benefits of chronological optimization in capacity planning for electricity markets

8/13/2019 Benefits of Chronological Optimization in Capacity Planning for Electricity Markets

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Capturing the benefits of high performance computing for investment

decisions in electricity markets: an emphasis on capacity expansion in

a carbon-constrained and uncertain future

Charles Ikenna Nweke (BEng)

Project submitted in partial fulfilment of the requirements for the degree of

MSc (Energy and Resources)

UCL School of Energy and Resources, Australia

I, Charles Ikenna Nweke confirm that the work presented in this report is my own.

Where information has been derived from other sources, I confirm that this has

b i di d i h


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b i di t d i th t

Abstract

As worldwide demand for energy continues to grow, the need for continuous expansion of

global electricity supplythe most usable form of energy - cannot be overemphasized. The

drive to match demand with adequate supply has led to the restructuring of many

electricity regimes around the world. Also the increasing awareness of the threats posed by

greenhouse gas pollution is undoubtedly influencing operating and investment decisions in

different electricity frameworks. For these and other reasons, investment in renewable

energy technologies has resulted in major installations of wind farms across the world in

the last decade.

Meeting the demand growth in a sustainable way requires significant penetration of

renewable energy sources which could be intermittent in nature (e.g wind and solar). In

turn, capacity expansion planning (CEP) to accommodate the different technologies and

perhaps market frameworks requires a shift from orthodox methods used in the past.

Optimization models used in the past to aid investment decisions have always been

constrained by computing performance hence allowing certain level of detail.

The objective of this thesis was to assess the viability of a more thorough method of


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being modelled. Nevertheless, this work exposes the frailties of the different methods that

energy planners and analysts need to be wary of knowing that huge commitments will rely

on the outcome of their models.


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Acknowledgement

This thesis is the outcome of countless hours of dedicated work guided by the trust,

encouragement, ideas, and inspiration of others near and far. I am particularly indebted to

my supervisor Dr. Mohan Kolhe for his input and guidance from the very beginning. Also I

am very grateful to the academic staff at UCL School of Energy and Resources, Australia for

their input in preparing me for this cross-disciplinary research I dared to embark on. Of

course all these wouldnt have been a smooth sail without the impact of the non-academic

staff at UCL SERAus, particularly Ms Maria Stavrinakis, whose unflinching support and

reassurances kept me going in difficult times. I am very grateful to the management of

Energy Exemplar pty and Mr Glenn Drayton (PhD) in particular for supporting this research

and providing the platform necessary to access and utilize the endless data required for

this work. Lastly I would like to acknowledge the backing and reinforcement from my familyand friends who in one way or the other have helped me all through this programme.


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Table of Contents

Abstract...ii

Acknowledgements.iii

List of

Figures. vi

List of

Tables.....vii

Acronyms &

Abbreviations.. viii

Definition of

Terms.x

1 INTRODUCTION ................................................................................................................. 1

1.1 Problem definition and motivation .................................................................................... 2

1.2 Objective and scope of the Thesis ...................................................................................... 3

1.3 Energy Exemplars Next-generation power market simulator ........................................... 4

1 4 O i d W k Fl f Th i 5


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3 INVESTMENT DECISIONS IN ELECTRICITY MARKETS .......................................................... 34

3.1 Regulated Electricity Framework ..................................................................................... 35

3.2 Liberalized Electricity Industry ......................................................................................... 36

3.3 Implications of Capacity Imbalance .................................................................................. 39

3.4 Case Study: The Australian National Electricity Market (NEM) ....................................... 42

4 MODELLING LONG-TERM INVESTMENT DYNAMICS USING PLEXOS ................................. 45

4.1 PLEXOS LT Plan ................................................................................................................. 45

4.1.1 PLEXOS LT Plan formulation ............................................................................................. 46

4.2 Model Overview ............................................................................................................... 51

4.2.1 Bulls eye representation of model variables................................................................... 51

4.3 Model Input ...................................................................................................................... 52

4.4 Scenario Analysis .............................................................................................................. 59

4.5 General Model Hypotheses .............................................................................................. 59

5 SIMULATION AND RESULTS ............................................................................................. 61

5.1 Simulation Parameters and Conditions ............................................................................ 61

5.2 Base Case Simulations ...................................................................................................... 62

5.3 Sensitivity Analysis ........................................................................................................... 67


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List of Figures

Figure 1-1: A 3-pointer high-level thesis work flow ............................................................................................... 6

Figure 2-1: Moore's law and the Intel chip evolution in the past four decades [15] ............................................. 9

Figure 2-2: A basic modern processor configuration [22] .................................................................................... 11

Figure 2-3: Capacity expansion planning optimization [37] ................................................................................. 16

Figure 2-4: Relationship between Benders' triplet and Decision triplet [47] ....................................................... 24

Figure 2-5: South Australian demand curve for March 2011 ............................................................................... 29

Figure 2-6: Load Duration Curve representation of Figure 2-5 ............................................................................ 29

Figure 2-7: A 12-block approximation of the LDC using least-squares method ................................................... 30

Figure 2-8: Chronological representation of the load profile using 50 blocks ..................................................... 31

Figure 2-9: 200-Block representation of the demand applied in this research.................................................... 32

Figure 3-1: Regulated electricity framework ........................................................................................................ 35

Figure 3-2: Participant stock and flow diagram in a liberalized electricity framework [56] ................................ 38

Figure 3-3: The Australian wholesale market reserve margin and peak prices after liberalization [3] ............... 40

Figure 3-4: The Spanish wholesale market after liberalization [3] ........................................................ ............... 40Figure 3-5: Optimal mix and level of capacity ...................................................................................................... 41

Figure 3-6: Right capacity with sub-optimal base-load ........................................................................................ 41

Figure 3-7: Excess capacity with optimal base-load ............................................................................................. 41

Figure 3-8: The Australian NEM showing the 5 inter-connected regions [76] ..................................................... 42

Figure 3-9: Shift towards renewable energy sources in the last decade [79] ...................................................... 44

Figure 4-1: Bulls eye representation of the SA model......................................................................................... 52

Fi 5 1 B d l i l ti h i th i t k l d t t l it d i 63
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List of Tables

Table 2-1: Comparisons of grid and cloud computing ......................................................................................... 14

Table 2-2: Formats for a typical minimization linear program ............................................................................. 17

Table 4-1: LT Plan key formulation elements ....................................................................................................... 47

Table 4-2: Some common problem variables used in LT Plan .............................................................................. 48

Table 4-3: Description of variables used to define inter-temporal constraints ................................................... 50

Table 4-4: PLEXOS input objects and properties .................................................................................................. 53

Table 5-1: Successful wind penetration levels attained by 2010 compared with simulation results .................. 66Table 5-2: Comparisons of computing demands for LDC and Chrono simulations .............................................. 74

Table 8-1: New entrants build Costs ($/kW) 2010-2030 [12] ............................................................................... xii

Table 8-2: Input parameters for new entrant technologies [12] ......................................................................... xiii

Table 8-3: New entrant gas prices 2010-2030 [85] .............................................................................................. xiv

Table 8-4: Hypothetical improvements in heat rates (GJ/MWh) for new entrant thermal plants [12] ............... xiv

