optimisation in power system planning - energy...
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UNIVERSITY OF ADELAIDE
SCHOOL OF ELECTRICAL AND ELECTRONIC ENGINEERING
Optimisation in power system
planning Final Report
(Due at: October 2012)
Supervisor: Dr. Rastko Zivanovic
Students : Chengwang Yu (1215980)
Nianlun Yu (1218019)
Yi-Li Liao (1158760)
i
Acknowledgements
This research project would not have been possible without the support of many
people. We wish to express our gratitude to the supervisor, Dr Rastko Zivanovic who
provided invaluable technical and academic skill support and guidance to us. The
deepest appreciation is also to the school of Electrical and Electronics engineering
staffs and faculty members who were really helpful for our final project: Dr. Danny
Gibbins, Mr. David Bowler, Mr. Mark Innes and Mr. Ryan King.
Special thanks also to all consultants involved in our project, who have been very
supportive to us: Ms. Lian Chen and Mr. Bradley Harrison from ElectraNet Pty Ltd; as
well as Mr. Vincent Tripodi and Mr. Felipe Valdebenito from Energy Exemplar Pty
Ltd.
We want to articulate my thankfulness to our great parents; who praise me for every
progress I had made and dedicated all their life to support me without asking
anything as return.
Last but not the least, to each one in the project team, we had all owed words of
gratefulness and apology for all the good and difficult moments during this year.
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Executive Summary
The final report provides the detailed software configuration in demonstrating the
application of PLEXOS in Australian National Electricity Market (NEM). This project
aims to explore the modulation and optimisation methods for regulatory electricity
market with the assistance from power system market modelling software: PLEXSO.
Some parts of Australian transmission networks model has been tested during the
project. The demonstration results about that will be analysed to determine the
project accomplishment.
iii
Acronyms
AEMO Australian Energy Market Operator
AER Australian Energy Regulator
CT Current Transformer
DNSP Distribution network service provider
ERIG Energy Reform Implementation Group
FCAS Frequency control ancillary service
JFS Joint Feasibility Study
LT Long Term
NCAS Network control ancillary services
NEL National Electricity Law
NEM National Electricity Market
NER National Electricity Rules
NSW New South Wales
RIT-T Regulatory Investment Test for Transmission
SA South Australia
SRAS System restart ancillary service
TNSP Transmission Network Service Provider
VIC Victoria
Table of Contents
CHAPTER 1: INTRODUCTION .............................................................................................................. 1
1.1. PROJECT OBJECTIVES ........................................................................................................................... 1
1.2. BACKGROUND ................................................................................................................................... 1
1.3. SCOPES ............................................................................................................................................ 2
1.4. SYSTEM FLOW CHART ......................................................................................................................... 2
1.5. TASK ALLOCATIONS ............................................................................................................................. 3
CHAPTER 2: LITERATURE RESEARCH ................................................................................................... 5
2.1. NATIONAL ELECTRICITY MARKET ........................................................................................................... 5
2.1.1. NEM structures ..................................................................................................................... 5
2.1.1.1. Australia Energy Market Operator ................................................................................................ 5
2.1.1.2. Other NEM participators ............................................................................................................... 6
2.1.1.3. Ancillary services ........................................................................................................................... 6
2.1.1.4. Advantages and disadvantages of the structure ........................................................................... 7
2.2. REGULATORY INVESTMENT TEST FOR TRANSMISSION ................................................................................ 7
2.3. POWER SYSTEM FORECASTING FACTORS .................................................................................................. 8
CHAPTER 3: THE NETWORK MODEL ................................................................................................. 10
3.1. MODEL BACKGROUNDS ..................................................................................................................... 10
3.1.1. Generation .......................................................................................................................... 10
3.1.2. South Australian to Victoria energy export ......................................................................... 11
3.1.3. Victoria to South Australia import study results ................................................................. 12
3.1.4. Incremental option .............................................................................................................. 13
3.1.5 Intraregional network augmentation .................................................................................. 15
3.1.6 Least-cost optimisation ................................................................................................................... 18
3.2 DEBUGGING ..................................................................................................................................... 19
CHAPTER 4: INTERFACE DESIGN ....................................................................................................... 27
4.1. BRIEF ............................................................................................................................................. 27
4.2. PSS®E ........................................................................................................................................... 27
4.3. PYTHON ......................................................................................................................................... 28
4.4. DESIGN .......................................................................................................................................... 28
4.4.1. Data Extraction ................................................................................................................... 28
4.4.2. Data processing................................................................................................................... 31
4.4.3. CSV generation .................................................................................................................... 32
4.5. FUTURE DEVELOPMENT ..................................................................................................................... 33
5. 3-NODE MODEL IMPLEMENTATION ............................................................................................. 34
5.1. 3-NODE MODEL PARAMETERS ............................................................................................................. 34
5.2. PSS®E INTERFACE AND SIMULATION..................................................................................................... 36
5.3. PLEXOS INTERFACE AND SIMULATION ................................................................................................. 38
CHAPTER 6: PROJECT MANAGEMENT .............................................................................................. 46
6.1. DETAIL PROJECT PHASE DESCRIPTION .................................................................................................... 46
6.2. KEY MILESTONES .............................................................................................................................. 49
6.3. RISKS ANALYSIS ................................................................................................................................ 50
6.3.1. Encounter issue ................................................................................................................... 50
6.3.2. Risk overcome ..................................................................................................................... 51
CONCLUSION ................................................................................................................................... 53
REFERENCES ..................................................................................................................................... 54
APPENDIX A – REDUCED NODAL MODEL .......................................................................................... 57
APPENDIX B – AUGMENTATION OPTIONS ....................................................................................... 58
APPENDIX C – GANTT CHARTS ......................................................................................................... 59
APPENDIX D - RISKS DESCRIPTION AND SOLUTIONS ........................................................................ 61
APPENDIX E - RISKS ANALYSIS .......................................................................................................... 63
Table of Figures
FIGURE 1: SYSTEM FLOW CHART ........................................................................................................ 3
FIGURE 3.1: POWER FLOW WARNING .............................................................................................. 19
FIGURE 3.2: WARNING TRANSMISSION LINE .................................................................................... 20
FIGURE 3.3: WRONG DATA SETTING ................................................................................................ 20
FIGURE 3.4: FIXED POWER FLOW SETTING ....................................................................................... 21
FIGURE 3.5: TIMESLICE SETTING ...................................................................................................... 22
FIGURE 3.6: REMAINING WARNINGS ............................................................................................... 24
FIGURE 3.7: MEMBERSHIP SETTING FOR BANNABY ......................................................................... 24
FIGURE 3.8: MEMBERSHIP SETTING FOR CANBERRA ........................................................................ 25
FIGURE 3.9: MEMBERSHIP SETTING FOR CAPITAL ............................................................................ 25
FIGURE 3.10: MEMBERSHIP SETTING FOR MARULAN ...................................................................... 25
FIGURE 3.11: REMAINING ERROR..................................................................................................... 26
FIGURE 4.1: EXECUTION LIFECYCLE OF THE INTERFACE PROGRAM .................................................. 28
FIGURE 5.1: THE SINGLE LINE DIAGRAM OF THE 4-BUSES SYSTEM ................................................... 34
FIGURE 5.2: PSSE INTERFACE ............................................................................................................ 36
FIGURE 5.3: CREATE BUS DATA ........................................................................................................ 36
FIGURE 5.4: CREATE BRANCH DATA ................................................................................................. 37
FIGURE 5.5: CREATE LOAD DATA ...................................................................................................... 37
FIGURE 5.6: CREATE GENERATOR DATA ........................................................................................... 37
FIGURE 5.7: CREATE TRANSFORMER ................................................................................................ 37
FIGURE 5.8: BUS CODES SETTING ..................................................................................................... 37
