concentrated solar thermal market modelling · energeia developed, configured and ran its...

Concentrated Solar Thermal

Market Modelling

Prepared by ENERGEIA for Jeanes Holland and Associates

May 2018

Version 0.3 of 45 May 2018

1 Executive Summary

This report details the underpinning quantitative analyses undertaken to support the development of an evidence based, independent, long-term, holistically developed roadmap for Concentrated Solar Thermal (CST) in Australia. The quantitative modelling was used to estimate the technology’s potential in both small and large applications to contribute to Australia’s energy security and industrial production potential until 2040.

1.1 Background

The key objective of the modelling undertaken to support the CST roadmap development program was the identification of the potential sources of value from CST storage across a range of small and large-scale market segment, and off-grid and grid-connected sub-segments within those segments.

1.2 Scope and Approach

Energeia developed, configured and ran its whole-of-power system simulation platform (uSim) to model the CST value streams (to 2040) and potential CST market sizing across the following three different market segments:

• Off-Grid Small-Scale Customers – Off-Grid mines, representative of large, remote customers, which are looking to reduce their electricity costs, which are currently based on diesel, or diesel and solar PV

• Off-Grid Small-scale Communities – Fringe-of-Grid communities, representative of remote, grid connected communities, where governments and networks are looking to reduce cost-to-serve by potentially taking them off grid during peak demand or permanently

• Grid-Connected Large-Scale – Transmission-connected CST acting as large-scale dispatchable renewable energy generation and ancillary services (primarily frequency regulation) to the Australian Energy Market Operator (AEMO)

These market sub-segments were combined into four different scenarios that considered the interaction between small-scale market development opportunities and different rates of deployment of large-scale CST based on global rates of cost decline:

• Low Case – what is the uptake of large-scale CST in Australia if global deployment is slower than forecast, and learning rates are slower than expected?

• Base Case – what is the uptake of large-scale CST in Australia given the most likely case for global deployment, learning rates and therefore capital costs?

• No Regrets Case – how would the Base Case be improved if targeted interventions were made to encourage the development of small-scale CST in various sub-segments, in addition to the uptake of large-scale systems in the transmission network?

• High Case – what would be the net uptake of CST systems if the targeted interventions in small-scale market sub-segments were complemented by a faster global deployment of CST, and resultant fast learning rates and steeper cost declines?

1.3 Modelling Results

1.3.1 Market Sizing

Figure 1 shows the cumulative CST uptake capacity across the modelled period. The Base Case CST uptake is 3.94 GW to 2040, all large plants in the wholesale energy market, assuming the most likely case for CST deployment rates globally. An additional 1.14 GW can be taken up in addition to this if targeted interventions to encourage the application of CST to small-scale markets at the fringe-of-grid is encouraged.

Analysis of the modelling of uptake by market segment and sub-segment reveals that the small-scale market applications are taken up in the early 2020s, between 5 and 10 years earlier than the large-scale markets.


Figure 1 – Cumulative CST Uptake (GW)

Source: Energeia Modelling

1.3.2 Economic Benefit

The economic benefits delivered by CST uptake, net of interventions, is shown in Figure 2. In the Base Case, $2.023 bn of value is delivered (on a present value basis), which increases to $3.376 bn under the No Regrets Case.

Figure 2 – Value Stack by Scenario (Present Value, $ bn, real 2018)


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Disclaimer

While all due care has been taken in the preparation of this report, in reaching its conclusions Energeia has relied upon information provided third parties as well as publicly available data and information. To the extent these reliances have been made, Energeia does not guarantee nor warrant the accuracy of this report. Furthermore, neither Energeia nor its Directors or employees will accept liability for any losses related to this report arising from these reliances. While this report may be made available to the public, no third party should use or rely on the report for any purpose.

The modelling results are supplied in good faith and reflect the knowledge, expertise and experience of the consultants involved. Energeia does not warrant the accuracy of the model nor accept any responsibility whatsoever for any loss occasioned by any person acting or refraining from action as a result of reliance on the model. The model is for educational purposes only.