Table 8-5: Fossil fuels and their GHG production rates from combustion [85] ................................................... xvi

Table 8-6: CO2price trajectory ($/tCO

2-e) for financial years ending in 2030 [80] .............................................. xvi

Table 8-7: Notional LRET targets for South Australia .......................................................................................... xvii

Table 8-8:South Australian Winter maximum demand projections [79] ........................................................... xviii

Table 8-9: Summer maximum demand projections [79] ................................................................................... xviii

Table 8-10: Energy projections between 2010 and 2020 [79] .......................................................................... xviii

Table 8-11: 2009-10 base year profile [79] .......................................................................................................... xix

Table 8-12: Demand-side participation (DSP) set at different RRN prices for South Australia [80] .................... xix
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Acronyms & Abbreviations

AEMO Australian Energy Market Operator

CCGT Combined Cycle Gas Turbine

CCS Carbon Capture and Storage

Chrono Chronological

CEP Capacity Expansion Planning

CF Capacity Factor

CMOS Complementary Metal-Oxide Semiconductor

CPT C02Price Trajectory

DP Dynamic Programming

DSP Demand-side Participation

EGS Enhanced Geothermal System

ESOO El t i it St t t f O t iti


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MILP Mixed-integer Linear Programming

MLF Marginal Loss Factor

MSL Minimum Stable Level

NEM National Electricity Market (Australia)

NPV Net Present Value

NTNDP National Transmission Network Development Plan

OCGT Open Cycle Gas Turbine

OR Operations Research

POE Probability of Exceedence

RAM Random-Access Memory

RHS Right Hand Side

RRN Regional Reference Node

SA South Australia

SRMC Short Run Marginal Cost


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Definition of Terms

Auxiliary Increment (Aux Incr): This describes the auxiliary loss per megawatt of generation

in a station. It is computed from the auxiliary use which denotes the total of all station

auxiliary loads or in-house energy consumption.

Cache: A relatively small high-speed memory (usually situated within the processor) that

improves computer performance.

Capacity Factor: This percentage measures the utilization of the generation resource over a

period of time. It is defined mathematically as Generation over a period/ (Total Capacity x

time).

Carbon Shadow Price: It is the notional price, which if implemented directly (through say, a

tax) would achieve the same abatement as that targeted by the mitigation measures

actually in place. It is defined as the social cost of emitting a marginal tonne of carbon or

the social benefit of abating a tonne.

Demand-side Participation (DSP): A situation where customers vary their electricity


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Integer: Defined as a whole entity in its complete integral state

Integerization: It is a term used to describe enforcement of infrastructure

builds/retirements to remain as integer quantities in capacity expansion planning. It could

also be applied to unit commitment optimizations to ensure unit on/off decisions are

integer-enforced.

Interconnector: A line or group of transmission lines that links the transmission networks in

adjacent regions.

Intermittent: A term used commonly in this thesis to describe generating units which

outputs are not readily predictable. These include solar generators and wind turbine

generators.

Levelized Cost of Electricity: This is the lifetime discounted cost of a generating asset

expressed in cost per unit of power generated.

Marginal Loss Factor (MLF): This is a multiplier used to describe the marginal electrical

energy loss for electricity used or transmitted.

Mi i S bl L l (MSL) h l i (MW) hi h i i


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Reliability of Supply: The likelihood of having adequate capacity (generation, DSP, or both)

to meet demand.

Run rate (up/down): This is the rate at which a generating unit ramps up/down generation

level from an offline condition to the MSL and vice-versa (MW/minute). This is defined for

the pre-conditions like hot, warm, and cold states; meaning that a generator may start-up

faster when in hot state (within 48

hours of shutdown).

Short-run Marginal Cost (SRMC): This is the change in total generation costs recorded per

unit change in the quantity of power produced by a plant.

Start-up costs: This accounts for the expenses (fuel, labour, emissions, etc.) involved in

bringing a unit from an offline condition to any output level above the MSL.

Unit Commitment (UC): This refers to a sequence of generator on/off decisions made over

time (usually in the short-term). It seeks an optimal and feasible combination of on/off

decisions for all generating units across a given horizon.

Unserved Energy (USE): This defines the amount of energy that cannot be supplied due to


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1 INTRODUCTIONWorld energy demand has grown consistently over the past decades, yet one-fifth of the

worlds population are currently without access to electricity.1Global electricity demand

according to the International Energy Agency (IEA) is expected to increase by more than

200% of the 2007 level in less than half a century [1]. In similar terms energy demand and

CO2emissions could double by 2050 [1]; a path unarguably unsustainable if this demand is

to be met using conventional means alone. Addressing the global growth in electricity

demand while gradually moving towards a low-carbon economy has led to an increasing

reliance on renewable energy sources such as wind and solar energy [2]. For varying

reasons ranging from energy security to environmental concerns, many countries haveencouraged investment in renewable energy technologies which has resulted in major

installations of wind farms especially, across the United States, Europe, Australia and China

within the last decade.

Fundamentally, electricity markets over the world have experienced various forms of

i h l 20 di i l d lib li d f k


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tools that will aid in achieving minimal total costs of investment while maintaining

adequate reliability hence avoiding consumer price hikes as much as possible.

Historically, accurate decision-support models for electricity planning have been

constrained by computing power [7, 8] among other issues. Even with the fairly predictable

properties of conventional sources of generation there had been various levels of

compromise between model detail and computing requirements [7]. Following the

increasing share of intermittent generation in various parts of the world, modelling

techniques used in capacity expansion planning (CEP) are being asked to cope with the

complexities of the different technologies that are being integrated into electricity grids.

New methods are expected to maximize computing resources within their physical

constraints to ensure the highest possible accuracies for various purposes.

1.1 Problem definition and motivationElectricity generation accounts for a third of total worldwide fossil fuel use and contributes

about 41% of global energy-related CO2emissions; hence efforts to significantly curb GHG

emission will no doubt be achieved through a transformation of the sector [1]. The overall

objective of minimizing total costs in the midst of uncertainties inherent in long-term


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conventional thermal systems [7, 8]. This method reduces the resolution of system

variations such as the intermittency effects of distributed renewables on inter-temporal

impacts of outages and commissioning of thermal plants, leading to a loss of resolution

that may produce sub-optimal solutions to the original detailed problem.

Chronological modelling of the demand on other hand significantly improves the modelling

of intermittency effects on unit-commitment optimization of conventional plants [9] but at

a cost higher computational time and requirements. There are no doubts whatsoever

that the electricity mix is gradually changing (with preference given to cleaner but

unorthodox technologies like wind turbines), and at the same time there have been

significant improvements in computing performance over the years. More so historical

trends where single processor performance upgrade was paramount are tending towards

parallel multi-core systems [10]. It is in this light that this research looks into the increasingcomplexity of accurately modelling the growing percentage of intermittent generation by

effectively utilizing these advancements in computing. Retaining the chronological

information of detailed models in long-term planning is fast becoming indispensable if unit

commitment decisions are to be considered [6, 9] in finding the optimal future technology

mix in line with policies aimed at decarbonizing the world economy.


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unlocking possible benefits of recent improvements in computing performance and

infrastructures for broad purposes. This thesis focuses on answering important questions

like:

What share of unpredictable renewable injection in a market is permitted afterwhich more robust and apprehensive modelling technics will be needed in capacity

expansion planning?