FIGURE 5.9: REPORT OF RESULT ....................................................................................................... 38
FIGURE 5.10: SINGLE LINE DIAGRAM OF SAMPLE POWER SYSTEM .................................................. 38
FIGURE 5.11: INTERFACE OF PLEXOS ................................................................................................ 39
FIGURE 5.12: CREATE A NEW REGION .............................................................................................. 40
FIGURE 5.13: CREATE NODE ............................................................................................................. 40
FIGURE 5.14: LOAD PARTICIPATION FACTOR SETTING ..................................................................... 40
FIGURE 5.15: CREATE NODE[REGION] RELATIONSHIP ...................................................................... 41
FIGURE 5.16: CREATE TRANSMISSION LINES .................................................................................... 41
FIGURE 5.17: LINES SETTING ............................................................................................................ 41
FIGURE 5.18: CREATE GENERATORS ................................................................................................. 42
FIGURE 5.19: CREATE TRANSFORMER .............................................................................................. 42
FIGURE 5.20: CREATE CONSTRAINTS ................................................................................................ 42
FIGURE 5.21: INPUT DATA ................................................................................................................ 42
FIGURE 5.22: REGION LOAD FILE ...................................................................................................... 43
FIGURE 5.23: INTERFACE OF SIMULATION........................................................................................ 43
FIGURE 5.24: SAMPLE RESULTS ........................................................................................................ 43
FIGURE 5.25: INTERFACE OF RESULTS REVIEW ................................................................................. 44
FIGURE 5.26: GENERATION PROPERTIES .......................................................................................... 45
FIGURE 6.1: PROJECT FLOW CHART FROM THE PROJECT PLAN ........................................................ 47
Table of Tables
TABLE 1: WORK BREAK DOWN STRUCTURE ....................................................................................... 4
TABLE 3.1: AUGMENTATIONS FOR SOUTH AUSTRALIA TO VICTORIA EXPORT LIMIT ....................... 12
TABLE 3.2: AUGMENTATIONS FOR VICTORIA TO SOUTH .................................................................. 13
TABLE 3.3: MARKET BENEFITS OF THE INCREMENTAL OPTION ......................................................... 14
TABLE 3.4: TIMING OF THE INCREMENTAL OPTION WHEN ENTERED ALONE .................................... 14
TABLE 3.5: INTRAREGIONAL NETWORK REINFORCEMENT (THE FAST RATE OF CHANGE) ................. 16
TABLE 3.6: INTRAREGIONAL NETWORK REINFORCEMENT (DECENTRALISED WORLD) ...................... 17
TABLE 3.7: INTRAREGIONAL NETWORK REINFORCEMENT (OIL SHOCK AND ADAPTATION) ............. 18
TABLE 3.8: THE DEFINITION OF TIMESLICE SYMBOLS ....................................................................... 22
TABLE 3.9: TIMESLICE DEFINITION OF 20110114 JFS ELECTRANET MODAL ....................................... 23
TABLE 5.1: BUSES IN THE SAMPLE POWER SYSTEM .......................................................................... 35
TABLE 5.2: GENERATORS IN THE SAMPLE POWER SYSTEM .............................................................. 35
TABLE 5.3: LOAD IN THE SAMPLE POWER SYSTEM ........................................................................... 35
TABLE 5.4: LINES IN THE SAMPLE POWER SYSTEM ........................................................................... 35
TABLE 6.1: LIST OF PROJECT PRODUCTS ........................................................................................... 50
TABLE 6.2: SUPPOSED RISKS FROM THE PROJECT PLAN ................................................................... 50
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Chapter 1: Introduction
1.1. Project objectives
The network model provided by ElectraNet Pty Ltd (ElectraNet) is very huge and
complicated, which involves the whole Australian transmission network including the
data of the generators, transmission lines, interconnections and transformers
located in the most region of Australia such as New south wale, Victoria, South
Australia, Queensland, Tasmania, and Capital Territory. The aim of this project is to
optimise the transmission network based on the concept of Australian regulatory
investment for transmission network (RIT-T). Since this project is a systematic work,
that will being divided as several steps including initial literature research, network
model review, network model debugging, communication interface design for Power
System Simulator for Engineering (PSS®E) and PLEXOS, then finally the detailed
system planning optimisation. As the first research team who touch on such project,
research group concentrated on some initial preparing works of this project such as
background research, PLEXOS interface and function review, fixing some setting bugs
happened in the original network model, and PSS®E and PLEXOS communication
interface design. Based on these finding during the progress of this project, the
research team laid down the project report as the relevant project guideline and
some useful advises for the students who will continue to this project.
1.2. Background
Australian electricity market is a regulatory wholesale national electricity market
whose operation is based on a gross pool mode with ex-ante settlement. The
wholesale electricity market is monitored by the Australian Energy Regulator (AER).
The role of AER is responsible for compliance with and enforcement of the national
electricity rules (NER). According to the electricity rules, the AER produce the RIT-T.
The RIT-T provides a single framework for all transmission investment. The
responsibility of the RIT-T is to identify the transmission investment option which
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can maximise economic benefits and maintain the balance between network
reliability and low economic cost as network upgraded.
Economic modelling is now integral part of transmission investment analysis
particularly in markets where regulatory approval is required before investment can
proceed such as Australian regulatory electricity market.
Economic modelling provides insight into how new assets will affect system
reliability, operating costs and market dynamics. The analysis process includes
understanding how can get the maximum benefits of a proposed investment
compare to the whole network maintenance and upgrade costs. Regarding to this
situation, some economic utilises software are available to undertake effective
economic analysis based on spread sheet calculation about expected power flow in
system dispatch model , which model electric variables such as energy, interruptible
load and detailed complex transmission networks.
1.3. Scopes
In original design document, project scope is limited to optimise the existing power
system network by operating on PLEXOS Model that is provided by ElectraNet.
However due to the late coming of PLEXOS Model, PLEXOS License and Xpress-MP
license, there are some changes happened in the project objectives by comparing to
the previous project plan. The current project scope is:
Background literature research
Base network marketing model study and base model debugging
3 node market model designing on PLEXOS
3 node network modelling on PSS®E
The interface between PLEXOS and PSS®E Design which can produce .csv file
for PLEXOS model from PSS®E simulation outcome.
1.4. System Flow Chart
The project team is proposed to approach this project with the following step shown
in Figure 1
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* RIT-T: Regulatory investment test for transmission
Figure 1: System flow chart
1.5. Task allocations
The model provided by ElectraNet staff is a real and commercial power system
market modelling network, which includes the whole Australian transmission
networks, and this means it is hard to address up by individual. By considering the
fair workload basis, personal characteristics and the diverse talents of each member,
tasks were allocated. And apart from the tasks stated above, the team work would
together most of the time.
Project team consists of three masters students: Chengwang Yu (Ryan), Nianlun Yu
(Aaron) and Yili Liao (Phoenix). Each team member in this team has individual role in
this project. The task for each member can be demonstrated in the Table 1.
Model Finalisation
RIT-T *
System Modeling in PLEXOS
Case Investigation
System Modification in PSS®E
Modify system in
PSS®E
Feed reformed
system into PLEXOS
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Member task
Chengwang Yu (Ryan)
PLEXOS Base models debugging, Communication interface design
Nianlun Yu (Aaron)
3 node network market modeling on PLEXOS and PSS®E, Communication interface design
Yili Liao (Phoenix)
3 node network investigation, Communication interface design and associated programming
Table 1: Work break down structure
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Chapter 2: Literature Research
2.1. National Electricity Market
Before the 1900s, the historical Australian power system was in a regulated
monopoly structure; and under such structure, power demand of all the customers
in the nation was expected to be settled by a single electricity service provider.
However due to the fact that customers desire a better power efficiency and lower
electricity price; from March 1990, the reform of Australian electricity industry was
gradually processed and attempt to a new formation called deregulated market
structure. The name NEM made its debut on December 1998 [Wikipedia, 2012]; and
since then, Australian wholesale electricity market and its associated synchronous
electricity transmission grid are all under this name.
With the contribution of all market participators, Nowadays, NEM is able to provide
reliable electricity production about 200,000 gigawatt hours of energy each year, to
customers, approximately 19 million residents, all over the nation based on an
interconnected national grid that runs through Queensland, New South Wales, the
Australia Capital Territory, Victoria, South Australia and Tasmania.
2.1.1. NEM structures
2.1.1.1. Australia Energy Market Operator
To serve the purpose of managing NEM, Australia Energy Market Operator (AEMO)
was established a couples of year after in July 2009 [AEMO, 2010]. Maintaining the
wholesale market for trading electricity between generators and retailers are the
primary role of AEMO. A general power market process begins from generators
making their contribution on electricity productions and will be reported to AEMO
which is in charge of production management and transference to electricity retailer
or consumers. During this process, spot prices of power are determined by price
offers and bids in the market under supervision of AEMO.
On the other hand, ensuring a safe and reliable operation manner for Australian
power system under the NER is another major function of AEMO. To achieve this
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goal, AEMO also controls the key technical standards of the Australian power
system, including frequency, voltage, network loading and system restart process,
which are called ancillary services. Detail of ancillary services will be described later
in Chapter 2.1.1.3.
2.1.1.2. Other NEM participators
Other major participators of NEM including the following, and they all played their
own role to ensure a prosperous operation of NEM.
Distribution Network Service Providers (DNSPs)
Transmission Network Service Providers (TNSPs)
Market Customers
Traders
2.1.1.3. Ancillary services
As mention in 2.1.1.1, AEMO is responsible to provide ancillary services. To be more
specific, the term “ancillary services” describes the services which are vital to
support system capacity and energy transmission from generator to market
customer as well as maintaining dependable operations of the transmission system.
