UNIVERSITY OF ADELAIDE SCHOOL OF ELECTRICAL AND ELECTRONIC ENGINEERING. Optimisation in power system planning. Final Report. (Due at: October 2012)

Transcription

1 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 ( ) Nianlun Yu ( ) Yi-Li Liao ( )

2 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. i

3 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. ii

4 Acronyms AEMO AER CT Australian Energy Market Operator Australian Energy Regulator Current Transformer DNSP ERIG FCAS JFS LT NCAS NEL NEM NER NSW Distribution network service provider Energy Reform Implementation Group Frequency control ancillary service Joint Feasibility Study Long Term Network control ancillary services National Electricity Law National Electricity Market National Electricity Rules New South Wales RIT-T SA SRAS TNSP VIC Regulatory Investment Test for Transmission South Australia System restart ancillary service Transmission Network Service Provider Victoria iii

5 Table of Contents CHAPTER 1: INTRODUCTION PROJECT OBJECTIVES BACKGROUND SCOPES SYSTEM FLOW CHART TASK ALLOCATIONS... 3 CHAPTER 2: LITERATURE RESEARCH NATIONAL ELECTRICITY MARKET NEM structures Australia Energy Market Operator Other NEM participators Ancillary services Advantages and disadvantages of the structure REGULATORY INVESTMENT TEST FOR TRANSMISSION POWER SYSTEM FORECASTING FACTORS... 8 CHAPTER 3: THE NETWORK MODEL MODEL BACKGROUNDS Generation South Australian to Victoria energy export Victoria to South Australia import study results Incremental option Intraregional network augmentation Least-cost optimisation DEBUGGING CHAPTER 4: INTERFACE DESIGN BRIEF PSS E PYTHON DESIGN Data Extraction Data processing CSV generation FUTURE DEVELOPMENT... 33

6 5. 3-NODE MODEL IMPLEMENTATION NODE MODEL PARAMETERS PSS E INTERFACE AND SIMULATION PLEXOS INTERFACE AND SIMULATION CHAPTER 6: PROJECT MANAGEMENT DETAIL PROJECT PHASE DESCRIPTION KEY MILESTONES RISKS ANALYSIS Encounter issue Risk overcome CONCLUSION REFERENCES APPENDIX A REDUCED NODAL MODEL APPENDIX B AUGMENTATION OPTIONS APPENDIX C GANTT CHARTS APPENDIX D - RISKS DESCRIPTION AND SOLUTIONS APPENDIX E - RISKS ANALYSIS... 63

7 Table of Figures FIGURE 1: SYSTEM FLOW CHART... 3 FIGURE 3.1: POWER FLOW WARNING FIGURE 3.2: WARNING TRANSMISSION LINE FIGURE 3.3: WRONG DATA SETTING FIGURE 3.4: FIXED POWER FLOW SETTING FIGURE 3.5: TIMESLICE SETTING FIGURE 3.6: REMAINING WARNINGS FIGURE 3.7: MEMBERSHIP SETTING FOR BANNABY FIGURE 3.8: MEMBERSHIP SETTING FOR CANBERRA FIGURE 3.9: MEMBERSHIP SETTING FOR CAPITAL FIGURE 3.10: MEMBERSHIP SETTING FOR MARULAN FIGURE 3.11: REMAINING ERROR FIGURE 4.1: EXECUTION LIFECYCLE OF THE INTERFACE PROGRAM FIGURE 5.1: THE SINGLE LINE DIAGRAM OF THE 4-BUSES SYSTEM FIGURE 5.2: PSSE INTERFACE FIGURE 5.3: CREATE BUS DATA FIGURE 5.4: CREATE BRANCH DATA FIGURE 5.5: CREATE LOAD DATA FIGURE 5.6: CREATE GENERATOR DATA FIGURE 5.7: CREATE TRANSFORMER FIGURE 5.8: BUS CODES SETTING FIGURE 5.9: REPORT OF RESULT FIGURE 5.10: SINGLE LINE DIAGRAM OF SAMPLE POWER SYSTEM FIGURE 5.11: INTERFACE OF PLEXOS FIGURE 5.12: CREATE A NEW REGION FIGURE 5.13: CREATE NODE FIGURE 5.14: LOAD PARTICIPATION FACTOR SETTING... 40

8 FIGURE 5.15: CREATE NODE[REGION] RELATIONSHIP FIGURE 5.16: CREATE TRANSMISSION LINES FIGURE 5.17: LINES SETTING FIGURE 5.18: CREATE GENERATORS FIGURE 5.19: CREATE TRANSFORMER FIGURE 5.20: CREATE CONSTRAINTS FIGURE 5.21: INPUT DATA FIGURE 5.22: REGION LOAD FILE FIGURE 5.23: INTERFACE OF SIMULATION FIGURE 5.24: SAMPLE RESULTS FIGURE 5.25: INTERFACE OF RESULTS REVIEW FIGURE 5.26: GENERATION PROPERTIES FIGURE 6.1: PROJECT FLOW CHART FROM THE PROJECT PLAN... 47

