Modelling of the Victorian renewable energy target scheme ... · Modelling of the Victorian renewable energy target scheme scenarios ... Nature and scope of our work ... Modelling

Modelling of theVictorian renewableenergy targetscheme scenarios

Department of Environment, Land,Water and Planning18 September 2017

A member firm of Ernst & Young Global LimitedLiability limited by a scheme approved under Professional Standards Legislation

NoticeErnst & Young (“we” or “EY”) has been engaged by the Department of Environment, Land, Water andPlanning (“you”, “the Department” or the “Client”) to provide advice and modelling assistance toinform implementation of Victoria’s Renewable Energy Target scheme (the “Project”) in accordancewith our Agreement for the Provision of General Services dated 16 September 2016.

The enclosed report (the “Report”) was prepared on the specific instructions of the Department solelyfor the purpose to provide advice and modelling assistance to inform implementation of Victoria’sRenewable Energy Target, and should not be relied upon for any other purpose. The Report should beread in its entirety including the applicable scope of the work and any limitations. A reference to theReport includes any part of the Report.

EY has prepared the Report for the benefit of the Department and has considered only the interests ofthe Department. EY has not been engaged to act, and has not acted, as advisor to any otherparty. Accordingly, EY makes no representations as to the appropriateness, accuracy or completenessof the Report for any other party's purposes.

No reliance may be placed upon the Report or any of its contents by any recipient of the Report for anypurpose and any party receiving a copy of the Report must make and rely on their own enquiries inrelation to the issues to which the Report relates, the contents of the Report and all matters arisingfrom or relating to or in any way connected with the Report or its contents.

EY disclaims all responsibility to any other party for any loss or liability that the other party may sufferor incur arising from or relating to or in any way connected with the contents of the Report, theprovision of the Report to the other party or the reliance upon the Report by the other party.

No claim or demand or any actions or proceedings may be brought against EY arising from or connectedwith the contents of the Report or the provision of the Report to any party. EY will be released andforever discharged from any such claims, demands, actions or proceedings.

Our methodologies chosen are based, in part, on the assumptions stated and on information providedby the Department. We do not imply, and it should not be construed that we have performed audit ordue diligence procedures on any of the information provided to us. We have not independentlyverified, or accept any responsibility or liability for independently verifying, any such information nordo we make any representation as to the accuracy or completeness of the information. We accept noliability for any loss or damage, which may result from your reliance on any research, analyses orinformation so supplied.

EY have consented to the Report being published for informational purposes only. EY have notconsented to distribution or disclosure beyond this. The material contained in the Report, including theEY logo, is copyright and copyright in the Report itself vests in the Department. The Report, includingthe EY logo, cannot be altered without prior written permission from EY.

EY’s liability is limited by a scheme approved under Professional Standards Legislation.


18 September 2017

Executive Director, Renewable EnergyDepartment of Environment, Land, Water and Planning8 Nicholson StreetPO Box 500 East MelbourneVictoria 8002 Australia

Modelling of the Victorian renewable energy target scheme scenarios

Dear Executive Director,

In accordance with the Agreement for the Provision of General services dated 16 September 2016(“Agreement”), Ernst & Young (“we” or “EY”) has been engaged by the Department of Environment,Land, Water and Planning (“you”, the “Department” or the “Client”) to provide advice and modellingassistance to inform implementation of Victoria’s Renewable Energy Target (VRET) scheme.

The enclosed report (the “Report”) sets out the outcomes of our work. You should read the Reportin its entirety. A reference to the report includes any part of the Report.

Purpose of our Report and restrictions on its use

Please refer to a copy of the Agreement for the restrictions relating to the use of our Report. Weunderstand that the deliverable by EY will be used for the purpose of informing the VictorianGovernment’s implementation of the VRET policy and communicating to the public on thequantitative modelling undertaken (the “Purpose”).

This Report was prepared on the specific instructions of the Department solely for the Purpose andshould not be used or relied upon for any other purpose.

We accept no responsibility or liability to any person other than to the Department, and accordinglyif such other persons choose to rely upon any of the contents of this Report they do so at their ownrisk.

Nature and scope of our work

The scope of our work, including the basis and limitations, are detailed in our Agreement and inthis Report.

Our work commenced on 16 September 2016 and was completed on 13 September 2017.

In preparing this Report we have considered and relied upon information from a range of sourcesbelieved after due enquiry to be reliable and accurate. We do not imply and it should not beconstrued that our enquiries could have identified any matter that a more extensive examinationmight disclose. However, we have evaluated the information provided to us by the Department aswell as other parties through enquiry, analysis and review and nothing has come to our attention toindicate the information provided was materially mis-stated or would not afford reasonable groundsupon which to base our Report.

Ernst & Young111 Eagle StreetBrisbane QLD 4000 AustraliaGPO Box 7878 Brisbane QLD 4001

Tel: +61 7 3011 3333Fax: +61 7 3011 3100ey.com/au


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The work performed as part of our scope considers information provided to us. This modellingconsiders several combinations of input assumptions relating to future conditions, which may notnecessarily represent actual or most likely future conditions. Additionally, modelling work performedas part of our scope inherently requires assumptions about future behaviours and marketinteractions, which may result in forecasts that deviate from future conditions. There will usually bedifferences between estimated and actual results, because events and circumstances frequently donot occur as expected, and those differences may be material. We take no responsibility that theprojected outcomes will be achieved, if any. EY’s role was limited to modelling assumptions selectedby the Department under different scenarios and has acted on the instructions of the Department.

Our conclusions are based, in part, on the assumptions stated and on information provided by theDepartment and other information sources used during the course of the engagement. NeitherErnst & Young nor any member or employee thereof undertakes responsibility in any waywhatsoever to any person in respect of errors in this Report arising from incorrect informationprovided by the Department or other information sources used.

This letter should be read in conjunction with our Report, which is attached.

The Report is structured as follows:

► Section 1 contains the executive summary

► Section 2 describes the background and purpose of the work

► Section 3 provides an overview of the VRET scheme

► Section 4 details our methodology and provides an overview of the scenario assumptions

► Section 5 highlights key outcomes of the modelling

► Appendix A provides the detail of the scenario assumptions

The modelling outcomes are based on a range of assumptions that define the scenarios. Impacts ofthe VRET scheme are assessed by comparing the VRET scenario outcomes to a reference scenario.We note that the assumptions have been selected by the Department. We acknowledge that there isa significant range of alternative assumptions that, in isolation or in aggregate, could transpire toproduce outcomes that will differ to those that have been modelled. These possible alternativefutures have not been considered in this Report.

Yours sincerely

Ian Rose Michael FenechExecutive Director Partner


Table of contents1 Executive summary ........................................................................................................ 1

1.1 Background ............................................................................................................. 1

1.2 Scenarios presented ................................................................................................. 1

1.3 Overview of modelling methodology .......................................................................... 2

1.4 Key modelling outcomes ........................................................................................... 2

2 Introduction .................................................................................................................. 7

2.1 Background to engagement ...................................................................................... 7

2.2 Purpose of this Report .............................................................................................. 7

2.3 Structure of this Report ............................................................................................ 7

3 Victorian Renewable Energy Target (VRET) ...................................................................... 9

3.1 Overview of the VRET scheme design ........................................................................ 9

3.2 The VRET payment scheme....................................................................................... 9

4 Modelling methodology ................................................................................................ 12

4.1 Modelling framework .............................................................................................. 12

4.2 Forecasting the electricity market – an iterative approach .......................................... 12

4.3 Market simulations ................................................................................................. 14

4.4 Large-scale Generation Certificate prices ................................................................. 18

4.5 Retail bill impact .................................................................................................... 19

4.6 Jobs and investment in Victoria ............................................................................... 20

4.7 Modelling assumptions ........................................................................................... 22

4.8 Limitations ............................................................................................................ 24

5 Modelling outcomes ..................................................................................................... 26

5.1 Generator capacity mix ........................................................................................... 26

5.2 Generation energy mix ........................................................................................... 27

5.3 Wholesale electricity prices ..................................................................................... 29


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5.4 Retail bill impacts ................................................................................................... 30

5.5 Renewable energy investment ................................................................................. 32

5.6 Renewable energy jobs ........................................................................................... 33

5.7 Emissions .............................................................................................................. 35

Appendix A Modelling assumptions ..................................................................................... 36


1 Executive summary

1.1 BackgroundThe Department of Environment, Land, Water and Planning (the Department) commissioned EY toundertake modelling to inform the Victorian Government’s development of the Victorian renewableenergy target (VRET) scheme. This modelling was conducted over the period from September 2016to July 2017. EY was asked to model the National Electricity Market (NEM) from 2017-18 to2049-50 and forecast the impacts of the VRET scheme on the following:

► Wholesale electricity prices, with a focus on Victoria. This includes:► Impact on the wholesale electricity price component of electricity consumer retail bills

► Large-scale generation certificate (LGC) prices. This includes:► Impact on the LGC price component of electricity consumer retail bills

► The generation mix in Victoria

► Payments to new entrant wind and solar PV generators under different VRET scheme designs.This includes:► Impact of payments on electricity consumer retail bills if the cost of payments were passed

through to consumers

► Jobs and investment stimulated in Victoria

Other aspects were not considered, including the impact of the VRET on transmission anddistribution network investment. EY understands that the Department is investigating the matter ofnetwork investment separately with AEMO. EY had no role in advising the Department on policymatters.

1.2 Scenarios presentedOver the period of engagement with the Department, EY has conducted several scenarios withdifferent assumptions. At the time of writing this Report some of the scenarios were no longer valid.This Report presents the modelling outcomes of two VRET scenarios chosen by the Department toexplore the impact of VRET targets in comparison with a reference case:

► Reference Case: Forecasts Victorian and national electricity market outcomes under acontinuation of existing and announced policy settings but without the VRET policy,

► VRET 3400 MW: The VRET introduces around 3,400 MW of additional renewable energygeneration capacity progressively by 2025, and

► VRET 5150 MW: Victoria introduces around 5,150 MW of additional renewable energygeneration capacity progressively by 2027.

The modelling outcomes are based on a range of assumptions that define the scenarios. Theseassumptions were selected by the Department.

Table 1 summarises the key modelling assumptions. All dollars in this Report presented are June2016 (real).

The Department of Environment, Land, Water and PlanningModelling of the Victorian renewable energy target scheme scenarios

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Table 1: Key modelling assumptionsMarket modelling assumption Value/source

Electricity demand, rooftop PV and behind-the-meterbattery uptake

From the 2016 AEMO National ElectricityForecasting Report

1

Emissions reduction policy: a price on carbon that resultsin the NEM achieving its share of Australia’s emissionsabatement target of 26-28% by 2030

Increasing to $30/t CO2-e by 2030, then heldconstant

Long-term gas prices in Latrobe Valley $8.15/GJ2

Long-term coal prices for Loy Yang A/B, Yallourn $0.61/GJNew entrant wind farm capacity factors 36%New entrant single-axis tracking solar PV capacity factors 26.5% (varies from site to site)Economic lifetime for new entrant wind and solar PVgenerators

25 years

Weighted-average loss factors for wind and solar PVgenerators (for VRET cost calculations)

0.95

VRET design assumption Value/description

Payment scheme Hybrid: a combination of contract fordifferences on wholesale market revenues and

fixed paymentsPPA lifetime under VRET for renewable projects 15 years

WACC3 (pre-tax real) for VRET new entrants 7%

1.3 Overview of modelling methodologyThe market simulations were conducted using EY’s in-house market dispatch modelling software,2-4-C®. Generation mix and wholesale electricity prices were forecast on a half-hourly basis for theperiod 2017-18 to 2049-50. The forecast outcomes are a result of scenario input assumptions suchas carbon pricing, Large-scale Renewable Energy Target (LRET) policy, electricity demand, rooftopPV uptake, generator fuel prices, generator capacity developments and generator biddingstrategies. The market modelling procedure employed by EY involves running many iterative marketsimulations with the 2-4-C® model to arrive at a final set of outcomes based on economically-drivennew entrant and retirement of generator capacity. The scenario assumptions used in this Report andthe full modelling methodology are described in Section 4 and Appendix A.

