ELEC9715 Electricity Industry Operation and Control
ELEC9715 - assignment 1, t1 2025
Assignment 1
This assignment will be distributed to you Tuesday of week 4. It is due midnight Tuesday at the start of week 6. Your submission should be a pdf document. The assignment must be submitted individually on the course moodle via Turnitin, and must be your own work. The UNSW policy on student plagiarism can be found at www.unsw.edu.au and you should note that the university uses automated software to check assignments. Note that you must save a copy of your Excel workbook (or databases and code if you use another tool such as matlab or python). This will need to be uploaded into Moodle as well as the PDF report, but will only be checked if there are concerns about possible plagiarism.
The assignment will be marked out of 20 (5 marks per question). One mark per question is for your explanation of how you answered the question, 2 marks are for your actual analysis, 1 mark is for the discussion and 1 mark is for the quality of the presentation – ie. does your report look professional. No explanation of how you undertook the analysis (it only needs to be brief) or discussion of your findings is lost marks. If you want the full 4 marks for presentation over the assignment you have to make it look like a professional consulting report.
The two assignments over the course are worth 25% of your final assessment. Late submission without good reason, as explained in an email to the course lecturer prior to the due submission time, will see marks reduced as per the details in the Course Guide. Late submissions must be directly emailed to the lecturer as well as uploaded into Moodle. In keeping with the recommended hours per week of study for a six unit of credit course, (around 10 hours of self-directed work per week additional to the 4 contact hours), we expect that you will spend in the order of 15 to 20 hours of so in total on this assignment. The assignments are excellent preparation for the final exam and it is essential, and a UNSW requirement, that you do it yourself.
Again, you should look to make your assignment like a consultancy report – ie. professional presentation with figures, tables and graphs. It is excellent practice for the technical report writing you have to do as an engineer in the electricity industry sector. You should briefly outline your methods for answering the questions in the report - engineers show their working. All tables and discussion must be pasted in as tables and text rather than pictures so that they are searchable via text (a requirement for Turnitin) - if you put tables or have discussion in your report that can’t be ‘text’ searched then it will not be marked. Finally, I am serious about the importance of engineering reports explaining how they undertook the analysis and discussing what the findings actually mean. Engineers shouldn’t just present numerical analysis but try to also help (often non-engineer) readers to understand how they did the calculations and what then answers mean.
I’ll run a couple of assignment consultation sessions online and in-person at the end of week 4 and then week 6 if you want some assistance with what is a pretty long and, in some places, challenging assignment.
Electricity industry operation and control is determined by the operational capabilities of all supply, network and end-use equipment. A key question is how the operational characteristics of existing and potential new generation technologies, as well as electricity demand, will shape future industry operation.
Question 1:
The Australian Energy Market Operator (AEMO) has recently updated its technical and cost estimates for all existing and a range of possible new utility generators for its planning studies. These studies are an input into its forthcoming 2026 Integrated System Plan (ISP). This requires AEMO to model operation of the Australian National Electricity Market (NEM) under a range of possible future generation and network scenarios. This, in turn, requires that they estimate key technical characteristics and capital and operating costs of all existing generation and potential new generation technologies. The latest (2024) data for this is available on the AEMO website as an Excel spreadsheet. It’s a very interesting read and you may well wish to look at it and see how AEMO undertakes its modelling. However, to simplify the assignment we also provide a cut-down workbook in the file on the course moodle and/or MS Teams. The cut down assignment AEMO modeling work book has a pre-prepared sheet with estimates (based on the AEMO data but with some additional assumptions) of the minimum and maximum operating levels operating costs and carbon emissions for all the coal and gas generating plants in NSW, as well as hydro and utility wind and solar plant and battery energy storage in the State – existing and committed (meaning definitely coming).
Plot the generation supply curve (Incremental variable cost $/MWh versus MW system generation for economic dispatch) for the existing NSW thermal plant mix (all coal and gas-fired generators) for two possible carbon price scenarios - $0/tCO2 which is the current level since the removal of Australia’s carbon price in 2014)and $75/tCO2 which is the official Value of Emission Reductions (VER) for 2025 for rule making in the NEM. For the second carbon scenario, all the fossil fuelled plants are required to pay for each tCO2 they emit, adding to their operating costs from Variable O&M and fuel purchasing. In reality, the VER is what is called a ‘shadow’ price, and generators don’t have to pay it. Instead it is used for rule making and regulation settings on the basis that they really should pay it. One day it may actually return as a real carbon price.
For simplicity ignore transmission losses and constraints. All the coal and gas units are committed - that is on-line and required to operate somewhere between their minimum and maximum output.