How do the different levels of renewable integration affect marginal costs ofelectricity in a wholesale market with and without considering intertemporal

effects in the model; and how does it affect investment in other options?

What are the benefits of chronological modelling over the aggregated loadduration curve (LDC) approach given the present developments in computing

technology and what are the likely trade-offs assuming Moores theory holds for

the evolution of processing power in the near future?

Overall this research aims predominantly at retaining chronological information for

production and capacity constraints in a bid to show how decisions could be aided in the

long term which could potentially lead to huge savings following future investments paths.


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enabling the incorporation and conversion of real-time wind-speed forecasts into wind

generation forecasts are but some of the recent improvements in PLEXOS, allowing market

traders and operators more accurately anticipate swings of up to several thousand

megawatts in wind generation and the corresponding impact on market prices *13+.

The foundation of this research work with Energy Exemplar centred on seeking ways to

improve not only accuracy of decision support tools but also the credibility of such

imperfect but effective techniques. This research work employs the many benefits of the

constantly evolving PLEXOS platform including its use of stochastic optimisation of the

growing uncertainties helped by the efficient utilisation of Mixed integer linear

programming (MILP) techniques [14].With expert support from the Energy Exemplar team

this work effectively probes the status quo in long term planning and the orthodox

contribution of decision-support tools with respect to technology constraints. The questionof whether new methods are well overdue in the planning process makes the subject of

this report and will be investigated over the coming sections, employing high resolution

simulations on a developed South Australian test model.

1.4 Overview and Work Flow of Thesis


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In Chapter 3, the two most distinctive electricity frameworks are compared and contrasted

in relation to their explicability using discussed mathematical methods. The impact of

capacity expansion outcomes are discussed in detail, citing arguments for present and

future generations, vis--vis investment capital and payback responsibilities. Finally, a case

study of the investment trends over the period of a decade in restructured markets is

analysed with particular emphasis on the Australian NEM and policies driving the

investment patterns observed.

Initiation Phase:

Execution Phase:

Drafting of thesis body

Update Model/database

Develop and simulateexperiments/scenarios

Closing Phase:

Analyse results ofsimulations

Draw conclusions andmakerecommendations

Finalize thesis report

End


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In Chapter 5, results of the CEP simulations carried out on the described model are

compared and contrasted using LDC approximation against Chronological representation of

demand. Sensitivity analysis is performed on some key variables to show the model

appropriateness for this study as well as reflect impact of regulatory decisions on the

framework represented. Finding a compromise between model accurateness and

tractability is further discussed following analysis carried out for solving CEP problems in

chronological fashion. Chapter 6 outlines the conclusions of this research work, including

suggestions for prospective research and further investigation. The Appendix summarizes

important model data and parameters that make up the South Australian Model. Other

hypothetical data included in the model are outlined in the Appendix as well.


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2 APRACTICALPERSPECTIVEONHIGHPERFORMANCECOMPUTING(HPC)ANDOPERATIONSRESEARCH(OR)

Advancements in computing technologies particularly with the commensurate affordability

of processor components and wares have been enjoyed in virtually every work of life [15].

The past four decades has seen processor frequency grow arguably in par with Moores

predictions [15], however that steep growth seem to be stalling in recent times due to

thermal constraints with the Complementary metal-oxide semiconductor (CMOS)

technology [16]. Nonetheless the seemingly insatiable growth for more computing power

has meant that processor chip manufacturing companies like Intel and AMD are leaning

towards multi-core processing and network-distributed parallel computing, widely viewed

as cloud computing in recent times.

The application of HPC in power systems operation and planning requires significant

computing performance particularly with the need to consider more uncertainties and

variables from the liberalization of markets and the growing share of generation from


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necessarily follow the increase in computing performance as figure 2-1 shows. Note a steep

growth between 1995 and 2005 preceded by a steady growth rate in processor speed from

1971.

A more recent study by Stutter [20] on the development pattern followed by chip

compositions (number of transistors per chip), clock speed of processors, power

requirements, and the parallelization of instructions between 1970 and 2010 showed

similar and very interesting results. Whereas the number of transistors continued to

increase in exponential fashion, the other characteristics in contrast have flattened out

right from the early 2000s. These plateaux explain the difficulty chip vendors have endured

in exploiting greater clock speeds viz: excessive heat generation, increasing power

consumption as well as issues with leakage of current [20, 21]. It also means that the

advent of multicore processing has the capability of enabling higher performancecomputing while keeping power requirements fairly constant. The challenge however is

that, applications will have to be redesigned if there are any chances of taking full

advantage of the parallelization benefits multicore processors bring to the fore [20].

This work does not discuss the validity or otherwise of Moores law notwithstanding that it


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2.2 Single Processors to Parallel ComputingParallel computing and multicore computers are emerging as the preferred option needed

to deal with the computational workload from power system models [16] given the

physical constraints vendors have had to deal with in improving performance of single

processors. The irony however has been that parallel processing has not really lived up to

the expectations in terms of linear gains in performance over the single core likes. Say for

instance using a dual core chip does not necessarily provide twice the performance of asimilar single chip processor. This is partly due to coordination and handshaking between

the cores and the architecture of the applications running on the parallel processing chip

[20].

Over the past four decades performance throughput has been achieved by CPU designers

(mostly using single core processors) based on clock rate or speed, cache size, and

execution enhancement [20, 21]. In contrast, recent trends have shown an inclination

toward a multi-core or many-coresera which is being helped by improvements in cache

designs and hyper-threading abilities in chips and applications [20, 21].

2.3 Multicore Processing


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multi-threaded applications to take advantage of the performance gains. Depending on the

compatibility of the hardware configuration for various purposes, well-written multi-

threaded applications were expected to offer performance boost of up to 40% in 2005 [20].

So far Microsoft and Apple operating systems have led the race among operating

systems compatible with multicore chip processors [22].

High performance growth in computing is not only reliant on drivers like multi-cores and

hyper-threading. Feeding the high-performing cores with the required amounts of data

necessary to prevent latency and bottlenecks requires a proportional expansion of on-chip

memory and/or faster access to data in memory subsystems *21+. The notion that Cache is

kingindeed summarizes the extreme importance of the on-chip cache size in performance

gains by greatly reducing processor latency during data execution and transfer [20]. Figure

2-2 shows a modern generic processor configuration showing the relationship between theon-chip Level 1 (L1) Cache and the off-chip Level 2 (L2) Cache in a core. The challenge has

always been with increasing the sizes of the memory blocks closer to the processor given

the relative difference in their magnitudes. For example, most computer systems have

memory configuration of the range: L1 ~ 32KB, L2 ~ 2MB, Memory (RAM) ~ 4GB and Hard

Disk ~ 500 GB [22].


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dedicated caches others being shared by groups of cores or globally by all cores will have to

be done in such a way that does not counteract the expected gains in performances [21].

2.4 Grid ComputingJust like electrical grids where electric current is available for end-use in a pool injected

from centralized and distributed plants, so also does a computational grid provide

computing resources (particularly processing power and data storage systems) for carrying

out complex problems [23]. Grid computing is an unconventional form of HPC2 which

originated in the 1990s after a ground-breaking publication by Ian Foster and Carl

Kesselman [24]. This concept has since been embraced as a cost-effective option for

tackling CPU-intensive problems in organisations and also across geographical locations,

adopting what has become volunteer computing [24].