9 Table of Tables TABLE 1: WORK BREAK DOWN STRUCTURE... 4 TABLE 3.1: AUGMENTATIONS FOR SOUTH AUSTRALIA TO VICTORIA EXPORT LIMIT TABLE 3.2: AUGMENTATIONS FOR VICTORIA TO SOUTH TABLE 3.3: MARKET BENEFITS OF THE INCREMENTAL OPTION TABLE 3.4: TIMING OF THE INCREMENTAL OPTION WHEN ENTERED ALONE TABLE 3.5: INTRAREGIONAL NETWORK REINFORCEMENT (THE FAST RATE OF CHANGE) TABLE 3.6: INTRAREGIONAL NETWORK REINFORCEMENT (DECENTRALISED WORLD) TABLE 3.7: INTRAREGIONAL NETWORK REINFORCEMENT (OIL SHOCK AND ADAPTATION) TABLE 3.8: THE DEFINITION OF TIMESLICE SYMBOLS TABLE 3.9: TIMESLICE DEFINITION OF JFS ELECTRANET MODAL TABLE 5.1: BUSES IN THE SAMPLE POWER SYSTEM TABLE 5.2: GENERATORS IN THE SAMPLE POWER SYSTEM TABLE 5.3: LOAD IN THE SAMPLE POWER SYSTEM TABLE 5.4: LINES IN THE SAMPLE POWER SYSTEM TABLE 6.1: LIST OF PROJECT PRODUCTS TABLE 6.2: SUPPOSED RISKS FROM THE PROJECT PLAN... 50

10 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 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 1 P a g e

11 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 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 System Flow Chart The project team is proposed to approach this project with the following step shown in Figure 1 2 P a g e

12 Case Investigation System Modeling in PLEXOS RIT-T * Modify system in PSS E Feed reformed system into PLEXOS System Modification in PSS E Model Finalisation * 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. 3 P a g e

13 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 4 P a g e

14 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 NEM structures 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 5 P a g e

15 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 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 Ancillary services As mention in , 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. 6 P a g e

16 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 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 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 7 P a g e

17 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 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) 8 P a g e

18 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) 9 P a g e

19 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 largescale 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 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 10 P a g e

20 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 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 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 11 P a g e

21 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. Export limit (MW) Case Augmentation required Thermal limit Stability limit (1) Third 500/275 kv transformer at Heywood and dynamic lie rating of Tailem Bend-to-South East lines (2) (1) MVAr capacitor bank at 275 kv South East substation (3) (2) + 40% series compensation of Tailem Bend-to-South East lines, + 80 MVAr SVC at Tailem Bend and reactive support at Tailem Bend 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 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 12 P a g e

22 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 (2) (1) MVAr capacitor bank at 275 kv South East substation (3) (2) + 40% series compensation of Tailem Bend-to- South East lines, + 80 MVAr SVC at Tailem Bend and reactive support at Tailem Bend Table 3.2: augmentations for Victoria to South [AEMO, 2011] 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 13 P a g e

23 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, Decentralised World 162, Oil Shock and Adaptation 137, Green grid 185, 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] 14 P a g e

24 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 P a g e

25 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. 16 P a g e

26 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. 17 P a g e

27 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] 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]. 18 P a g e

28 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. 19 P a g e

29 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. 20 P a g e

30 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. 21 P a g e

31 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. 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 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 JFS ElectraNet base case that is the model researcher worked on can be demonstrated in the following table. 22 P a g e

32 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 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 23 P a g e

33 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 24 P a g e

34 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. 25 P a g e

35 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. 26 P a g e

36 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 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 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 27 P a g e

37 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 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 Design The following diagram indicated the general flow of the interface execution lifecycle. Data Extraction Data Processing CSV Generation Figure 4.1: Execution lifecycle of the interface program 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 28 P a g e

38 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 29 P a g e

39 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) Siemens Energy, Inc., Power Technologies International (PTI) This program is a confidential unpublished work created and first licensed in 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. 30 P a g e

40 SIEMENS POWER TECHNOLOGIES INTERNATIONAL 50 BUS POWER SYSTEM SIMULATOR--PSSE University INITIATED ON FRI, OCT :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 :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 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 31 P a g e

41 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 D input 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. D generated 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 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. 32 P a g e

42 ==========================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. 33 P a g e

43 5. 3-node model implementation 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). 34 P a g e

44 Bus Number Bus Name Bus Type Base voltage kv Area number Zone Number 1 Gen Bus1 Swing Bus Gen Bus2 Gen Bus Bus3 Load Bus Load Bus Load Bus 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 Bus Gen Bus Table 5.2: Generators in the sample power system Bus Name Load MW Load MVAr Load Model Bus 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 Bus Bus3 Gen Bus Bus3 Gen Bus Table 5.4: Lines in the sample power system 35 P a g e