1.4 Key modelling outcomesVictorian renewable generation

Figure 1 shows the Victorian forecast renewable generation in each scenario, isolating rooftop PVand hydro generation from large-scale wind and solar PV generation. Hydro and rooftop PVgeneration is equivalent in the three scenarios. The wind and solar amounts for the VRET 3400 MWscenario show the additional generation with respect to the Reference Case, and for theVRET 5150 MW the generation shown is in addition to the VRET 3400 MW scenario.

1 Available at: https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/National-

Electricity-Forecasting-Report2 These prices are arguably low compared with more recent published gas price trajectories

3 Weighted average cost of capital


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Figure 1: Renewable energy generation – all scenarios (wind + solar refers to large-scale wind and solar PV)

► Under the Reference Case, renewable electricity generation in Victoria reaches to 25% of totalgeneration by 2020-21, driven mainly by the Federal LRET. After the LRET reaches itsmaximum in 2020, Victoria’s renewable generation is forecast to grow more slowly, which isprimarily due to the expected uptake of rooftop solar PV systems.

► Under the VRET 3400 MW scenario, Victoria is forecast to achieve 25% renewable generationone year earlier in 2019-20 and 40% by 2024-25.

► Under the VRET 5150 MW scenario, Victoria is also forecast to achieve the 25% target in2019-20 but 40% is reached one year earlier in 2023-24.

Wholesale electricity prices

Figure 2 shows the forecast impact on average wholesale market prices in Victoria due to the VRETas the price differences in the two VRET scenarios and the Reference Case.

Figure 2: Forecast average Victorian wholesale market price difference between the VRET scenarios andthe Reference Case


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► Victorian wholesale market prices are forecast to be reduced by the VRET throughout the2020s in the scenarios as additional renewable capacity is installed in Victoria due to the VRET.

► Lower wholesale prices are projected to occur with higher renewable generation capacitybecause this capacity supplies electricity to the wholesale market at a very low marginal cost,displacing generation with a higher marginal cost.

► Wholesale price reductions are greater in the VRET 5150 MW scenario than in theVRET 3400 MW scenario due to more renewable capacity being installed in Victoria.

► The price differences are diminished by around 2029-30 primarily due to an assumed change inbidding strategies for wind and solar PV generators as the LRET subsidy ceases.

These forecast price differences are an outcome of the assumptions selected by the Departmentacross the scenarios.

Impact on electricity bills

In each scenario, two of the primary components were modelled to show the possible impact of theVRET on electricity bills for consumers in Victoria taking into account certain parameters. These arethe costs associated with the payments to electricity generators under the scheme, and savingsfrom reduction in wholesale market prices. Based on the scenarios modelled and the assumptionsselected by the Department, overall wholesale market price savings are estimated to exceed thecosts of payments.

Lower wholesale prices under the VRET 3400 MW scenario are estimated to reduce the wholesalecomponent of a typical Victorian household electricity bill by around $294 a year on average overthe life of the scheme. A typical small business is projected to save around $144 a year in thewholesale component on average over this period, which for a large company with 20 GWh ofannual consumption is equivalent to $144,000 a year.

If the VRET scheme payments to renewable generators were passed on to Victorian consumers, arepresentative household could save around $15 a year on average over the life of the scheme, asmall business could save around $75 a year and a large company could save around $75,000 ayear over the life of the scheme. These modelling outcomes are all based on the assumptions behindthe scenarios modelled, and as stated earlier only factor in the wholesale market and paymentimpacts on electricity bills.

Impact on jobs and investment

As described in Section 4.6, the impact of the VRET on jobs and investment from the renewableenergy sector in Victoria was undertaken using an Input Output (IO) modelling approach. Thisapproach is bound by a number of assumptions, which should be taken into consideration ininterpreting the results.

► For the VRET 3400 MW scenario the modelling outcomes are around $1.5 billion of additionaleconomic activity from Victoria’s renewable energy sector (out of capital expenditure of around

4 All bill impacts in this section were calculated by discounting the modelled annual bill impacts to 2019-20, and presenting

the result in June 2016 dollars. The life of the scheme for the VRET 3400 MW scenario is consider to be from 2019-20 to2040-41 as this covers all the years of payments to generators.Consumption assumed by the Department: Representative household: 4,026 kWh/year; Small business: 20 MWh/year; Largecompany: 20 GWh/year.


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$5.1 billion5), around 6,050 new two-year construction jobs6 and around 500 ongoing jobsfrom the renewable energy sector.

► Under the VRET 5150 MW scenario, the forecast is $2.1 billion of gross additional economicactivity from Victoria’s renewable energy sector (from capital expenditure of about $7.2billion), around 9,050 two-year construction jobs and about 750 ongoing operational jobs.

Figure 3 shows these summary outcomes for construction jobs and investment in the two VRETscenarios. The jobs outcomes presented did not take into account any increases in Victoria’s shareof the (national and international) renewable energy supply chain (manufacturing, design, etc.) as aresult of the VRET. Total net investment and jobs outcomes from all sectors of the Victorianeconomy are not shown.

Figure 3: Renewable energy construction jobs and investment under the VRET

Impact on emissions

The VRET is likely to lead to reductions in Australia’s greenhouse gas emissions from electricitygeneration. Over the period to 2049-50, the cumulative electricity sector emissions under theVRET 3400 MW scenario are forecast to be around 140 Mt of CO2-e lower than under the ReferenceCase. This is approximately equivalent to 10 months of emissions from the national electricitymarket today. Larger emissions reductions are forecast under the 5150 MW VRET scenario asshown in Figure 4.

5 These numbers were calculated using the same discounting method as for levelised retail bill impacts.

6 Construction-related jobs are expected to be created during the construction phase of the new renewable energy capacity

being driven by the VRET scheme. The total number of two-year construction jobs presented are calculated by adding up thetotal numbers of annual jobs from the modelling over the full construction phase of the VRET and dividing by two.


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Figure 4: Total Australian emissions reduction due to VRET policies in electricity generation sector

The contents of this executive summary should be read in conjunction with the details included inthe rest of this Report.


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2 Introduction

2.1 Background to engagementEY has been engaged by the Department from September 2016 to the date of this Report to providemodelling assistance to inform the implementation of the VRET scheme. Specifically, EY has beenasked to model the NEM for an outlook period to 2049-50 and forecast the impacts of the VRETscheme on the following quantities:

► Wholesale electricity prices, with a focus on Victoria. This includes:► Impact on the wholesale electricity price component of electricity consumer retail bills

► Large-scale generation certificate prices. This includes:► Impact on the LGC price component of electricity consumer retail bills

► The generation mix in Victoria

► Payments to new entrant wind and solar PV generators under different VRET scheme designs.This includes:► Impact of payments on electricity consumer retail bills if the cost of payments were passed

through to consumers

► Jobs and investment stimulated in Victoria.

Over the 12 months of engagement EY has conducted several scenarios with different assumptions.At the time of writing this Report some of the scenarios were no longer valid, primarily due to theretirement of the Hazelwood power station occurring by 30 March 2017. The scenarios presented inthis Report include the retirement of Hazelwood power station.

2.2 Purpose of this ReportThis Report presents the outcomes of two VRET scenarios chosen by the Department, which weremodelled by EY in July 2017. These scenarios informed the Department’s launch of the VRETpolicy7 in August 2017. The scenarios explore the impacts of the VRET scheme with two differentVRET targets: one achieving 3,400 MW of large-scale wind and solar PV in Victoria by 2025 and theother achieving 5,150 MW by 2027. The impact of the VRET for each of these scenarios is analysedby comparing the outcomes with a reference case:

► Reference Case: Forecasts Victorian and national electricity market outcomes under acontinuation of existing and announced policy settings but without the VRET policy

► VRET 3400 MW: The VRET introduces around 3,400 MW of additional renewable energygeneration capacity by 2025; and

► VRET 5150 MW: Victoria introduces around 5,150 MW of additional renewable energygeneration capacity by 2027.

This Report also describes the modelling methodology in detail, plus the VRET scheme design. EYhad no role in advising the Department on policy matters.

2.3 Structure of this ReportThe remainder of the Report is structured as follows:

7 EY had no role in advising the Department on policy matters.


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► Section 3 provides an overview of the VRET auction scheme

► Section 4 details our methodology and provides an overview of the scenario assumptions

► Section 5 highlights key outcomes of the modelling

► Appendix A provides the detail of the scenario assumptions

All prices in this Report refer to real June 2016 dollars unless otherwise labelled. All annual valuesrefer to the fiscal year (1 July – 30 June) unless otherwise labelled.

The modelling outcomes are based on a range of assumptions that define the scenarios. Impacts ofthe VRET scheme are assessed by comparing the VRET scenario outcomes to a reference scenario.We note that the assumptions have been selected by the Department. We acknowledge that there isa significant range of alternative assumptions that, in isolation or in aggregate, could transpire toproduce outcomes that will differ to those that have been modelled. These possible alternativefutures have not been considered in this engagement.


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3 Victorian Renewable Energy Target (VRET)

3.1 Overview of the VRET scheme designThe VRET will incentivise renewable capacity to be built in Victoria. This may be achieved through avariety of mechanisms. As instructed by the Department, the VRET scenarios modelled for thisReport are based on a series of auctions. A reverse auction for up to 650 MW of new renewablecapacity was announced by the Victorian Government on 23 August 20178.

On a high level, VRET reverse auctions are a process where potential projects submit a bid for theirrequired payments to the Victorian Government and the lowest bids are awarded. The VictorianGovernment would then sign contracts with the successful bidders to allow them to secure financingand build their project. More broadly referred to as VRET Support Agreements (which may or maynot have been arranged through reverse auctions), these contracts are the framework under whichthe payments are made to generators. They would also likely include conditions that incentivise thegenerators to be built on time and then maximise their energy output. The VRET SupportAgreement term is 15 years.

3.1.1 Interaction with the LRETAs communicated to EY by the Department, the aspects of the VRET design that interact with theLRET are as follows:

► The successful projects in the first VRET auction of up to 650 MW will receive revenue fromLGCs, either implicitly as part of the VRET payments or directly. The Victorian Governmentexpects that this capacity would have been built elsewhere in Australia without the VRETauction. As such, these projects would be expected to only require a relatively small VRETpayment that allows them to be built in Victoria, rather than another region in Australia, whilereceiving the same LGC revenue.► The outcomes presented in this Report are based on the assumptions that all new entrant

projects operational by late 2020 would receive LGC revenue. For the modelling outcomes,the Department assumed this to be the first 650 MW plus a 200 MW solar PV capacityauction in 2020-21.