The following points indicate the main functions of ancillary services,
Automatic generation control
Governor control
Load shedding
Rapid generator unit loading
Reactive power
System restart process
Under the effect ancillary services, system would be well protected and would have
ability to encounter any disturbances occurs within the network.
NEM ancillary services are mainly divided into 3 categories, they are
Frequency control ancillary services (FCAS),
“FCAS are used by AEMO to maintain the frequency on the electrical system,
at any point in time, close to fifty cycles per second as required by the NEM
frequency standards.”
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Network control ancillary services (NCAS)
“(NCAS are used to) Control the voltage at different points of the electrical
network to within the prescribed standards; or Control the power flow on
network elements to within the physical limitations of those elements.”
System restart ancillary services (SRAS).
“SRAS are reserved for contingency situations in which there has been a
whole or partial system blackout and the electrical system must be restarted.”
[AEMO, 2010]
At present, AEMO operates 8 separate markets for the delivery of ancillary services
under these 3 categories. These ancillary services markets are the side markets
associated NEM. Under agreement with AEMO as well as rules and regulations of the
market, service providers and trader can participate in these service markets by
submitting service offer or biding service provided by others.
2.1.1.4. Advantages and disadvantages of the structure
The most noticeable advantage, which is also the fundamental purpose of NEM is,
the market structure allow competitions between market participators, and AEMO
as the market operator hence is able to select the most appropriated future
development plan from diverse proposals. Such process has the benefits of
increasing power network system efficiency by AEMO’s circumspect proposal
selection and reducing electricity prices from market competitions; so that
ultimately these revolutions can hopefully generate benefits to not only service
providers or traders but also the whole commonwealth.
Nevertheless, the disadvantage of this structure is also obvious, which is the hidden
stability risk of the market. Strictly speaking, the stability of NEM is heavily depends
on two factors, one is the compensation from AEMO and the other is the balance
between market participators. While such issue is seem to be less significant in
country with regulated monopoly structure electricity market.
2.2. Regulatory Investment Test for Transmission
According to the NER, the National Electricity Laws (NEL) and the Electricity Act,
networks which proposed by Transmission Network Service Provider (TNSPs) is
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required to satisfy power demand, reliability standards and able to generate market
benefit.
Before the debut of RIT-T, a Regulatory Test issued by AER was used to determine
the efficiency and market benefit of a specific electricity investment. It was however
got substituted by RIT-T later on in 2010 due to some amendments in NER. The
purpose of a RIT-T is identical to its previous model, Regulatory Test. Nevertheless,
the major distinctness between these two tests is “the new RIT-T process removes
the distinction between reliability-driven projects and projects motivated by the
delivery of market benefits (from the Regulatory)”, as stated by ElectraNet. By
amalgamating the reliability and market benefits sections in the Regulatory Test as
suggested by Energy Reform Implementation Group (ERIG), the new RIT-T examine
all electricity investment by providing only one framework. Hence all proposed
electricity transmission projects are now being review against both standards on
technical aspects as well as the potential of delivering broader benefits to NEM.
A typical RIT-T process flow involves publication of two to three reports, which will
submit to AER as well as publish across NEM.
Project Specification Consultation Report
Project Assessment Draft Report (if required)
Project Assessment Conclusions Report
The purposes of these reports are to serve as primary reference during the
consultation about a certain development plan between TNSPs and other NEM
participators.
2.3. Power system forecasting factors
During the first semester, the project team had attended a lecture with “Power
System Forecasting” as topic. It is provided by a guest lecturer from Monash
University, who put forward the following factors, and demonstrated their
importance in power system forecasting process.
Calender effect (resident tend to use less electricity during holiday, such as
Christmas day)
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Weather condition (usage of electricity will vary significantly due to different
weather conditions even within a short period)
Climate change (long term climate change is proven to have major impact on
electricity usage)
Economic change (global economic environment will have impact on material
prices such as carbon, which will ultimately lead to profit of power system
change from time to time)
Technology development (increasing dependence on new technology will
increase the power demand in the future, but innovation on technology is
also likely to decrease electricity usage due to efficient improve)
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Chapter 3: The Network Model
3.1. Model backgrounds
On 19 November 2010, ElectraNet and AEMO conducted a market model, which is
related to the joint feasibility study (JFS) of transmission development options that
improves the interconnector transfer capability in the region between South
Australia and other NEM load centres. Refer to Appendix A for the reduced nodal
model of the whole current network. Two northern part of Australia options
demonstrate similar characteristics. The high growth rates are modelled by the fast
rate of change scenario, which are the most pronounced in Queensland and New
South Wales. The northern options play a role in the national electricity market as
energy exporters from South Australia. For example, the expansion project of the
Olympic Dam load in this scenario leads to the reduction of congestion on the South
Australian network. In general, South Australia imports the energy from the northern
interconnectors that is located in Victoria. Refer Appendix B for graphical
presentation of these planning options.
According to the Oil shock and adaptation scenario, the northern interconnector is
operated relatively symmetrically as true two-way links, with energy import to and
export from South Australia. The incremental option and the southern option
facilitate south Australian energy export in all scenarios. Take the south options, for
example, it reaches 80% to 90% of its capability at peak [AEMO, 2011]. Between
2015 and 2020, the frequency of South Australia to Victoria flow driven by the large-
scale renewable energy target and favourable conditions for renewable generation
in South Australia will increase from 30% to 80% of the time [AEMO, 2011]. The
magnitude of power flow can be reduced during this transition through period as
dependence on coal generation in the Latrobe Valley declines.
3.1.1. Generation
Based on the growth of power demand regarding each scenario, new generation is
added into the base case. By 2020, between 14,000 MW and 21,000 MW of
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generation is set up. By 2030, the new generation will be increased to between
25,000 MW and 41,000 MW as assumption [AERO, 2010].
The major type of generation is gas generation in all of the scenarios. Since the gas
price is very high in the Oil Shock and Adaptation scenario, the new coal generation
is the most significant in that scenario, which is located in Queensland and fitted
with carbon capture and sequestration technology.
In South Australia, new generation is distributed relatively evenly around its three
planning zones, with predominant technologies being wind in the north, gas in
Adelaide and geothermal in the south east.
3.1.2. South Australian to Victoria energy export
Across all scenarios, energy export from South Australia will increase quickly to 2020,
followed by decreasing to 2025, then increase again toward 2030.
The transformer limit of a third identical transformer at Heywood would be raised to
approximately 920 MW. However, the capacity of the South East-to-Heywood 275
kV lines will limit the South Australia to Victoria transfer capacity, which has thermal
limits of 591 MVA in summer and up to 675 MVA in winter [AEMO, 2011].
Heywood interconnector can potentially achieve the export of capability of up to 650
MW without any new transmission line as the following network augmentations.
Installation of the South Australian South East regional 132 kV transmission
system real time dynamic line rating equipment and 275 kV lines from Tailem
Bend-to-South East-to-Heywood
Installation of 100 MVAr capacitor bank at South East 275 kV terminal station
In addition to these augmentations, export capability of up to 650 MW could
potentially be achieved without any new transmission lines, with the network
augmentations below:
Addition connection of 40% series compensation on the South East-Tailem
Bend lines
Setup of 100 MVAr capacitor bank and 80 MVAr static var compensator at
Tailem Bend
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Installation of a programmable logic controller based voltage control system
to switch capacitor banks at South East and at Tailem Bend
All the main 275 kV backbone transmission lines would be operated at higher design
current rating according to the location of wind and other generation sources. All
secondary system limitation such as the current transformer (CT) ratio, line traps and
over-load protection can be ignored in the incremental support studies. The follow
table demonstrates the details of augmentations that are required to increase the
export capability from Heywood, South Australia.
Case Augmentation required
Export limit (MW)
Thermal limit
Stability limit
(1) Third 500/275 kV transformer at Heywood and dynamic lie rating of Tailem Bend-to-South East lines
650 550
(2) (1) + 100 MVAr capacitor bank at 275 kV South East substation
650 700
(3) (2) + 40% series compensation of Tailem Bend-to-South East lines, + 80 MVAr SVC at Tailem Bend and reactive support at Tailem Bend
650 700
Table 3.1: Augmentations for South Australia to Victoria export limit
[AEMO, 2011]
In the network of Victorian area, the south west 500 kV corridor is worked with its
thermal capabilities. If critical levels of generation are linked on the existing
Heywood-to-Moorabool 500 kV lines, long term plans include that to build a third
Heywood-to-Moorabool 500 kV line.