Grid computing has served to address two major needs: making IT assets remotely

available and cumulating computing power [23]. Accomplishing these needs has been

made possible through the use of applications capable of farming out bits of a program to

thousands of several heterogeneous computers, examples of which include the

SETI@home project [23, 34] and the Globus Toolkit [24, 25, 34]. Grids have been applied to


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3% of the total emissions [34]. Hence it is pertinent that the insatiable longing for more

computing performance required to optimize operations and decision-making, in a carbon-

constrained world we find ourselves at the moment, be treated with concern in order not

to incur a rebound effect in the energy efficiency of the supposed solution. This I believe is

where distributed high performance computing (DHPC) has a huge role to play through

more patronage of grid and cloud computing.

2.5 Cloud ComputingThis is similar to grid computing but it goes a step further in its method of resource

provisioning [35]. Cloud computing offers in addition to grid computing, utility models for

processing capacity, business, and software functionalities [27] as value-added services by

vendors to customers. Among Stanoevska-Slabeva et als [34] review of cloud computing

includes a definition that cloud computing

is a large-scale distributed computing paradigm that is driven by economies of scale, in

which a pool of abstracted, virtualized, dynamically-scalable, managed computing power,

storage, platforms, and services are delivered on demand to external customers over the

Internet.


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future power systems [29] arguably will depend majorly on the architecture of modelling

applications to harness its infrastructural scalability; not diminishing its perceived promise

of reducing carbon footprints of the IT industry [35].

2.6 Grid versus Cloud ComputingQuestionably two of the most common forms of DHPC being used for computationally

complex and data-intensive problems, grid and cloud computing have a handful of similar

characteristics as well as peculiarities between them. The fact that many have argued3that

cloud computing evolved from grid computing means that they easily share similarities like

their ease of scalability. For both systems, CPU, network bandwidth and storage utilization

fluctuates with user demands and are scaled through load balancing of applications running

on separate systems [35]. Another obvious similarity of both DHPC systems are their multi-

tasking and multi-tenancy attributes which allows customers to perform concurrent single

or multiple applications at various instances [35]. Some differences between grid and cloud

computing are summarized in table 2.1 below.

Table 2-1: Comparisons of grid and cloud computing


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2.7 Operation Research MethodsOperations research (OR) have applications in all works of human endeavours particularly

as it concerns decision making in economic, engineering, healthcare, military and in

transportation processes [31, 32]. Kaufmann and Faure [32] defines OR as the art of

applying precise reasoning to problems which, for a variety of reasons, cannot be

formulated in the usual precise terms of science. OR seeks to find a best or an optimal

solution out of a number of suitable practical solutions to any given problem [31]. Theroots of OR are dated as far back as during the world war II when scarce resources had to

be optimally distributed, and its application has since evolved following the increasing

intricacy and specialty of organisations from the industrial revolution up to this present

time [31].

The relevance of OR in energy planning can be traced to its numerous optimization tools,

particularly linear programming, which are now highly computer-based due to the

complexity of the tasks involved in real-life problems [31, 33]. Arguably one of the most

renowned science breakthroughs of the 20th

century, linear programming still dominates a

large share of all scientific computations handled by computers [7, 31] and can be argued


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Subject to: Physical, technical, and environmental constraints, etc

(2.1b)

Such a problem would generally be tackled using optimization tools like linear

programming or dynamic programming. Depending on the nature, size and complexity of

the problem, further optimization techniques like decomposition methods [7, 36] may be

applied to make the problem computationally tractable and efficient, whereas stochastic

optimization techniques could be employed to enhance the correlation between the model

and its real-life problem through its peculiar modelling of probabilistic elements hence

increasing accuracy [7, 33,].

Capacity expansion planning (CEP) generally refers to determining the optimal location,

capacity, and timing of new generation and/or transmission builds and retirements.

Optimization models typically seek answers to the where, how much and when the

capitally-intensive resources need to be built and/or retired by minimizing expected

expansion costs subject to operational and technical constraints [37]. The total expansion

costs here involves investment costs as well as production costs of meeting short term

demands as shown in figure 2-3. The curves give an illustration of the opposite slopes


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2.7.1.1 Linear Programming (LP)Linear programming comprises the planning of activities to achieve an optimal result,

among reasonable options, that most replicates a mathematical model [31]. Typically

aiming to maximize or minimize an objective function, LP utilizes some mathematical

assumptions like proportionality, additivity, divisibility, and certainty or determinism [31,

36] to obtain an optimal solution without violating any of the binding constraints. While the

linear formulation of most practical models satisfy majority of these assumptions, satisfying

the certainty assumption is hardly the case hence the wide application of sensitivity

analysis [31, 7].

A typical linear program is pre-formulated into either standard or canonical formats

depending on the nature of its variables and the constraints [36], after which an

appropriate mathematical algorithm solves the model. An example of these formats for a

minimization linear program from Bazaraa et al [36] is written as shown in table 2-2.

Table 2-2: Formats for a typical minimization linear program

Standard form Canonical form


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and/or retirements, fuel costs, other fixed and variable maintenance costs, environmental

externalities, as well as social costs of unserved energy. The constraints would generally

include supply/demand adequacy, capacity reserve requirements (based on forced outage

and maintenance rates of the plants), emission constraints, and other system technical

restrictions like transmission line capacity.

The earliest applications of optimization in the 1950s for capacity planning were modelled

using linear programming to find least cost expansion solutions within constrained

operational boundaries [7, 14]. Recent improvements to the LP theory now permit

automatic recovery from infeasible problems whereby the supported solvers are able to

efficiently relax some constraints in order to repair the infeasibilities [41]. However one key

challenge that linear programming faces in solving CEP problems is the presence of integer

variables associated with investment decisions [37] or unit starts/stops decisions [43] forinstance, as well as non-linear constraints that define power flow equations [37]. In

summary some major shortcomings of linear programming with respect to CEP that make it

less attractive as a stand-alone method include the following [7]:

1. Its tendency to approximate or round up discrete elements like the capacity of


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2.7.1.2 Mixed integer linear programming (MILP)This is an enhanced form of linear programming which is able to handle problems wheresome of the decision variables are restricted to integers in the optimal solution [37]. An

MILP generally expands the scope or size of an optimisation problem by the inclusion of

integrality constraints which otherwise would be treated by a linear relaxation (LR)

program. This ensures that some limitations of LP as described in (1), (2), and (5) above are

treated more accurately; of course at exponential computational costs [37, 7]. For instance

binary decision variables (whether to build or not to build, to start or shutdown plants, etc.)

makes CEP a combinatorial optimization problem whose solution space grows

exponentially with the variables [37].

To reduce the computational burden MILP-based models employ heuristics as well as the

branch and bound technique. The branch and bound algorithm works by adopting a

wishful approach that ignores the integrality constraints at the start and hopes that the LP

relaxationsolved would meet the necessary integer requirements [37, 38]. If the relaxation

does not meet the integer requirements the branch and bound method picks one of such

variables and branches, creating sub-problems with the integrality constraint of the


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Unlike linear programming, dynamic programming starts by solving a sub-problem for its

optimal solution and gradually expands the problem by finding the following optimal

solution from the preceding one until the problem is solved completely [7, 31].