► The successful projects for the VRET auctions not operational by 2020 are in addition to theLRET. Those projects will not receive LGC revenue and will only receive VRET payments inaddition to market revenue. The Victorian Government will manage this by taking the LGCscreated by these projects out of the market. Thus the VRET capacity commissioned post 2020will have very little impact on the LGC market.

► The VRET may have a small impact on LGC prices due to the VRET-driven capacity puttingdownward pressure of wholesale market prices. This includes implicit LGC prices throughbundled PPAs (which would depend on the PPA structure) and potentially LGC spot prices.

3.2 The VRET payment schemeThe VRET scheme modelled for the outcomes in this Report is a hybrid scheme combining a contractfor difference (CfD) payment and a fixed payment for generators. This hybrid scheme was designedby the Department. The two payment mechanism components are described in the followingsections.

8https://www.energy.vic.gov.au/renewable-energy/victorian-renewable-energy-auction-scheme


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3.2.1 Contract for difference componentThe CfD component of the VRET hybrid payment scheme sets a price (strike price) for electricity (in$/MWh) that a generator is to receive for their electricity generation in the wholesale market9. EYunderstands that the Victorian Government will set technology-specific CfD strike prices for eachauction.

Figure 5 illustrates the CfD component of a VRET Support Agreement for a generator showinghypothetical annual wholesale market prices and CfD settlements with respect to a contract strikeprice. As shown in the chart, CfD component of the payment provides a top-up, or reduction toensure the generator receives the price for its generation.

Figure 5: Illustration of annual CfD settlements (hypothetical values)

3.2.2 Fixed payment componentThe fixed payment component of the VRET Support Agreement specifies fixed payments for agenerator over and above its wholesale electricity revenue. Estimating the required fixed paymentfor a new entrant generator to be economically viable requires forecasting its wholesale marketrevenue over the contract period and then determining how much additional revenue it needs torecover its fixed and variable costs.

3.2.3 The VRET hybrid payment schemeIn each VRET Support Agreement settlement period (considered to be annual for the modellingpresented this Report), an eligible generator will receive a net payment based on the combination ofthe following settlements:

► A fixed payment in the form of $/MW as bid by the generator in the auction. Being in $/MW, thefixed payment is based on the nameplate capacity (in MWAC) of the generator, rather than theenergy produced.

► A CfD payment (in $/MWh) based on a strike price for the expected wholesale market pricereceived by the generator. Being in $/MWh, the CfD settlement depends on the energyproduced by the generator.

9 The total wholesale market revenue received by a generator depends on its applicable transmission and distribution loss

factors, which vary from generator to generator.


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Based on the CfD strike price, wind and solar PV generators offer bids into the auction for the fixedpayment component. The hybrid design requires the Victorian Government to undertake potentialwholesale market forecasting to determine suitable strike prices for each auction. Due to the CfDpart of the contract, wholesale market price risk is borne by the Victorian Government; otherwisethis risk would be borne by the generators. This is expected to allow generators to offer lower pricesinto the auction than if the CfD component was not included. To take this into account, a 7% WACC10

was used to determine annual loan repayments and the NPV of the costs of new entrant generatorswith VRET Support Agreement (7.5% WACC10 is used for other generators as shown in Table 1).

While the fixed payment in $/MW would not, in isolation, incentivise generators to maximise theirenergy produced, the CfD component of the VRET Support Agreement does provide this incentive.This is because the CfD component is paid in $/MWh and as such a generator will earn more revenuefrom this component the more energy it generates.

To demonstrate, by way of example, the two components of the hybrid scheme payments, Figure 6illustrates these two payments for a generator in $/MWh alongside the wholesale market revenue.

Figure 6: Illustration of annual revenues for a generator with a VRET Support Agreement (hypotheticalvalues)

11

10 Selected by the Department, in consultation with EY. All WACC numbers quoted are pre-tax real.

11 Note that the wholesale market price and strike price are uplifted by the fixed payment with respect to the y-axis for the

purposes of illustration. Note also that the influence of network loss factors applied to a generator’s revenues are omittedfrom the hybrid description and chart for simplicity.


4 Modelling methodology

4.1 Modelling frameworkTo model the impact of the VRET policy on NEM wholesale prices and the Victorian economy, EY’sbroad approach is to compare two scenarios: one without and one with the VRET policy. Thisapproach requires all other assumptions to be the same for the two scenarios so that the impact ofthe VRET policy can be isolated. Figure 7 illustrates this broad approach, and shows how the samemarket forecasting methodology is applied to each scenario and the outcomes of this depend on theassumptions adopted. In the VRET scenarios, the annual installed capacity of wind and solar PV tobe driven by the VRET is assumed to be fixed and EY used its market forecasting methodology tomodel retirements and other new entrant generators around the VRET outcomes. The detailedmethodology for EY’s market forecasting is described in Section 4.2.

Figure 7: Broad approach on modelling the impact of the VRET policy

4.2 Forecasting the electricity market – an iterative approachThe term “market forecasting” in this Report refers to the process of forecasting the expectedgeneration mix and wholesale prices in the electricity market, as an outcome of selected inputassumptions. The market modelling procedure employed by EY involves running many iterativemarket simulations with the 2-4-C® model to arrive at a final set of outcomes. The process involvesthe following steps:

1. Determine a set of input assumptions. These assumptions include policy drivers such as theLRET and a trajectory for a price on carbon emissions as well as an electricity demand forecast,generator costs and technical parameters and many others as described in Section 4.7 andAppendix A.

2. Set up an initial market simulation. Using all the assumptions, conduct an initial time-sequential half-hourly market simulation over the forecast period. Assess the annual netrevenues of each generator using the method of calculating net revenue described below, anddetermine if any new entrants or retirements would be commercially driven for net revenueoutcomes outside a tolerance range.

Analyse VRET impactsAnalyse VRET impacts by comparing forecast

outcomes in VRET Scenario to Reference Scenario.

Reference Scenario assumptionsCore scenario assumptions chosen by the Department.

Market forecasting

VRET policy assumptionsAnnual VRET auction capacities in MW for wind and

solar PV as specified by the Department.

Market forecasting

Reference Scenario VRET Scenario


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3. Iterative modelling to achieve final simulation. Adjust the new entrants and retirements;re-simulate several times until all generators have a net revenue within a specified tolerance12.EY considers that when wind and solar PV generators reach their project lifetime, the sites arelikely to be upgraded to new wind and solar PV generators. This is because these technologiesare likely to still be preferred in decades to come and due to project development savings fromrebuilding on an already acquired, approved and developed brownfield site. As such EY doesnot consider retirements of wind and solar PV generators in this iterative process.

4.2.1 Calculating a generator’s net revenueAll capacity developments made within the market forecasting procedure are determined byassessments of the net revenue of generators modelled within 2-4-C®. A generator’s net revenue iscalculated for any particular year using the equation (1) below.

Net revenue = pool revenue−O&M costs− annualised capital cost repayments− fuel costs (1)

where

Pool revenue is the total annual wholesale market revenue earned over each half-hourly tradinginterval in the year. In the modelling, this is the sum-product of the modelled dispatched generationand the wholesale market price over all trading intervals, multiplied by an assumed loss factor forthe generator.

O&M costs is the total fixed and variable operation and maintenance costs. Variable operationalcosts may include an emissions cost associated with an emission reduction policy.

Annualised capital cost repayments is the annualised capital cost of the generator, taking intoaccount the assumed economic life and weighted average cost of capital (WACC) for the generator.

Fuel costs is the total cost of the fuel used in the generator’s modelled production of electricalenergy throughout the year. The fuel cost is always zero for wind and solar PV.

The net revenue equation does not consider other potential revenue sources (other than poolrevenue). The major sources of generator revenue that are excluded are listed below, with thereasoning for each.

► LGCs. With the present capital costs of large-scale wind and solar PV power stations, and thepipeline of projects under development, EY believes the LRET will be met on time. Based on theannounced status of various renewable projects, EY developed a LRET new entrant list inconsultation with the Department.

► Ancillary services. There are several ancillary service markets in which generators canparticipate and earn revenue. According to publicly available market data, one of the moresignificant of these is the Frequency Control Ancillary Services (FCAS) market, wheregenerators can offer services to ramp up or down generation from a set point to manage thesupply-demand balance. The revenue generators currently earn for providing ancillary servicesis small compared to revenue from electricity sales. In 2015, the total value of FCAS in the NEMwas $112 million, being 1.4% of the $8.3 billion traded on the energy market in the NEM13. Forthis reason the interaction between the energy and ancillary markets was not considered in thenet revenue calculations.

Assessing a generator’s net revenue is conducted differently depending on whether they areexisting or a new entrant:

12 This tolerance is within the margin of uncertainty in the market modelling, and is established by experienced EY market

modelling staff.13

https://www.environment.gov.au/system/files/resources/97a4f50c-24ac-4fe5-b3e5-5f93066543a4/files/independent-review-national-elec-market.docx


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► Existing generators: There is no publicly available data for an existing generator’s capital costrepayments and in many cases the capital cost might be already paid off. As such, EY assessesthe year-on-year net revenue of existing generators in the modelling assuming no capital costrepayments are required, and retire them on a commercial basis if the net revenue is negative(and persists with negative revenue in subsequent years).

► New entrant generators: EY’s new entrant outcomes (beyond the LRET and VRET) are basedon the net present value (NPV) of a generator’s net revenue over its assumed economic lifetimeusing the assumed WACC (see Table 3 in Section 4.7).

4.3 Market simulationsThe market simulations were conducted using EY’s in-house market dispatch modelling software,2-4-C®. Figure 8 shows a flow diagram depicting the input assumptions and data processing used forthe market simulations.

Figure 8: Data flow diagram for the market simulations

Figure 8 shows that conducting a market simulation involves establishing a large set of inputassumptions and data. The key input assumptions and EY’s associated modelling methodology aredescribed in the following sections. The first of these, Section 4.3.1, describes the methodology andphilosophy behind forecasting the electricity market on a half-hourly basis. Some of the inputassumptions are processed in models external to the dispatch software, 2-4-C®, to determine thequantities to be used directly in the dispatch modelling. One of these determines the bids for eachgenerator, for which the methodology is described in more detail in Section 4.3.5. An overview of2-4-C® itself is provided in Box 1.

External Data

2-4-C® dispatchengine

Time-sequentialdispatch of power

system

25 Monte Carlosimulations of generator

forced outages perdemand/reference year

Generator bidsInput assumptions

Maintenance schedule

Demand side participation

Generator planting Fuel prices

Starting bids

Technical parameters

Carbon price

Retirements

Domestic storage uptake

Constraint equations

Trace extrapolator

Projecting half-hourlyprofiles into future yearsmaintaining consistent

weather-driven locationalprofiles

Historical half-hourlyregional demand

Locational wind andsolar energy resource

data

Wind and solar generation modelling

Modelled historical half-hourly rooftopPV, wind and solar generation availability

Future bids escalated bychanges in fuel, O&Mand emissions costs

Annual energy and peakdemand forecasts

Demand manipulation

Manipulation of half-hourly demandprofiles to meet future energy and peaks

Forced outage rates

Bidding assumptionsGeneral assumptions

Outage assumptions

Half-hourly profile assumptions

Half-hourlydispatch, pricesand weighted-average USE

Biddingassumptions

Generalassumptions

Half-hourly profileassumptions

OutageassumptionsTechnical parameters

Rooftop PV uptake


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4.3.1 Forward-looking half-hourly modellingEY’s approach to forward-looking half-hourly modelling for the Department was to base all the inter-temporal and inter-spatial patterns in electricity demand, wind and solar energy on the weatherresources and consumption behaviour in one recent historical year. Figure 9 depicts EY’smethodology for modelling future half-hourly electricity demand, rooftop PV generation and large-scale wind and solar PV available generation, in terms of the data used.