3.1.3. Victoria to South Australia import study results
The additional transformer at Heywood will increase the amount of import transfer
from Victoria to South Australia, under transmission ratings, demand patterns and
generation the limitation of import would increase beyond 650 MW. However, due
to thermal limitations of the underlying 132 kV system in the southeastern part of
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South Australia, the import will be limited to approximately 490 MW. The following
alternatives could make the import capability be more.
Decoupling the parallel 132 kV network in the South East from the 275 kV
system
Installation of series compensation on the South East-tailem Bend 275 kV
lines
However, the reduction in the South Australia to Victoria export stability limitation
may be caused by decoupling the parallel 132 kV network from the 275 kV main
system. As can be seen from this case, it is clear that this option could provide
benefits for export from Victoria to South Australia. Due to absence of investigation,
this option was excluded from this JFS. The above table illustrates the augmentations
that are required to increase the import capability into South Australia from Victoria.
Case Augmentation required
Export limit (MW)
Thermal limit
Stability limit
(1) Third 500/275 kV transformer at Heywood and dynamic lie rating of Tailem Bend-to-South East lines
650 665
(2) (1) + 100 MVAr capacitor bank at 275 kV South East substation
650 685
(3) (2) + 40% series compensation of Tailem Bend-to-South East lines, + 80 MVAr SVC at Tailem Bend and reactive support at Tailem Bend
650 800
Table 3.2: augmentations for Victoria to South
[AEMO, 2011]
3.1.4. Incremental option
The incremental option`s entry timing was optimised by this market model. The
reason why entry time was optimized is an early entry for scenarios including large
amounts of new wind generation in South Australia 2017/18 and 2019/20
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respectively, and a relative late entry in other scenarios such as Fast rate of change
(2025/26) and Oil Shock and Adaptation (2029/30) [ElectraNet, 2012]. The following
table demonstrates the market benefits of the incremental option under each
scenario
Option Total cost
(PV) Gross benefit
(PV) Augmentation cost
(PV) Net benefit
(PV)
Fast rate of change 185,891 26 0 26
Decentralised World 162,116 28 0 28
Oil Shock and Adaptation
137,471 22 0 22
Green grid 185,988 88 0 88
Table 3.3: Market benefits of the incremental option
[AEMO, 2011]
The following table shows the timing of the incremental option in each scenario.
Option Build year
Fast Rate of Change 2025/26
Decentralised World 2019/20
Oil Shock and Adaptation 2029/30
Green Grid 2017/18
Table 3.4: Timing of the incremental option when entered alone
[AEMO, 2011]
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3.1.5 Intraregional network augmentation
Basically, long term planning partial line upgrades are converted into time sequential
model of the whole projects. The required network upgrades for each scenario
should be launched according to the growth of additional energy demand in some
area and the detailed location or region of new entry generation, and the special
task of network upgrades is to improve network reliability (cutting off the useless
energy section).
Actually, the considered intraregional projects are not unique method for addressing
the issue of network congestion. It is predicted that less cost options could become
feasible when the real transmission congestion or other type of thermal congestion
are happened in the network.
The largest amounts of intraregional network projects would be required as the high
energy demand growth in the fast rate of change scenario. The projects will be
produced in this kind of scenario can be demonstrated in the following table, which
also include the entry time.
Region Project Entry Year
VIC Increase ratings on Rowville-Yallourn lines 2015
VIC Increase ratings on Dederang-Mount Beauty lines 2015
VIC Increase ratings on Dederang-South Morang lines 2020
VIC Add a third 500/330 kV transformer at South Morang 2020
VIC Add a second 500/220 kV transformer for Cranbourne 2020
NSW Increase ratings on Upper Tumut-Canberra line 2020
SA Add a double circuit between Tepko and Krongart 2020
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NSW Add 500 kV double circuit between Bannaby and Kemps Creek
2025
NSW Convert Yass-Canberra to double circuit 2025
NSW Convert Kemps Creek – Sydney South to double circuit
2025
NSW Add a Kemps Creek-liverpool line 2025
NSW Increase ratings on Marulan-Dapto, Marulan-Avon and Kangaroo Valley-Dapto lines
2025
NSW Increase ratings on Yass-Marulan and Yass-Bannaby circuits
2025
VIC Add a second 500/220 kV transformer at Kielor 2025
VIC Add a second 330/220 kV transformer at South Morang
2025
NSW Increase ratings on Sydney South-Haymarket 2029
NSW Increase ratings on Sydeny South-Beaconsfield west 2029
VIC Add a fourth 500/330 kV transformer at South Morang
2029
Table 3.5: Intraregional network reinforcement (the fast rate of change)
[AEMO, 2011]
Also, some projects are required in the scenario of decentralized world. The
following table illustrates the projects and the entry time regarding these projects.
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Region Project Entry year
SA Convert Davenport-Brinkworth-Para to double circuit
2018
NSW Convert Bannaby-Sydney West to double circuit 2020
NSW Add a Bayswater-Newcastle-Eraring 500 kV doubele circuit
2020
NSW Add a third Liddell-Tamworth 330 kV circuit 2020
NSW Add a kemps Creek to Liverpool 330 kV circuit 2020
VIC Add a second 500/220 kV transformer at Kielor 2020
VIC Add a third 500/330 kV transformer at South Morang
2020
VIC Add a third Geelong-Moorabool circuit 2020
NSW Add a second Kemps Creek-Sydney South line 2025
VIC Add a second 500/220 kV transformer at Cranhoume 2025
VIC Add a second 330/220 kV transformer at South Morang
2025
Table 3.6: Intraregional network reinforcement (Decentralised World)
[AEMO, 2011]
In addition to these scenarios, some projects have been considered in the Oil shock
and Adaptation scenario. The future intraregional project has been listed in the table
below including the entry year.
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Region Project Entry Year
VIC Increase ratings on Dederang-Mount Beauty lines 2015
SA Convert Davenport-Brinkworth-Para to double circuit
2019
NSW Convert Yass-Canberra to double circuit 2025
NSW Convert Bannaby-Sydney West to double circuit 2025
NSW Convert Kemps Creek-Sydney South to double circuit 2025
VIC Increase ratings on Yass-Marulan and Yass-Bannaby circuits
2025
VIC Add a third 500/330 kV transformer at South Morang
2025
VIC Add a third Geelong-Moorabool circuit (base case) 2025
Table 3.7: Intraregional network reinforcement (Oil Shock and Adaptation)
[AEMO, 2011]
3.1.6 Least-cost optimisation
In general, least-cost algorithms involve the objective function construction which
represents entire network costs. Least-cost algorithm is used to optimize the cost of
generation and transmission assets such as cost of network maintenance and
upgrade in long term (LT) planning module by using PLEXOS [Newham, 2010].
An alternative method that is market operation investments inspect whether the
network can be worked under economic condition by testing new generator and
transmission option. This approach is very time-consuming for longer-time duration
such as 20-year period since investments in different generators and transmission
options have impact on the investments economics [Ann, 2011].
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Basically, the objective function is the sum of all of the economic costs, which can be
modeled including fuel costs, emission costs, fixed generator costs and other
network maintenance and upgrade costs [Adam, 2011]. The variables of the
objective function contain generation dispatch, line flows as well as setting of power
plants and transmission lines. Constraints can make sure that the solution could
capture the physical limitations.
3.2 Debugging
Actually, researchers were warned that there are some errors and system warnings
happened in the original case when taking over this case from ElectraNet on the end
of May. The first task for us was trying to find out the location of these errors and
warnings in the model and fix up all of them to make whole model can work under
normal condition and obtain the final analysis report.
After running the base case, the error report demonstrated that one warning that
Minimum power flow on the transmission from Para 275 to Magill 2b should be less
than 0 or equal 0. This warning can be displayed in the following screenshot.
Figure 3.1: power flow warning
For fixing up this warning, researcher checked the data setting under the
transmission line option in the main tree as shown in the figure below.
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Figure 3.2: warning transmission line
Double click the warning transmission line to find out the wrong data setting as
below.
Figure 3.3: wrong data setting
Basically, it is wrong when the setting value of maximum power flow is the same as
the setting value of minimum power flow that because if these values are same, the
power flow going through transmission line must be 1000MW. In general, the
minimum power flow cannot be set as zero since it is impossible that no power flow
through relative transmission line. However, negative power flow setting can be
acceptable since if power is imported into the node from other node that means the
power flow direction is opposite with that of reference node. For avoiding the
overload power happened in the network, researcher set the magnitude of
minimum power flow is the same as that of maximum power flow, but be negative.
The fixed setting can be demonstrated in the following the figure.
