ELEC9714 Electricity Industry Planning + Economics Assignment 1, t2 2024

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ELEC9714 Electricity Industry Planning + Economics

t2 2024

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 Introduction 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.

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. $2.15billion rather than $2,147,362,014.32). Keep this mind during analysis – you might want to make simplifying assumptions in your estimations rather than getting 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 3 marks 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 2023 (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.

As an energy policy analyst working for the NSW Government you are part of a team intended to advise the State Government on what to do given the State’s very ambitious decarbonization targets for 2050 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.

You have been given the responsibility to estimate the optimal 'new build' generation plant mix for the NSW 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 26 years. Note that you can assume that no current plants in NSW will still be operating at that time (yes, a big assumption, that we will later relax by considering existing hydro).

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 and CO2 emissions intensity and CO2 storage costs where relevant. Note that these are summarized for technologies relevant to NSW in the assignment spreadsheet. These numbers have been modified from the 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 4%, while private market participants argue that they can only finance projects at 9%.

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 NSW 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.

Annualised fully dispatchable technology costs:

(a) Plot the total annual costs ($/MW/yr) for each of the appropriate 'new build' plants available for NSW 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, 4% discount rate and $100/tCO2, and 9% discount rate with no carbon price. Use the capital, economic lifetime and O&M, plant efficiency, and fuel costs provided specifically for the State in the spreadsheet. Note that these are based on 2030 Gencost estimates given the challenges of projecting longer-term future costs, even though you are solving the optimal generation mix for 2050. Cost forecasts have proven pretty hopeless over just a few years, let alone decades. You will need to calculate fixed ($k/MW/year) costs for the two interest rates (4% and 9%) 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) 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 NSW 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 NSW for the calendar year 2023. They estimate that electricity demand will increase by 50% 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 NSW). 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 NSW for the year 2050, ordered from highest to lowest demand over the year. (5 marks)

(c) From this load duration curve, and economically optimal plant capacity factor estimates from (a) above, estimate the optimal plant capacity mix for NSW for the 4% discount rate and $100/tCO2 carbon price scenario for 2050. Ignore issues of existing NSW generation plant (which is mostly old and can be expected to be retired over the next 20 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 NSW’s likely ‘least cost’ 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 4% 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 $200/kW/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 4% 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)

Incorporating demand side participation (DSP):

AEMO estimates that NSW might have around 2GW of price responsive demand in 2050. Effectively, this demand will choose not to run if the price is very high (here we assume $10000/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 $100/kW with no fixed or variable O&M costs and an economic life of 20 years. You might consider this equivalent to a 1000MW generator that has very low capital costs, but a $10000/MWh operating cost. Assume again the 4% 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 NSW 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 2GW enough? (5 marks)

Incorporating variable wind and PV:

AEMO has also provided you with hourly traces of actual PV generation (2.4GW of single axis tracking) and wind (2GW of on-shore) generation in NSW over 2023. Assume the 4% discount rate and $100/tCO2 scenario.

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 NSW electricity industry when adding 15GW of PV. Do not include the dispatchable PV plant or DSP calculated above. What % of PV generation is spilt? Recall that the traces you are given are for 2400MW of solar and 2000MW of wind so you will need to scale accordingly.

i) Estimate the optimal generation mix of the NSW electricity industry when adding 20GW of wind. What % of wind generation is spilt?

j) Estimate the optimal generation mix and total annual cost and emissions of the NSW electricity industry when adding both 15GW of PV and 20GW of wind. What percentage of wind and solar 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 existing energy constrained hydro generation:

NSW has around 5GW of energy constrained hydro generation already on-line or committed. These plants have very long lifetimes so will very likely still be generating in 26 years time. The average achievable capacity factor of this hydro generation is around 30% (that is, there is sufficient water to generate around 13TWh per year).

k) Estimate the optimal generation mix of the NSW electricity sector for the case where there is 15GW of PV and 20GW of wind and also this 5GW of energy constrained hydro generation. Note that there are no capital costs given these generators are already built, and you can ignore fixed and variable O&M costs given that they are low anyway. Again, assume the 4% discount rate and $100/tCO2 scenario.

Think carefully about when it would be right time to run this hydro generation given that you can only run it at rated capacity for around 30% of the time. At present, it mostly runs in peaking style roles to displace higher operating cost generation (gas OCGT) and this might be the best option here as well.

Briefly discuss your findings on the potential value of the existing hydro generation in NSW (10marks)

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? Estimate the total annual cost of the NSW electricity industry for the case where there is 15GW of utility solar and 20GW of utility wind and the 5GW of hydro for the 4% $100/tCO2 scenario in 2050. However, the State also installs 10GW of battery energy storage, with 6 hours of storage (ie. 60GWh 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?

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 1500 hours and discharges for 1500 hours). So you effectively have 10GW of extra load for 1500 hours of lowest demand and 10GW of generation for another 1500 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)