Figure 9: Flow diagram showing EY’s use of an historical year of electricity and atmospheric conditions datato make a half-hourly forecast

The top section of Figure 9 highlights the philosophy behind what features in the historical half-hourly data are projected forward, and what features are modified to capture future conditions.These are described in more detail as follows:

► The historically observed inter-temporal and inter-spatial impact of weather patterns aremaintained in the forecast by using data from the Australian Bureau of Meteorology (BOM).Historical hourly locational wind and solar resource data is used by EY to model half-hourly14

14 Hourly historical resource data is interpolated to half-hourly data.


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generation from rooftop PV, large-scale solar PV and wind generation. All the interactionsbetween wind and solar generation at different sites are projected forward consistently,maintaining the impact of actual Australian weather patterns. The available half-hourly large-scale wind and solar PV generation profiles are bid15 into the market to meet grid demand in the2-4-C® dispatch modelling. These may not be fully dispatched in case of binding networkconstraints or being the marginal generator and setting the price, with the volume above themarginal price being curtailed.

► Inter-temporal and inter-spatial (regional) electricity consumption behaviour is maintained inthe forecast. Historical half-hourly grid demand is obtained from AEMO and added to EY'shistorical modelled rooftop PV to produce the historical electricity consumption. By projectingconsumption forward instead of grid demand, the underlying half-hourly consumer behaviour ismaintained while specifically capturing the future impact of increasing rooftop PV generation inchanging the half-hour to half-hour shape of grid demand during each day. EY also separatelymodels behind-the-meter storage profiles and electric vehicle charging profiles to capture theirimpact on the shape of grid demand.

► The historical year(s) used in the modelling consist of various types of weather, which may ormay not be considered typical or average. With respect to demand, the historical electricityconsumption is processed to convert it into two types of weather-years for each future yearmodelled. One could be considered a moderate year, which uses AEMO’s 50% probability ofexceedence (POE) peak demand forecast16, while the other is considered a year with moreextreme weather, using AEMO 10% POE peak demand17.

► Overall, the half-hourly modelling methodology ensures that the underlying weather patternsand atmospheric conditions are projected in the forecast capturing a consistent impact ondemand, wind and solar PV generation. For example, a heat wave weather pattern thatoccurred in the historical reference year is maintained in the forecast for each future year. Theforecast is developed in the context of a moderate or extreme weather year from a demandperspective. The availability of renewable generation which is assumed to be operational withinthe modelling period is a function of the atmospheric conditions specific to each plant locationand as would have been experienced across the whole NEM during the same weather event.

Each future year was modelled with 50 individual iterations that make up one simulation. The 50iterations are comprised of:

► Two demand profiles (50% POE and 10% POE peak demand profiles), and► 25 Monte Carlo simulations of different generator forced outage profiles, based on the forced

outage probabilities for each generator, as estimated by AEMO.

All simulated years of half-hourly results are then collated with a weighted-average of 0.7 on the50% POE iterations and 0.3 on the 10% POE iterations. This weighting has been an industrystandard for more than a decade, and accounts for the effect of a distribution covering years thatare both less and more extreme than the simulated year types.

The methodologies to produce the forecast half-hourly demand, wind and solar profiles for themodelling are described in more detail in the following sections.

15 EY’s bidding methodology is described in Section 4.3.5.

16 The 50% POE peak demand forecast is expected to be exceeded for one half hour once in every 2 years.

17 The 10% POE peak demand forecast is expected to be exceeded for one half hour once in every 10 years.


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4.3.2 Half-hourly locational renewable generation modellingAs described earlier, and depicted in Figure 9, EY models future half-hourly generation availabilityfor individual wind and large-scale solar PV power stations, based on historical wind and solarresource data. An overview of the methodology for wind and solar is as follows:

► Wind: EY’s wind energy simulation tool (WEST) uses historical hourly short-term wind forecastdata from the BOM on a 12 km grid across Australia to develop wind generation profiles forexisting and future potential wind power stations used in the modelling. WEST manipulates theBOM wind speed data for a site and processes this through a typical wind farm power curve totarget a specific available annual energy in the half-hourly profile for each power station.Existing wind farms use the historical average achieved annual energy from actual data, whileall new wind farms use an assumed annual energy that varies depending on their location in theNEM. All new entrant wind farms in Victoria were modelled with an assumed long-term averagecapacity factor of 36%.

► Solar PV: EY’s solar energy simulation tool (SEST) uses historical hourly satellite-derived solarinsolation data on a 5 km grid across Australia, obtained from the BOM, along with BOMweather station data of temperature and wind speed. The resource data from the BOM isprocessed using the System Advisory Model (SAM) from the National Renewable EnergyLaboratory (NREL) to develop locational solar PV generation profiles. The annual energy outputvaries from site to site as a result of calibration to the performance of existing solar farms andthe locational resource data.

4.3.3 Half-hourly demand modellingTo forecast the half-hourly demand based on a historical year, EY first constructs the historicalelectricity consumption profile. This is made from adding together the historical half-hourlyoperational demand data published by AEMO and EY’s historical modelled rooftop PV generation.The historical rooftop PV is modelled with SEST using regional monthly rooftop PV capacity andannual generation published by AEMO. EY’s modelled half-hourly rooftop PV generation achievesAEMO’s published annual generation expectation and is based on various representative locationsand installation orientations of rooftop PV systems for each NEM region.

Using AEMO’s latest forecasts of annual regional electricity demand, EY’s Trace Extrapolator (TEX)tool applies statistical techniques to manipulate the historical demand profile to meet future annualenergy and seasonal peak demand forecasts. SEST is used to produce corresponding futurerooftop PV profiles based on AEMO’s forecast of rooftop PV uptake, and this is subtracted from thedemand consumption to give the half-hourly operational demand for application in 2-4-C®.

Box 1: Overview of 2-4-C®

The 2-4-C® software was developed soon after the NEM inception in 1998 and is maintainedentirely in-house by EY (formerly ROAM Consulting). The 2-4-C® dispatch engine is able toreplicate most functions of the AEMO real-time dispatch engine (NEMDE), meaning that 2-4-C® iscapable of simulating real market behaviours to the most rigorous level of detail possible in amulti-year forward-looking assessment. As with NEMDE, 2-4-C�® bases dispatch decisions on themarket rules, considering generator bidding patterns and availabilities to meet regional demand.The model takes into account full and partial forced outages and planned outages for eachgenerator, half-hourly renewable energy generation availability by individual power station as wellas inter- and intra-regional transmission capabilities and constraints.

4.3.4 Behind-the-meter battery storageEY’s behind-the-meter battery (domestic) storage profile tool produces a seasonal time-of-daycharge and discharge profile for behind-the meter battery storage for each region. The tool aims toproduce an aggregate profile that responds to peak demand usage tariffs and lower priced daytimeeffective tariffs due to battery owners also owning rooftop PV systems. Rather than assuming aparticular retail tariff structure for future battery owners, it is assumed that the tariffs will relate to


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the net demand profile on the distribution network – consumption minus rooftop PV generation. Asa result the tool produces a fixed time-of-day discharge profile that optimally reduces the seasonalpeak net demand and a charge profile that operates during the lowest periods of residual demand.

This domestic storage profile is added/subtracted to the operational demand for 2-4-C® modelling.The amount of domestic storage modelled in each future year is a scenario assumption.

4.3.5 BiddingFor this report, EY constructed bidding profiles for each individual generator based upon publiclyavailable historical data with the objective to match observed market outcomes as closely aspossible. This strategy yields results that accurately model a generator’s market behaviour for themajority of the time, implicitly capturing their bidding behaviour with respect to their portfolio andcontracting positions. In any single trading interval, each generating unit is modelled with a bidoffering their capacity at up to 10 price-quantity pairs, as in the actual market. For example, a coalunit will bid a certain proportion of its load at or near the market floor price (-$1000/MWh) toreflect its self-commitment intention, and incremental proportions of its capacity at positive pricesto reflect their running costs and higher priced bids potentially up to the MPC to recover fixed costsand be exposed to opportunistic pricing events in the market.

Any known or assumed factors that may influence existing or new generation are taken into accountin modifying these bidding profiles for the modelled future years. These include water availability,changes in regulatory measures, fuel costs or fuel availability, carbon abatement policy or changesin total portfolio generation capacity where applicable.

4.3.6 Network constraintsIn the NEM, generators are dispatched to meet demand, subject to transmission constraints. AEMOpublishes data sets of system-normal transmission network constraint equations for use in forward-looking market modelling studies, including AEMO’s own studies. EY used AEMO’s 2015 constraintequation data set for this report.

4.3.7 ReliabilityThe reliability standard for the NEM is that the expected proportion of total grid energy demand thatis not met in a year should not exceed 0.002%. Unmet demand is referred to as unserved energy(USE).

During the modelling the amount of USE is monitored and it is ensured that the reliability standardis met in any given year.

4.4 Large-scale Generation Certificate pricesThe LGC subsidy for a renewable energy generator can be received in different ways, such asthrough selling LGCs directly on the LGC spot market or implicitly as an LGC contract price as partof a Power Purchase Agreement (PPA) contract bundling together the energy and LGC revenues fora generator.

For the purposes of determining the LGC revenue received by a Victorian new entrant renewablegenerator, EY has estimated the implicit LGC contract price. This is calculated as the fair value of theLGC subsidy required for a new entrant renewable generator entering into a PPA to recover its fixedand variable costs. The duration of this PPA is from the commissioning date until the end of LRET(2030), and as such the required LGC revenue is distributed over that period. In reality the totalsubsidy could be distributed over fewer years for specific projects with a shorter PPA duration. Wehave used the most competitive renewable plant type in the largest region (NSW) to set the LGCcontract price in each simulated year, considering these as the marginal new entrants for LGCpricing. This total LGC subsidy required by the marginal renewable generator is determined bytaking the NPV of the fixed and variable costs (including capital costs) and subtracting the NPV of


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the projected average wholesale market revenue for NSW renewable projects of the same type. Therenewable technology requiring the lowest subsidy for a particular commissioning year is made themarginal generator and sets the LGC contract price. This technology is typically wind or single-axistracking solar PV.

Only a small, but growing percentage of LGCs are traded on the spot market, with most large-scalerenewable projects entering into PPAs with retailers that include a bundled LGC price. The LGC spotprice is subject to many different drivers that are de-coupled from the commercial drivers of LGCcontract prices discussed above. LGCs are traded on the spot market in short-term quantities andthus pricing is influenced by the circumstances of those trades rather than long-term financingconsiderations. The LGC spot market has traditionally been thinly traded allowing the possibility forspeculative trades to influence the price and as such LGC spot prices have been observed to changequickly following political announcements. Post 2020, the LGC spot price will likely be determinedby whether LGCs are in excess or shortfall in a particular year, which will depend on the amount ofrain, wind and sunshine in that year among other factors.

4.5 Retail bill impactAs stated previously, EY was tasked with estimating the impact of the VRET policy on three aspectsof retail electricity bills as an outcome of the scenarios modelled. These aspects are:

► wholesale price component► LGC price component► cost of payments to generators under the VRET Support Agreements if this cost were not

absorbed by the Victorian State budget and passed through to electricity consumers.