DP is characterized by the way its problems are handled. Hillier and Lieberman [31]

characterizes DP problems as follows:

They are divisible into stages where decisions are made for every stagesequentially;

Each stage is defined by a finite or infinite states representing the possibleconditions the system could take at such stages;

The policy decision at each stage serves to transform the current state to a stateassociated with the beginning of the next stage with the optimal solution of the

overall problem expected to be solved at the final stage. This way, optimal

solutions for the remaining stages are independent of decisions adopted in

preceding stages.

Possess a recursive relationship that identifies the decision for stage , given thedecision for stage , is available; in which case the solution proceeds in a


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account for this, DP algorithm applies some form of heuristic rules to simplify problems by

way of eliminating some feasible states which may affect the accuracy of the decisions

reached [37]. As an example, STRATEGIST energy modelling software reduces the

computational burden on DP problems by specifying a limit to the feasible states that is

treated in single years of a capacity expansion problem [37].

2.7.2 Decomposition MethodsDecomposition, as the name implies, refers to the breaking down of large complex

problems into smaller problems for better tractability and more efficiency while preserving

the focus of the problem [7, 33, 36]. Real-life problems including CEP applications present

linear programs with millions of columns and rows which represent the variables that need

to be reduced into sub-problems of manageable sizes. Decomposition methods (e.g

Dantzig-Wolfe technique [36], Benders method [7, 39, 47], and Langrangian relaxation [36])

are but some common ways of handling such problems. Another method of breaking down

solution complexity include the branch and bound technique [37, 38] among others.

As one of the approaches for solving optimization problems, decomposition is not an

entirely new concept. Ku [7] portrayed decomposition methods as an alternative to linear


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1962 (hence the name Benders decomposition) and reviewed by Geoffrion [48] a decade

later creating a method now known and applied across various industries as the

Generalized Benders decomposition [47]. Other forms of Benders decomposition have

been discussed based on their applications across different fields and include the stochastic

Benders decomposition [40], as well as the logic-based Benders decomposition and the

combinatorial Benders decomposition [39]. This thesis however emphasises the

generalized Benders decomposition method which has been key not only in power

systems decision problems but specifically in its algorithm similarity with CEP problem

formulations [47, 49]. Hence it offers a lot more computational efficiency (due to its ability

to exploit parallel computing); its generalized form makes it relevant to different types of

decision problems (such as in power systems operations, maintenance scheduling and in

planning); and its flexibility and scalability makes it ideal for integrating into existing

applications [47].

The generalized Benders decomposition algorithm decomposes large-scale mixed-integer

programming (MIP) problems into a master problem (which could take linear, non-linear,

integer or continuous forms) [47, 48] and a sub-problem (which may be a linear or non-

linear convex program) that serves to feedback linear constraints, known as Benders cuts to


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; and is a lower bound of the initial problem (2.3a) and is updated iteratively by the 2ndstage problem.

To check if constraint (2.3c) is satisfied based on the decision , a slack vector isintroduced to test the feasibility of the sub-problem. If any violations occur in the sub-

problem, afeasibility cut5is added to the master problem and it is then re-solved.

2ndStage:

(2.5a) (2.5b)The sub-problem decides on a feasible considering the initial constraint (2.3c) with thegiven

from the 1st stage problem. If the solution is not optimal, an optimality cut6 is

added to the master problem and it is re-solved.

These iterations are repeated as new feasibility and optimality cuts are generated until the

process converges to an optimal solution in a finite number of iterations [39].

The generalized Benders decomposition is well amenable to power system decision


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Generalized Benders approach has been proven to suit analyses involving thermal units,

limited energy and storage units, as well as non-dispatchable generators, and load

management services. Its application in generation planning using the Electric Generation

Expansion Analysis System (EGEAS) to adequately estimate incremental costs of meeting

allowed unserved energy reliability targets was acknowledged by the International Atomic

Energy Agency (IAEA) [8].

Figure 2-4: Relationship between Benders' triplet and Decision triplet [47]

The decision triplet is time-decomposedin an optimization problem as they can be treated

separately for different periods of the horizon. Also, each service in the decision triplet can

typically befunctionally-decomposed; hence leaving a lot of positives yet be leveraged with


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One application of the two-stage SP in CEP is the scenario-wise decomposition. This form of

Stochastic optimization decomposes all possible paths (or scenarios) of a problem based on

discrete probability functions which they follow [43]. This assumes that the distributions of

the random parameters are represented by discrete finite scenarios* +, withprobabilities * +. The equation (2.7) is a standard representation of a two-stagescenario-wise SIP formulation [44].

Minimize:

(2.7)Subject to:


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resultant capacity mix. Hence the problem becomes S times larger than a deterministic

case [43]; however parallelization of the optimization process by most solvers on multi-

cores ensures that such problems are not necessarily Stimes less tractable.

2.8 Dealing with Uncertainties and Imperfections inherent withDecision Support Tools

Decision making in practice is subject to different sources of uncertainty regardless of the

magnitude of the impacts of such decisions. In the same vein, models that try to mimic

reality are only simplifications of the many uncertainties that abound in real-life decision-

making. These uncertainties for instance stem from input data to a model (data

uncertainties), parameters used in simulating the model (parameter uncertainties), and the

approximations of reality that such a model represents (model uncertainties) [50]. Some

ways these uncertainties have been dealt with particularly for long-term decisions include

the use of scenario analysis and sensitivity analysis.

2.8.1 Scenario analysisThe Oxford English Dictionary defines a scenario as a sketch, outline, or description of an

imagined situation or sequence of events which could include a synopsis of future


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scenario to reflect a possible future outlook, and then assessing the implications of each

scenario [7, 37]. An example of a scenario analysis using modelling tools is the

development and assessment of the Australian Large-scale renewable energy target (LRET)

in the NEM. A scenario could be constructed to evaluate the effect of meeting the overall

LRET target in 2020 through a fifty percent contribution from investments in Wind

generation in South Australia.

2.8.2

Sensitivity analysis

Unlike scenario analysis that tries to deal with uncertainties by evaluation of hypothetical

future occurrences, sensitivity analysis is more concerned with identifying the relative

effects of the various components of a decision support process. Saltelli et al in [51] defines

Sensitivity analysis as a study of how uncertainty in the output of a model (numerical or

otherwise) can be apportioned to different sources of uncertainty in the model input. It can

be carried out to screen input and output data for a model, the model parameters, as well

as the overall effectiveness of a model for a particular purpose. A method to perform

sensitivity analysis to deal with data uncertainties is explicitly described in [52]. Bertsch et

al [50] focuses on testing the sensitivity of parameters for multi-criteria decision analysis


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Figure 2-5: South Australian demand curve for March 2011


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figure 2-7. Increasing the number of blocks per LDC as well as the concentration of LDCs in

the planning horizon improves the approximation to the original load profile. For instance

modelling with one LDC per week obviously approximates the load better than with one

LDC per month (using the same number of blocks); in which case commissioning and de-

commissioning of plants can only be made weekly or monthly respectively.