For further information, please contact:

Energeia Pty Ltd Suite 902, 172 Clarence Street Sydney NSW 2000

T: +61 (0)2 8097 0070 E: [email protected] W: www.energeia.com.au

http://www.energeia.com.au/


Table of Contents 1 Executive Summary ....................................................................................................................................... 2

1.1 Background .......................................................................................................................................... 2

1.2 Scope and Approach ........................................................................................................................... 2

1.3 Modelling Results ................................................................................................................................. 2

Disclaimer ............................................................................................................................................................... 4

2 Background ................................................................................................................................................... 7

2.1 Project Brief ......................................................................................................................................... 7

2.2 Project Objective .................................................................................................................................. 7

3 Scope and Approach ..................................................................................................................................... 8

3.1 Scope ................................................................................................................................................... 8

3.2 Approach .............................................................................................................................................. 8

4 Assessment Approach ................................................................................................................................. 10

4.1 Value Streams ................................................................................................................................... 10

4.2 Market Segmentation ......................................................................................................................... 11

5 Scenario Development ................................................................................................................................ 19

5.1 Scenario Framework .......................................................................................................................... 19

5.2 Scenario Design ................................................................................................................................. 20

6 Modelling Results ........................................................................................................................................ 22

6.1 CST Outcomes .................................................................................................................................. 22

6.2 Market Impacts .................................................................................................................................. 24

Appendix 1: Small-Scale Market Sub-Segments .................................................................................................. 29

Off-Grid Mines .................................................................................................................................................. 29

Fringe-of-Grid/Off-Grid Microgrids .................................................................................................................... 31

Commercial and Industrial Customers ............................................................................................................. 32

Appendix 2: Base Case Market Scenarios ............................................................................................................ 33

Market Scenario Development ......................................................................................................................... 33

Market Scenario Results .................................................................................................................................. 36

Appendix 3: Key Scenario Factors ........................................................................................................................ 41

Deployment Rates ............................................................................................................................................ 41

Energy Market Factors ..................................................................................................................................... 41


Table of Figures

Figure 1 – Cumulative CST Uptake (GW) ............................................................................................................... 3 Figure 2 – Value Stack by Scenario (Present Value, $ bn, real 2018) .................................................................... 3 Figure 3 – CST Market Sub-Segments – Defined by Market Segment and Connection Status.............................. 8 Figure 4 – Average Daily Dispatch – Off-Grid Mine System ................................................................................. 12 Figure 5 – Levelised Cost of Electricity Comparison – On-Grid vs. Off-Grid Mine (2025) ..................................... 13 Figure 6 – Fringe-of-Grid Towns Relative to Large Towns, by Distance Band ..................................................... 13 Figure 7 – Average Daily Dispatch – Fringe-of-Grid/Off-Grid Microgrid ................................................................ 14 Figure 8 – Capacity and Generation by Fuel Type – Fringe-of-Grid/Off-Grid Microgrid ........................................ 14 Figure 9 – Levelised Cost of Electricity Comparison – Fringe-of-Grid/Off-Grid Microgrid (2025) .......................... 15 Figure 10 – FCAS Supply and Demand Forecast ................................................................................................. 16 Figure 11 – CST Discharge Opportunities vs. Spot Price (2032) .......................................................................... 17 Figure 12 – CST Revenue and Costs (2032) ........................................................................................................ 18 Figure 13 – Scenario Modelling to define the Strategic Space ............................................................................. 19 Figure 14 – Cumulative CST Uptake (GW) ........................................................................................................... 22 Figure 15 – CST Market Segment Share (%) ....................................................................................................... 23 Figure 16 – Value Stack by Scenario (Present Value, $ bn, real 2018) ................................................................ 23 Figure 17 – Value Share by Scenario (%) ............................................................................................................. 24 Figure 18 – Results Variance to the Base Case ................................................................................................... 25 Figure 19 – Net Entry of Generation and Storage (Cum. 2018-40) ....................................................................... 26 Figure 20 – Volume Weighted Average Pricing (5-Yearly 2020-40)...................................................................... 26 Figure 21 – Customer Bills (Cum. Wholesale Bills in 2040 by State, Real $ bn) .................................................. 27 Figure 22 – NEM Carbon Dioxide Equivalent Emissions (5-Yearly 2020-40) ....................................................... 28 Figure 23 – New Mines – Forecast to 2040 .......................................................................................................... 29 Figure 24 – Off-Grid Mines – CST Configuration by Size ..................................................................................... 30 Figure 25 – Off-Grid CST Mines – Cumulative to 2040 ......................................................................................... 30 Figure 26 – Off-Grid CST Mines – Cumulative Generation Capacity to 2040 ....................................................... 31 Figure 27 – Fringe-of-Grid Market Potential – Zone Substation Count by DNSP ................................................. 31 Figure 28 – Cumulative Fringe-of-Grid CST Uptake (GW) – No Regrets Case .................................................... 32 Figure 29 – Results Variance to Base Case – Market Scenarios ......................................................................... 37 Figure 30 – CST Uptake Capacity in GWs installed (Cum. 2018-40, Market Scenarios) ...................................... 37 Figure 31 – Net Entry/Exit of Generation and Storage (Cum. 2018-40, Market Scenarios) .................................. 38 Figure 32 – Volume Weighted Average Pricing (5-Yearly 2020-40, Market Scenarios) ........................................ 39 Figure 33 – Customer Bills (Cum. Wholesale Bills in 2040 by State, Real $ bn, Market Scenarios)..................... 39 Figure 34 – NEM Carbon Dioxide Equivalent Emissions (5-Yearly 2020-40, Market Scenarios).......................... 40 Figure 35 – CST Large-Scale Technology Cost Forecast ..................................................................................... 41 Figure 36 – Rooftop Solar PV Costs per kW installed ........................................................................................... 42 Figure 37 – Battery Energy Storage System Costs per kWh installed .................................................................. 42 Figure 38 – Large Scale Renewable Capital Cost per kW installed ...................................................................... 43 Figure 39 – Large Scale Levelised Storage Cost per MWh installed .................................................................... 44

