ELEC9714 Assignment 1, t2 2025 page 1
ELEC9714
Electricity Industry Planning + Economics
School of Electrical Engineering and Telecommunications
University of NSW
Assignment 1
This assignment will be distributed to you in week 3 via the course Moodle. It is due on Sunday
midnight of week 5 (just before the start of flexibility week). The assignment must be submitted via
Moodle as a single pdf file. Aim to make your assignment look like a professional consultancy report
and paste in Excel plots and calculation tables. The assignment must be submitted individually and
must be your own work. The UNSW policy on student plagiarism can be found on the
www.unsw.edu.au website. Note also the information on plagiarism detailed in the elec9714 Course
Outline which is available on the course Moodle. Note that UNSW uses automated plagiarism
software. Because of this, all text and tables need to be ‘searchable’ within the pdf – ie. not pasted in
as graphics. Again, the only acceptable pasted graphics are the plots, not any tables and not any of
your discussion. If the marker spots text graphics your assignment will not be marked. You are also
required to upload your Excel Spreadsheet or similar working file. This will not be marked but may be
checked if there are concerns about assignment similarities across two or more students. Be sure to
include the standard EE&T assignment coversheet.
The assignment will be marked out of 100, with an overall marks breakdown for each part. For each
part, 25% of the marks is for explaining how you undertook the analysis, 50% of the mark is for your
answers, and 25% of the mark for your discussion of the findings. If you don’t discuss your results
then you can only get maximum 75% of the assigned mark, assuming you explained your method and
got the right answer. Finally, no spurious precision please. You are modeling industry costs in 2050
using heroic assumptions. I expect 3 digits maximum (eg. $2.15b rather than $2,147,362,014.32).
Keep this mind during analysis – you might want to make simplifying assumptions in your estimations
rather than trying to get your calculations exact.
The two assignments over the course are in total worth 25% of your final assessment. This
assignment therefore contributes 12.5% of your final mark. Note that late submission without good
reason will see you lose 10% of your mark per day it is late. I suggest you contact me prior to the
submission date if you expect to be late in order to discuss arrangements.
An Excel spreadsheet is available on the Moodle. It provides data from the CSIRO Gencost report. It
also contains 30 minute demand data and representative State utility PV and wind traces, using
actual NEM data from calendar year 2024 (provided by NEMSight).
You are strongly encouraged to use Excel or a similar spreadsheet package to undertake this
assignment – indeed, you will need to use some form of data analysis software. You will also want to
spend the time to work out how to automate the calculations as much as possible. There are lots of
parts to this assignment that extend the initial analysis – make it easy to change key parameters such
as the carbon price by making it an external parameter which your formulas references, rather than
hard-coding it in. A little automation will make your life much easier, and it is very valuable to have
good Excel skills – it is the one techno-economic energy modeling tool that you can guarantee will be
available to all power system engineers in whatever role they have.
ELEC9714 Assignment 1, t2 2025 page 2
As an energy policy analyst working for the Queensland Government you are part of a team intended
to advise the State Government on what to do given the former State Government’s ambition
decarbonization targets for 2050, concerns regarding renewables by the current government and the
current energy crisis across the Australian National Electricity Market due to coal and gas market
pricing shocks, and an aging coal and gas generation fleet in the State.
Note that the State Government is concerned that the National Electricity Market is incapable of
providing the State with secure affordable power and is actively considering ‘going it alone’ including
government funding of generation investment, and no reliance on interconnector flows with NSW or
any of the other States. Coal generation investment is now again under active consideration.
You have been given the responsibility to estimate the optimal ‘new build’ generation plant mix for
the Queensland Electricity Industry in 2050. This will be an input into Government decision making
on what types of generation deployment they should be facilitating over the coming 25 years. Note
that you can assume that no current plants in Queensland will still be operating at that time.
The Gencost database provides estimated technical and economic characteristics for a range of
potential new build plant including overall capital costs, economic life, fixed O&M costs, variable
O&M costs, efficiency, fuel costs as well as CO2 emissions intensity and CO2 storage costs where
relevant. Note that these are summarized for technologies relevant to Queensland in the assignment
spreadsheet. These numbers have been modified from the latest (December 2024) Gencost
estimates for several technologies in the assignment.