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Chronological method generally requires more blocks than the LDC approach with

minimum resolutions of two blocks per day to ensure daily peak/off-peak cycles are

captured [41]. PLEXOS approximates the time-dependant load by fitting blocks into the

curves; the more blocks defined the more temporal characteristics are accounted for in the

planning horizon and the more computational resources and time demanded by the model

simulation.


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Figure 2-9: 200-Block representation of the demand applied in this research

Comparing the slicing of the demand curve in Figure 2-8 and 2-9, one can clearly see that

using more blocks is likely to increase the accuracy of the simulation results given that the


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In this research the use of time-dependent demand profiling (chronological demand) over

aggregated load duration curves (LDC) is used to demonstrate how the present computing

systems is able to accommodate more complex and realistic models to aid better decisions.


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3 INVESTMENTDECISIONSINELECTRICITYMARKETS

Energy policies are being introduced by states and regions around the world for various

reasons including improved reliability and security of supply, environmental concerns, and

to exploit the economic benefits of competitiveness in energy markets. Electricity is

arguably the most useable form of energy, perhaps the reason why its market is very

closely linked with economic growth and social well-being of a people [1, 7, 53]. The past

four decades has recorded an average annual growth of 3.5% in electricity demand globally

[14]. The next four decades on the other hand anticipates that up to 20% of the total world

energy investments (USD 32 trillion dollars) will be committed to the electricity sector [1].

These investments when analysed individually follow market signals like prices andsupply/demand dynamics [54] as one would expect, but most importantly tend to be

influenced by the frameworks under which such markets operate such as in monopoly,

oligopoly or in liberalized markets.

The peculiar nature of investments in energy infrastructures, particularly in electricity


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the monopoly and liberalized frameworks are discussed and reviewed. The different

mechanisms currently adopted in these markets and their effects on capacity expansion

and investment decisions are discussed focusing on the Australian (NEM) perspective.

3.1 Regulated Electricity FrameworkThe idea of a regulated framework is very common in a monopolistic system whereby a

vertically integrated utility controls generation, transmission and distribution of electricity

to customers in a region. For a monopolistic electric utility, the obligation to meet demand

at a certain reliability level implies a responsibility of investing in new infrastructure [59].

Figure 3-1 shows a simplified generic representation of an electricity monopoly structure

that illustrates the relationship between a single utility and the end-users or a major

market player with monopolistic influence in a market. In a vertically integrated regulated

monopoly the utility controls all business functions and is obligated to meet entire

customer demand in the region; there is no competition hence the need for government

regulation of the monopoly to prevent abuse of power [60]. A regulated framework could

also have some form of competition particularly at the generation level however


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monopolies in practice fall, somewhere between the vertically integrated and the

unbundled monopoly described in [60].

The level of regulation in monopolized markets is seen to be critical in the scientific

representation of these markets as it reduces a great deal of uncertainties in capacity

expansion planning. With limited competition in the market, an optimization exercise

carried out for the monopolies (being regulated) should yield similar results with one

carried out by government regulators (for instance) seeking benefit maximization of allplayers in the market. This is supported by the fact that uncertainties like load demand

growth, discount and interest rates, and even future electricity prices are more harmonized

giving the planning process more of a deterministic outlook. In effect, monopolistic players

are less exposed to investment risk in exchange for lower returns while the customers bear

the consequences of bad investment decisions [59, 57]one way is through an imposition

of cross-subsidized prices on the customers12[57]. This guaranteed return for investors also

hinders technological innovation; a scenario which is unlikely to happen in the liberalized

markets where competition breeds incentives for breakthroughs [59]. The non-existence of

a market means that prices do not accurately reflect supply adequacy. In addition

investments in transmission tends towards eliminating congestion entirely which may not


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electricity for customers as well as freedom of negotiation with suppliers of choice, and a

better reflection of demand/supply relationships through the adoption of spot markets.

Whether all of these objectives have been achieved so far in the liberalized frameworks

mentioned above remains to be seen; however distortions from regulations such as the

implementation of price ceilings among others are likely hindrances to these objectives

[72]. Due to the complexity of electricity networks, horizontal unbundling of supply utilities

have more or less been carried out on the generation and retail levels. Ownership and

control of transmission and distribution infrastructure in liberalized frameworks is mainly

that of the regulatory authority or market operator who have as important a role as the

generators in the overall sustainability of unbundled markets.

Unlike in the monopolized framework, decisions to invest and retire capacity are made in a

decentralized profit-oriented move by the competitors in the industry. Market participants

are forced to draw conclusions from price signals and face numerous uncertainties which

mean imperfect foresight of future market conditions [3, 58]. This framework incurs higher

risk profiles to planners who not only have to tend with uncertainties in exogenous

variables like fuel cost and demand but also with short-term and strategic behaviours of

competitors and the regulatory authorities [67]. Future revenue streams are not


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reliability levels by the operators [64, 67]. Ironically, market dynamics and economic theory

which are the basis upon which the liberalized frameworks are proposed appear imperfect

due to inherent characteristics of energy infrastructures namely: its investment lumpiness

and the non-linear benefits of economies of scale [58].

Figure 3-2: Participant stock and flow diagram in a liberalized electricity framework [56]


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3.3 Implications of Capacity ImbalanceThe objective of an optimal expansion plan is to expand capacity to a (equilibrium) pointsuch that marginal cost exceeds the benefit of retaining the status quo [58]. There have

been a number of literatures comparing the electricity frameworks and their ways of

dealing with demand uncertainty as well as their implications on the reserve margins of

various electricity industries around the world. Ku [7] and Haynes et al [69] discussed the

pros and cons of under- and overcapacity from a CEP perspective; however, Green [68]

discusses the implications of investment in the different capacity mixes (i.e. baseload,

intermediate and peak generation) on market prices. Kilanc and Or [54] have argued based

on experiments carried out, that investments in power markets do not always go in tandem

with demand growth and capacity retirements due to imperfect foresight as well as

investment decision and construction delays among others. Here I synopsize the effects of

capacity inadequacy and imbalance that need to be considered while modelling long term

investment decisions for electricity markets.

Capacity over-design or overcapacity has enjoyed the better share of arguments over time

and have actually occurred more in practice [58, 69]. However the majority notion that


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Figure 3-3: The Australian wholesale market reserve margin and peak prices after liberalization [3]


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competitive market with respect to the demand profile in a region. Figures 3-5, 3-6, and 3-7

summarize the results of the capacity model discussed in [69].

Figure 3-5: Optimal mix and level of capacity

Total Revenue

Total Cost

Total revenue curve is higher than the

total cost beyond period T* meaning

the base-load generators will operate


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3.4 Case Study: The Australian National Electricity Market (NEM)The Australian NEM began operations in 1998 after restructuring of the previousframework leading to a competitive wholesale electricity market. It boasts of the worlds

longest interconnected power system with a distance spanning about 5,000 kilometres,

connecting five regions viz: New South Wales, South Australia, Queensland, Victoria, and

Tasmania. The NEM records electricity transactions of more than $10 billion annually in

meeting demands of over eight million users in the Eastern part of Australia. The Australian

Energy Market Operator (AEMO) is charged with operating the NEM as well as coordinating

the planning of infrastructure for the efficiency, reliability, and security of the system based

on the National Electricity Law and Rules [76].