Retailers may choose to pass through cost changes to customers in different ways. To add furthercomplication, there are many different tariffs applied to different types of electricity customers inVictoria and other regions in the NEM. To simplify the modelling outcomes, EY has estimated retailbill impacts from the scenarios modelled as an average over all retail bills.

Wholesale price component

The annual wholesale price component of a retail bill is estimated from the modelling outcomes byevenly distributing the total wholesale cost over consumption. Specifically, this is calculated as thesum-product of Victorian demand (sent-out) and wholesale market price outcomes over everymodelled half-hour outcome, and then divided by the total as-delivered load in Victoria. The impactof a VRET policy on the wholesale price component is then the difference in this componentbetween the VRET policy scenario and the Reference Case.

LGC price component

The VRET scheme is expected to have a small impact on LGC prices. This is because the VRET-drivenrenewable capacity developed in Victoria in addition to the LRET-driven capacity will initially putdownward pressure on Victorian wholesale market prices, which in turn drives reduced prices inother states in the NEM (albeit to lesser extent than in Victoria). The modelled slightly lower pricesin NSW give LRET-driven new renewable generators in that state a slightly lower wholesale marketrevenue in the VRET scenarios. As described in Section 4.4, EY estimates LGC contract prices basedon the marginal new entrant renewable generator in NSW. The slightly lower wholesale marketrevenue in the VRET scenarios for NSW renewable generators leads to the calculation of a slightlyhigher forecast LGC contract price.

The LGC price component on retail bills is modelled by estimating the average implicit retailerpayments to generators for LGCs through power purchase agreements. Assuming all existingcontracts continue, the impact of a VRET scheme of the LGC component of retail bills in eachmodelled year is estimated as:


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► The difference in the average retailer LGC payment for new entrants between a VRET scenarioand the Reference Case

► Multiply this difference by the total liable Victorian demand for LGCs, multiplied by the relevantrenewable power percentage for that year.

The total liable demand for LGCs is calculated by determining the total as-delivered load in Victoriaand subtracting the state’s share of EITE load provided by the Department. The renewable powerpercentage is the legislated LRET target that specifies the percentage of liable demand for whichretailers must source LGCs in a given year without paying a penalty.

Cost of VRET Support Agreement payments

If the cost of payments to generators under the VRET policy were not absorbed by the Government,EY has assumed that all distribution system demand that is not classified as an emissions-intensivetrade-exposed entity (EITE) in Victoria would be liable for cost allocation. The total liable demand forthe cost of payments is therefore the same as for the LGC price component.

In each year, the total cost of payments is calculated as the total renewable generation value of thepayments under the Hybrid payment design. For the VRET-driven capacity built before 2020, thesepayments are in addition to EY’s forecast LGC revenue. The total cost of payment is then divided bythe total liable Victorian demand. The impact of a VRET policy on the pass-through of payment costsis then the difference in this component between the VRET scenario and the Reference Case.

4.6 Jobs and investment in VictoriaTo model the direct and indirect impact of the selected policies on Victorian investment andemployment, we have undertaken Input Output (IO) modelling. This approach is bound by a numberof assumptions which are outlined below and should be considered when interpreting the results.

IO modelling calculates how increased expenditure that occurs in one industry affects the broadereconomy through established inter-industry and other economic relationships. These relationshipsare represented in matrix form, which position industries as being both suppliers and consumers oftheir output. For example, a retail business sources products to sell from a wholesaler who in turnsources their stock from the manufacturer. Of course there are many other inputs into the operationof the retailer, such as electricity (sourced from distributors and generators) and construction of thepremises (by the construction industry). The degree to which each industry requires input fromother industries is expressed in the form of coefficients.

The same coefficients serve to distribute expenditure of an industry throughout the economy andthe effect is then measured. These effects are measured in a number of ways, including:

► Total Output – the (final) value of transactions for goods and services generated in theeconomy.

► Value-add or Gross Regional Product (GRP) – the value of outputs produced in a region orindustry less the cost of inputs sourced from other regions. The sum of value-add across allindustry sectors in a specific region is known as the GRP and is a direct measure of productivityincreases.

► Employment – number of jobs or value of wages generated in the economy to service increaseddemand.

This Report presents the outcomes from EY’s IO modelling for the investment and jobs created fromthe renewable energy sector in Victoria. Total investment (expenditure) reported is the value-addedinvestment discussed above, while employment figures are derived from the appropriate multipliersin the IO tables.


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Total effects consist of two components: direct effects of an organisation (i.e., revenues or output)and the flow-on (or ‘indirect’) effects of the organisation’s operations. Indirect effects are calculatedthrough the use of multipliers and can be further broken down into the following two subcomponents:

► Industrial flow effect – the additional output generated by industries that supply inputs toproduction to the industry where the expenditure occurred; and

► Consumption effect – the flow of expenditure to all industries that results from the spending ofsalaries and wages by local employees.

The total effect is the sum of the direct and indirect effects. These components are explained inTable 2.

Table 2: Components of a multiplierEconomic contribution item Corresponding source

Direct economic contribution Total revenues (including any value added taxes) and employmentgenerated by an activity

Flow-on (Indirect) economiccontribution – industrial effect

Indirect contribution generated by an industry as it purchases inputgoods and services generating revenue for other businesses andrequiring these businesses to employ additional personnel

Flow-on (Indirect) economiccontribution – consumption effect

Indirect contribution generated by an industry as its employees spendtheir wages and salaries on household consumption, providing revenuefor other businesses and incentive for these businesses to hire morepersonnel

Total direct and indirecteconomic contribution Sum of direct and indirect economic contribution

Advantages, limitations and assumptions

There are many advantages of using IO modelling as the basis for economic contribution studies.These include:

► The approach produces relatively simple outputs that are easy to understand and communicate

► It can be used to estimate the sectoral impact of industry-specific changes in final demand

► The process is transparent; not a ‘black box’ where the method of calculation is unknown.

However, there are also limitations of the IO approach that result from the use of criticalassumptions and these should be well understood when interpreting the results of the analysis.Typically these limitations are more pronounced when attempting to use IO modelling to analysecomplicated policy impacts which affect many industries, taxes, resource allocation, etc., on a large,national scale. Although this analysis is intended to present a broad overview of the potentialimpacts, it is still important to be mindful of the most significant limitations, which in this contextinclude:

► The approach assumes that unlimited supplies of production inputs such as labour (lack ofsupply side constraints) are available

► It does not account for price changes that may result from increased competition for scarceresources

► The analysis is built on a static picture of the economy that does not consider dynamicadjustments that occur from a shock (fixed coefficients)


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► It considers the average effects rather than the marginal effects, meaning that IO models donot take into account economies of scale, unused capacity or technological change

► Economic contribution is a gross measure rather than a net measure of the economic role of anindustry. Economic contribution studies do not consider substitution impacts. To fullyunderstand these issues, approaches such as computable general equilibrium (CGE) modellingmay be required.

Data sources for modelling economic impacts

The characteristics of regional economies can be diverse and to account for this we have used fourdistinct IO tables as the basis of the economic contribution analysis. These regionalised IO tableswere sourced from an external party, REMPLAN, who are considered to be national experts in thisarea. These regional tables are derived from the national IO table released by the Australian Bureauof Statistics (ABS) and are shaped using a range of local economic characteristics.

Developing IO inputs from modelled outcomes

We have reviewed public reports, industry publications, and our own market analysis to determinerepresentative figures for the investment required during the construction and operation phases ofelectricity sector projects.

In particular, we have utilised the following key reports that involved stakeholder consultation oninvestment and job creation:

► ROAM Consulting report to the Clean Energy Council on the Renewable Energy Target18

► SKM report to the Clean Energy Council on job creation and investment in the renewablesector19

We have also reviewed a number of projects in each technology category:

► Solar: Review of Nyngan, Broken Hill, Moree and Royalla announcements of job creation

► Wind: Range of press releases

4.7 Modelling assumptionsAn overview of the key input assumptions selected by the Department are shown below in Table 3.More detail on the key input assumptions can be found in Appendix A. These assumptions underpinthe outcomes of all of the scenarios presented in this Report.

Table 3: Overview of key assumptions for the scenariosAssumption

Assumptions affecting demand / energy consumption

Load - energy and peak demand

Neutral economic growth scenario from AEMO’s 2016 NationalElectricity Forecasting Report (NEFR)

20.

Both 10% and 50% probability of exceedance (POE) peak demandsmodelled.

Rooftop PV Moderate rooftop PV uptake scenario from 2016 AEMO NEFR,

18https://www.cleanenergycouncil.org.au/dam/cec/policy-and-advocacy/ret/roam-modelling-april-2014/LRET-policy-

analysis-full-report/LRET%20policy%20analysis%20-%20full%20report.pdf19

https://www.cleanenergycouncil.org.au/dam/cec/policy-and-advocacy/reports/2012/Wind-Farm-Investment-Employment-and-Carbon-Abatement-in-Australia/Wind%20Farm%20Investment,%20Employment%20and%20Carbon%20Abatement%20in%20Australia-1.pdf20

Available at: https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/National-Electricity-Forecasting-Report


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Assumption

consistent with the broad economic assumptions that underlie theNeutral energy forecast.

Behind-the-meter storage uptake Neutral scenario projections as per the 2016 AEMO NEFR.

Assumptions regarding market policies

Large-scale Renewable EnergyTarget (LRET)

Present legislated target21

of 33,000 GWh by 2020, and constantuntil scheme end in 2030, with additional voluntary surrenderscapturing demand created by schemes such as GreenPower and otherauction processes (including respective VRET auctions if applicable).

Carbon price

As directed by the Department, EY has applied a carbon price thatresults in the NEM achieving its share of Australia’s emissionsabatement target of 26-28% below 2005 levels by 2030. This carbonprice is lower than that specified in the 2016 AEMO NEFR.

Assumptions affecting market supply

New renewable generation

New renewable planting in the NEM to meet the LRET with theassumption that the LRET is met by 2019-20. In the case of the VRETscenarios, an additional 650 MW of renewable capacity is modelled inVictoria from 2019-20, instead of in other regions.

Thermal generation developmentsEY modelled committed and announced retirements. Assumedthermal generation retirements and developments are as listed inTable 6 and Table 7 in Section A.6.

Fuel prices New entrant fuel prices from AEMO’s planning assumptions22

,developed for the 2016 Electricity Statement Of Opportunities.

Existing generator parametersOperating parameters, and costs including fixed and variableoperation and maintenance costs for each existing generator arefrom the 2014 AEMO Planning Studies.

New entrant parameters includingtechnology capex

New entrant parameters based on 2015 CO2CRC Australian PowerGeneration Technology Report

23, except wind and solar PV capital

costs updated in June 2017, adjusted lower based on recent marketanalysis conducted by EY and the Department.

Weighted Average Cost of Capital(WACC)

Pre–tax WACC of 7.5% (real) for all generators not receiving the VRETsubsidy.Pre–tax WACC of 7% (real) for all renewable projects receiving theVRET subsidy

24. This lower WACC is used to reflect the lower

investment risk for these generators due to the CfD component of theVRET payments providing a guaranteed revenue.This WACC adjustment is based on considered revisions to the WACCparameters, notably a reduction in debt and equity margins, and anadjustment to the debt/equity ratio.