A particular challenge is assessing the impact of a future carbon price on emissions from the
electricity sector in Australia. Rather than making a (large) assumption, you will consider two
scenarios of carbon price – $0/tCO2 which is the present situation in Australia, and $100/tCO2 which
is roughly the current carbon price in the EU that applies to the electricity sector. While a carbon
price might seem unrealistic at present, we can only hope that serious carbon pricing is implemented
over the coming two decades. And State governments could of course choose to ‘shadow’ price
carbon for planning purposes even in the absence of concerted Federal Government or international
progress. Keep in mind that when generators pay a carbon tax, then the government earns revenue.
This revenue can be used to reduce other taxes and, arguably, therefore doesn’t actually represent
an industry cost. However, carbon pricing also reflects, at least in part, the damage costs associated
with each tCO2 (ie. the government may need those revenues to pay for the overall economic
damage that climate change is causing).
Another challenge for the team is that the government is not yet clear if it will have to make the
investment directly, or whether it will aim to incentivize private market participants to invest in the
‘optimal’ generation mix. The State government can effectively borrow money at 5%, while private
market participants argue that they can only finance projects at 10%. To date, state governments in
Australia has undertaken some investment directly, but also contracted to buy power purchase
agreements from private providers.
The potentially relevant generating plants for your analysis are provided in the assignment
spreadsheet. You’ll note there is no brown coal or biomass or new hydro costings given that brown
coal is not relevant in Queensland and there are only very limited new biomass and hydro
opportunities in the State Of course, not all these plants are fully dispatchable and capable of
operating at up to 100% Capacity factor over the year so you will need to think carefully about which
plants you include for part a) and consequent analysis.
ELEC9714 Assignment 1, t2 2025 page 3
Annualized fully dispatchable technology costs:
(a) Plot the total annual costs ($/MW/yr) for each of the appropriate ‘new build’ plants available
for Queensland with the capacity factor of the plants varying from 0 to 1 over a year
(representing 0 to 8760 hours of operation in a year) for two scenarios, 5% discount rate and
$100/tCO2, and 10% discount rate with no carbon price. Use the capital, economic lifetime
and O&M, plant efficiency, and fuel costs provided specifically for the State in the
spreadsheet. Note that these are based on the 2030 Gencost estimates given the challenges
of projecting longer-term future costs, even though you are solving the optimal generation
mix for 2050. Cost forecasts have proven pretty hopeless over just a few years, let alone
decades. You will need to calculate fixed ($k/MW/year) for each relevant generation option
for the two interest rates (5% and 10%) and variable ($/MWh) costs (for no carbon price and
$100/tCO2) for each technology. The assignment spreadsheet provides you with a plotting
template to assist you in drawing these plots once you have calculated these costs for each
technology for each of the two scenarios.
Note that the solar thermal cost is higher than in Gencost because it has been adjusted to be
capable of 100% capacity factor operation. In practice this is very challenging as these plants
need clear skies. However, no generation technologies offer 100% CF operation in practice
over extended periods due to maintenance, forced outages etc. Also, note that the hydrogen
engines have zero emissions because it is assumed that the hydrogen is from renewable sources.
Highlight the economically optimal (ie. lowest annual cost) plant type as capacity factor increases
from 0 to 1 and estimate or calculate the ‘break point’ capacity factors.
Think carefully about which technologies can actually be included in such analysis – the ability to
operate the plant at any overall annual capacity factor up to 1 (ie. plant runs at rated output for
the entire year) and to definitely be available when needed (such as those peaking plants which
won’t run often but have to be there when there is high demand)) is key here. Discuss briefly both
how you undertook this analysis (ie. explain what you did with the data to get the plots), and
what these findings highlight about comparative technology costs and the impacts of carbon
pricing and interest rates on these. You might also want to consider the implications of including
currently technical unproven technologies in your analysis. Note that this discussion is not
optional – engineers need to explain how they do their quantitative (numerical) analysis, yet also
what it means. Remember, your client here is the Queensland Government – the senior executives
will generally not have engineering backgrounds. (20 marks)
Optimal dispatchable generation mix:
AEMO has provided you with estimated half hourly demand data for Queensland for the calendar
year 2024. They estimate that electricity demand will increase by 30% to the year 2050 as businesses
and households move from using gas to electricity for heating (space and water) and cooking, and
industry also electrifies more processes currently using gas, coal or oil. You can, however, assume
that the general ‘shape’ of the demand profile won’t change over that time (ie. you can just scale the
30 minute data to estimate a 2050 demand trace for Queensland). We will come back to this
question of load profile later.