You will first need to calculate the ‘sent out’ operating cost – short run marginal cost or SRMC - ($/MWh) for each generator for each carbon price. The spreadsheet is set up to assist in this. Note that you should still explain your working in the assignment so its clear you know how the calculation works. You’ll then need to sort them from lowest to highest incremental operating costs. You can of course bundle multiple plants if they are the same technology with the same costs (eg. PV, wind).
i) Plot the two supply curves (one for each carbon price scenario) on a single graph. These curves represent the cost of the power system providing an additional MWh of demand - that is System Short Run Marginal Cost or SRMC as a function of demand for electrical energy over the entire range of economic dispatch. Think carefully about how minimum operating levels should be represented – we looked at this in the class on economic dispatch. Discuss the implications of a carbon price on economic dispatch of the NSW thermal plant, and particularly any impacts on the merit order (that is, the order of generating plant technologies from lowest to highest operating cost). You’ll find a useful template for plotting supply curves in the assignment workbook.
We are now going to consider all current NSW generation including the coal and gas plants but also hydro, wind and utility PV, and even Battery Energy Storage Systems (BESS).
ii) You now need to add existing hydro generation to the supply curve. Note that there are really three types of hydro generation to be modelled. Run of river plants effectively run whenever there is water flow - for some schemes these plants effectively look like constant output generators (more water than required at all times of the year) with only variable operating costs to cover. The problem as discussed in lectures, is that a lot of hydro plants are energy constrained - that is, their water actually has an opportunity cost/value. The third type is pumped hydro plants. Plot the NSW supply curve for a zero carbon price now including all the hydro units (except of course Snowy 2.0 which is pumped hydro and still some way from being finished) assuming that they are run of river and have zero operating cost. On the same graph also plot the hydro if we were to treat it as energy constrained plant where it bids into the market at its opportunity cost. You can assume this is $300/MWh (standard call option contract value, as you’ll learn when we cover peaking plant operation). Discuss the impacts of treating hydro differently in the economic dispatch supply curve.
iii) Now add existing and committed wind and solar to the supply curve. Keep the hydro all offering at $300/MWh. The main challenge here is the high variability of wind and solar. Section ii) shows the supply curve if the wind isn’t blowing and the sun isn’t shining. Now plot the supply curve for a zero carbon price for three cases (all on the same graph) – 1) midday on a sunny day across NSW when the utility solar is all operating at rated capacity (it gets pretty close) but there is no wind, 2) on a windy winter evening in NSW when the utility wind power is operating in aggregate at 80% of installed capacity (never really see all the State’s wind generation all at rated output at the same time) but there is, of course, no solar power, and 3) when its both sunny and windy with all (100%) utility PV and 80% of wind generating Discuss the implications of NSW wind and solar on the generation supply curve and its implications for meeting State demand as it varies from a minimum of around 3000MW, at average demand of around 7500MW to its maximum of around 13800MW. In particular, do you envisage periods where coal and gas plants would ideally be turned off, or when there may be insufficient generation to meet demand?
iv) Now add the existing and committed battery energy storage systems (BESS) and pumped hydro to the curves from iv) above. For reasons that will be explained later in the course, one way to model energy constrained and pumped hydro plants and BESS is to consider them as having an relatively high operating cost. For this assignment we will assume they have an operating (actually opportunity operating) cost of $300/MWh (hint, this is based on the standard call option pricing in the NEM).
Briefly summarise the implications of all the above scenarios for economic dispatch in the NSW region as wind and solar deployment increases and coal plant continue to retire.
Question 2:
This question involves analysis of actual generating plant operation in the NEM. You have been given access to NEMSight - an extremely powerful commercial package for analysing NEM data. Details for accessing NEMSight are available on the course Moodle. You will want to use its ‘Time Machine’ function to analyse a number of NSW generators and characterize their operation over the calendar year 2024. Note that you can plot graphs by fuel type or participant (which gives you the individual units). Choose one plant in NSW for each of the following generation technologies:
- Coal
- OCGT (gas turbine) plant
- Utility wind or solar farm
- battery energy storage system (BESS)
You will want to eyeball historical data for your chosen plant to make sure there aren’t any surprises - eg. not operating for most of the year (a particular issue with some of the renewable generators that have only recently been commissioned, or may not have even been connected yet). NEMSight offers very useful charting of data, and if you wish to analyse it further you can then right click on the chart and it will allow you to copy the data as a table which you can then paste into Excel. In the assignment excel workbook I have already provided 5 minute prices and scheduled demand for calendar year 2024 so you can actually place your chosen generator data into that sheet for your analysis.
i) For each of your chosen plant, use 5 minute dispatch data to estimate as best able the following:
a. Highest ramp rate seen over the year (up or down) in %RatedCapacity/min. Don’t consider starts and stops in this calculation – ramp rate is the change in output over 5 minutes when the plant stays operating. This can be a little tricky with very fast plant like OCGTs, pumped hydro and definitely BESS where they can go from zero to rated output pretty quickly. For coal plants on the other hand, they might go from zero to minimum operating level pretty quickly in the data, but the plant was actually started some time prior to this.
b. achieved capacity factor over the year % (actual output divided by possible output if plant operated at its max output for every hour of the year)
c. Number of starts in the year (ie. Going from zero output to generation)
d. operating profit over the year, using the operating cost estimates from Question 1 above for the zero carbon price scenario, and the regional spot prices (available from NEMSight but also provided in the assignment workbook).