Renewable generation capacityfactors

In the market dispatch modelling, available capacity factors forexisting renewable projects are based on actual generation fromrecent years. Capacity factors assumed for new entrant generatorsdepend on the region/zone in which they are installed. See Table 8 inSection A.8 for the values used.The VRET financial calculations are based on wind achieving alifetime-average capacity factor of 35% and single-axis tracking solarPV achieving a lifetime capacity factor of 26.5%.

21 Available at: https://www.legislation.gov.au/Details/C2016C00286

22 AEMO’s latest planning assumptions as at July 2016

23 Available at: http://www.co2crc.com.au/publication-category/reports/

24 These two WACC values are based on IPART Review of Regulated Retail prices (August 2015 model)

(http://www.ipart.nsw.gov.au/Home/Industries/Research/Market_Update/Spreadsheet_of_WACC_model_-_August_2015)and were selected by the Department in consultation with EY


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4.8 LimitationsEY has only addressed the scope that the Department has requested. There are also inherentlimitations in the modelling outcomes. The following sections outline the major limitations in theoutcomes, separated into scope and modelling limitations.

4.8.1 Scope limitationsNetwork impacts

The focus of our scope of work is on the large-scale generation sector. The Department did not askEY to consider the impact of the VRET scheme on the distribution or transmission sectors. As suchour modelling has not incorporated a detailed review of the technical issues that could result fromthe increased penetration of renewable generation in Victoria.

It is possible that the investment required to achieve the renewable energy target would requireinvestment in network infrastructure. Our analysis has only considered a reasonable geographicalspread of new entrant renewable projects based on broad considerations of the availabletransmission network, site suitability and the underlying renewable resource. EY has not consideredin detail how renewables would be distributed across the Victorian electricity system to best utiliseexisting network infrastructure. If investment in network infrastructure were required to facilitaterenewable generation development, this would likely lead to increases in retail electricity pricesthrough higher network charges. Network limitations may also reduce the availability of high qualitywind and solar sites, and lead to increased costs.

System security

Our modelling scope does not constitute a detailed technical review of the impact that an increasedpenetration of renewable generation, and/or a reduction in synchronous thermal generation, willhave on issues of system security such as inertia and stability and on frequency control ancillaryservice markets.

Scenarios and assumptions

This Report presents the outcomes for two VRET scenarios as chosen by the Department. These arebased on a set of assumptions selected by the Department that define these scenarios. Theseassumptions are from publicly available data. There are many other plausible assumptions that mayeventuate, which would form alternative scenarios that could lead to materially different outcomes.One such assumption is to for retirements of coal generators to occur due to age. However, apartfrom the committed retirements listed in Section A.6, EY has forecast coal retirements based oneconomic modelling outcomes only.

Furthermore, some of the scenario assumptions have recently updated sources in the public domainand some of these are materially different. The rate of change of key parameters such as theoutlook for gas prices has changed markedly in the recent 12 months. Aside from capital costs ofwind and solar PV, the scenarios presented use market assumptions selected by the Department inSeptember 2016. Assumptions that have changed and could be significant for the VRET modellingoutcomes include:

► The outlook for gas prices► The renewable generators assumed to be built for the LRET, including the total capacity

expected to be built in Victoria with and without the VRET► For instance, the recent outcomes of the Melbourne Trams tender25 are not included

► Relatively recent announcements on thermal capacity, such as the retirement of two Torrens Aunits and replacing those with the new Barker Inlet power station in 201926

25http://www.premier.vic.gov.au/renewable-energy-a-jobs-boom-for-victoria/

26http://www.abc.net.au/news/2017-06-07/agl-announces-new-sa-power-station/8596016


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► No explicit modelling of electric vehicles (EVs) was conducted for their impact on electricitydemand

► No consideration of large-scale storage uptake in the NEM was considered► The demand forecast assumptions for the scenarios presented assume Portland smelter will

remain in full operation for the duration of the modelling period. However, there is a risk thatthe Portland smelter will retire after 1 July 2021 when its current contract expires27

► There have been significant changes in bidding behaviour of existing generators across theNEM in the past 12 months, but the impact of this has not been considered in the scenariospresented.

Other issues

Our scope of work has focused largely on the direct impacts of the VRET scheme on the electricitymarket. Net jobs and investments outcomes across the entire Victorian economy have not beenpresented. In the presented analysis, EY has not attempted to identify or quantify the actions thatmay need to be taken by the Victorian Government in areas affected by changes in the energymarket (e.g., education and training to assist in the structural adjustment in the local economy).Furthermore, our analysis has not attempted to quantify the externalities (other than carbonemissions) of electricity generation from different sources.

4.8.2 Modelling limitationsWithin each modelling scenario, we have not assessed the impact of the renewable energy auctionon customer demand. In scenarios where retail electricity prices decrease due to the VRET scheme,this could result in increased customer demand due to long-term demand elasticity. This has notbeen included in our analysis.

We have not considered any changes to bidding behaviour of generators that might arise due to theincreased penetration of renewable generation as a result of the VRET. Furthermore, if theincreased penetration of renewable generation results in a more rapid retirement of existinggenerators, this could result in increased concentration in the wholesale market and could provideopportunities for generators to exploit incidences of transient market power. This has not beenconsidered in the modelling.

Our modelling assumes that the LRET will be met should underlying project economics indicate thatLGC values are sufficient to provide an adequate rate of return to the renewable investment requiredto achieve the annual targets. The impact of any further change to the LRET that results in areduction in scheme targets or a fundamental change to the scheme design have not beenconsidered in any of the scenarios presented.

We acknowledge that all modelling is underpinned by numerous assumptions. Actual outcomes mayvary significantly from those forecast by energy market models. However, the results represent onlya subset of possible changes in the market that may influence the effect of the VRET scheme. Inparticular, the power stations have unique operating considerations that cannot always be reflectedin publicly available data sources. This means that the forecast response of generators to changes inpolicy or other factors may be inaccurate.

27http://www.afr.com/business/energy/electricity/agl-energy-seals-power-deal-for-alcoas-portland-smelter-20170119-

gtv2ug


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5 Modelling outcomes

5.1 Generator capacity mixFigure 10 shows the forecast annual large-scale capacity mix28 by fuel type in Victoria in theReference Case. The chart shows the result of the assumed uptake of 600 MW of wind capacity inVictoria to contribute to meeting the LRET by 2020. However, the assumptions combine to produceinsufficient pricing signals for new entrant wind capacity after the LRET. Due to the modelled solarresource in Victoria and the other scenario assumptions such as the expected solar capacity costs,solar farms are not forecast to enter the market until 2032-33 in the Reference Case. The carbonprice assumed to be reintroduced in 2020-21, and increasing through to 2030-31 to help achievenational emissions reduction targets, places financial pressure on coal fired generators in Victoria.As a result, some coal-fired capacity is replaced by new entrant gas-fired capacity in the 2030s.

Figure 10: Victorian large-scale generator capacity by fuel type – Reference Case

Figure 11 shows the Victorian capacity by fuel type in the VRET 3400 MW scenario. Theachievement of the VRET in this scenario sees continued growth in renewable generation capacityuntil the mid-2020s. As a result, over the long term, less gas-fired generation capacity is forecastunder this scenario than under the Reference Case.

28 Rooftop PV capacity is not shown.


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Figure 11: Victorian large-scale generator capacity by fuel type – VRET 3400 MW

As shown in Figure 12, similar outcomes can be observed in the VRET 5150 MW scenario, with alittle less new gas-fired capacity being forecast.

Figure 12: Victorian large-scale generator capacity by fuel type – VRET 5150 MW

5.2 Generation energy mixPresently, Victorian generation is predominantly sourced from brown coal, which operates at highercapacity factors than most other types of generation capacity. Figure 13 shows that in theReference Case, renewable electricity generation in Victoria increases up to around 25% of totallarge-scale Victorian generation by 2019-20, driven by the LRET, and up to 34% by the end of thestudy period in 2049-50, largely as a result of continued uptake of rooftop PV systems over the restof this period.


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Figure 13: Victorian generation by fuel type – Reference Case

Figure 14 shows the generation outcomes for the VRET 3400 MW scenario. Victoria’s renewableelectricity generation is forecast to increase from around 9,000 GWh in 2017-18 to around22,000 GWh in 2024-25 as Victoria achieves its renewable energy targets. Over the longer term,this increased renewable generation is forecast to result in less gas-fired generation, slightly lowerlevels of coal generation but higher generation overall than under the Reference Case. This meansthat less electricity generation is produced in other regions since the total demand is the same inthese scenarios.

Figure 14: Victorian generation by fuel type – VRET 3400 MW

Figure 15 shows the generation outcomes for the VRET 5150 MW scenario. Victoria’s electricitygeneration sector changes in a similar manner to that observed under the VRET 3400 MW scenariobut these changes are slightly magnified as Victoria introduces more renewable generation underthis scenario. As a result, Victoria’s total electricity generation is slightly higher under this scenariothan under the VRET 3400 MW scenario, while the output of coal and gas-fired electricitygeneration is slightly lower. This means that slightly less electricity generation is produced in otherregions compared to the VRET 3400 MW scenario.


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Figure 15: Victorian generation by fuel type – VRET 5150 MW

5.3 Wholesale electricity pricesFigure 16 shows the forecast impact of the VRET on annual average Victorian wholesale electricityprices in the VRET scenarios. In the 2020s the VRET is forecast to reduce prices compared to theReference Case in both VRET scenarios as more renewable generators are developed to meet the2020 VRET target.

Lower prices are expected with increasing penetration of renewable generators as they supplyelectricity to the market at a very low marginal cost, displacing thermal generation with a highermarginal cost. This allows renewables to bid low prices into the market for their generation, loweringoverall wholesale prices.

The wholesale market price impact diminishes in both scenarios from 2030 partly due to the newentrant solar PV capacity in Victoria in the Reference Case, and partly due to wind and solar PVgenerators being assumed to change their bidding strategy (as described in Section A.9).

Figure 16: Victorian wholesale electricity price difference between VRET scenarios and Reference Case


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5.4 Retail bill impactsAs described in Section 4.5, this Report presents the impact of the VRET scheme on threecomponents of retail electricity bills: wholesale prices, LGCs and VRET Support Agreementpayments. Two funding options were considered in the modelling, a funding through the VictorianState Budget or funding through the pass-through to consumer bills.

Figure 17 shows the annual outcomes for retail bill impact forecast in the VRET 3400 MW scenarioin the case of the funding being absorbed in the Victorian State Budget. Hence, only the wholesaleprice and LGC components are shown.

Figure 17: Annual retail bill impact forecast for the wholesale and LGC components only as an average of allliable customers – VRET 3400 MW scenario

Figure 18 shows the annual outcomes for the three modelled components of retail bills (with thefunding passed through to consumers) in the VRET 3400 MW scenario.

Figure 18: Annual retail bill impact forecast for the three modelled components as an average of all liablecustomers – VRET 3400 MW scenario


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If payments to generators under VRET Support Agreements were recovered through consumerretail bills, the VRET payments would initially increase as the VRET-driven renewable capacity isinstalled. These payments would then reduce due to the following reasons:

1. Rising wholesale prices in response to carbon pricing would reduce the CfD component of thesepayments, and

2. As the 15-year VRET Support Agreements expire.

The VRET scenarios reduce the wholesale component of retail bills in the 2020s compared to theReference Case due to the VRET-driven renewable capacity lowering wholesale market prices. Thiscomponent more than offsets the cost of the VRET Support Agreement payments. This reductiondiminishes in the 2030s, following the trends in the wholesale market prices presented inSection 5.3. Beyond 2030 the effect on retail bills is relatively neutral with minor fluctuations dueto minor variations in wholesale prices.

