes the representation of the function y(t),
through the addition of sinusoidal terms defined by amplitude, frequency and phase
angle.
( ) ∑ ( )
( 3-17)
Moreover equation (3-18) can be represented in the frequency domain using the Fourier
transform. A representation of this concept is shown in equation (3-19) where every
term of the addition represents the natural mode of the function y(t) (Goodwin et al.,
2000). When two functions in time have the same frequency content, but they are
different in magnitude, it means that both functions have the same components
∑ ( ) , where the only difference is related to the magnitude of the function, that
is the Bi coefficients.
( ) ∑ ( )
( 3-20)
For the purpose of this study, the same concept has been applied in order to build the
forecast of the gas demand. The assumption is that the frequency content of annual
demand profile is keep constant at least with the lower frequencies. This is reasonable
due to the main frequencies of the demand functions are seasonally determined by
weather conditions and human behaviour (Weron, 2007).
The year used as a basis for the mathematical model is 2013, where daily data was
obtained from the National Gas Bulletin Board (Figure 3-5). The hypothesis in using
this year assumes that the main frequencies (related to weather conditions and human
54
behaviour) will be presented in the following years and the only difference will be
defined by the amplitude (coefficients) Bi. AEMO publishes the projections for the
annual demand in the forthcoming years (Annex 4). Using this information and an
algorithm developed in the language Visual Basic language, a daily demand profile was
developed. The algorithm takes into account the main frequencies of the year 2013 and
the amplitudes determined by data provided by AEMO. The algorithm is presented in
the Annex 2.
Figure 3-5 Brisbane’s daily demand profile [TJ] for the year 2013 (Bulletin, 2013)
3.6.5.1 Gas Demand Profiles created
AEMO categorized the domestic demand of gas using the following segments (AEMO,
2013a):
Mass market (MM), comprising residential and business demand of less than 10
TJ/a.
Large industrial (LI), comprising consumers with gas demand greater than 10
TJ/a.
Gas-power generation (GPG)
Using the projections supplied by AEMO in the Gas Statement of Opportunities
(GSOO) and with the demand profiles created, the following demand profiles were
created
55
Figure 3-6 Brisbane’s demand profile forecasted [TJ] (2013 -2031)
Figure 3-7 Gladstone’s demand profile forecasted [TJ] (2013 -2031)
56
Figure 3-8 Mount Isa’s demand profile forecasted [TJ] (2013 -2031)
Figure 3-9 New South Wales’ demand profile forecasted [TJ] (2013-2031)
57
Figure 3-10 South Australia’s demand profile forecasted [TJ] (2103-2031)
Figure 3-11 Tasmania’s demand profile forecasted [TJ] (2013-2031)
58
Figure 3-12 Victoria’s demand profile forecasted [TJ] (2013-2031)
59
4 The formulation of the Long Term Planning Problem in
PLEXOS®
4.1 Introduction
Australia is endowed with a variety of both renewable and non-renewable resources. In
fact, Australia is worldwide known by its uranium and coal reserves (Goverment, 2010).
Furthermore, electricity generation through renewable sources is significant in the states
of Tasmania and South Australia. In a first shallow analysis of the energy situation in
Australia, it could be incorrectly concluded that electricity generation is uncomplicated
and inexpensive due to the variety of natural resources in the country.
Despite the positive framework described above, Australia is at the verge of an
energetic dilemma due to the uncertainty of the variables that determine energy
investment, for instance: carbon tax, fuel prices and electricity demand patterns. Indeed,
those variables add uncertainty and risk to the investment sector.
The current chapter will present the reader the software used for modelling the
Australian Electricity and Gas system. It also will explain the objective function behind
the optimisation problem proposed in this thesis.
4.2 Energy Planning Tool
Energy planners need specialized tools in order to manage the complexity of this
changing commercial and regulatory landscape. The software chosen for this project is
PLEXOS® from the company Energy Exemplar. PLEXOS® is a integrated gas and
electricity software which provides specialized answers to different planning cases.
The integrated gas-electric model allows detailed modelling of the physical delivery of
gas from fields, through pipelines and storage to gas and electricity demand points. The
60
gas and electric models are solved simultaneously allowing decision makers to trade-off
gas investments, constraints and costs against other alternatives.
The long-term planning problem analysed in this thesis uses a mixed integer
deterministic optimization where electricity and gas systems are co-optimized. The
software’s process can be categorized in three stages: long, medium and short term,
where the extension of the process is linked to the period of time studied.
In the long-term (LT) process, the expansion planning is performed. Generation and
transmission investments (different alternatives) are provided before the simulation
begins. The algorithm will optimise the different choices and also it will take into
account the current generation and transmission capacity in order to find if some
retirements are also needed. The optimisation problem takes into account energy costs,
constraints in transmission systems (lines and pipelines) and demand profiles. It should
be mentioned that usually, long-term planning problems use load blocks for
representing each load duration curve (LDC). However for the purpose of this thesis the
chronological approach is used (Nweke et al., 2012).
To solve the capacity expansion problem presented in this paper implies to find an
optimal answer for the mixture of the new generation and transmission capabilities in
conjunction with the gas network system analysed. It should be mentioned that not only
addition of elements could occur, but also retirements of assets can happen. The
objective function is to minimize the net present value (NPV) of the total system cost
evaluated over a long-term planning horizon (Berry, 2012), which means to solve
simultaneously the expansion problem and the dispatch process of electricity and gas
from a long-term framework.
61
The minimisation of the objective function described by the equation (4-1) will result in
a co-optimised expansion plan where generation and inter-regional (electricity and gas)
options are considered. In addition restrictions are included in order to meet reliability
requirements. From data obtained from the AEMO which points out the transmission
power flows between the states, it is possible to evaluate possible transmission upgrade
of the current transmission system.
As it was mentioned previously, Linear Programming is basis for the formulation the
understanding of any optimisation problem. The main characteristic from an
optimization framework of energy expansion problems is the use of integer variables
and non-linear constraints (linked with power flow equations). In addition, the use of
discrete variables provides the optimisation problem with a combinatorial formulation
mostly determined by decisions to build new assets (Newham, 2008). The high number
of decision variables will imply a exponential number of calculations, which is usually
refers as the ‘Curse of Dimensionality’(Newham, 2008).
The energy expansion problem addressed in this work is formulated in PLEXOS as a
Mixed-Integer Linear Program (MILP). That is the variable decisions of the problem
can be discrete and continuous. Usually, the numbers of generation units built and
retired are categorized as discrete variables. It worth to mention that the formulation of
a MILP implies a higher computation time in comparison to use only continues decision
variables.