Table of Tables

Table 1 – Market Segment Summary – Sub-segments by Scale and Connection Point ...................................... 11 Table 2 – Scenario Summary – Relationship between Market Sub-Segments and Scenario Cases .................... 20 Table 3 – Detailed Final CST Scenario Design ..................................................................................................... 21 Table 4 – Results Summary .................................................................................................................................. 24 Table 5 – Market Scenario Design ........................................................................................................................ 35 Table 6 – Results Summary – Market Scenarios .................................................................................................. 36 Table 7 – Consumption Tariffs .............................................................................................................................. 43 Table 8 – Cost Reflective Tariff ............................................................................................................................. 43


2 Background

Jeanes Holland and Associates (JHA) engaged Energeia’s market modelling services to model potential value streams for Concentrated Solar Thermal (CST) in Australia in small-scale and large-scale applications.

2.1 Project Brief

This market modelling report supports the overall development of an independent, long-term, holistically developed roadmap for CST in Australia, positioning the potential of the technology in both small and large applications to contribute to Australia’s energy security and industrial production potential into the middle part of this century.

2.1.1 Prior Modelling

The modelling work in this project is built on previous modelling completed for the Australian Solar Thermal Research Initiative (ASTRI) by Energeia1. That previous modelling was completed for ASTRI working collaboratively with ARENA to understand when and where marginal storage requirements emerge in the National Electricity Market (NEM), and to identify CST’s maximum pricing point and optimal technical configuration, i.e. heliostat, storage and power block component ratios, by state and voltage level, over time.

2.1.2 Scenario Drivers

Energeia developed and modelled scenarios to test a range of strategic questions impacting on the potential deployment of CST across a range of market segments and sub-segments, including:

• What are the value streams potentially accessible to CST?

• What role can CST play in microgrid applications?

• What are the benefits of accelerated CST deployment?

• How will increasing decentralisation of the energy system affect CST?

• How do declining technology costs for renewable energy generation impact CST uptake over time?

• What are the competitive risks to CST uptake from large scale storage projects?

• What are the regulatory risks to CST uptake from the National Energy Guarantee (NEG) or from future emissions policies?

2.2 Project Objective

The key objective was the identification of the potential sources of value for CST with storage for a range of different market sub-segments, including both small and large scales, in off-grid and grid-connected applications.