Figure 19 shows the retail bill impact of all three components in the VRET 5150 MW scenario.Similar trends are forecast to the VRET 3400 MW scenario, with higher VRET payments offset bydeeper reductions in wholesale prices.

Figure 19: Annual retail bill impact forecast for the three modelled components as average of all liablecustomers – VRET 5150 MW scenario

Table 4 shows the average (levelised29) VRET impacts on the different consumer groups in theVRET 3400 MW scenario under the two different funding scenarios mentioned above. The negativenumbers for all the results indicate that the VRET scheme results in a net reduction in retail billstaking the three modelled components into account.

29 The levelised bill impacts were calculated by discounting the modelled annual bill impacts to 2019-20 (but with the results

presented in June 2016 dollars) as requested by the Department. The “life of the scheme” for this calculation was definedby the Department as being from 2019-20 to the final year of VRET Support Agreement payments. The discount rate usedfor all consumer groups is 7.5%. The final numbers for each consumer group are obtained by multiplying the levelised impacton the retail electricity price by their respectively assumed annual electricity consumption.


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Table 4: Average (levelised29) modelled electricity bill impact on Victorian consumer groups in theVRET 3400 MW scenario over life of scheme

Bill impact (Government absorbsscheme costs)

Bill impact (Government does notabsorb scheme costs)

$/year % of bill30 $/year %/bill

Small household31 -19.0 -2.2 -10.1 -1.2

Representative household -28.9 -2.6 -15.4 -1.4Large household -34.1 -2.6 -18.1 -1.4Small business -144 -3.1 -76.4 -1.7Medium company -2,590 -3.7 -1,370 -2.0Large company -144,000 -6.7 -76,400 -3.6

Table 5 shows the equivalent levelised VRET impacts on consumers in the VRET 5150 MW scenario.

These electricity bill impact outcomes are all based on the assumptions behind the scenariosmodelled. As mentioned in the modelling limitations in Section 4.8.2 of this Report, they only factorin impacts on the wholesale market, LRET and VRET Support Agreement payments.

Table 5: Average (levelised) modelled electricity bill impact on Victorian consumer groups in theVRET 5150 MW scenario over life of scheme

Bill impact (Government absorbsscheme costs)

Bill impact (Government does notabsorb scheme costs)

$/year % of bill $/year %/bill

Small household31 -26.1 -3.0 -9.8 -1.2Representative household -39.8 -3.6 -15.1 -1.4Large household -46.9 -3.6 -17.8 -1.4Small business -198 -4.3 -74.9 -1.6Medium company -3,560 -5.2 -1,350 -2.0Large company -198,000 -9.2 -74,900 -3.5

5.5 Renewable energy investmentAs part of the work, EY analysed the impact of the VRET scheme on investment in Victoria. Thisincludes both direct investment, as well as flow-on effects to the Victorian economy.

The forecast impact of the VRET scheme on additional investment in Victoria from renewableenergy development is shown in Figure 20 for the two scenarios. It shows the change in investmentoutcomes expressed as the NPV of additional investment in wind and solar PV in Victoria as a resultof the VRET, compared to the Reference Case.

Based on the scenario assumptions, the VRET 3400 MW scenario is forecast to result in around$1.5 billion33 of additional economic activity from Victoria’s renewable energy sector, out of capitalexpenditure of around $5.1 billion32. Under the VRET 5150 MW scenario, the forecast is $2.1 billionof additional economic activity from Victoria’s renewable energy sector, from a total capitalexpenditure of about $7.2 billion.

The investment outcomes presented do not take into account any increases in Victoria’s share of the(national and international) renewable energy supply chain (manufacturing, design, etc.) as a result

30 Total bill prices by consumer group as per ABS data and from the AEMC, 2016 Residential Electricity Price Trends

31 Consumption assumed by the Department: Small household: 2,641 kWh/year; Representative household:

4,026 kWh/year; Large household: 4,745 kWh/year; Small business: 20 MWh/year; Medium company: 360 MWh/year;Large company: 20 GWh/year.32

Capital expenditure includes capital and connection cost for large-scale wind and solar PV generators built under theVRET, using the discounting method described above (see footnote 29)


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of the VRET. Total net investment modelling outcomes from all sectors of the Victorian economy arenot shown.

Figure 20: Gross additional economic activity (no pattern) arising from renewable energy investment overlife of scheme under each scenario and total capital expenditure (pattern)

33

5.6 Renewable energy jobsAs with investment, direct job creation has been considered, as well as potential flow-on job creationin other industries in Victoria. For each generator technology, jobs are considered arising from bothconstruction and operation of the generators. Construction-related jobs tend to be only for aroundtwo years while generators are being built. Operation-related jobs are ongoing and are for operatingthe generators, including day-to-day operation, financial considerations and maintenance, plusindirect jobs in other sectors of the Victorian economy.

Figure 21 shows the forecast annual numbers of jobs created across the Victorian economy fromthe renewable energy sector activity due to the VRET as full-time equivalents (FTEs). The chartsshow jobs forecast to arise from the wind and solar developments in the VRET 3400 MW scenario.These are the additional jobs expected to be created due to the VRET only and exclude jobs createdfor other renewable capacity in Victoria, such as due to the LRET. The chart shows the constructionjobs created during the construction phase of the VRET-driven renewable capacity, while the numberof ongoing operation jobs increases as the capacity is commissioned and then remains constant asthese projects remain in operation until beyond 2049-50. Construction of wind capacity is assumedto require two years and solar PV one year, so the wind construction jobs start one year earlier thanfor solar PV.

33 These numbers were calculated using the same discounting method as for levelised retail bill impacts (see footnote 29 on

page 33)


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Figure 21: Forecast of annual gross additional jobs created due to the VRET scheme from wind and solardevelopments - VRET 3400 MW scenario

Figure 22 shows the total VRET-driven renewable energy jobs forecast for the VRET 5150 MWscenario.

Figure 22: Forecast of annual gross additional jobs created due to the VRET scheme from wind and solardevelopments - VRET 5150 MW scenario

The total number of two-year construction jobs for the VRET scheme can be calculated from theforecast annual outcomes by summing the total number of forecast annual construction jobs anddividing by two. Using this calculation, the VRET 3400 MW scenario results in around 6,050 newtwo-year renewable construction jobs and the VRET 5150 MW scenario around 9,050 two-yearrenewable construction jobs. As shown in Figure 21 and Figure 22, these total numbers of two-yearjobs are not all available at the same time.

After construction has finished, the total number of operation jobs created due to the newrenewable capacity in operation is 500 in the VRET 3400 MW scenario and 750 in theVRET 5150 MW scenario.


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The jobs outcomes presented do not take into account any increases in Victoria’s share of the(national and international) renewable energy supply chain (manufacturing, design, etc.) as a resultof the VRET. Total net job modelling outcomes across all sectors of the Victorian economy are notshown.

5.7 EmissionsThe VRET is forecast to lead to significant reductions in Australia’s greenhouse gas emissions fromelectricity generation. The majority of the emissions reductions result from lower levels of coal-firedgeneration, which are only slightly offset by an increase in black coal generation elsewhere in theNEM. In the longer term, the VRET scenarios lead to less gas-fired generation installed due to thehigher amount of renewable generators and this further lowers ongoing emissions. Figure 23 showsthe total emissions reductions forecast for the two VRET scenarios relative to the Reference Case inthe NEM by 2049-50. Over the period to 2049-50, the cumulative electricity sector emissionsunder the VRET 3400 MW scenario are forecast to be around 140 Mt of CO2-e lower than under theReference Case. This is equivalent to approximately 10 months of emissions from the NEM today.Larger emissions reductions are forecast under the VRET 5150 MW scenario as shown in Figure 23.

Figure 23: Cumulative emissions reduction in the NEM over the life of scheme under each scenario


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Appendix A Modelling assumptions

An overview of the modelling assumptions was provided in Table 3 in Section 4.7 of this Report.This appendix provides the detail and quantities behind these assumptions. All of these assumptionswere selected by the Department when the modelling commenced in September 2016, except forthe capital cost assumptions, which the Department updated in June 2017 for the scenariospresented.

New entrant renewable capacityA.1.1 Reference Case and VRET scenariosIn all scenarios it is assumed the LRET is met on time by 2020. The LRET target is described belowin Section A.1.2. Based on committed and announced new entrant renewable projects, theDepartment formed a view of a likely list of projects to be installed in the NEM to meet the LRET.This includes assumptions for the contribution to the LRET from existing Hydro generators in theNEM and new generation outside the NEM, such as in Western Australia. For the Reference Case theproject list is without any VRET policy. Figure 24 shows the total generation expected to be achievedby wind and solar PV combined in the NEM in the Reference Case for the next three years.

Figure 24: Generation sent-out by wind and solar PV in the Reference Case

The announced 650 MW auction of the VRET is designed to be complementary to the LRET, i.e., itwill not create additional demand for LGCs on the national level. This part of the scheme is modelledby inclusion of the respective additional capacity of renewable energy in Victoria, substituting LRETcapacity in other states.

Figure 25 shows the capacity of wind and solar PV modelled in Victoria selected by the Departmentfor the three scenarios.


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Figure 25: Victorian renewable capacity installed (wind-no pattern, solar-pattern) – all scenarios

A.1.2 The large-scale renewable energy targetThe present legislated targets require 33,000 GWh per annum of eligible renewable energy from2020 to 2030, as illustrated in Figure 26. Additional voluntary certificate surrenders are alsoexpected, due to several state or territory policies, and consumer choice schemes as well such asthe GreenPower program. The amount of additional demand assumed for renewable energy due tothese schemes are also shown in the figure.

Figure 26: Large-scale renewable energy target trajectory

Demand and energy consumptionOne of the primary considerations when forecasting the electricity market is the future demand forelectricity, which is expressed in terms of annual energy consumption and seasonal peak demands.


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EY has used the 2016 AEMO NEFR34 as the source of electricity demand and energy projections asthe most up-to-date source at the time of modelling.

The trajectories in annual operational energy consumption (to be met by large-scale generationfacilities) for each region of the NEM assumed in this scenario are shown in Figure 27. They areconsistent with the Neutral economic growth scenario as published by AEMO in the 2016 NEFR andextrapolated by EY up to 2049-50. The 2016 NEFR Neutral outlook is fairly flat, partly due to anincreases in demand due to growing population being offset by increasing uptake of rooftop PV aswell as higher levels of energy efficiency in domestic appliances, commercial buildings andmachinery.

Figure 27: AEMO Neutral annual regional energy forecast in the NEM

Peak demands are materially influenced by weather conditions, particularly hot temperatures insummer and cold temperatures in winter, driving cooling and heating air conditioning loads,respectively. The peak demand (and near-peak demand conditions) increases the risk of extremeprice volatility, and therefore the magnitude of the peak demand in any given year is a materialfactor in determining overall wholesale market pricing trends. EY has used two of AEMO's publishedpeak demand forecasts34 representing a 10% POE and an average (50% POE) peak demand level.The 50% POE peak represents a typical year, with a one in two chance of the peak demand beingexceeded in at least one half hour of the year. The 10% POE peak demand represents a one in tenchance of being exceeded in at least one half hour of the year. EY simulates both targets and createsweighted-average results.

Figure 28 below shows the regional peak demand in the NEM for the 10% POE projection used in thisscope of work from the Neutral economic growth scenario from the AEMO NEFR 2016, extrapolatedup to 2049-50.