4.3 Formulation of the problem
In order to formulate the problem the following variables (Table 4-1) and parameters
are defined for the formulation of the core expansion problem:
Table 4-1, decision variables used for the expansion planning problem
62
Variable Description Type
Number of generating units build in year y for Generator g Integer
Dispatch level of generating unit g in period t Continuous
Unserved energy in dispatch period t Continuous
Capacity shortage in year y Continuous
Table 4-2 parameters for the formulation of the expansion planning problem
Element Description Unit
D
Discount rate. We then derive
( )
which is the discount factor applied to year y,
and DFt which is the discount factor applied to
dispatch period t
Duration of dispatch period t Hours
Overnight build cost of generator g $
Maximum number of units of
generator g allowed to be built by the end of
year y
Maximum generating capacity of each unit of
generator g MW
Number of installed generating units of
generator g
Value of lost load (energy shortage price) $/MWh
Short-run marginal cost of generator g which is
composed of Heat Rate × Fuel Price + VO&M15
Charge
$/MWh
Fixed operations and maintenance charge of
generator g $
Average power demand in dispatch period t MW
System peak power demand in year y MW
Margin required over maximum power demand
in year y MW
Capacity shortage price $/MW
FOMChargeg Fixed operations and maintenance charge of $
15
VO&M = variable operation and maintenance costs
63
generator g
The following formulation will include only build decisions, due to the extension of the
equation to address the problem. The objective function of the expansion problem
minimized the net present value of the summation of the generation investment, fixed
operations and maintenance costs and operation costs over a planning horizon. The
expansion problem is based on the least-cost algorithm which is formulated using mixed
integer programming where the objective function represents the total system costs.
Equation (4-1) represents the summarized formulation of the objective function used in
this thesis. The first term of the objective function is linked with the investment cost of
new assets to be built, composed by individual unit build cost* multiplied by the amount
built**
(CapEx). The second term refers to the production cost, composed by individual
unit production cost***
multiplied by individual unit production****
(OpEx). Finally, the
third term is defined by the multiplication between VOLL and Unserved Energy.
∑∑(
)
∑(∑
)
( 4-1)
The following constraints impose physical and system limitations to the cost
minimisation MIP model.
Subject to
1) Supply and Demand Balance. This restriction defines that the total supply has to
meet the total demand in any dispatch period. Any supply short-fall resulting in
an involuntary load curtailment appears as unserved demand (Shortaget) in this
64
equation to satisfy the supply-demand balance requirement. Therefore the supply
and demand balance is represented as:
∑
( 4- 2)
2) Production Feasible. This restriction addresses on a feasible amount of supply
available for any dispatch period. Generator outages, approximated by forced
outage rate (FOR) and maintenance outrage (MOR) derate the energy
contribution from a generator in a dispatch period.
( 4-3)
3) Expansion Feasible. This restriction relates the limit on the maximum number of
new generation entry that can be built in a year.
( 4-4)
4) Integrality. This constraint refers to the discrete feature of new generation build
for a specific year.
( 4-5)
In the next chapter results of the model presented in Chapter 3 and 5 will be depicted.
65
5 Results
This section will present the results of the model developed for this thesis. In the first
section, the results from the gas model will be presented. This is compulsory due to the
need to validate the gas model. The following section will show the results of the
expansion planning co-optimisation of the gas and electricity network.
5.1 The Gas Model
For this section the demand zones are modelled as three demand profiles which are built
with data from the AEMO (AEMO, 2013b) and the National Gas Market Bulletin
Board (Bulletin, 2013). In Figure 5-1 the gas network modelled is shown. As it was
mentioned there are seven demand zones for the network modelled. In addition the
study of the integration of the LNG projects is presented in this section.
66
Figure 5-1 Gas network modelled in PLEXOS®, the main demand zones included in this study are:
Mount Isa, Gladstone, Brisbane, Adelaide, Sydney, Melbourne and Hobart
5.1.1 Scenario 1
Scenario 1 does not include the projects in Curtis islands and also only 2p16
reserves are
included in the analysis. Figure 5-2 shows the price of gas in the different demand
zones described for this simulation. Tasmania has the higher cost due to the extensive
network of pipelines to supply this state. Prices along the network simulated vary
between 4.5 and 7 $/GJ. This range has important implications for the evaluation about
export or import gas according to the international value.
16
Despite of 3p reserves were included in the model; the optimal solution did not
include these type of reserves due to the high price.
67
Figure 5-2 Gas cost at the demand nodes
Figure 5-3 End volume basins (TJ) (Bowen-Surat is not included)
Figure 5-3 shows the rates of reduction of the different basins included in this study.
Bowen-Surat was not included in this figure due to its larger reserves, which can be
observed in Figure 5-4. According to the information consulted, the Bowen-Surat basin
68
will provide most of the production for the LNG projects located in Queensland. For
that reason in Figure 5-4 production in this basin is almost constant.
Figure 5-4 End volume Basins (TJ)
5.1.2 Scenario 2
The only difference for the model simulated in this scenario is the inclusion of the LNG
projects in the gas network.
From the integration of LNG demand into the gas network analysed, from Figure 5-6 it
can be observed that there is an increase in the rate of extraction from the Bowen-Surat
basin driven by the deployment of nonconventional coal gas seam gas which likely will
supply the future demand of LNG in Curtis Island (Queensland).
It is interesting to notice that with the development of the three mentioned projects, the
production of the Bowen-Surat Basin increases addressing possible restrictions on the
current network. From the data analysed and the model simulated, gas fields located on
69
the Bowen-Surat Basin would have to ensure their overall production above 1,6 [PJ] in
order to supply demand requirements including domestic and international market.
Figure 5-5 End volume basin (TJ) not including Bowen-Surat
70
Figure 5-6 End volume Basins (TJ) including Bowen-Surat
5.2 The Co-optimized model
The National Electricity Market is structured as a zonal price. Figure 5-7 a) shows the
current configuration of the NEM conformed by 16 zones. For the purpose of the work
presented, the configuration was simplified grouping 5 nodes for the NEM. This
assumption allows reflecting interactions between the states in terms of power flows.
The co-optimized model is the junction between the networks depicted in Figure 5-1
and Figure 5-7 b). For a single electricity model, fuel prices are exogenous values which
are input data for this model. For the co-optimized model the gas price values and the
reserves are dynamic inputs. The linking between both systems is performed through
the gas node which allows connection with the gas power generation plant.
71
Figure 5-7 a) The NEM b) Model used for this thesis
Similarly to the results presented in the previous section, in the following section two
scenarios are presented:
Scenario 1: Co-optimization of the NEM and the South Eastern Australian gas
network
Scenario 2: Co-optimization of the NEM and the South Eastern Australian gas
network with the integration of LNG projects on Curtis Island, Queensland.
In addition the following inputs are included in the model:
Electricity Demand. Similarly to the demand profiles included in the gas section,
the expansion planning has to deal with supply the forthcoming demand build
72
new generation capacity. As main difference with the gas model, gas demand
related to electricity generation is modelled dynamically due to the direct link
with the gas demand. For that reason the integrated gas model has two static
demands (commercial and industrial) and one dynamic demand linked to the
electricity generation. Figure 5-8 describes how electricity demand is forecasted
for the forthcoming years where Queensland and New South Wales have the
higher growing rates in comparison with other states.
Figure 5-8 Electricity Demand in the NEM (AEMO)
Another important input that has relation to the gas consumption is the how
much renewable energy is required over a period of analysis. For the current
model the following restriction was included in the model.