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Marginal pricing is reflected in the incremental increase/decrease in generation or demand

in a spot market where AEMO dispatches generators18 to meet demand at 5-minute

intervals. The (marginal) dispatch prices are averaged every half-hour to determine the

spot prices for each trading interval upon which generators are paid for their supply in each

region. The spot prices though dictated by supply and demand balance (also considering

system and transmission constraints) are capped, as prescribed by the National Electricity

Rules (NER); presently at a maximum of $12,500/MWh and a minimum of negative

$1000/MWh. By way of considering system reliability and transmission constraints into

pricing, the NEM adopts an approximate form of locational pricing by the use of marginal

loss factors (MLF)19calculated at every node based on the generation to demand ratio at

the nodes20

. These locational signals inform market participants about energy variation

within the nodal locations hence guiding the operation of existing generation while

affording new entrants the opportunity of making more informed decisions regarding loss

reduction in the choice of location and technology of plants [77]; this serves the overall

efficiency of the market in a decentralised decision-making pool of participants.

While investment decisions can be seen as decentralized strategic moves by the competing

market participants in the NEM, AEMO retains planning and coordination of the market to


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Figure 3-9: Shift towards renewable energy sources in the last decade [79]

AEMO plays a vital role in consolidating the supply adequacy, network planning and future

investment opportunities through its suite of planning documents like the Electricity

Statement of Opportunities (ESOO) [79], National Transmission Network Development Plan

(NTNDP) [80], and a host other documents prepared for each regions like the South

Australian Supply and Demand Outlook (SASDO) [81]. With carbon pricing on the verge of

coming online in Australia from the next financial year [82], and its expected push on

renewable energy investment, more rigorous centralized planning is expected to inform


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4 MODELLINGLONG-TERMINVESTMENTDYNAMICSUSINGPLEXOS

Electricity markets exhibit some structural patterns and dynamics that are easily captured

by mathematical models which aid in testing the likely impacts of policy outcomes in a

system. PLEXOS LT plan offers a very flexible and rigorous environment for the formulation

and optimization of Mixed-Integer Linear Programs (MILP or MIP) that effectively mirror

the dynamics in a whole range of market frameworks. Here an electricity model21 is

developed using available data and some hypothesized data that closely mirror the South

Australian wholesale market and would be used to provide answers to questions posed in

the objectives section of this thesis; this is of course in response to the long-term dynamics

of the liberalized framework practiced in the NEM. Policies constraining carbon emissions

in the long term here in Australia are well accounted for in the model including effects of a

likely carbon pricing commencing in 2012.

This chapter discusses the PLEXOS LT Plan components which includes variables,

parameters, and the problem formulation. The model is described in detail, endogenous


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only carried out in deterministic fashion as the PLEXOS LT Plan can stochastically find the

single optimal solution in the face of uncertainties in any input like wind generation, load,

fuel prices, etc.

In modelling long-term capacity expansion for electricity markets PLEXOS captures a variety

of possibilities through its many features including expansion and retirement of

infrastructure (such as generators, AC/DC transmission lines, interfaces) in multi-stages,

physical generation and load contracts from participants directly to customers, as well asmodelling mutual exclusivity among projects. A whole range of generating options can be

modelled from conventional thermal plants (with or without CCS) to hydro (run-of-river or

with storages) down to little details like run up rates and ramp rates (depending on cooling

states of generators).

4.1.1 PLEXOS LT Plan formulationThe LT Plan formulation uses Mixed-integer programming for solving capacity expansion

problems using a set of user-defined elements and the necessary problem variables as

shown in table 4-1 and 4-2 respectively. The objective function minimizes the net present

value of build/retire costs, fixed operations and maintenance costs, and the expected


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Subject to:

Energy Balance (4.1):

Feasible Energy Dispatch Accounting for Maintenance and Forced Outage Rates (4.2):

( )

Feasible Builds/Retirements (4.3):

Integrality (4.4):


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Element Description Unit

Duration of dispatch period Hours Overnight build cost of generator $/KW Maximum number of units of generator allowed to

be built by the end of year

Maximum generating capacity of each unit of

generator MW

Number of installed generating units of generator Value of lost load (energy shortage price) $/MWh

Short-run marginal cost of generator

which is

composed of [Heat Rate] [Fuel Price] + [VO&M

Charge]

$/MWh

Fixed operations and maintenance charge ofgenerator $/KW/year

Average power demand in dispatch period MWSystem peak power demand in year MW


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Variable Description

Unserved energy in dispatch period Capacity shortfall in year Excess capacity in year

Constraints (4.1), (4.2), and (4.3) define minimum constraints that typically distinguish

capacity expansion planning from mid- and short-term planning exercises. The integrality

constraint in (4.4) makes the problem more realistic in representing the lumpiness of the

investments, mothballing, or retirement decisions; however it also increases the complexity

of the problem, although with MIP-compliant solvers such problems are tractable. The LT

Plan formulation by default does not require reserve margin constraints in (4.5) (even

though it will be involved in one of the scenarios developed in the model for this thesis)

which means that a trade-off between shortage costs24and the economics of expansion

determines whether infrastructure is built or not; and the resulting reserve margin

may/may not meet reliability standards.


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GenTurnOff (t):

GenOffk>= GenMDTt for k=tto t+MDT, and all t

Start Cost and Shutdown Cost (4.9):

Objective (Minimize) (4.9.1):

Table 4-3: Description of variables used to define inter-temporal constraints

Variable Description

This is the maximum output capacity of the unit in period This decision variable represents the number of units operating in each

dispatch period,


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[ (

)] (4.0.1)

4.2 Model OverviewFor this thesis, an optimization-based model incorporating all the existing thermal and wind

generators in South Australia are modelled. The SA region is treated as a stand-alone

market but with inter-regional transfers between SA and Victoria modelled as a wholesale

market object. Owing to this arrangement electricity is traded between SA and Victoria

(within seasonal interconnector transfer limits) in response to correlations between

endogenously determined prices in SA and exogenous historical Victorian averages defined

as part of the model input. This model serves the purpose of benefit maximization for all

market participants from a market operator point of view assuming a perfectly competitive

framework.

The model excludes transmission limits and congestion modelling in its optimization of

production costs within the SA region however locational losses incurred by all individual

generators are represented by the marginal loss factors as defined in [84] for the

2011/2012 financial year. Model parameters and Input data and based on industry-wide


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approximated impact of omitting transmission parameters in this model is cushioned in

build outcomes based on capacity addition limits of certain generation technologies in

different locations; just as the use of MLFs ensures that congestion isnt entirely ignored in

short-term dispatch decisions.

The SA region is modelled as an aggregated case, whereby all generators and the regional

load are connected to a node where the pool price is decided. The settlement method isnt

affected however, as outputs at generator terminals are balanced against the auxiliary

station demands and the marginal losses for the different generators. Correlating marginal

costs of generating in the SA region with hourly prices defined in the Victoria region 26

determines if power is purchased (imported) or sold (exported) depending on the seasonal

interconnector flow limits defined in the model.