(b) Using this data and growth projection, estimate and plot a load duration curve for
Queensland for the year 2050, ordered from highest to lowest demand over the year. (5
marks)
ELEC9714 Assignment 1, t2 2025 page 4
(c) From this load duration curve, and economically optimal plant capacity factor estimates from
(a) above, estimate the optimal plant capacity mix for Queensland for the 5% discount rate
and $100/tCO2 carbon price scenario for 2050. Ignore issues of existing Queensland
generation plant (much of which can be expected to be retired over the next 25 years or so).
You can also assume that all new build generating plant is 100% reliable.
Again, think carefully about which technologies can actually be included in such analysis. You can
eyeball your generation plot against the load duration curve as shown in the lectures or convert
the breakpoint capacity factors to the 30 minute dispatch across the year (capacity factor
30 minute dispatches). Discuss briefly how you undertook this analysis (ie. explain what you did
with the data to get the plots), and what these findings highlight about Queensland likely ‘least
cost’ new build generation mix with a significant carbon price. You will also want to provide your
generation mix results in a Table. (15 marks).
d) Now estimate the total annual cost ($m/yr) of the electricity industry in 2050 for the 5%
discount rate and $100/tCO2 scenario. Include of course annualized capital costs, the
associated fixed O&M costs, and the variable O&M, fuel and carbon costs associated with
actual operation of the plants. Also estimate the total electricity industry greenhouse gas
emissions.
An easy way to estimate operating costs is by measuring the areas under the load duration curve
(LDC) for each of the technologies in the optimal mix. That gives you hours
an associated variable cost $/MWh for each plant. You really should spend the time to work out
how to automate this as you will be doing more of these annual industry cost calculations in the
assignment. Think of how to automate the process of taking your generation mix estimates (MW
of each technology) and then breaking down the dispatched energy in the LDC by which
generation technology is providing it. (10 marks).
Fully dispatchable and non-constrained variable renewables:
With sufficient energy storage, it might be argued that wind and solar can be made fully dispatchable
and non-energy constrained plant, in a similar way to the solar thermal plant. For wind and PV this
would involve oversizing wind and solar and adding a lot of battery storage. If you had a PV site with
full sunshine every day, close to the equator so you get 12 hours of sun per day regardless of season,
then assuming 6 hours of full sunshine a day (yes a big assumption), the PV plant will generate 1MW
for those 6 hours and store 18MW for later use in the other 18 hours. For a slightly more realistic
example, let’s assume a 1MW 100% capacity factor PV plant would require 5MW of PV and 36MWh
of battery storage (three full nights of storage for the 1MW plant to cover occasional cloudy days and
losses.) Assume battery costs of $150k/MWh, which seems very plausible given current price falls
(although CSIRO’s Gencost report doesn’t agree) and a total fixed O&M cost of $150k/MW/year for
this plant with no variable O&M. For simplicity assume a 20 year economic life (very long for battery
energy storage plants its true, but short for the PV which is about half the capital cost)
e) Add this dispatchable generator to your generation options plot and present it here. Would such a
PV plant capable of 100% CF operation be part of the optimal generation mix under the 5% and
$100/tCO2 carbon scenario? Discuss your findings for this ‘extreme’ case of making variable
renewables equivalent to full dispatchable and non-constrained plant. (10 marks)
ELEC9714 Assignment 1, t2 2025 page 5
Incorporating demand side participation (DSP):
AEMO estimates that Queensland might have around 3GW of price responsive demand in 2050.
Effectively, this demand will choose not to run if the price is very high (here we assume $5000/MWh
(which is well below the current market ceiling price but does represent an extraordinary electricity
price for many industry participants). AEMO estimates capital costs to create such load flexibility of
on average $100k/MW with no fixed or variable O&M costs and an economic life of 20 years. You
might consider this equivalent to a peaking generator that has very low capital costs, but a
$5000/MWh operating cost. Assume again the 5% discount rate and $100/tCO2 scenario.
g) Estimate how many MW and how many hours a year this demand side participation might
actually be called upon in the Queensland electricity industry in 2050. Briefly discuss your
findings, and their implications for the value of demand side participation. You can add this DSP
as another generation technology in your plot to see over what range of capacity factors it’s the
least cost ‘generation’ option. Do not include the dispatchable PV plant in this calculation You’ll
want to check how many MW of demand will optimally be met by DSP – is the projected 3GW
enough? (5 marks)
Incorporating variable wind and PV:
AEMO has also provided you with 30 minutes traces of well performing PV and wind generation in
Queensland. These have been normalized as 1MW of PV and wind capacity profiles.