73
Figure 5-9 Renewable requirements [MW]
5.2.1 Scenario 1, Co-optimisation of the Electricity and gas model including
Bowen-Surat Basin
Results related to scenario 1 addresses the co-optimisation of the NEM and the South
Eastern Gas Network including the Bowen-Surat basin. For the purposes of the present
paper, this basin was chosen due to its high content of coal seam gas having the higher
quantity of reserves among the basin in this part of Australia.
Figure 5-10 shows the evolution of the electricity price over the period of study. The
states of Tasmania and South Australia shows a decrease in the electricity price
especially in the year 2022 which coincides with the constrains related to the renewable
target set in the model in 2022. On the contrary Queensland and Victoria have a
growing tendency in relation to the electricity price.
74
Figure 5-11 shows the generation capacity is built over the period of analysis. In relation
to the extension of this report only the year 2016 will be explained. A detail description
of these results is shown in Annex 5. In the year 2016 most of the capacity built is
composed by wind and CCGT:
New South Wales: Wind 2700 [MW] and Combine Gas Cycle Turbine 2400
[MW]
Victoria: Wind 1839 [MW] and Combine Gas Cycle Turbine 3655 [MW]
South Australia: Wind 2300 [MW]
Tasmania Wind 750 [MW]
Queensland: Combine Gas Cycle Turbine 3600 [MW]
Figure 5-10 Electricity Price over the period simulated ($/MWh)
75
Figure 5-11 Generation Capacity Built (MW)
On the contrary to the Figure 5-11, Figure 5-12 shows the results of the capacity retired
for the period modelled. All the units retired correspond to coal power plants. The
retirement is driven by the carbon price used, the renewable target and the gas prices.
76
Figure 5-12 Generation Capacity Retired (MW)
5.2.2 Scenario 2, Co-optimisation of the Electricity and gas model: sensitive
analysis of the Bowen-Surat Basin
The following analysis points out the importance of the Bowen-Surat basin in the
context of the planning of the National Electricity Market. Projects in Curtis Island will
depend highly of this basin, which implies a trade-off between domestic and
international consumption. The following scenario does not include the Bowen-Surat’s
reserves. Therefore electrical planning will be determined by the gas consumption from
the basins described in Figure 5-13.
77
Figure 5-13 End volume basin (TJ) not including Bowen-Surat in the domestic consumption
The allocation of the reserves from the Bowen-Surat to the international market results
in an increase in the electricity price (Figure 5-14) in comparison with Figure 5-10.
South Australia is one of the states more affected by this allocation due to its high
dependency on gas for electricity generation.
78
Figure 5-14 Electricity Price over the period simulated ($/MWh)
Figure 5-15 Generation Capacity Built (MW)
79
Figure 5-15 described the results of the generation capacity built for scenario 2. There
are clearly differences between results from Figure 5-11 and Figure 5-15 which are
determined by the absence of the reserves from Bowen-Surat basin. For instance most
of the investment in allocate for wind energy generation highlighting the lack of gas
power generation.
.
Figure 5-16 Generation Capacity Retired (MW)
Figure 5-16 described the generation capacity retired which is characterized by only
coal power plants. However in comparison with the results from scenario 1, the quantity
retired is less than results from Figure 5-12. This result is logic due to the quantity of
base generation is built. Therefore, if less base capacity is built, less base capacity
should be retired, which is the case of coal power plants.
80
6 Conclusions and Future Work
Australia is facing an energy planning dilemma. Electricity generation is predominantly
sourced by highly emission intense coal power plants. In 2007 Australia ratified the
Kyoto Protocol which set the first steps towards a low carbon future having as an
important milestone the implementation of the carbon tax an emission trading system
(ETS). Australia faces difficult decisions and considerable uncertainty about its future
energy path: business as usual versus a shift to a low carbon economy.
In addition, unconventional gas resources have become commercially attractive due to
technological breakthroughs in exploration and environmental advantages in regards to
CO2 pollution with regards to electricity generation. Moreover the growing
international market for LNG has been a game-changer in the energy sector and
Australia is right in the middle of this expected boom.
As it was described in this report the complexity of energy models increases when more
variables are included in it. The importance of integrate gas and electricity has
economic and physical implications especially in the current context of the Australian
system, for that reason the significance of modelling a coupled market. In addition, the
current approach taken from AEMO where both markets are analysed separately in an
iterative process proves that the approach taken in this thesis is the correct. The reasons
to support this argument are three: mathematical, computing time and dynamic
interaction between both markets. Because AEMO performs an iterative process where
there are at least two simulations (electricity and gas models) for each market, the
model presented in this thesis reduces the time for modelling both systems. In relation
to the argument of dynamic interaction, it is always a more correct approach to integrate
dynamic interaction between two systems instead of static interactions as AEMO
81
performs its analysis. Finally, the mathematical approach mentioned is related to the
mathematical certainty or probe that the optimal solution for a coupled system is always
better than the optimal solution obtained from a decoupled analysis. In other words, the
objective function of the co-optimised problem evaluated in the optimal point will be
less than the addition of the two objective functions in their respective optimal points.
The mathematical proof of this last argument is beyond the objectives of the present
work; however it is highly recommended to be proposed as a research project for the
complexity and stringency associated with it.
As it was shown in the results from the sensitive analysis of scenario 2, the lack of
reserves from the Bowen-Surat basin has important implication on the investment for
gas power generation affecting the state of South Australia due to its dependency to this
type of plant.
Another interesting point addressed in the sensitive analysis relates the quantity of units
retired associated with coal fired power plants. Because investment in gas power
generation decreases when the Bowen-Surat basin in not included, the quantity of base
coal power plant retired also diminishes due to demand has to match with supply.
In relation to the gas network, the information presented in this report has shown that
significant new investments in gas pipelines have improved interconnection and hence
the market competition. Also based on the results either in the single gas model section
or in the co-optimised section there is enough gas in the Bowen-Surat basins to supply
the three projects on Curtis Island in a period over 20 years. As it was mentioned the
current project was developed taking into account 2P reserves in the optimal answers
from PLEXOS. Despite of 3P reserves were included in the model, the optimal solution
provided by the software did not use 3P reserves due to its high cost, it would be
82
interesting to analyse what are the implications of a sensitive analysis that will force the
use 3P reserves. In addition, the approach performed in relation to 2P and 3P reserves
was without the use of a statistical point of view. That is 2P and 3P reserves imply a
50% and 10% respectively of certainty of the economic benefits from the reserve.
There are several research paths taking as basis the model developed. The first has to do
with the implications of including the international market of LNG into the model.
Through this proposal, different policies can be tested in order to satisfy international
and domestic demand. In addition, future projects related to shale gas or 3p reserves
could complement the research proposal mentioned where probability should be
included.
In relation to the electricity model, it is interesting to use the current model in order to
tests stochastic variables using the features that PLEXOS provides. Some of the
variables that could be addressed are: wind speed, fuel prices, solar patterns and
electricity consumption behaviour.
Finally, as the Australian gas market is growing at fast speed, it would be interesting to
analyse which alternative for the gas market would be appropriate taking into account
the influence of an international price for LNG.