45 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 36 P a g e

46 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: MW 0.00 MVAR 0.06 MVA AT BUS 1 [GEN BUS ] SYSTEM TOTAL ABSOLUTE MISMATCH: 0.08 MVA 37 P a g e

47 SWING BUS SUMMARY: BUS# X-- NAME --X BASKV PGEN PMAX PMIN QGEN QMAX QMIN 2 GEN BUS ==============================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 interface of PLEXOS is shown as below: 38 P a g e

48 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: 39 P a g e

49 Then, create nodes of system: Figure 5.12: Create a new region 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 40 P a g e

50 Assign Nodes to Region: Create Transmission Lines: Figure 5.15: Create Node[Region] relationship Connect Lines: Figure 5.16: Create Transmission Lines Figure 5.17: Lines setting 41 P a g e

51 Create Generators and connect them to the Network (assume two generators are seme that Max Capacity=500, Fuel Price=1.5, Heat Rate=10): Create transformer: Figure 5.18: Create Generators Create constraints: Figure 5.19: Create transformer Create input data (Excel file): Figure 5.20: Create constraints Figure 5.21: Input data 42 P a g e

52 Define Region Load: Figure 5.22: Region Load File Prepare Simulation: The results shown below: Figure 5.23: Interface of simulation Figure 5.24: Sample Results 43 P a g e

53 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 44 P a g e

54 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). 45 P a g e

55 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. 46 P a g e

56 Background Research Individual Case Investigation System Modeling Calibration Model Testing Evaluation RIT-T * Model Finalisation * 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, 47 P a g e

57 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 48 P a g e

58 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 Key milestones The status of the milestones is concluded on the date 12 th 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. 49 P a g e

59 Deliverables Project Plan (Completed) Mid-project reviews reports (Completed) Final project report (Completed) Seminar and demonstration Project plan seminar (Completed) Final Seminar (Yet to be held) 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 Encounter issue In the planning document submitted early this year, numbers of risks were supposed as shown below. Risk Over budget Member sickness or injury Lack of staff support Lack of equipments Lack of financial sponsorship Concept fault Behind schedule 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 50 P a g e

60 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 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 51 P a g e

61 ElectraNet, and finally with his help when the team visited ElectraNet again, the issue was fixed up. 52 P a g e

62 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. 53 P a g e

63 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 W. Ann, G. Tom, Assessing Competition Benefits Under The RIT-T, NERA Economic Consulting, 31 May ElectraNet, Lower Eyre Peninsula Reinforcement, RIT-T: Project Specification Consultation Report, February ElectraNet, South Australian Annual Planning Report 2011, 2012, available at: 6. AER, Regulatory investment test for transmission, Australian Energy Regulator, June AER, Regulatory investment test for transmission and regulatory investment test for transmission application guideline final decision, Australian Energy Regulator, June AER, Regulatory investment test for transmission application guideline, Australian Energy Regulator, June Grid Australia, RIT-T Cost Benefit Analysis, Grid Australia Handbook Version 1.1, November Essential Services Commission Of South Australia, Electricity Transmission Code, available at: 1 July ElectraNet, AEMO, ElectraNet-AEMO Joint Feasibility Study: South Australian Interconnector Feasibility Study final report, Available at: February P a g e

64 12. ElectraNet, AEMO, ElectraNet-AEMO Joint Feasibility Studey: South Australian Interconnector Market Modelling Report, available at: February ElectraNet, AEMO, ElectraNet-AEMO Joint Feasibility Study: South Australian interconnector Feasibility Study Market Modelling Report, available at February AERO, An Introduction to Australia`s National Electricity Market, Australian Energy Market Operator, AERO, National Transmission Network Development Plan, Australian Energy Market Operator, SIMENES, PSS E Product Suite, May 2012, Available at: G. Arindam, Fault Calculations in Power Systems, Collaborative power engineering centres of excellence, The Australian Power Institute, 2010, available at: S. Islam, Real and Reactive Power and Load Flow Analysis, Collaborative power engineering centres of excellence, The Australian Power Institute, 2010, available at: 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 Z.Y.Dong, Supply Chain Fudamentals, Demand Side Management and Forecasting, Collaborative power engineering centres of excellence, The Australian Power Institute, 2010, available at Z.Y.Dong, Market Simulation software: PLEXOS, Collaborative power engineering centres of excellence, The Australian Power Institute, 2010, available at 55 P a g e

65 22. Z.Y.Dong, Market Simulation: Load Forecasting and Demand Side Management, Collaborative power engineering centres of excellence, 2011, available at 56 P a g e

66 Appendix A Reduced nodal model 57 P a g e

67 Appendix B augmentation Options 58 P a g e

68 Appendix C Gantt Charts 59 P a g e

69 60 P a g e