By assuming that PV and wind have sufficiently low operating costs that they will always be fully
dispatched (unless spilling), you can create a residual load duration curve for different penetrations of
PV and/or wind, which you then use to determine the optimal dispatchable generation mix.
h) Estimate the optimal generation mix of the Queensland electricity industry when adding 8GW of
PV and 8GW of wind. Do not include the dispatchable PV plant or DSP calculated above. What %
of wind and solar generation is spilt? Assume the 5% discount rate and $100/tCO2 scenario.
Recall that the traces you are given are for 1MW of solar and 1MW of wind so you will need to
scale accordingly.
i) Estimate the optimal generation mix of the Queensland electricity industry when adding 12GW of
PV and 12GW of wind. What % of wind and PV generation is spilt?
Briefly discuss your findings – in particular, does wind and/or PV reduce overall industry costs?
How much spill of wind and solar are you seeing? (15 marks).
Incorporating high renewables and storage:
Are you interested to know what the whole mix might look like with high renewables as well as
storage to avoid at least some of that renewables spill, and avoid having to run as much conventional
generation? Estimate the total annual cost of the Queensland electricity industry for the case where
there is 12GW of utility solar and 12GW of utility wind for the 5% $100/tCO2 scenario in 2050.
However, the State also installs 5GW of battery energy storage, with 6 hours of storage (ie. 30GWh
energy capacity). Assume again this storage costs $150k/MWh, ignore O&M costs, and there are no
roundtrip losses (actually around 10% losses with Li-ion energy storage). Does the storage make
economic sense in terms of reducing total industry costs? Do not include the dispatchable PV plant or
DSP calculated above.
ELEC9714 Assignment 1, t2 2025 page 6
There are many ways to model such storage but the simplest is to assume that you’ll operate the
storage to reduce the periods of highest demand, while charging during the periods of lowest
(negative demand). With 6 hours storage you might assume that over the year the plant charges for
around 2000 hours and discharges for 2000 hours. So you effectively have 5GW of extra load for 2000
hours of lowest (ideally negative) demand and 5GW of generation for another 2000 hours of the
highest demand. You can estimate a new residual load duration curves and solve the least cost mix of
dispatchable generation. More complex techniques are to solve actually battery plant dispatch for
the 30 minute demand trace over the year but I don’t suggest you try this unless you enjoy linear
programming. (10 marks)
Incorporating EV charging:
l) Discuss how you might incorporate EV charging into the analysis. Let us assume that rapid
transport electrification means that there are 4 million EVs in Queensland in 2050. Assume also
each vehicle typically charges at 3kW (note that typically they already charge at around 7kW for
household charging, let alone supercharging). Each vehicle drives around 40km/day (around
14600km/year), consuming around 6kWh/day or 2.2MWh/year (EV efficiency around
150Wh/km). Total daily consumption charging these EVs is therefore around 24GWh. Consider
two extreme cases for vehicle charging.
1) on average vehicles charge between 6-10pm every day, starting when people get home from
work. This effectively adds a block of new Queensland demand of say 6GW for that 4 hours.
2) all vehicles charge between 10am-2pm every day, utilizing a wide range of work based and
public charging infrastructure. Again, there is additional Queensland demand of 6GW for those 4
hours every day.
For both cases, estimate the optimal generation mix for the 5% discount rate and $100/tCO2
carbon price scenario and 12GW of wind and 12GW of utility solar. Don’t include the demand-
side participation, dispatchable PV or utility PV in the analysis. Calculate the total industry cost.
What is the difference in total annual industry cost depending on which charging profile is used.
And does the average $/MWh for electricity increase or decrease for each scenario. Briefly
discuss the value of thoughtful EV battery charging.
Think of ways to modify the load duration curve given this new load, and then reordering to get a
‘residual’ duration curve that you can then use to determine the optimal generation mix. There is
a useful demand profile tool in the assignment spreadsheet that allows you to enter a daily
controllable load profile, which then gets scaled up over the entire year.