83
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86
A. Annex 1- Lagrange Multiplier
The Lagrange Multiplier is commonly used to address optimization problems in order to find
the minimum or maximum values of an objective function. In the area of power system, this
approach is used for resolving the economic dispatch problem (Wood and Wollenberg, 1984).
An optimization problem is composed by an objective function which could be maximized or
minimized; and also composed by several constraints according with the requirements of the
problem. The most common problem in a power system is related to the dispatch problem.
Usually the objective in a power system is to minimize the cost of electricity generation among
N generators as it is defined by the equation (A-1). is the power generated by unit with a
cost associated of .
Electricity cannot be stored efficiently and economically, therefore the overall quantity of
electricity generated should match with demand (load). This constraint is defined by the
equation (A-2).
∑ ( )
(A-1)
∑
(A-2)
In order to solve the optimization problem proposed in the equation (A-1) a Lagrangian function
is defined in equation Error! Reference source not found.. It should be noted that according to
he Lagrarian strategy, constrains (A-2) have to be multiplied by the factor.
( ) ∑ ( )
( ∑ )
(A-3)
Equation (A-4) defines the problem of dispatch optimization based on the minimization of
generation cost. That is generator will be ranked in a term of which of them is the cheapest. In
order to find the optimum point, the partial derivates of the Lagrarian function are equalized to
zero which follows resolving the resulting equations:
( ∑
)
(A-4)
From the equation (A-4), it can be inferred that the marginal cost of every generating unit is
equal to the Lagrange multiplier. Usually this multiplier receives the name of shadow price for
several generators that belong to a same portfolio. Every generator from the portfolio in order to
be dispatch optimally should operate at the same marginal cost.
87
B. Annex 2 – Gas Demand Profile Algorithm
Sub read_demand()
Dim demand_file As Excel.Workbook
Dim list_files As Excel.Workbook
Dim demand_sheet As Excel.Worksheet
Dim demand_files As Excel.Worksheet
Dim i As Integer
Dim output_workbook As Excel.Workbook
Dim output_sheet As Excel.Worksheet
Dim path_demand As String
Dim file_demand_name As String
Dim z As Integer
path_demand = "E:\investigacion\tesis australia\gas\data from BB\Flow\"
Excel.Workbooks.Open (path_demand & "list_files.xlsx")
Set list_files = Excel.Workbooks("list_files.xlsx")
Set demand_files = list_files.Worksheets("Sheet1")
z = 1
Do While (demand_files.Cells(z, 1) <> "")
file_demand_name = demand_files.Cells(z, 1)
Excel.Workbooks.Open (path_demand & file_demand_name)
Set demand_file = Excel.Workbooks(file_demand_name)
Set demand_sheet = demand_file.Worksheets(Left(file_demand_name, 31))
'Gladstone
Set output_sheet = Sheet1
Call extraer("Queensland Gas Pipeline", "Queensland Gas Pipeline (QGP)", demand_sheet,
output_sheet)
'Brisbane (RBP)
Set output_sheet = Sheet2
Call extraer("Roma - Brisbane Pipeline", "Roma to Brisbane Pipeline (RBP)", demand_sheet,
output_sheet)
'Adelaide (MAP)
Set output_sheet = Sheet3
Call extraer("Moomba to Adelaide Pipeline System", "Adelaide (ADL)", demand_sheet,
output_sheet)
'Adelaide (SEAGas)
Set output_sheet = Sheet4
Call extraer("SEA Gas Pipeline", "Adelaide (ADL)", demand_sheet, output_sheet)
'Hobart (TGP)
Set output_sheet = Sheet5
Call extraer("Tasmania Gas Pipeline", "Tasmanian Gas Pipeline (TGP)", demand_sheet,
output_sheet)
'Mount Isa (CGP)
Set output_sheet = Sheet6
Call extraer("Carpentaria Pipeline", "Carpentaria Gas Pipeline (CGP)", demand_sheet,
output_sheet)
'Moomba Gas Plant
88
Set output_sheet = Sheet7
Call extraer("Moomba Gas Plant", "Moomba (MOO)", demand_sheet, output_sheet)
'Sydney (EGP)
Set output_sheet = Sheet8
Call extraer("Eastern Gas Pipeline", "Sydney (SYD)", demand_sheet, output_sheet)
'Canberra (EGP)
Set output_sheet = Sheet9
Call extraer("Eastern Gas Pipeline", "Aust. Capital Territory (ACT)", demand_sheet,
output_sheet)
'EGP
Set output_sheet = sheet10
Call extraer("Eastern Gas Pipeline", "Eastern Gas Pipeline (EGP)", demand_sheet,
output_sheet)
'Sydney (MSPS)
Set output_sheet = sheet11
Call extraer("Moomba to Sydney Pipeline System", "Sydney (SYD)", demand_sheet,
output_sheet)
'Canberra (MSPS)
Set output_sheet = sheet12
Call extraer("Moomba to Sydney Pipeline System", "Aust. Capital Territory (ACT)",
demand_sheet, output_sheet)
'MSPS
Set output_sheet = sheet13
Call extraer("Moomba to Sydney Pipeline System", "Moomba to Sydney Pipeline System
(MSP)", demand_sheet, output_sheet)
'LNG Storage Dandenong
Set output_sheet = sheet14
Call extraer("LNG Storage Dandenong", "Victorian Principal Transmission System",
demand_sheet, output_sheet)