Note that some plants may have been ‘down’ for extended periods over the past year – best to select another plant. A number of these plants, particularly thermal coal and gas plants and hydro, have multiple units that can cause some complexities for the analysis. You should analyse a single unit. Finally, note that we will be checking for assignments that analyse the same generators given there is a choice available– this assignment is meant to be done individually. My advice is to first eyeball the data of a range of plants to get a feel for the general operation of different plants. Then you can write simple data analytics, using a wide range of helpful Excel functions, to characterize their operational characteristics. Useful EXCEL functions for this include MAX and MIN and AVERAGE (eg. MAX(b:b) returns the largest number in column b). You can get ramp rates by adding a column which calculates the difference between adjacent cells containing 5 minute power outputs (MW). And the IF function is very useful for filling an added column with a counter if power output is zero).
Be sure to put your results in a table. Please comment on your findings, and the operational flexibility of different generation technologies, and the potential implications for NSW’s electricity sector operation.
ii) Using 5 minute NSW scheduled demand data for calendar year 2024 (available from NEMsight or in the assignment workbook, determine the following:
a. Average demand (MW) over the year
b. Highest 5 minute demand (and when it occurred – date and time).
c. Lowest 5 minute demand (and when it occurred – date and time)
d. Highest up ramp rate (MW/min)
e. Highest down ramp rate (MW/min)
f. Effective capacity factor of demand (%) with respect to highest 5 minute demand seen over the year 2024.
Be sure to put your results in a table and discuss their implications, particularly with respect to the variability of NSW demand compared with its wind and utility PV.
Question 3:
Distributed energy resources (DER), increasingly now being called Consumer Energy Resources (CER) are becoming an increasingly important generation source – Australia has a lot more rooftop PV than utility PV (25GW versus around 10GW). Other DER technologies include household appliances which have energy storage so that you can move around the time that they run – electric storage hot water systems are a particular example.
An Excel spreadsheet is available on the course Moodle and/or MS Teams that has 30 minute household data for approximately 100 houses in the Ausgrid network region of Sydney for a complete year. Each house has metered load (kWh over 30 minutes) for both what is termed General Consumption (GC) and Controlled Load (CL). GC measures all household electricity consumption other than controlled loads. The CLs are typically hot water systems and/or pool pumps - which are electronically controlled through ripple control as instructed by the distribution network service provider. CLs are separately metered because they can be flexibly dispatched by the network operator and therefore pay a lower tariff (c/kWh) rate. Somewhere around half of NSW households have CLs although this is falling as off-peak hot water systems are replaced.
The dataset also includes Gross (total) Generation (kWh over 30 minutes) from the household PV system. While the Ausgrid data set has different capacity PV systems on each house we have standardized the PV system size to 6kW – the average PV system size across Australia these days.
You will analyse the house number that matches the last two digits of your student number – eg. if your student number is s1234567 you will analyse house 67. Note that some houses have very questionable data suggesting metering errors or PV system failure – if that is the case for your house, please note this in your report, explaining the issue, and then use the next house number. Note that you must use the house data corresponding with your student number or explain why you didn’t – I wanted to copy my friend’s assignment is not an acceptable answer and you will get zero marks for the question.
For your particular house,
i) estimate as best able from the 30 minute data over the year:
- highest GC demand (kW) and day (date day/month) and time it occurred (24 hour eg. 17:00 = 5pm)
- average GC demand (kW) over the year
- highest CL demand (kW) and date (day/month) and time at which this occurred (only relevant of course if your house has CL)
- average CL demand (kW) over the year
- proportion of total household load which is CL over year (%)
-annual electricity bill for the house assuming no PV, GC tariff of 35c/kWh and CL tariff of 12c/kWh
ii) For the PV system, estimate as best able from the 30 minute data over the year
- average PV capacity factor (%) (with respect to provided 6kW PV system capacity) over year.
- peak net export of PV generation (kW) if any (that is, greatest PV generation exports to grid after removing GC and CL demand) and the day (date day/month) and time at which this occurred.