1 Energeia (2017), ‘Concentrated Solar Power Cost Targets: Prepared by Energeia for the Australian Solar Thermal Research Initiative’


3 Scope and Approach

Energeia worked closely with JHA and the CST Roadmap team to identify a range of strategic drivers impacting on the CST uptake in Australia and the impact of CST uptake on the market.

3.1 Scope

The project was designed to simulate the impacts of CST deployment into the Australian energy system, including:

• the impact of large-scale applications, bidding into the NEM spot energy market or via a time of day/dispatchability based Power Purchase Agreements (PPA), and;

• range of future NEM configurations implied by current national policy and regulatory debates;

The scenarios were compared based on their impact on:

• uptake and economic benefit of CST, and;

• impacts on the NEM (against the energy trilemma of system security, user affordability and environmental sustainability).

3.2 Approach

An iterative approach was adopted, which allowed the Energeia and broader CST Roadmap teams to work in partnership to agree on inputs, assumptions, scenario design and reporting.

3.2.1 Assessment Framework

CST storage impact on the Australian energy systems was assessed through a framework that valued the storage benefits CST could provide and identified the market segments and sub-segments within which it could work.

3.2.1.1 Value Streams

In developing the value stack for each of the applications for CST considered in the market modelling, Energeia has considered the type of services (customer, network and market) that CST can provide and the quantum of value available from each of these services.

3.2.1.2 Market Segments

The market segment definition considered small-scale (<30MW) and large-scale (>100MW) market segments, as shown in Figure 3.

Figure 3 – CST Market Sub-Segments – Defined by Market Segment and Connection Status

Source: Energeia

Market Segment

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Five market sub-segments were identified, investigated and screened (for both the economic benefit and their potential for implementation), and two small-scale and one large scale segments were taken forward for detailed modelling.

3.2.2 Scenario Development

Scenarios were developed for modelling CST uptake, and modelling market risks to the uptake outlook.

3.2.2.1 Uptake Modelling

Scenarios combining Energeia’s modelling of the large-scale market (i.e. the wholesale energy market) and the targeted initiatives to encourage the development of CST applications in small-scale niche markets were developed based on both CST deployment rate and the potential value of small-scale applications of CST.

3.2.2.2 Risks to the Uptake Forecast

Once a Base Case was developed, that reflected the most likely case of CST deployment globally, Energeia conducted a scenario modelling process across a range of market risk factors to understand the impacts on CST uptake. This analysis is detailed in Appendix 2: Base Case Market Scenarios.

3.2.3 Modelling and Key Assumptions

3.2.3.1 uSim Simulation Platform

For each modelled scenario, Energeia configured their uSim simulation platform2, ran the simulation, and reported and reviewed the results.

3.2.3.2 Key Assumptions

The simulation platform was updated with key assumptions for the current cost and performance of CST systems, which was supplied by ITP Renewables, namely:

• CST operating and capital costs

• CST technology and operating parameters

• CST learning rates or technology cost decline curves

In addition, ITP Renewables provided an annual Direct Normal Irradiance (DNI) profile for Longreach for a typical CST plant, which was scaled by Energeia to cover the remainder of the NEM.

3.2.3.3 Key Performance Indicators

Energeia agreed the following basket of key performance indicators (KPIs) with JHA to track the impact of the different modelled scenarios on the NEM, customers and the environment:

• Generation Fleet Composition

• NEM Prices (in the form of wholesale market prices and the wholesale component of consumer bills)

• System Reliability

• Carbon Emissions

Frequency Control Ancillary Services (the 8 different FCAS markets3) were also considered as a potential source of value for CST and were tested as a potential viable value stream.

2 Energeia has developed uSim, an agent based, bottom-up model of customer behaviour that forecasts the configuration and adoption of a range of emerging consumer side energy technologies based on scenarios of retail energy prices, technology prices and historical customer investment patterns. It represents over $5 million of investment over the last six years in proprietary research around technology cost curves and customer adoption drivers. 3 Frequency Control Ancillary Services are split into Regulation (raise and lowering minor changes in frequency) and Contingency (fast, slow and delayed raise and lower services – at 6 seconds, 60 seconds and 5 minutes) services. Accessed from: https://www.aemo.com.au/-/media/Files/PDF/Guide-to-Ancillary-Services-in-the-National-Electricity-Market.pdf


4 Assessment Approach

To identify the value of CST storage in different markets, Energeia developed an assessment framework to characterise the value of CST in different market segments.