'Lang Lang Gas Plant Victoria
Set output_sheet = sheet15
Call extraer("Lang Lang Gas Plant", "Victoria", demand_sheet, output_sheet)
'Longford Gas Plant Gippsland (GIP)
Set output_sheet = sheet16
Call extraer("Longford Gas Plant", "Gippsland (GIP)", demand_sheet, output_sheet)
'Orbost Gas Plant Gippsland (GIP)
Set output_sheet = sheet17
Call extraer("Orbost Gas Plant", "Gippsland (GIP)", demand_sheet, output_sheet)
'Iona Underground Gas Storage Port Campbell (PCA)
Set output_sheet = sheet18
Call extraer("Iona Underground Gas Storage", "Port Campbell (PCA)", demand_sheet,
output_sheet)
'Minerva Gas Plant Port Campbell (PCA)
Set output_sheet = sheet19
Call extraer("Minerva Gas Plant", "Port Campbell (PCA)", demand_sheet, output_sheet)
'Otway Gas Plant Port Campbell (PCA)
Set output_sheet = sheet20
Call extraer("Otway Gas Plant", "Port Campbell (PCA)", demand_sheet, output_sheet)
89
'NSW-Victoria Interconnect Victorian Principal Transmission System
Set output_sheet = sheet21
Call extraer("NSW-Victoria Interconnect", "Victorian Principal Transmission System",
demand_sheet, output_sheet)
'Longford to Melbourne Victorian Principal Transmission System
Set output_sheet = sheet22
Call extraer("Longford to Melbourne", "Victorian Principal Transmission System",
demand_sheet, output_sheet)
'South West Pipeline Victorian Principal Transmission System
Set output_sheet = sheet23
Call extraer("South West Pipeline", "Victorian Principal Transmission System", demand_sheet,
output_sheet)
'Wallumbilla Roma(ROM)
Set output_sheet = sheet24
Call extraer("Wallumbilla", "Roma (ROM)", demand_sheet, output_sheet)
'South West Queensland Pipeline South West Queensland Pipeline (SWQ)
Set output_sheet = sheet25
Call extraer("South West Queensland Pipeline", "South West Queensland Pipeline (SWQ)",
demand_sheet, output_sheet)
'Kogan North Roma (ROM)
Set output_sheet = sheet26
Call extraer("Kogan North", "Roma (ROM)", demand_sheet, output_sheet)
'Kincora Roma(ROM)
Set output_sheet = sheet27
Call extraer("Kincora", "Roma (ROM)", demand_sheet, output_sheet)
'Peat Roma(ROM)
Set output_sheet = sheet28
Call extraer("Peat", "Roma (ROM)", demand_sheet, output_sheet)
'Spring Gully Roma (ROM)
Set output_sheet = sheet29
Call extraer("Spring Gully", "Roma (ROM)", demand_sheet, output_sheet)
'Strathblane Roma(ROM)
Set output_sheet = sheet30
Call extraer("Strathblane", "Roma (ROM)", demand_sheet, output_sheet)
'Taloona Roma(ROM)
Set output_sheet = sheet31
Call extraer("Taloona", "Roma (ROM)", demand_sheet, output_sheet)
'Berwyndale South Roma (ROM)
Set output_sheet = sheet32
Call extraer("Berwyndale South", "Roma (ROM)", demand_sheet, output_sheet)
'Fairview Roma(ROM)
Set output_sheet = sheet33
Call extraer("Fairview", "Roma (ROM)", demand_sheet, output_sheet)
'Scotia Roma(ROM)
Set output_sheet = sheet34
Call extraer("Scotia", "Roma (ROM)", demand_sheet, output_sheet)
'Rolleston Roma(ROM)
Set output_sheet = Sheet35
90
Call extraer("Rolleston", "Roma (ROM)", demand_sheet, output_sheet)
'Yellowbank Roma(ROM)
Set output_sheet = Sheet36
Call extraer("Yellowbank", "Roma (ROM)", demand_sheet, output_sheet)
'Ballera Gas Plant Ballera (BAL)
Set output_sheet = Sheet37
Call extraer("Ballera Gas Plant", "Ballera (BAL)", demand_sheet, output_sheet)
'SEA Gas Pipeline SEA Gas (SEA)
Set output_sheet = Sheet38
Call extraer("SEA Gas Pipeline", "SEA Gas (SEA)", demand_sheet, output_sheet)
'Moomba to Adelaide Pipeline System Moomba to Adelaide Pipeline System (MAP)
Set output_sheet = Sheet39
Call extraer("Moomba to Adelaide Pipeline System", "Moomba to Adelaide Pipeline System
(MAP)", demand_sheet, output_sheet)
demand_file.Close
z = z + 1
Loop
End Sub
Function extraer(condition1 As String, condition2 As String, in_sheet As Excel.Worksheet,
out_sheet As Excel.Worksheet)
Dim x As Integer
Dim y As Integer
x = 2
If (out_sheet.Cells(1, 1) = "") Then
y = 1
Else
y = 1
Do While (out_sheet.Cells(y, "A") <> "")
y = y + 1
Loop
End If
Do While (in_sheet.Cells(x, "A") <> "")
If (in_sheet.Cells(x, "A") = condition1 And in_sheet.Cells(x, "B") = condition2) Then
out_sheet.Cells(y, "A") = in_sheet.Cells(x, "A")
out_sheet.Cells(y, "B") = in_sheet.Cells(x, "B")
out_sheet.Cells(y, "c") = in_sheet.Cells(x, "c")
out_sheet.Cells(y, "d") = in_sheet.Cells(x, "e")
y = y + 1
End If
x = x + 1
Loop
End Function
91
C. Annex 3 – Data used for the Gas Model (IES, 2013, SKM, 2013) Table C-1 Reserves by basin and type - PJ
Gas Source Geological Basin 2P 3P
Conventional Bass 254 254
Conventional Bowen/Surat 160 203
Conventional Cooper-Eromanga 1835 1835
Conventional Gippsland 3890 3890
Conventional Gunnedah 0 0
Conventional Otway 720 720
CSG Bowen/Surat 39148 57783
CSG Galilee 0 0
CSG Moranbah 2472 5504
CSG Clarence-Moreton 445 2922
CSG Gloucester 669 832
CSG Gunnedah 1426 1426
CSG Sydney 282 457
CSG Cooper 0 0
Table C-2 Maximum production capacity – TJ/day
Basin TJ/day
Bowen-Surat 1099