34 Available at: https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/National-

Electricity-Forecasting-Report


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Figure 28: AEMO Neutral annual 10% POE regional peak demand forecast in the NEM

Commercial and residential rooftop PV uptakeThe uptake in rooftop PV systems in recent years has been driven by favourable government policiesand attractive payback periods. While many of the supportive government policies have now beenremoved (or significantly scaled back), AEMO expects continued significant growth in rooftop PVuptake due to decreasing costs of PV systems and increasing retail energy costs.

Figure 29 shows the combined commercial and residential rooftop PV uptake trajectory used in allscenarios, which is part of AEMO’s Neutral scenario from the 2016 AEMO NEFR.

Figure 29: AEMO Neutral installed rooftop PV capacity forecast for the NEM

Behind-the-meter storage uptakeAll scenarios use the sum of AEMO’s household and commercial battery storage uptake trajectoriesfrom the Neutral scenario in AEMO’s 2016 NEFR. Figure 30 shows this uptake in each region.


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Figure 30: Household battery storage uptake trajectory per region

EY’s storage profile tool optimises the charge and discharge profile of the installed householdbattery storage capacity to “flatten” (reduce the difference between the maximum and minimum)the time-of-day average sent-out demand profile as much as possible. This is based on theassumption that small-scale storage will be operated to optimally reduce a customer’s electricity bill,particularly using the incentives created by time-of-use electricity tariff structures. Since time-of-use electricity tariffs are typically designed to incentivise customers to use less electricity when it ismore expensive, and electricity is more expensive when demand is high, we considered the time-of-use tariff to correspond to the time-of-day average demand profile on the network.

This optimisation methodology produces an annual fixed time-of-day charge and discharge profilefor battery storage in each region and each simulated year. Figure 31 shows an example storageprofile created for 2026-27 in Queensland, along with the time-of-day average rooftop PV, demandnet of rooftop PV and final operational demand (net of rooftop PV and storage) profiles.


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Figure 31: Example household battery storage charge and discharge profile in 2024-25 in Victoria

Emissions reduction policy – a carbon priceAfter the LRET is achieved in 2020, the Federal Government’s Emission’s Reduction Fund andSafeguard Mechanism are currently the key policy settings to drive decarbonisation of the electricitysector to contribute to Australia’s emissions abatement targets.

Given the Australian Government's commitment at the UNFCCC COP21 Paris Climate Conference inDecember 2015, additional policy mechanisms may be required to achieve the national emissionsabatement target of 26-28% below 2005 levels by 2030.

AEMO's 2016 NEFR applied a carbon price that they assumed would be required to drive theemissions abatement needed to achieve this target. The NEFR assumes that an energy policychange will result in direct costs to the electricity sector equivalent to a carbon price of $25.11/tCO2-e in 2020 21, rising to $50.22/t CO2-e by 2030-31.

Our preliminary modelling showed that this carbon price trajectory would be likely to hastengenerator retirements such that emissions abatement in the electricity sector would more thanachieve the sector’s “fair share” of the national abatement target. In the scenarios, based on theinstructions from the Department, we have modelled a lower carbon price than that applied byAEMO, which achieves emissions abatement by 2030 that more closely matches the NEM’sproportional share of the target.

Figure 32 shows the carbon price trajectory used in the scenarios.

Recently, alternative mechanisms to achieve emissions reductions in the electricity sector havebeen discussed in Australia, such as the Clean Energy Target (CET) recommended in the FinkelReview35. At the time of writing this Report, it is unclear if a CET will be implemented for Australia’selectricity sector and how a CET would interact with the VRET.

35http://www.environment.gov.au/energy/national-electricity-market-review


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Figure 32: Carbon pricing trajectory

Thermal generation developmentsDue to a modest energy growth outlook as per the AEMO 2016 NEFR Neutral scenario andincreasing competition from renewable projects to meet the LRET, retirement or mothballing(temporary removal from service) of thermal generation is likely over the modelled period. Inaddition to the recently retired Hazelwood power station by 30 March 2017, further announcedretirements from the market are listed in Table 6. EY has included these in the scenarios.

Table 6: Retirements due to age or operational concernsPowerstation

Region Type Capacity(MW)

Timing Comments

Smithfield NSW CCGT36 171 2017-18

Announced retirement(http://www.aemo.com.au/Electricity/Planning/Related-Information/Generation-Information)

Liddell NSW BlackCoal 2,000 2022

Announced retirement(https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/Generation-information)

The continued operation of the NEM’s ageing black and brown coal fleet is an importantconsideration when modelling the market over the short, medium and long term. The key marketdrivers assumed in the scenarios, and the technical considerations of each power station, align toproduce a material level of retirement risk, particularly for each region’s oldest and/or highest costgenerators. Given the oversupplied market NEM-wide, as well as the mild demand growth conditions,it is not expected that significant re-investment in many of these assets will be forthcoming. It isunlikely that generators approaching the end of their technical life, or those with fuel concerns, willfind it financially viable to either undertake a major refurbishment or negotiate a favourablealternative future fuel supply. To take into account rising costs associated with ageing coalgenerators into the modelling, for the scenarios presented EY has assumed a 2% annual uplift in thefixed operation and maintenance costs for coal generators. A higher or lower uplift would changethe modelling outcomes with respect to the point in time at which a coal power station would beretried for economic reasons.

Thermal plants returning to service from a period of storage are exceptions to the typical behaviourof thermal generators in the NEM. Swanbank E as shown in Table 7 is the only mothballed generator

36 Combined Cycle Gas Turbine


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that has announced a return to service. Given the challenging market conditions at the time of themodelling and the high cost of fuel, despite the announcement, it is assumed that Swanbank Eremains offline for the duration of the modelled period.

Table 7: Thermal plant returning to servicePowerstation

Region Type Capacity Timing Comments

Swanbank E QLD CCGT 385 Remainsoffline

Return of this CCGT plant is announced to be on1 January 2018.(http://www.stanwell.com/news/press-releases/swanbank-e-power-station-return-service/).However it is assumed the plant remains offlinefor the duration of the modelled period.

New entrant capital costsNew entrant capital cost projections are a primary driver of VRET scheme costs as well as the futuregeneration mix and wholesale market outcomes forecast in the scenarios. In consultation with EY,the Department has based the capital cost trajectories for the scenarios on projections published inthe CO2CRC Australian Power Generation Technology Report23 but wind and solar capital costsadjusted lower in the next ten years. This is based on recent market analysis conducted by EY andthe Department using publicly available information. Figure 33 shows the capital cost projectionsused in the scenarios for the new entrant technologies modelled. These capital costs excludeconnection costs.

Figure 33: New entrant capital costs for different technologies

Capacity factors for wind and solar PV generatorsCapacity factors assumed for new entrant generators depend on the region/zone in which they areinstalled. Table 8 provides an overview of the capacity factors used in the different NEM zones foreach technology. Wind capacity factors for all new entrant wind are modelled as per the table below.Solar capacity factors stated in the table are a guide to what is modelled. Solar capacity factors areable to be modelled according to the locational solar resource, with respect to the calibration of EY’sSolar Energy Simulation Tool to existing solar farm capacity factors.


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Table 8: New entrant capacity factors per technology targeted for the different NEM zonesTechnology type Region NTNDP zone Capacity factor

Wind QLD NQ 33.0%Wind QLD CQ 33.0%Wind QLD SWQ 33.0%Wind QLD SEQ 33.0%Wind NSW NNS 36.0%Wind NSW NCEN 36.0%Wind NSW CAN 36.0%Wind NSW SWNSW 36.0%Wind VIC NVIC 36.0%Wind VIC CVIC 36.0%Wind VIC MEL 36.0%Wind VIC LV 36.0%Wind SA SESA 36.0%Wind SA ADE 36.0%Wind SA NSA 38.0%Wind TAS TAS 38.0%Tilted Plate PV QLD NQ 26.0%Tilted Plate PV QLD CQ 25.5%Tilted Plate PV QLD SWQ 25.0%Tilted Plate PV QLD SEQ 24.0%Tilted Plate PV NSW NNS 25.0%Tilted Plate PV NSW NCEN 24.0%Tilted Plate PV NSW CAN 23.0%Tilted Plate PV NSW SWNSW 25.5%Tilted Plate PV VIC NVIC 24.0%Tilted Plate PV VIC CVIC 24.5%Tilted Plate PV VIC MEL 22.0%Tilted Plate PV VIC LV 20.0%Tilted Plate PV SA SESA 22.0%Tilted Plate PV SA ADE 23.0%Tilted Plate PV SA NSA 25.0%Tilted Plate PV TAS TAS 21.5%Single axis tracking PV QLD NQ 31.0%Single axis tracking PV QLD CQ 30.5%Single axis tracking PV QLD SWQ 30.0%Single axis tracking PV QLD SEQ 28.0%Single axis tracking PV NSW NNS 29.5%Single axis tracking PV NSW NCEN 28.0%Single axis tracking PV NSW CAN 26.0%Single axis tracking PV NSW SWNSW 30.0%Single axis tracking PV VIC NVIC 28.0%Single axis tracking PV VIC CVIC 28.5%Single axis tracking PV VIC MEL 25.0%Single axis tracking PV VIC LV 22.5%Single axis tracking PV SA SESA 25.0%Single axis tracking PV SA ADE 27.0%Single axis tracking PV SA NSA 29.0%

Bidding of wind and solar PV capacityAll wind and solar generators are assumed to bid their available capacity at -$40/MWh up to2029-30. This assumption is based on recent observed bidding behaviour from existing wind andsolar generators37. The negative price bid is understood to reflect the LGC revenue for thegenerators through power-purchase agreements. From 2030-31, the wind and solar capacity is

37 Based on bidding data published by AEMO.


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assumed to bid $0/MWh for half of the capacity (since there is no longer an LGC revenue), and apositive price shadowing the cheapest CCGT bid (based on CCGT short-run marginal cost) for anyavailable capacity above that. This is based on the hypothesis that there will be large portfolios ofwind and solar generation owned by single entities post 2030 that can bid strategically to increaseoverall wholesale market revenue. These single entities need to own significant renewable capacityso that they can bid at the shadow price and in doing so have some generation not dispatched whenthey set the price, but overall they have an increase in revenue across the fleet. Entities owning onlya few renewable generators cannot do that because of competition and will be underbid and lose toomuch generation in dispatch to compensate. An alternative assumption on wind and solar biddingwould lead to different scenario outcomes.

Coal pricesFigure 34 shows the assumed coal prices used in the modelling for the existing coal power stations.The prices are from AEMO’s 2016 Electricity Statement of Opportunities38 (ESOO).

Figure 34: Coal prices for current operating power stations as released by AEMO in their 2016 ESOO

Gas pricesThe price for natural gas is a key influence on market prices, influencing the bidding of gas firedgenerators. We do not consider the impacts of short-term gas contracts in our modelling, ratherconsidering the pricing effect of long-term gas contracts for gas powered generators. Figure 35below shows the assumed gas price trajectory in sample NEM ‘zones’ for uncontracted gas supplies.As existing gas generators’ current gas contracts roll off, EY expects that these generators will beforced to adopt this price trajectory for their future gas contracts.

38 https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Planning-and-forecasting/NEM-Electricity-

Statement-of-Opportunities


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Figure 35: Forecast gas prices for the capitals in each region (from 2016 ESOO)


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