4.1 Value Streams

Energeia adopted a best practice approach to characterise the storage value streams available to CST in the Australian context, given the nature of the NEM and the emerging NEG requirements. Successful deployment of CST depends on unlocking and stacking a range of different storage value streams.

4.1.1 Best Practice Approaches

State-of-the-art approaches4 to stacking up the range of different storage value streams typically classify storage value streams into three broad categories:

• Customer Services – Application of storage to manage the cost and availability of power for customers through the avoidance of time-of-use and demand charges, increased solar PV self-consumption and the provision of back-up power.

• Network Services – Storage can help defer investment in transmission and distribution assets (by managing peak demand impacts), avoid or reduce the need to call on peaking power plants (normally higher priced gas power generation units), and help manage transmission constraints.

• Market Services – A range of services including energy arbitrage (storing energy at low prices and discharging at higher prices, arbitraging between the different prices at different times in the day) and frequency regulation (such as the FCAS markets in the NEM).

Energeia has not considered the regulatory barriers preventing CST from monetising the services provided, and the necessary policy changes required to ensure that these values could be unlocked5.

4.1.2 National Electricity Market Considerations

The availability of customer, network and market benefits for storage is limited by the market design currently in place in the NEM:

• NEM Markets - The NEM’s market design is an energy-only model (as opposed to capacity-energy markets used in some jurisdictions) which means that CST storage needs to earn revenue from either the wholesale energy or ancillary services markets.

• National Energy Guarantee - The NEG is still uncertain in its structure, application and enforcement, but based on the initial release of the high-level design, there will be market mechanisms in place to recover reliability and emissions compliance costs. This may provide an additional source of value for energy storage, as implementing storage solutions to reliability and emissions compliance issues may be more cost effective than paying any required compliance costs.

4.1.2.1 Final Value Streams

For each market sub-segment, Energeia estimated the following value streams relative to the no-CST counterfactual:

• Energy costs (i.e. bidding of CST storage into the NEM spot market)

• Ancillary services costs (e.g. frequency response)

4 Rock Mountain Institute (2015) ‘The Economics of Battery Energy Storage: How multi-use, customer-sited batteries deliver the most services and value to customers and the grid’. Accessed from: https://www.rmi.org/wp-content/uploads/2017/03/RMI-TheEconomicsOfBatteryEnergyStorage-FullReport-FINAL.pdf 5 Recommended policy actions to unlock the value of CST in off-grid small-scale and on-grid large-scale applications are outlined in the CST Roadmap final report.


• Reliability compliance costs (i.e. generation, transmission and distribution capacity to meet 1-in-10-year peak demand)

• Emissions compliance costs (e.g. the existing Renewable Energy Target, the Paris target or any future emissions target)

4.2 Market Segmentation

In the small and large-scale market segments identified, CST energy storage applications could operate in a range of market sub-segments defined by a network connection and customer type or market access.

Table 1 – Market Segment Summary – Sub-segments by Scale and Connection Point

CST Market Sub-Segments Scale Connection

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Meter Distribution

Trans- mission

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Off-Grid Communities ✓ ✓

Behind-the-Meter Customers (Electricity Only)

✓ ✓

Behind-the-Meter Customers (CHP) * *

Fringe-of-Grid (Distribution Connected) * *

Large-Scale (Transmission Connected

✓ ✓

Source: Energeia Analysis. Notes: (i) Green ticks represent modelled market sub-segments; (ii) Orange markers represent market sub-

segments that were not analysed as part of this study.

4.2.1 Small-scale

Energeia modelling showed that two implementable off-grid small-scale applications had an economic benefit. The identified market sub-segments were:

• Firstly, allowing large consumers, such as mines, to stay (or go) off-grid, and;

• Secondly, allowing networks to manage their future expenditure profile in the replacement or augmentation of existing assets on the fringe-of-grid by taking parts of the distribution network off-grid into a microgrid independent of the grid.

These customers are being driven to reject, or defect from, the grid by high connection costs, high network fixed charges, the desire for increased reliability and the falling cost of energy storage and solar PV.