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Figure 3.4: fixed power flow setting
After reloading the model, it is obvious that the warning has been fixed up by
applying this method.
In addition to this warning, there was a serious error existing in the original case. The
error report demonstrated that failed to interpret the pattern “M1,2,7,8, WEEKEND”
which appears to have invalid meta-patterns related to timeslice “weekend”. Since it
is the first time for researcher to think about the issue involving the timeslice setting,
the members of research team have no enough relative working experience on this
section. Therefore, researcher reviewed the help document of PLEXOS that is
attached in the software installation folder. As the section about timeslice class on
the help documentation demonstrated, every data entered in the properties setting
window in PLEXOS is static or dynamic type.
In general, static data all have a constant value no matter how the time period
whereas dynamic data change over time or additionally, apply to some given period
of time. In addition to static data, dynamic data can be required based on the length
of the user-set trading period duration, which can be defined as varying from 5
minutes to 24 hours.
When some data need to be used and repeated in a pattern, basically, operator
should use the timeslice field to enter the data value, rather than make a list about
them with a serious of date-tagged entries. In general, patterns can be set into the
timeslice for the data setting by time of day, day of week, day of month, and month
of year. The following screenshot illustrates the timeslice setting on the data pane of
PLEXOS interface window.
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Figure 3.5: timeslice setting
The PLEXOS interface can classify data based on pattern as well as date. Hence, it is
best to two digits to define the simple patterns like daily value i.e. D01, D02, D03,
D04, etc, that must be mentioned that the values should display in the correct order.
The definition of these symbols can be demonstrated in the following Table.
Table 3.8: the definition of timeslice symbols
Timeslices can be created using patterns, date from, date to, and even read from a
text file. Timeslice objects can be created as needed and timeslice names can be
used in Data files, but timeslices or patterns should not be used to filter data in a
data file. The timeslice definition of 20110114 JFS ElectraNet base case that is the
model researcher worked on can be demonstrated in the following table.
Symbol Range Meaning
H 1-24 Hours of the day ( 1= midnight to 1.00am, 24=11:00pm to 12 midnight)
W 1-7 Day of week
D 1-31 Day of month
M 1-12 Month of calendar year
P 1- NUM.Trading periods in Day
Trading period of day
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Timeslice Property Value Units Pattern
off-peak include -1 - H1-6,23-24
peak include -1 - H7-24
weekday include -1 - W2-6
weekday off-peak include -1 - W2-6,H1-9,20-24
weekday peak include -1 - W2-6,H10-19
weekend include -1 - W1,7
weekend off-peak include -1 - W1,7,H1-9,20-24
weekend peak include -1 - W1,7,H10-19
Saturday include -1 - W7
Sunday include -1 - W1
Spring include -1 - M3-5,H8-18
Summer include -1 - M1-2,12,H7-19
Autumn include -1 - M3-5,H8-18
Winter include -1 - M6-8;M3-5,9-11,H1-7,19-24
Spring/Autumn include -1 - M3-5,9-11,H8-18;M1-2,12,H1-6,20-24
Table 3.9: timeslice definition of 20110114 JFS ElectraNet Modal
The error researcher met was that failed to interpret the pattern “M1,2,7,8,
WEEKEND” which appears to have invalid meta-patterns related to timeslice
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“weekend”. The researchers concentrated on the timeslice setting format in the
property panel.
Researcher tried several types of timeslice setting format, finally, it is found that the
reason why result in error here is one additional space between comma and
“weekend”. The address method is to hit control key and “f” key in the meantime
and type the wrong timeslice setting into search option for finding all of issued
setting, and then replace them as correct type. Finally, all of timeslice settings were
addressed and the base model can be run under a normal condition.
Due to the late coming of FICO, researcher had no enough time to work on the
further warnings and error which are happened after running the model we fixed at
the first step of debugging. Therefore, unfortunately, they have not been addressed
yet. However, the research members would like to give some advices to other
student who will continue to work on this project. The following screenshots
demonstrate the existing warning and errors.
Figure 3.6: remaining warnings
From the report shown in the figure above, it could be supposed that the warning is
relative to object setting. Therefore, researcher reviewed the data setting in the base
model. The following screenshots illustrate the location of warning of data setting in
model.
Figure 3.7: membership setting for Bannaby
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Figure 3.8: membership setting for Canberra
Figure 3.9: membership setting for Capital
Figure 3.10: membership setting for Marulan
Researchers suggest the student who will continue to work on this project that
changing the value in membership setting may be an effective method to fix up
these issues. That will be a huge working for them. Because as search on key word of
these warning the researcher did, the team can see that a large amount of
membership that are relative to warning. The following researchers need to take a
long time to check the membership setting for each warning.
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In addition to the warnings, one error is still happened in the base model. As the
error report demonstrated in the following screenshot of report, object reference is
not set to an instance of an object.
Figure 3.11: remaining error
Researchers have no idea about this error. The project team have tried to change the
reference element setting such as reference node setting. It is still happened in the
case unfortunately. But researchers still believe that this error may result from the
reference setting.
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Chapter 4: Interface Design
4.1. Brief
As mentioned before, there are two software, PSS®E and PLEXOS are being
employed in this project. According to the system flow chart presented in Figure 1,
Chapter 1, during the optimisation stage, data communication between PLEXOS and
PSS®E is required to achieve the project goal of meeting not only technical but also
economic standards. In this chapter, the project team will introduce the concept of
designing an interface script which is expected to process raw result data from PSS®E
into appropriable PLEXOS input data.
4.2. PSS® E
As one of the “most comprehensive, technically advanced and widely used” *SIMENS
2010] industrial standards power system simulator, the historic PSS®E has its first
appearance in 1976. PSS®E is specially designed to estimate power system
performance with multiple methodologies, which are contributed by PSS®E’s
integrated probabilistic analyses and its advanced dynamics modelling capabilities.
The ability scope of PSS®E includes primarily but not limited to power flow
calculation and power flow optimisation; it is also master in areas such as,
Balanced or Unbalanced Fault Analysis
Dynamic Simulation
Extended Term Dynamic Simulation
Open Access and Pricing
Transfer Limit Analysis
Network Reduction
Generally, actions such as model constructions and request for simulations can all be
performed via the comprehensive graphic user interface. Nevertheless, PSS®E also
supports external scripts based on different programming languages (e.g. IPLAN,
Python and FORTRAN) to import its generated solution or even extend its original
functions. Application Programming Interface (API) and associated programming
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Manual are provided by the PSS®E developer, SIMENS, to serve as secondary
development references.
In this particular project, Python was chosen by the project tem to program the
interface scripts.
4.3. Python
According to the entry statement in Wikipedia, Python is an object-oriented
interpreted programming language. The syntax of Python language is known as
remarkably clear and expressive due to the design philosophy of “beautiful”,
“explicit” and “simple” in code readability.
Furthermore, being a general purpose high level language, Python has outstanding
ability of supporting various programming paradigms, which include its fundamental
object-oriented programming as well as other paradigms such as imperative
programming, functional programming, aspect-oriented programming and generic
programming. Although Python is capable to be used in diverse non-scripting
context, in many occasions, Python serves as a scripting language. For instance, the
communication interface is designed base on Python script.
4.4. Design
The following diagram indicated the general flow of the interface execution lifecycle.
Figure 4.1: Execution lifecycle of the interface program
4.4.1. Data Extraction
In order to obtain the ultimate aim of presenting appropriate PLEXOS input data, the
first step is to extract the associated raw data from PSS®E.
Before the actual extraction process, connection between PSS®E and Python must be
produced for future communication purpose. This can be achieved by loading the
PSS®E development library modules into a particular Python script. However, since
Data Extraction
Data Processing
CSV Generation
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those PSS®E special modules are not in the collection of python pre-recognised
module, one extra step of adding the address of PSS®E development library to
Pythons path search list is required. Otherwise, errors will be complained by Python,
with messages like below,
Traceback (most recent call last):
File "C:\Python25\Lib\site-packages\pythonwin\pywin\framework\scriptutils.py",
line 310, in RunScript
exec codeObject in __main__.__dict__
File "E:\test\error_msg.py", line 1, in <module>
import psspy
ImportError: No module named psspy
To import the module properly, the following code should be used.
==========================Python script start===========================
# import essential modules for expending path search list import os import sys
# define the directory of the PSSE module PSSE_LOCATION = r"F:\Program Files\PTI\PSSEUniversity32\PSSBIN"
# add such address to the path search list sys.path.append(PSSE_LOCATION)
# notify PSSE where its library is being added os.environ['PATH'] = os.environ['PATH'] + os.pathsep + PSSE_LOCATION
# now import the psspy module which is the place where most API defined import psspy
===========================Python script end============================
After the code above, PSS®E module is considered as successfully loaded into
Python. In order to run PSS®E smoothly, one more PSS®E module called redirected
should be imported. The main function of this module is to directing output from
PSS®E to Python. In this particular project, the function psse2py() defined in the
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redirect module will be used to convert text messages appearing on the popup
windows which are created by PSS®E into Python message form and prevent those
windows from popping up.