Cooper-Eronmanga 490
Sydney 26
Bass 70
Gippsland 1245
Otway 848
Clarence-Moreton 100
Gloucester 90
Gunnedah 100
Galiee No modelled
Table C-3 Production costs by basin and type -$/GJ
Type Geological Basin 2P 3P
Conventional Bass 4.77 5.02
Conventional Bowen/Surat 4.4 4.84
Conventional Cooper-Eromanga 4.2 4.62
Conventional Gippsland 4.76 5.01
Conventional Otway 4.77 5.02
CSG Bowen/Surat 4.42 4.86
CSG Clarence-Moreton 4.82 5.3
CSG Galilee 5.01 5.51
CSG Gloucester 4.42 4.85
CSG Gunnedah 4.62 5.08
CSG Moranbah 4.62 5.08
CSG Sydney 5.58 6.08
Unconventional Cooper-Eromanga 6.01 6.61
92
Table C-4 Pipeline capacities and tariff – TJ/day and $/GJ
Pipeline From Townsville Tariff
($/GJ)
Max
cap
North Queensland Gas Pipeline Moranbah Townsville 1.42 68
Carpentaria Gas Pipeline Ballera Mt Isa 1.4 119
Queensland Gas Pipeline Wallumbilla Gladstone 0.87 249
Roma to Brisbane Pipeline Wallumbilla Brisbane 0.49 232
South West Queensland Pipeline Ballera Wallumbilla 1.04 694
South West Queensland Pipeline
Reverse Flow
Wallumbilla Ballera 1.04 595
QSN Link Ballera Moomba 0.40 694
QSN Link Reverse Flow Moomba Ballera 0.40 595
Moomba to Sydney Pipeline Moomba Young 0.75 439
Moomba to Sydney Pipeline Young Dalton 0.06 439
Moomba to Sydney Pipeline Dalton Sydney 0.13 439
Moomba to Sydney Pipeline
Reverse Flow
Sydney Dalton 0.13 315
Moomba to Sydney Pipeline
Reverse Flow
Dalton Young 0.06 315
Moomba to Sydney Pipeline
Reverse Flow
Young Moomba 0.75 315
Dalton to Canberra pipeline Dalton Canberra 0.15 439
Eastern Gas pipeline Longford Hoskinstown 0.71 288
Eastern Gas pipeline Hoskinstown Sydney 0.43 288
Longford to Canberra via Eastern
Gas Pipeline
Hoskinstown Canberra 0.43 77
NSW-VIC Interconnect (VIC to
NSW)
Melbourne Culcairn 0.32 92
NSW-VIC Interconnect (VIC to
NSW)
Culcairn Wagga
Wagga
0.06 92
NSW-VIC Interconnect (VIC to
NSW)
Wagga Wagga Young 0.09 92
Longford-to-Melbourne Pipeline Longford Dandenong 0.2 1030
Longford-to-Melbourne Pipeline Dandenong Melbourne 0.07 1030
South West Pipeline Port Campbell Melbourne 0.27 429
South West Pipeline Reverse
Flow
Melbourne Port
Campbell
0.27 429
SEAGas Pipeline Port Campbell Penola 0.25 314
SEAGas Pipeline Penola Adelaide 0.5 314
Moomba to Adelaide Pipeline Moomba Whyte
Yarcowie
1 253
Moomba to Adelaide Pipeline Whyte
Yarcowie
Adelaide 0.3 253
Moomba to Adelaide Pipeline
Reverse Flow
Adelaide Whyte
Yarcowie
0.3 380
Moomba to Adelaide Pipeline
Reverse Flow
Whyte
Yarcowie
Moomba 1 380
Tasmanian Gas Pipeline Longford Bell Bay 1.3 130
Tasmanian Gas Pipeline Bell Bay Hobart 1.0 130
Queensland Hunter Pipeline Wallumbilla Gunnedah 1.0 230
Queensland Hunter Pipeline Gunnedah Newcastle 0.75 230
93
(then to
Sydney)
Queensland Hunter Pipeline
Reverse Flow
Gunnedah Wallumbilla 1.0 230
Central Queensland Pipeline Moranbah Gladstone 0.7 0
Lions Way Pipeline Casino
(Clarence-
Moreton Basin)
Ipswich
(then to
Brisbane)
0.5 74
Stratford to Hexham Pipeline Stratford
(Gloucester
Basin)
Hexham
(then to
Sydney)
0.35 100
D. Annex 4 – Gas projections (AEMO, 2013b) Table D-1. South Australia annual gas demand
Historic
Planning
Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand (PJ)
2008 73 13 2
5 11
1
2009 63 14 2
5 10
1
2010 63 14 24
10
0
2011 60 13 2
4 97
2012 63 13 2
2 98
2013 52 13 21 86
2014 43 13 21 77
2015 31 13 21 65
2016 31 13 21 65
2017 29 13 21 62
2018 27 13 21 61
2019 27 14 21 62
2020 27 14 21 62
2021 27 14 21 62
2022 28 14 21 63
2023 28 14 21 63
2024 28 14 21 63
2025 28 14 21 63
2026 28 14 21 64
2027 29 14 21 64
2028 29 14 22 65
2029 30 14 22 66
2030 33 15 22 70
2031 39 15 22 75
94
2032 39 15 22 76
2033 40 15 22 77
Table D-2. 2013 Victorian annual gas demand
Historic Planning Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand (PJ)
2008 23 120 90 233
2009 17 122 84 223
2010 7 122 84 213
2011 9 122 82 214
2012 16 124 79 219
2013 9 125 78 212
2014 1 126 77 204
2015 1 127 76 204
2016 1 128 76 205
2017 2 130 76 208
2018 3 132 77 211
2019 3 133 77 213
2020 3 135 78 215
2021 3 136 78 217
2022 4 137 77 218
2023 4 138 77 219
2024 4 139 77 221
2025 4 141 78 223
2026 5 142 78 225
2027 5 144 79 228
2028 6 146 79 231
2029 6 148 80 233
2030 7 149 80 236
2031 10 151 80 241
2032 11 153 80 243
2033 11 154 79 245
Table D-3. Queensland domestic annual gas
Historic
Planning
Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand (PJ)
2008 50 8 96 15
5
95
2009 62 7 96 16
4
2010 92 6 10
7 20
4
2011 79 6 11
6 20
2
2012 78 6 125
20
9
2013 52 6 130 189
2014 55 7 136 198
2015 41 7 140 188
2016 17 7 143 167
2017 16 8 144 168
2018 13 8 145 166
2019 11 8 146 165
2020 8 8 148 164
2021 10 8 150 169
2022 12 8 154 174
2023 13 9 157 179
2024 15 9 160 184
2025 17 9 161 188
2026 21 9 163 193
2027 26 10 164 199
2028 29 10 165 203
2029 33 10 166 209
2030 38 10 170 218
2031 42 10 177 229
2032 45 10 183 239
2033 49 11 186 246
Table D-4. Tasmanian annual gas demand