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properties. These properties can be made static if fixed for the whole simulation horizon or

dynamic if more flexibility is to be introduced, particularly in temporal fashion. The

complete list of parameters and input data for every generic object used in this model is

very extensive hence provided in the Appendix. Table 4-4 highlights the generic objects

used in building this specific model as well as brief definitions of their properties and

source of information (where applicable). Intended for use with a long term planning

horizon, the input data are made as dynamic as possible to reflect not only periodic

variations but also season fluctuations and yearly trends for up to twenty years. Data which

reflect such dynamism include generator ratings; build costs, emission prices, wind and

solar profile, Victorian market prices, and fuel prices. Specific times are captured even

further with time slices defined to model peculiar outcomes on Weekdays, Weekends,

Peak, Off-peak, Summer, and Winter, as well as in combination like in Summer Off-peak for

instance.

Table 4-4: PLEXOS input objects and properties

Property Description Source of data

Generator Objects


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Max Ramp Up Maximum ramp up rate that applies between the

Min stable level and the rating

AEMO

Run Up Rate Ramp rate that applies while running the unit from

zero to Min stable level.

Hypothetical

Aux Incr Auxiliary energy consumed per unit of generation.

Also important for modelling the high auxiliary

demands of capturing and storing carbon.

ACIL Tasman

[85]

Marginal Loss

Factor

MLFs defined for each generator in relation to

locational supply/demand profiles

AEMO

FO$M Charge Annual fixed operation and maintenance charge Energy Exemplar

Firm Capacity Contribution of each generator to capacity reserves Energy Exemplar

Forced Outage A hypothetical probability of failure for each unit Energy Exemplar


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Max Units Built Maximum number of units permitted to be

constructed in aggregate over the planning horizon

reflective of transmission congestion in different

zones

AEMO

Max Units

Retired

Maximum number of units permitted to be retired

from an existing station over the horizon

AEMO

Fuel Objects

Price Price of Fuel ACIL Tasman

[85]

Transport

charge

Additional charge on fuel due to transport

requirements

AEMO

Emission Objects


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DSP Bid

Quantity

Bid quantity for demand-side participation AEMO

DSP Bid Price Bid price for demand-side participation AEMO

VoLL Value of lost load used to model the spot price

ceiling

AEMO

Price of Dump

Energy

Price of dump energy per MWh used to mirror the

spot price floor

AEMO

Generator

Settlement

Model

Determines price paid to generators for electricity

supplied

Parameters to

mirror NEM

Load

Settlement

Determines price paid by customers Parameters that

reflect the NEM


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Market Object

Price Price of imported/exported energy per MWh based

on historic prices in Victoria

AEMO

Price Scalar Scalar on Market price Hypothetical

Price Incr Increment to dynamic market prices Hypothetical

Max Sales Maximum sales or exports out of the SA region

subject to seasonal line limits

AEMO

Max Purchases Maximum purchases or imports from outside the

SA region

AEMO

Constraint Objects

RHS Used to represent a value that binds the constraint Specific to


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Minimum Inertial Constraint (4.6):

Annual minimum contribution of renewable generation based on LRET (4.7):

Where:

is the left-hand side generation coefficient for each of the plants represent the dispatch from base-load and intermediate thermal units (in this

model it comprises coal plants, CCGT, and geothermal units).

represents a penalty price for violating that constraint. Where it is notincluded it means the constraint cannot be violated as in (4.7).


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4.4 Scenario AnalysisPLEXOS tool provides a flexible user interface which ensures parametric study is easilycarried out in its models to aid understanding of market dynamics, the effects of

participant strategies and system-wide policies. Focusing on the South Australian region in

the NEM, a limited number of scenarios were developed to demonstrate the sensitivity of

this model and tool, including their potential use for more practical purposes.

The scenarios were constructed to characterize certain changes on capacity expansion in

SA including: long-term demand growth patterns using different probabilities of

exceedence (POE); differing demand-side participation and related economics; carbon price

levels; system reliability dynamics represented by constraints; regional imports and

exports; the LRET targets, capacity obligations of generators and reserve margins;

Technological advancement and learning curves; not omitting annual and aggregate build

limits on certain technologies due to project exclusivity or locational congestions. The

effects of some of these scenarios are shown in the sensitivity analysis discussed in a later

section.

4.5 General Model Hypotheses


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and at no extra costs28. Priority isnt given to any projects due to congestion matrices or

proximity to transmission and mutual exclusivity of certain projects are equally not

considered. The absence of intra-regional transmission network means that losses are only

factored in generator settlements using MLFs defined for every financial year by AEMO and

this study assumes the same MLFs are maintained for the entire planning horizon. Inter-

regional transfers between SA and Victoria are modelled in response to an input price data

file dynamically scaled to reflect price increase under the carbon pricing scenarios.

The ratings for existing thermal plants are assumed to be constant for the first ten years

following data drawn from AEMOs forecastseasonal ratings for the plants in SA. However,

the thermal plants are assumed to gradually deteriorate in the following decade. For new

units however, the efficiencies are fixed for the planning span. Technological learning

curves are believed to be adequately incorporated into the model using more efficient heat

rates for new entrants (used in one of the scenarios) and in the annual build cost curves

defined for every technology. Therefore taxes and depreciation were assumed to be

factored into the build costs and annual de-rating of plants for this model.


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5 SIMULATIONANDRESULTS

In this chapter simulations are carried out on the model presented in the previous chapter

baring a few assumptions as discussed. Having been developed to show a simplified model

of the South Australian electricity framework, simulation results are analysed in terms of

the fundamental causes of the simulated results and relative behaviour rather than exact

figures reported. Most of the model outcomes have been observed in markets such as the

Australian NEM as described at the end of chapter three.

Simulation results are used to illustrate the developments thus far in HPC and my

arguments biased towards proportionate improvements in decision-making pertaining to

investments in electricity markets. This is demonstrated by the use of the relatively less

tractable chronological modelling of load demand in capacity planning compared to

orthodox methods. The long-run response of power markets to policy changes is further

exhibited showing sensitivity analysis of key variables using some developed scenarios

involving reserve margins, carbon pricing, renewable targets, and so on.


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defined as input data for different projects. This discount rate also applies perpetually

after the end of the horizon as the optimization considers on-going existence of the market

after the planning horizon ends.

Unit commitment optimization is based on linear relaxation rather than the optimal integer

production decisions to reduce the MIP problem formulation. Stochastic samples of

endogenous variables like wind profile, generator forced outages, and maintenance

outages are modelled in every simulation using Monte Carlo sampling method with the

stochastic results fixed to aid accurate comparison of the Chronological and LDC methods.

The simulation was carried out in PLEXOS 6.202 R11 environment using Xpress-MP solvers.

5.2 Base Case SimulationsThe base case includes all existing scheduled and semi-scheduled generating plants using

ratings reported by AEMO as of July 2011 and includes new entrants expected to be

commissioned by the end of 2011. Committed and proposed investments beyond 2011 are

ignored and subject to economic optimization by the algorithms. Carbon pricing is defined

to come into stream at the beginning of the 2012-13 financial year and retirement of fossil

plants is subject to fixed costs, dispatch economics, reliability levels and other


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Close observation reveals less capacity in figure (5-1b) ending at about 8000 MW compared

to 9000 MW in (5-1a) despite both reserve margins tailing out at 5% at the end of the

planning horizon related to the defined minimum level in the model.

benefits of chronological optimization in capacity planning for electricity markets

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