==========================Python script start===========================
# now import the redirect module
import redirect
# text message popup windows conversion
redirect.psse2py()
# PSSE initialisation where the integer within the bracket indicate how many buses
are requested
psspy.psseinit(50)
===========================Python script end============================
The following message will showed on the complier windows as indication if PSS®E is
being initialised successfully.
PSSE University Version 32
Copyright (c) 1976-2012
Siemens Energy, Inc.,
Power Technologies International (PTI)
This program is a confidential unpublished work created and first
licensed in 1976. It is a trade secret which is the property of PTI.
All use, disclosure, and/or reproduction not specifically authorized
by PTI is prohibited. This program is protected under copyright
laws of non-U.S. countries and by application of international
treaties. All Rights Reserved Under The Copyright Laws.
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SIEMENS POWER TECHNOLOGIES INTERNATIONAL
50 BUS POWER SYSTEM SIMULATOR--PSSE University-32.1.1
INITIATED ON FRI, OCT 19 2012 2:28
The next step will be importing the working model which is pre-constructed in PSS®E
for data extraction.
==========================Python script start===========================
# define the model directory
CASE_LOCATION = r"E:\test\Untitled.sav"
# import model
psspy.case(CASE_LOCATION)
==========================Python script start===========================
If model is loaded successfully, Python will print out the following message which
indicates the status of the model.
CASE E:\test\Untitled.sav WAS SAVED ON TUE, OCT 16 2012 22:58
DEFAULT OPTIONS MODIFIED:
GRAPHICS TERMINAL TYPE: 26
After this point, all sorts of results can be generate by using different functions
stated API, such as bus properties, loads or voltages. Detail about these functions
can be found in the API document and associated programming manual provided by
SIMENS.
4.4.2. Data processing
Results estimated by PSS®E are comprehensive, and in most cases, not all the data
are meaningful to PLEXOS. Hence it is important for the interface script to pick only
the suitable data. Furthermore, in the case when PSS®E is not able to generate all
the appropriate input parameters for PLEXOS, the interface script may even need to
generate prediction values with results from PSS®E and feed them into the script’s
32 | P a g e
own algorithms which are based on some mathematics model. For instance, project
assumed PSS®E has no ability to generate results with respect to long term time
period (e.g. in the interval of half an hour or one hour), PLEXOS is however using
data versus time as its input in some simulations. In this case, the scripts should
firstly define model algorithms for each parameter that need to be estimated. The
flow of a simple case such process can be represented mathematically as below.
Where
Dinput is the predicted/estimated future value generated by the interface
script which will be feed into PLEXOS
A is the algorithms of a specific mathematics model. It can be as simple as a
linear equation or as complicate as large matrix.
Dgenerated is the current time data generated by PSS®E
On the other hand, the script is required to have ability to generate header and
other associated text label according to data property.
4.4.3. CSV generation
After all the data is in the form which PLEXOS can accept, the interface script should
ultimately convert all the data into a CSV format file. Such procedure can be
achieved by using a build-in Python CSV module to generate desire files. Code for the
CSV generation is shown below in the next page.
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==========================Python script start===========================
# import csv module
import csv
# Create a file for writing.
csv_out = open('mycsv.csv', 'wb')
# create the csv writer object.
mywriter = csv.writer(csv_out)
# for the objects in the list to form a column in the CSV file
for row in zip(YEAR_NUMBER, MONTH_NUMBER):
mywriter.writerow(row)
==========================Python script start===========================
4.5. Future development
The space for future development on this interface script is wide opened. Possible
considerations include making a GUI, making mathematic model editor to prediction
algorithms.
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5. 3-node model implementation
5.1. 3-node model parameters
As mentioned in previous chapter, a power system can be modelled by PSS®E first
and the outputs (i.e. csv file) can be feed back into PLEXOS as input data. By running
PLEXOS with specified constraints, economical benefit solution can be obtained. In
this report, the team would like to create a 4-buses power system for short-term to
simulate and illustrate the process above in detail.
To begin with, the Topology of the 3-Nodes System is shown in below figure:
Figure 5.1: The single line diagram of the 4-buses system
This is a 3-nodes power system with three generators and a one-side load. The
parameters for this network is summarized in Table 1 through Table 5 listed below
(in next page).
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Bus Number
Bus Name Bus Type Base voltage
kV Area
number Zone
Number
1 Gen Bus1 Swing Bus 230 1 1
2 Gen Bus2 Gen Bus 230 1 1
3 Bus3 Load Bus 230 1 1
4 Load Bus Load Bus 16.5 1 1
Table 5.1: Buses in the sample power system
Bus Name Rating MVA
Max Capacity MW
Qmax MVAr
Qmin MVAr
Resistance p.u.
Reactance p.u.
Gen Bus1 700 500 400 -250 0.01 0.3
Gen Bus2 700 500 400 -250 0.01 0.3
Table 5.2: Generators in the sample power system
Bus Name Load MW Load MVAr Load Model
Bus3 100 50 Constant Power
Table 5.3: Load in the sample power system
From Bus Name To Bus Name Resistance Reactance Max Flow MW
Gen Bus1 Gen Bus2 0.01 0.008 1000
Bus3 Gen Bus2 0.01 0.008 1000
Bus3 Gen Bus1 0.01 0.008 1000
Table 5.4: Lines in the sample power system
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5.2. PSS® E interface and simulation
PSS®E interface has following functions and analyses: power flow and related network functions, optimal power flow, open access, fault analysis, network equivalence, one-line diagrams and program automation. Operators can use PSS®E to introduce, modify and delete network data using a spread sheet. Key elements of the interface shown below:
Figure 5.2: PSS®E Interface
The PSS®E power flow simulator is a well-known and reliable power flow simulation
tool for simulating power systems of up to large scale in size. the team can model
the same ‘Sample Power System’ case using PSS®E.
Create bus data:
Figure 5.3: Create bus data
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Create branch data:
Figure 5.4: Create branch data
Create load data:
Figure 5.5: Create load data
Create generator data:
Figure 5.6: Create generator data
Create transformer data:
Figure 5.7: Create transformer
Bus Codes setting:
(Hint: Swing Bus code is 3, PV buses code is 2, PQ buses code is 1)
Figure 5.8: Bus codes setting
After completing the setting, select ‘power flow’ then solution in the drop-down list,
the result can be shown as:
==============================result start==============================
REACHED TOLERANCE IN 19 ITERATIONS
LARGEST MISMATCH: -0.06 MW 0.00 MVAR 0.06 MVA
AT BUS 1 [GEN BUS1 100.00]
SYSTEM TOTAL ABSOLUTE MISMATCH: 0.08 MVA
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SWING BUS SUMMARY:
BUS# X-- NAME --X BASKV PGEN PMAX PMIN
QGEN QMAX QMIN
2 GEN BUS2 100.00 -566.1 500.0 -9999.0
275.8 9999.0 -250.0
==============================result start==============================
Also, the report of the result can be exported as below:
Figure 5.9: Report of result
From the figure above, it is easy to find out the real power, reactive power and
complex power of each bus connection. Also, it shows the losses corresponding to
each direction of lines.
Finally, the team can generate the single-line diagram based on the relationship of all
the components in the system as below:
Figure 5.10: Single line diagram of Sample Power System
5.3. PLEXOS interface and simulation
Energy Exemplar's PLEXOS for Power Systems (PLEXOS Desktop Edition) simulates
the development and operation of the NEM and provides both a least-cost
development plan of generation and transmission over the long-term along with
hourly generation dispatch and transmission utilisation. The version 6.207 interface
of PLEXOS is shown as below:
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Figure 5.11: Interface of PLEXOS
The numbering above refers to:
1. Main tree with the System tree and the Simulation tree – this tree shows the
objectsin the database organized into Collections.
2. Membership tree – this tree displays all relationships between objects.
3. Properties tree – this tree lists the properties enabled for objects selected in the
Main tree.
4. Display window.
Based on the same parameters assumed before, the team created a new input
database in PLEXOS corresponding to the PSS®E model. Firstly, researcher need to
create a new Region object called ‘Sample Power System’ shown below:
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Figure 5.12: Create a new region
Then, create nodes of system:
Figure 5.13: Create node
It is noted that ‘Node’ project can be found by clicking ‘Config’ button in the top of
interface and tick it in the open window.