Historic Planning Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand (PJ)
2008 9 0 4 14
2009 7 0 4 12
2010 10 0 4 14
2011 12 1 4 16
2012 12 1 4 17
2013 6 1 5 12
2014 1 1 5 7
2015 1 1 5 7
2016 0 1 5 6
2017 1 1 5 7
2018 1 1 5 7
2019 1 1 5 7
96
2020 1 1 5 7
2021 1 1 6 7
2022 1 1 6 8
2023 1 1 6 8
2024 1 1 6 8
2025 1 1 6 8
2026 1 1 6 8
2027 1 1 6 8
2028 1 1 6 8
2029 1 1 6 9
2030 2 1 6 9
2031 2 1 6 9
2032 2 1 6 10
2033 2 1 6 10
Table D-5. New South Wales and Australian Capital Territory annual gas demand
Historic
Planning
Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand
(PJ)
2008 12 42 69 123
2009 28 43 69 139
2010 32 43 69 144
2011 29 42 67 138
2012 33 42 69 144
2013 30 42 66 138
2014 29 43 65 136
2015 20 43 65 128
2016 15 44 67 126
2017 23 45 69 137
2018 24 46 72 142
2019 25 47 74 146
2020 27 48 75 149
2021 20 48 76 144
2022 13 49 76 138
2023 13 50 75 138
2024 13 51 75 138
2025 13 51 75 139
2026 13 52 75 141
2027 14 53 76 143
2028 14 54 77 145
2029 15 54 78 147
2030 17 55 79 150
2031 18 56 80 154
2032 19 57 81 156
97
2033 19 57 83 159
98
E. Annex 5 – Capacity built results Scenario 1: Co-optimization of
the gas and electricity system. (AEMO, 2013b) Table E-1. South Australia annual gas demand
Historic
Planning
Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand (PJ)
2008 73 13 2
5 11
1
2009 63 14 2
5 10
1
2010 63 14 24
10
0
2011 60 13 2
4 97
2012 63 13 2
2 98
2013 52 13 21 86
2014 43 13 21 77
2015 31 13 21 65
2016 31 13 21 65
2017 29 13 21 62
2018 27 13 21 61
2019 27 14 21 62
2020 27 14 21 62
2021 27 14 21 62
2022 28 14 21 63
2023 28 14 21 63
2024 28 14 21 63
2025 28 14 21 63
2026 28 14 21 64
2027 29 14 21 64
2028 29 14 22 65
2029 30 14 22 66
2030 33 15 22 70
2031 39 15 22 75
2032 39 15 22 76
2033 40 15 22 77
Table E-2. 2013 Victorian annual gas demand
Historic Planning Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand (PJ)
99
2008 23 120 90 233
2009 17 122 84 223
2010 7 122 84 213
2011 9 122 82 214
2012 16 124 79 219
2013 9 125 78 212
2014 1 126 77 204
2015 1 127 76 204
2016 1 128 76 205
2017 2 130 76 208
2018 3 132 77 211
2019 3 133 77 213
2020 3 135 78 215
2021 3 136 78 217
2022 4 137 77 218
2023 4 138 77 219
2024 4 139 77 221
2025 4 141 78 223
2026 5 142 78 225
2027 5 144 79 228
2028 6 146 79 231
2029 6 148 80 233
2030 7 149 80 236
2031 10 151 80 241
2032 11 153 80 243
2033 11 154 79 245
Table E-3. Queensland domestic annual gas
Historic
Planning
Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand (PJ)
2008 50 8 96 15
5
2009 62 7 96 16
4
2010 92 6 10
7 20
4
2011 79 6 11
6 20
2
2012 78 6 125
20
9
2013 52 6 130 189
2014 55 7 136 198
2015 41 7 140 188
2016 17 7 143 167
2017 16 8 144 168
100
2018 13 8 145 166
2019 11 8 146 165
2020 8 8 148 164
2021 10 8 150 169
2022 12 8 154 174
2023 13 9 157 179
2024 15 9 160 184
2025 17 9 161 188
2026 21 9 163 193
2027 26 10 164 199
2028 29 10 165 203
2029 33 10 166 209
2030 38 10 170 218
2031 42 10 177 229
2032 45 10 183 239
2033 49 11 186 246
Table E-4. Tasmanian annual gas demand
Historic Planning Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand (PJ)
2008 9 0 4 14
2009 7 0 4 12
2010 10 0 4 14
2011 12 1 4 16
2012 12 1 4 17
2013 6 1 5 12
2014 1 1 5 7
2015 1 1 5 7
2016 0 1 5 6
2017 1 1 5 7
2018 1 1 5 7
2019 1 1 5 7
2020 1 1 5 7
2021 1 1 6 7
2022 1 1 6 8
2023 1 1 6 8
2024 1 1 6 8
2025 1 1 6 8
2026 1 1 6 8
2027 1 1 6 8
2028 1 1 6 8
2029 1 1 6 9
2030 2 1 6 9
101
2031 2 1 6 9
2032 2 1 6 10
2033 2 1 6 10
Table E-5. New South Wales and Australian Capital Territory annual gas demand
Historic
Planning
Scenario
Forecasts
GPG MM LI Total GPG MM LI Total
Annual
demand
(PJ)
2008 12 42 69 123
2009 28 43 69 139
2010 32 43 69 144
2011 29 42 67 138
2012 33 42 69 144
2013 30 42 66 138
2014 29 43 65 136
2015 20 43 65 128
2016 15 44 67 126
2017 23 45 69 137
2018 24 46 72 142
2019 25 47 74 146
2020 27 48 75 149
2021 20 48 76 144
2022 13 49 76 138
2023 13 50 75 138
2024 13 51 75 138
2025 13 51 75 139
2026 13 52 75 141
2027 14 53 76 143
2028 14 54 77 145
2029 15 54 78 147
2030 17 55 79 150
2031 18 56 80 154
2032 19 57 81 156
2033 19 57 83 159
102
F. Annex 5 – Capacity built results Scenario 1: Co-optimization of
the gas and electricity system.
Generator Year Build Retire Net Build Cap.
Cost
(GW) (GW) (GW) ($Mln's)
--------------------------------------------------------------------------------------------------------------------
------------
BW01 2016 0.00 0.68 -0.68 0.00
BW02 2016 0.00 0.68 -0.68 0.00
BW03 2016 0.00 0.68 -0.68 0.00
BW04 2016 0.00 0.68 -0.68 0.00
ER01 2016 0.00 0.72 -0.72 0.00
ER02 2016 0.00 0.72 -0.72 0.00
ER03 2016 0.00 0.72 -0.72 0.00
ER04 2016 0.00 0.72 -0.72 0.00
LD01 2016 0.00 0.52 -0.52 0.00
LD02 2016 0.00 0.52 -0.52 0.00
LD03 2016 0.00 0.52 -0.52 0.00
LD04 2016 0.00 0.52 -0.52 0.00
REDBANK1 2016 0.00 0.15 -0.15 0.00
VP5 2016 0.00 0.66 -0.66 0.00
VP6 2016 0.00 0.66 -0.66 0.00
WW7 2021 0.00 0.50 -0.50 0.00
WW8 2021 0.00 0.50 -0.50 0.00
