ELEC9714代做、代写Java/Python编程设计

日期：2025-06-30 10:59

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 modelling 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 modelling 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 ambitious

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 have 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

Annualised 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 are zero emissions because it is assumed 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 X 17520

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 annualised 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 X dispatch MW with

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. (10 marks)