Define Load Participation Factors on Nodes:
As Node ‘Gen Bus2’ will carry all region loads, its Load Participation Factor will be 1
and other Node’s will be 0:
Figure 5.14: Load Participation Factor setting
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Assign Nodes to Region:
Figure 5.15: Create Node[Region] relationship
Create Transmission Lines:
Figure 5.16: Create Transmission Lines
Connect Lines:
Figure 5.17: Lines setting
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Create Generators and connect them to the Network (assume two generators are
seme that Max Capacity=500, Fuel Price=1.5, Heat Rate=10):
Figure 5.18: Create Generators
Create transformer:
Figure 5.19: Create transformer
Create constraints:
Figure 5.20: Create constraints
Create input data (Excel file):
Figure 5.21: Input data
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Define Region Load:
Figure 5.22: Region Load File
Prepare Simulation:
Figure 5.23: Interface of simulation
The results shown below:
Figure 5.24: Sample Results
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After running the model successfully, Plexos will provide a zip file with solution in the
same folder as the Sample Power System.xml file like this .
From the result above, it could be seen that the demand or generation of the power
system for a period. By multiplying the fuel price assumed before, the total cost of
generation and consumption can be obtained respectively.
The generated solution file can be opened from the File/Open menu and the results
of selected items are able to view.
Figure 5.25: Interface of results review
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For example, generation properties chart can be plotted as below:
Figure 5.26: Generation properties
Seen from the figure above, the curve shows the proportion of generation capacity
in terms of all the generators in the region. In the same way, the different charts can
be produced by using other objects.
In PSS®E, all network data components could be represented within worksheet style
tabs on the spreadsheet. Also, standard Windows capabilities for selecting and
copying text to the clip board or saving it to an external file are supported in both
views. This allows for easy transition between PSS®E and external applications such
as Excel or Word. One the other side, PLEXOS can export/import data from/to any
input database which can be written in 2 different formats, XML and Excel
Workbooks (CSV file).
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Chapter 6: Project Management
6.1. Detail project phase description
At the early stage of the project phase, the project believed that the whole project
stated in project description will be completed in one year length. As suggested by
the Dr. Rastko Zivanovic, the project team started with the project phase with
literature research about various aspect of the NEM while waiting for the computers
allocation for the team, software licenses validation as well as contact from
ElectraNet.
The initial proposal (refer to project document: Project Plan) was actually produced
before the ElectraNet visit. Due to the lack of future development information, the
project team was mainly focus on demonstrating the research and made a supposed
approach proposal in such plan document. The initial project flow from the first
submitted project plan is illustrated as below in Figure 6.1 (next page). This stated
flow stated was proposed by the team as first guess since similar approaches are also
been applied to actual planning procedures in real industrial project. In the situation
when information is not enough to generate a brand new approach, the team
believe adopting common procedure would be a safer movement. In this initial plan,
with the small area power system models provided by ElectraNet, team members
were required to simulate these systems individually on PLEXOS. Any model that is
involved has to pass technical tests to meet operational standards, before it is being
process to examination on economic aspects. Calibration should be applied if failure
occurred in technical tests. After the satisfactions on operation, team members
should perform RIT-T on each model and adjust the system to achieve maximum
economic benefit. If any major change is applied to any system during this
procedure, that particular system would needed to go through the technical test
once again to eliminate any safety issue, since safe operation is however still the
fundamental standards in system planning. Calibration and modification on both
economic and technical aspects should be continue until balance point is meet,
hence to complete the goal of optimising the power system.
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* RIT-T: Regulatory investment test for transmission
Figure 6.1: Project flow chart from the Project Plan
At late April, the project team had a visit to ElectraNet to discus about the project.
During the meeting with ElectraNet staffs, the following parts of the project were
changed or updated. First of all, instead of three individual small site systems, one
AEMO previous used nodal model already created in PLEXOS would be provided.
However such model had errors in some sections in the system and was not working
at that moment. The first task for the team would be to fix any error occurs during
simulation, advised by the ElectraNet staff, Mr. Bradley Harrison. On the other hand,
Background Research
Individual Case Investigation
System Modeling
Model Testing
RIT-T *
Calibration
Evaluation
Model Finalisation
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this AEMO nodal model involved the whole power system network in Australia and a
significant amount of purpose unknown scenarios were being set up in the software.
Secondly, the power system network planning program, PSS®E was being introduced
into this project to serve the role of network simulation tools. Since the model scope
change and the debut of PSS®E are both significant to the project, Harrison reckoned
the team would not able to complete the stated project goal. He suggested the
project should transform to a continue project and the team from the current year
should mainly focus on fixing up the error in the model on both PLEXOS and PSS®E,
as well as write interface programs for these two programs so that output data from
each software can be automatically feed into format that can be accepted by the
other software.
The project was then adjusted the project proposal and the detail approach
according to all the changes had been made during the meeting in ElectraNet. The
second attempted of system flow chart is shown previously in Figure 1, Chapter 1
and was being firstly presented in Mid-Semester Review Report Document. In the
modified project flow chart, the major revision is on the modelling and evaluation
procedure. PSS®E was being included in the progress for system modification
purpose. Note that in the initial system flow chart, there was evaluation procedure
in the RIT-T stage. Evaluation according RIT-T outcome require consideration on
judgement from AEMO and comment from other NEM participators. However as far
as this project concern, it is only on virtual basis and RIT-T document generate from
this project will not be accept by NEM. Hence the project team believes that such
evaluation procedure is out of the scope of and deleted it from the project flow.
Nevertheless, evaluation according to RIT-T outcome can still be a vital consideration
for future development if the AEMO nodal model is being applied to real power
system planning project again in future years. Gantt Charts were also initialised
according to this modified project flow chart by taken all the adjustment into
account, which is will be emitted later on in this chapter.
According to Harrison, they would not able to provide much help on this particular
AEMO nodal model due to the model is no longer being used by ElectraNet, the
project team reckoned investigation on this AEMO nodal model and get it working
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properly and interfaces design would be two largest tasks in this project. All the
points mentioned above were all being considered during the arrangement of the
Grantt Charts in Appendix C.
Due to the legal issue about confidential policy, the AEMO model was not able to be
provided by ElectraNet until the end of May. Meanwhile, the project team continued
the literature research as well as started making some small models on PLEXOS to
explore the functions of this software.
However, the new issue occurred immediately after the project team received the
AEMO model. It is PLEXOS program kept shutting down by Windows XP whenever
the team tried to execute simulation function for the AEMO nodal model. The team
tried to fix the issue by running model on different project computers, setup smaller
models, contact Energy Exemplar for assistants. The team then realised the issue
might be due to the allocated project computers were fail to meet the minimum
computer requirements stated in the PLEXOS manual. The project team placed in
couples of computer request forms during mid-year break to request better
computers, but none of the computer staffs response until late July. The team
decided to use personal laptops to run the program. At the meantime, the project
team went on investigate the Python language which is used to program the
software interfaces.
During early second semester, the project team managed to fix a major AEMO nodal
model error after the second visit to ElectraNet (Detail about the debugging refer to
Chapter 3.2). However soon after this forward movement, the team once again
suffered the lack of license issue of the analysing software FICO as well as PLEXOS.
These issues were not being fixed until Week 10 of the second semester.
6.2. Key milestones
The status of the milestones is concluded on the date 12th October and they are still
subject to development according to the progress made after this date.
In this project, numbers of official deliverables are required to be produced and
submitted by team members, as listed below.
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Deliverables
Seminar and demonstration
Project Plan (Completed) Project plan seminar (Completed)
Mid-project reviews reports (Completed) Final Seminar (Yet to be held)
Final project report (Completed) Demonstration section (Yet to be held)
Research poster (Under construction)
Documentation CD (Under construction)
Table 6.1: List of project products
6.3. Risks analysis
6.3.1. Encounter issue
In the planning document submitted early this year, numbers of risks were supposed
as shown below.
Risk
Over budget Lack of financial sponsorship
Member sickness or injury Concept fault
Lack of staff support Behind schedule
Lack of equipments Market changes
Lack of technical knowledge
Table 6.2: Supposed risks from the Project Plan
Detail risk description and supposed solution of these risks are stated Appendix D
and for associated risk analysis refer to Appendix E.
According to the progress up to now, some of these risks had indeed caused
resistance to project team.
Firstly as mentioned before, this project form has been significantly changed from its
prototype to its current form. The project team hence need to reconstruct all the
project flow design, work allocations as well as the detail timeline (Grantt Chart) for
the project after the initial plan is being submit. Besides, the increase workload
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showed the potential force of making this project to be a continue project rather
than to be completed in one year length.