CALL_B_1 2016 0.00 0.35 -0.35 0.00
CALL_B_2 2016 0.00 0.35 -0.35 0.00
COLNSV_1 2015 0.00 0.03 -0.03 0.00
COLNSV_2 2015 0.00 0.03 -0.03 0.00
COLNSV_3 2015 0.00 0.03 -0.03 0.00
103
COLNSV_4 2015 0.00 0.03 -0.03 0.00
COLNSV_5 2015 0.00 0.07 -0.07 0.00
CPP_3 2016 0.00 0.45 -0.45 0.00
CPP_4 2016 0.00 0.45 -0.45 0.00
GSTONE1 2016 0.00 0.28 -0.28 0.00
GSTONE2 2016 0.00 0.28 -0.28 0.00
GSTONE3 2016 0.00 0.28 -0.28 0.00
GSTONE4 2016 0.00 0.28 -0.28 0.00
GSTONE5 2016 0.00 0.28 -0.28 0.00
GSTONE6 2016 0.00 0.28 -0.28 0.00
KPP_1 2016 0.00 0.72 -0.72 0.00
MPP_1 2019 0.00 0.43 -0.43 0.00
MPP_2 2023 0.00 0.43 -0.43 0.00
STAN-1 2016 0.00 0.37 -0.37 0.00
STAN-2 2016 0.00 0.37 -0.37 0.00
STAN-3 2016 0.00 0.37 -0.37 0.00
STAN-4 2016 0.00 0.37 -0.37 0.00
TARONG1 2016 0.00 0.35 -0.35 0.00
TARONG2 2016 0.00 0.35 -0.35 0.00
TARONG3 2016 0.00 0.35 -0.35 0.00
TARONG4 2016 0.00 0.35 -0.35 0.00
TNPS1 2016 0.00 0.44 -0.44 0.00
APS 2016 0.00 0.16 -0.16 0.00
HWPS1 2016 0.00 0.20 -0.20 0.00
HWPS2 2016 0.00 0.20 -0.20 0.00
HWPS3 2016 0.00 0.20 -0.20 0.00
HWPS4 2016 0.00 0.20 -0.20 0.00
HWPS5 2016 0.00 0.20 -0.20 0.00
HWPS6 2016 0.00 0.20 -0.20 0.00
104
HWPS7 2016 0.00 0.20 -0.20 0.00
HWPS8 2016 0.00 0.20 -0.20 0.00
LOYYB1 2016 0.00 0.48 -0.48 0.00
LOYYB2 2016 0.00 0.49 -0.49 0.00
LYA1 2016 0.00 0.55 -0.55 0.00
LYA2 2016 0.00 0.55 -0.55 0.00
LYA3 2016 0.00 0.55 -0.55 0.00
LYA4 2016 0.00 0.55 -0.55 0.00
MOR1 2016 0.00 0.07 -0.07 0.00
MOR2 2025 0.00 0.03 -0.03 0.00
YWPS1 2016 0.00 0.36 -0.36 0.00
YWPS2 2016 0.00 0.36 -0.36 0.00
YWPS3 2016 0.00 0.38 -0.38 0.00
YWPS4 2016 0.00 0.38 -0.38 0.00
NPS1 2016 0.00 0.27 -0.27 0.00
NPS2 2016 0.00 0.27 -0.27 0.00
CAN Wind 2016 0.80 0.00 0.80
1,803.51
CAN Wind T2 2020 0.52 0.00 0.52
1,022.17
CAN Wind T2 2021 0.28 0.00 0.28
535.52
NCEN Wind 2019 0.31 0.00 0.31
630.22
NNS Wind 2016 0.31 0.00 0.31
703.87
NNS Wind T2 2020 0.31 0.00 0.31
612.91
SWNSW Wind 2016 0.13 0.00 0.13
289.56
SWNSW Wind 2017 0.38 0.00 0.38
811.34
105
SWNSW Wind T2 2021 0.29 0.00 0.29
547.82
NQ Wind 2019 0.15 0.00 0.15
307.07
NQ Wind 2020 0.11 0.00 0.11
222.24
SWQ Wind 2017 0.14 0.00 0.14
292.10
SWQ Wind 2018 0.13 0.00 0.13
267.23
CVIC Wind 2017 0.30 0.00 0.30
644.19
CVIC Wind 2018 0.24 0.00 0.24
500.54
CVIC Wind 2019 0.19 0.00 0.19
387.14
LV Wind 2019 0.08 0.00 0.08 165.11
MEL Wind 2016 0.82 0.00 0.82
1,839.49
NSA Wind 2016 0.77 0.00 0.77
1,732.96
NSA Wind 2017 0.05 0.00 0.05
100.21
NSA Wind 2018 0.02 0.00 0.02
41.60
NSA Wind 2019 0.06 0.00 0.06
115.40
SESA Wind 2016 0.23 0.00 0.23
519.85
SESA Wind 2019 0.17 0.00 0.17
334.57
SESA Wind 2020 0.06 0.00 0.06
112.92
TAS Wind 2016 0.33 0.00 0.33
742.09
106
TAS Wind 2018 0.46 0.00 0.46
945.92
TAS Wind T2 2018 0.12 0.00 0.12
249.05
TAS Wind T2 2019 0.02 0.00 0.02
45.36
TAS Wind T2 2020 0.05 0.00 0.05
99.98
TAS Wind T2 2021 0.08 0.00 0.08
151.26
CAN OCGT 2029 0.12 0.00 0.12
95.38
NCEN CCGT 2016 2.11 0.00 2.11
2,399.25
NCEN CCGT 2017 0.04 0.00 0.04
44.27
NCEN CCGT 2018 0.21 0.00 0.21
234.21
NCEN CCGT 2019 0.17 0.00 0.17
194.36
NCEN CCGT 2021 1.05 0.00 1.05
1,223.96
NCEN CCGT 2022 0.10 0.00 0.10
123.30
NCEN CCGT 2023 0.15 0.00 0.15
182.30
NCEN OCGT 2025 0.09 0.00 0.09
77.00
NCEN OCGT 2026 0.11 0.00 0.11
90.10
NCEN OCGT 2027 0.06 0.00 0.06
50.34
NCEN OCGT 2028 0.12 0.00 0.12
94.68
NCEN OCGT 2029 0.02 0.00 0.02
14.69
107
Can Biomass 2016 0.10 0.00 0.10
520.25
NCEN Biomass 2015 0.10 0.00 0.10
520.25
NCEN Solar PV (SFP) 2020 0.20 0.00 0.20
185.08
NNS Biomass 2017 0.10 0.00 0.10
520.25
CQ CCGT 2015 2.27 0.00 2.27
2,595.27
CQ CCGT 2016 2.21 0.00 2.21
2,511.95
CQ CCGT 2019 0.08 0.00 0.08
93.25
CQ CCGT 2023 0.62 0.00 0.62
745.37
CQ CCGT 2025 0.22 0.00 0.22
279.22
CQ CCGT 2026 0.13 0.00 0.13
170.18
CQ CCGT 2028 0.04 0.00 0.04
49.90
CQ CCGT 2029 0.38 0.00 0.38
499.24
SEQ CCGT 2016 1.02 0.00 1.02
1,160.99
SEQ CCGT 2017 0.14 0.00 0.14
158.36
SEQ CCGT 2018 0.04 0.00 0.04
46.06
CQ Solar CLF (FSP) 2020 0.02 0.00 0.02
38.04
NQ Biomass 2020 0.01 0.00 0.01
54.53
NQ Biomass 2021 0.09 0.00 0.09
465.72
108
NQ Solar PV (SFP) 2018 0.20 0.00 0.20
226.11
NQ Solar PV (SFP) 2019 0.20 0.00 0.20
205.24
NQ Solar PV (SFP) 2020 0.20 0.00 0.20
185.08
SWQ Solar PV (SFP) 2020 0.20 0.00 0.20
185.08
SESA Biomass 2018 0.10 0.00 0.10
520.25
SESA Biomass 2019 0.10 0.00 0.10
520.25
SESA Biomass 2020 0.09 0.00 0.09
465.72
SESA Biomass 2021 0.01 0.00 0.01
54.53
LV CCGT CCS 2028 0.02 0.00 0.02
48.15
LV CCGT CCS 2029 0.08 0.00 0.08
212.55
LV CCGT CCS 2030 0.09 0.00 0.09
236.77
MEL CCGT CCS 2026 0.15 0.00 0.15
367.30
MEL CCGT CCS 2027 0.21 0.00 0.21
521.99
MEL CCGT CCS 2028 0.15 0.00 0.15
378.88
NVIC CCGT 2016 3.21 0.00 3.21
3,655.93
NVIC CCGT 2017 0.17 0.00 0.17
187.50
NVIC CCGT 2018 0.02 0.00 0.02
21.08
NVIC CCGT 2019 0.03 0.00 0.03
31.81
109
NVIC CCGT 2021 0.18 0.00 0.18
207.21
NVIC CCGT 2022 0.11 0.00 0.11
133.18
NVIC CCGT 2023 0.09 0.00 0.09
111.40
NVIC CCGT 2024 0.07 0.00 0.07
86.08
NVIC CCGT 2025 0.16 0.00 0.16
206.03