- annual electricity bill for the house given the consumption tariffs above, and an export tariff (when the PV generation exceeds total GC and CL load in a 30 minute period) of 3c/kWh. Note that self consumed PV generation saves the household the consumption tariffs.
Again, I suggest you first graph the output for your house to ‘eye-ball’ its load and PV behavior. before then using some of the available Excel spreadsheet functions to identify the factors above. Always apply a sanity check to your answers, and be sure to use the units suggested above. In particular, note that 1kWh consumed in 30 minutes reflects a load consuming 2kW. You will need to change your meter data to get kW)
Be sure to put your results in a table. Please comment on your findings, and their implications for power system operation to meet residential load in NSW. Also discuss the potential role of controllable hot water systems as a flexible storage resource, and the performance of household PV systems.
Question 4:
Consider a very simplified version of the NSW generation fleet and State demand as a competitive electricity market as outlined in table 1 below. For convenience, you can assume that no generators have minimum operating levels hence no fixed variable costs (big assumption as you’ll see, those coal plants have a real minimum operating demand challenge at present) and that their incremental variable costs apply across their entire operating range. Assume that there are no transmission constraints or losses, and ignore the existing transmission interconnections between NSW and Victoria, and NSW and Queensland.
We assume that there are four major market participants in NSW as detailed in the table below. Each market participant can offer one quantity/price pair into the market for each of its generation technologies. Note that the PV and wind generation and Battery Energy Storage Systems (BESS) in the state are bundled into generic, multiple owner, participants for simplicity, and because they are likely to be market price takers rather than makers (although this is really changing and we are now starting to see wind and solar and BESS starting to exercise market power).
The market operates at hourly intervals. The market operator AEMO bids its scheduled load forecast for each hour into the market at a Market Ceiling Price (MCP) of $17,500/MWh. Note that there is one active demand market participant – an aluminium smelter which generally runs at 600MW 24/7 but bids all its demand such that it completely turns off if the price goes above $2000/MWh. AEMO does not include this plant in its forecasts of scheduled demand.
Assume for simplicity that the wind and solar are market participants who don’t earn income from PPAs but instead only the market price, and that the hydro and BESS is offered into the electricity market at the prices in table 1. There are big assumptions here, reflecting the opportunity cost of the water in different hydro schemes and energy arbitrage strategy for the BESS respectively (more on this in later weeks).
Consider three possible hourly scenarios of renewable generation and scheduled demand:
1) Wind generation of 2000MW, solar of 4000MW and scheduled market demand of 4000MW (sunny and windy day spring day with rooftop PV reducing scheduled demand to be met by utility generation.
2) Wind farms at 300MW and utility PV at 0MW with 13000MW of scheduled demand (a relatively still cold winter evening with lots of electrical heating).
3) A spring night with 1200MW wind, no utility solar of course, and scheduled demand of 8000MW.
Solve the following cases of market dispatch for each of the three renewables/demand scenarios:
(i) None of the generation participants are engaging in strategic (gaming) bidding into the market. What is the market clearing price (MCP) ($/MWh), dispatch (MW) and surplus/profit ($'000/hr) for each generation participant (for each of their generation options and in total). Also calculate the profit of the BESS assuming that it has 100% round trip efficiency (no losses) and pays an average $15/MWh. Please use tables to present these answers. Don’t forget what the battery storage plants might be doing – will they be charging, doing nothing or charging for each scenario, and how might that impact on price.
(ii) Participant AGL has now decided to attempt to exert its market power to improve profits. Assume that the other generators and the aluminium smelter will continue 'preference revealing' bidding. Assume also that AGL has excellent knowledge of the true maximum power outputs and incremental variable costs (and opportunity costs) of all their competitor's generating units, and the scheduled demand MW and price responsive behaviour of the smelter. How might AGL offer into the market (quantity, price) to maximise its profits under each of the different renewable energy and demand scenarios? What would then be the market clearing price, dispatch and profits for each generation participant. Again, think of what the BESS might be doing during the exercise of market power. And what about that price responsive demand?
(iii) Instead of participant AGL attempting to exert market power, it is now Snowy which is attempting strategic bidding in order to increase its profits. Assume that all the other generation participants and the BESS use 'preference revealing' bidding into the market. How might Snowy offer into the market (quantity, price) to maximise its profits under the different renewable energy and load scenarios? What would then be the market clearing price, dispatch and profits for each generator?
Be sure to put your results in tables as appropriate. Please comment on your findings, and their implications for market prices and the exercise of market power in the NSW region of the Australian NEM given growing penetrations of variable wind and solar generation, and growing BESS deployment. Also, what role might more price responsive demand play in the exercise of market power by generators. And can you see circumstances where wind and solar plants might try to exercise some limited market power themselves?