Additional behind-the-meter industrial customer and distribution network connected microgrid market sub-segments were considered and rejected:

• A market sub-segment for behind-the-meter small-scale CST applications was considered, assessing the potential for CST plants co-located with commercial and industrial customers to reduce their electricity costs through electricity self-generation and consumption.

• Potential use of behind-the-meter CST in industrial applications, as per the previous point, where there is an economic benefit of using heat from the CST as industrial process heat. This market sub-segment was not considered as being out of scope for this engagement.

• Fringe-of-grid networks that adopted a CST and renewables microgrid, that has the potential to go off-grid, but remains connected to the network to participate in the wholesale market. Energeia were unable to ascribe value to the bidding of the microgrid generation resources due network capacity and cost constraints.


4.2.1.1 Off-Grid Mines

Sub-Segment Description

Mines, with the combination of a (normally) remote location and large power loads, are often seen as prime candidates for self-generation microgrid solutions, but a range of factors need to be considered before putting in place an off-grid solution:

• The decision to connect to the main grid or to use a microgrid depends on their relative costs and risks. Network connection costs increase much faster with distance relative to microgrid costs. This is because distance increases the amount of assets for the grid connection, while the microgrid connection’s costs vary by distance due to operations, maintenance and diesel top-up.

• The mining segment features relatively high load factors, which means relatively high storage or diesel costs to power the mining site over-night. A high load factor also makes this segment relatively low-cost to serve by the grid, with the lowest contract costs to beat. Therefore, the primary competitive advantage of a microgrid for this segment is distance.

• Diesel has been the fuel of choice up until recently, with renewable energy sources becoming increasingly popular, driven by government subsidies and cost reductions. Mines are unlikely to be purely renewables plus storage microgrids, due to their generally short mine life, continuous operating hours and their high-value of reliability for which flexible generation with cheap capital costs is suited.

CST has been considered in combination with solar PV sourced energy, diesel-based generation and lithium-based battery storage technologies (excluding geothermal, wind, fuel cells, cogeneration and other combustion-based generation technologies). Given the inland and remote location of large numbers of mines in Australia, and the high levels of solar insolation in such locations, the application of both solar PV and CST is attractive, and the main value of CST is supporting the maximal utilisation of low cost solar PV during the solar trough by time-shifting of energy into peak consumption periods.

Potential Sub-Segment Value

Energeia modelled the average daily dispatch of generation throughout the year from solar PV, Li-ion storage, diesel generators and CST for an off-grid mine system as shown in Figure 4. As can be seen by inspection, solar PV provides the cheapest electricity during the middle of the day with the assistance of Li-ion storage during the shoulder periods which helps to ramp discharge from CST. CST mainly provides electricity outside the solar trough, and diesel generators in a back-up role (at 3% capacity factor).

Figure 4 – Average Daily Dispatch – Off-Grid Mine System


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Energeia modelled the annualised system costs and LCOE of a mine’s system based on its grid connection and its source of electricity, as shown in Figure 5. Off-grid options, either purely sourced by diesel generators or through a configuration that includes CST, have a lower annualised system cost and LCOE, than grid connections. Savings are considerable, with a CST, solar PV, Li-ion and diesel configuration 24% cheaper than diesel only systems, and 31% from grid connected systems.

Figure 5 – Levelised Cost of Electricity Comparison – On-Grid vs. Off-Grid Mine (2025)


4.2.1.2 Off-Grid Communities


Figure 6 – Fringe-of-Grid Towns Relative to Large Towns, by Distance Band

Source: Energeia

Remote towns that are currently expensive to serve, either due to high operating and maintenance costs (network operation and maintenance costs, and equipment replacement expenditure), low-reliability or impending

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asset replacement/augmentation requirements, could be disconnected from the grid to reduce costs. Energeia’s analysis and modelling of remote towns, as per Figure 6, found that distance to the main grid and the size of the population were both key factors driving the timing of microgrid cost-effectiveness. This is because the larger towns typically had more overnight load, which reduced their load factor and made them more expensive to serve from the grid. However, distance vs. size was still the primary driver.