Secondly, the computers with proper licensed PLEXOS installed were not allocated
until the end of week 2, Semester 1, and PLEXOS license server was being activated
in week 3. As written in previous chapter, two members suffer the error which is
PLEXOS got force quit just before it was about to execute a power system; the same
error also obsess the other member at random time during any power system
construction process.
Thirdly, the licenses for softwares were disabled during the second semester and
such issue turned out to be the most critical hit to the project progress. The issue is
due to PLEXOS program relying on a third party tool, FICO to generate simulation
report. Basically, the output data from PLEXOS are bunches of number which does
not make sense to ordinary end users. PLEXOS will then activate its internal interface
with FICO program from external server to call for output data analysis. Based on
these output data, FICO will generate some user friendly reports include result tables
and figures, eventually feedback these reports to PLEXOS for end user. Although the
communication interface with FICO is installed automatically together with PLEXOS
installation package, but license for FICO are completely isolated from PLEXOS
license, and FICO license require formal application to apply. During this year,
PLEXOS licences were expired during August. And PLEXOS were completely out of
function until PLEXOS licenses were granted again in late September, it is still
however not able to generate any simulation reports until FICO licenses were being
granted in mid-October.
6.3.2. Risk overcome
Since the project team is the first group to investigate such project, during the
project year, the team had encountered various issues. However, the project team
was managed to overcome some of the issues by forwardly communicating with
external professional engineers from ElectraNet and Energy Examplar. For instance,
for addressing the time slice setting issue on PLEXOS model. The team tried to obtain
help from Mr. Harrison, who is the power system market modelling specialist in
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ElectraNet, and finally with his help when the team visited ElectraNet again, the
issue was fixed up.
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Conclusion
Due to the incident of licenses issues, the project team was not able to finish all the
debugging works of the AEMO nodal model, however, the team had decided to
continue the study with a small 3-node model, and managed to obtain better
understanding on system modelling and market modelling.
As mentioned before, an interface is produced by the project team. However, such
interface has a bug in the section of data processing. Currently, the interface is
executable but failed to generate the correct time entry whenever year 2012 is
included in the scheme. Nevertheless the error will not occur if only 2012 is
modelled. Due to the lack of time, the team may not be able to fix the bug but the
team believes that it should only be a very simple algorithm error. On the other
hand, the mathematics model algorithms employed by the team to generate future
value is only an uncomplicated model. The model has strong bias and will not be able
to reflect the actual behaviour of real power flow. However, making a proper
mathematics model is time concerning; the team reckons it can be a future
development direction for students who continuing this project.
In addition to the suggested future work about interface design, it is advised that the
group who will continue to work on this project need to have a try to address the
remaining data setting bug in the base model, which are all mentioned in the
Chapter 3 of this report. The advises suggested by researcher could be a good guide
for future research students
From the risk met, the team realised communication skill and teamwork are both
vital in every engineering project, the whole project can be divided into several
sections and delivered to each member. Besides, for addressing big problem
happened in the project, the research team also learned that breaking big problem
into several small sections and solving them step by step is effective and efficient
method. Furthermore, acknowledge on power system market planning is also
developed during the year.
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References
1. N. Newham, “Challenges of Economic Modelling for Transmission
Investment”, Transpower NZ Ltd.
2. B. Adam, H. Gregory, N. Philip, W. Ann, “A Practical Application Of Real
Options Under The Regulatory Investment Test For Transmission”, NERA
Economic Consulting,May 2011.
3. W. Ann, G. Tom, “Assessing Competition Benefits Under The RIT-T”,
NERA Economic Consulting, 31 May 2011.
4. ElectraNet, “Lower Eyre Peninsula Reinforcement”, RIT-T: Project
Specification Consultation Report, February 2012.
5. ElectraNet, “South Australian Annual Planning Report 2011”, 2012,
available at: www.electranet.com.au.
6. AER, “Regulatory investment test for transmission”, Australian Energy
Regulator, June 2010.
7. AER, “Regulatory investment test for transmission and regulatory
investment test for transmission application guideline final decision”,
Australian Energy Regulator, June 2010.
8. AER, “Regulatory investment test for transmission application guideline”,
Australian Energy Regulator, June 2010.
9. Grid Australia, “RIT-T Cost Benefit Analysis”, Grid Australia Handbook
Version 1.1, November 2011.
10. Essential Services Commission Of South Australia, “Electricity
Transmission Code”, available at: www.escosa.sa.gov.au, 1 July 2011.
11. ElectraNet, AEMO, “ElectraNet-AEMO Joint Feasibility Study: South
Australian Interconnector Feasibility Study final report”, Available at:
www.aemo.com.au, February 2011.
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12. ElectraNet, AEMO, “ElectraNet-AEMO Joint Feasibility Studey: South
Australian Interconnector Market Modelling Report”, available at:
www.aemo.com.au, February 2011.
13. ElectraNet, AEMO, “ElectraNet-AEMO Joint Feasibility Study: South
Australian interconnector Feasibility Study Market Modelling Report”,
available at www.aemo.com.au, February 2011.
14. AERO, “An Introduction to Australia`s National Electricity Market”,
Australian Energy Market Operator, 2010.
15. AERO, “National Transmission Network Development Plan”, Australian
Energy Market Operator, 2011.
16. SIMENES, “PSS®E Product Suite”, May 2012, Available at:
www.energy.siemens.com/us/en/services/power-transmission-
distribution/power-technologies-international/software-solution/pss-e.
17. G. Arindam, “Fault Calculations in Power Systems”, Collaborative power
engineering centres of excellence, The Australian Power Institute, 2010,
available at: www.api.edu.au.
18. S. Islam, “Real and Reactive Power and Load Flow Analysis”,
Collaborative power engineering centres of excellence, The Australian
Power Institute, 2010, available at: www.api.edu.au.
19. Z.Y. Dong, “Market Simulation: Power systems supply chain fundamentals,
demand side management and forecasting”, Collaborative power
engineering centres of excellence, The Australian Power Institute, 2010,
available at www.api.edu.au.
20. Z.Y.Dong, “Supply Chain Fudamentals, Demand Side Management and
Forecasting”, Collaborative power engineering centres of excellence, The
Australian Power Institute, 2010, available at www.api.edu.au.
21. Z.Y.Dong, “Market Simulation software: PLEXOS”, Collaborative power
engineering centres of excellence, The Australian Power Institute, 2010,
available at www.api.edu.au.
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22. Z.Y.Dong, “Market Simulation: Load Forecasting and Demand Side
Management”, Collaborative power engineering centres of excellence,
2011, available at www.api.edu.au.
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Appendix A – Reduced nodal model
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Appendix B – augmentation Options
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Appendix C – Gantt Charts
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Appendix D - Risks description and solutions
Risk Description and Solution
Over budget Description: actual cost in the project is more than what is expected or allocated.
Solution: unlikely happen in this project. Discuss with supervisor if such risk does occur.
Member sickness or injury
Description: member unable to continue their work due to sicknesses or physical injuries.
Solution: member should maintain personal health by frequent exercise, and reschedule
timetable if such risk occur.
Lack of staff support
Description: staffs are not available when team members are seeking help in any area.
Solution: discuss the problem and try to solve any issue with other members, or leave the issue at
that moment and carry on the progress until staffs are available again.
Lack of equipments
Description: lack on computer, software and references etc.
Solution: if possible, try to find a substitution for that particular equipment. Whenever it is not
possible to find subsitution, discuss such issue with supervisor and other associate staffs.
Lack of technical knowledge Description: since none of the member is expert in the power system field, there might be some
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factors that member do not consider, in modeling progress
Solution: read as much reference as possible to gain knowledge, try as much as possibility as
possible to minimise error, use try and error approach, consult with supervisor or associate
professional.
Lack of financial sponsorship
Description: in later stage, this issue may occur due to the less consideration on economic
aspects.
Solution: remodel the system and take more economic aspects into account.
Concept fault Description: model fail to pass the technical test
Solution: remodel the system with different method or approach
Behind schedule Description: fall behind time schedule due to various reasons
Solution: reschedule timetable reasonably and put more effort on catching up the progress
Market changes
Description: price and other economic aspect change that may caused model become invalid
Solution: in the modeling progress, try to use as much as possible sample and review the model
frequently
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Appendix E - Risks analysis
Risk likelihood Seriousness Total score
Over budget 2 5 10
Member sickness or injury 2 9 18
Lack of staff support 5 5 25
Lack of equipments 3 9 27
Lack of technical knowledge 6 6 36
Lack of financial sponsorship 4 9 36
Concept fault 5 8 40
Behind schedule 7 7 49
Market changes 8 10 80
-