Figure 7 shows the average dispatch of generation from solar PV, Li-ion storage, diesel generations and CST required to switch a zone substation at the fringe-of-grid to an off-grid microgrid system. During the middle of the day, solar PV is the cheapest source of electricity while Li-ion storage helps with CST’s ability to ramp discharge. CST predominantly generates electricity in the early and late hours of the day supplying up to 12MW at 7pm. Diesel generators are also used to meet approx. 3% of load during periods of low solar resources throughout the year.

Figure 7 – Average Daily Dispatch – Fringe-of-Grid/Off-Grid Microgrid


The capacity of CST and diesel are sized to meet peak demand days, whilst solar PV and Li-ion storage are sized to meet midday demand as shown in Figure 8.

Figure 8 – Capacity and Generation by Fuel Type – Fringe-of-Grid/Off-Grid Microgrid


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The generation mix is different, with CST providing the majority (55%) of the load (primarily during the late afternoon, overnight and the early morning) around the low-cost solar trough, whilst back-up diesel provides the remaining 3% (averaged throughout the year).

The comparison of the grid-connected and off-grid annualised system costs and levelized cost of electricity (LCOE) of the fringe-of-grid zone substation shows that disconnecting from the grid has the potential to lower costs, as shown in Figure 9. Disconnecting from the grid can reduce annualised system costs by 15%.

Figure 9 – Levelised Cost of Electricity Comparison – Fringe-of-Grid/Off-Grid Microgrid (2025)


The combination of CST, solar PV, Li-ion and diesel resources in a microgrid can achieve a lower LCOE and annualised system costs than a traditional grid connection. This is mainly driven by the value of retail electricity, but also importantly through the cost savings of the network that are no longer required.

4.2.1.3 Fringe-of-Grid Communities


The application of a microgrid to resolve reliability and security issues, as per Section 4.2.1.2, for Fringe-of-Grid communities, where that microgrid remains connected to the distribution network. In this market sub-segment, the connection to the market, through the distribution network may allow the microgrid to earn additional value from the surplus generation from the unused microgrid generation capacity.


This application is unproven, and the network costs that made the application of the microgrid solution attractive, may make bidding into the wholesale market impracticable and uneconomic due to access constraints and network-use-of-service costs.

4.2.1.4 Behind-the-Meter Customers


Large commercial and industrial customers with large electricity loads and rising energy bills are increasingly looking at self-generation and energy storage solutions to minimise their consumption and peak charges. Conventionally, rooftop solar PV and battery energy storage solutions have been either applied separately or together to enable large users to reduce their electricity bills through the installation of behind-the-meter distributed energy resources (DER).

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A behind-the-meter use market sub-segment for these customers was identified for CST, which would offer both the solar generation and storage benefits of competing DER technologies in a single technology solution.

4.2.2 Large-scale

The economic case for CST in the large-scale markets is predicated on the ability of CST from either frequency control (network benefit) or energy sales during peak times (market benefits).

4.2.2.1 Ancillary Services Markets


The requirement for FCAS is predominantly driven by the increasing proportion of variable renewable energy generation in the generation mix. Renewable energy generators introduce more forecast error on the supply side, than has been historically experienced on the demand side6, increasing the call for FCAS corrections.


Energeia modelling forecasts, as per Figure 10, that market potential for FCAS increases by eight-fold from $30 million of generator revenue to $242 million by 2040. This increase is in turn driven by the doubling of renewable energy generation capacity in the 2018 to 2040 period. As FCAS requirements increase, the mix of generators providing the services change over time as coal (dark red) exits and is replaced by combined-cycle gas turbines (CCGT; dark blue) from 2025 and following. CST enters the market in 2030 and captures a minor market share (Energeia assumes that FCAS market shares are in line with the share of dispatchable generation in the energy market).

Figure 10 – FCAS Supply and Demand Forecast


Assuming that CST would obtain FCAS revenues in accord with their share of dispatchable generation, Energeia estimates that this would give the CST segment annual FCAS revenues of $5 million in 2040, spread across the 4.5 GW CST fleet (or approximately $0.001 / MW of installed capacity). Despite FCAS exponential growth, there is not sufficient revenue to deliver a meaningful value stream to CST.

6 The demand 5-minute forecast error averages 1.37%, as against a forecast 5-minute error rate of 3.19% for wind and 8.72% for solar. As renewable energy share of the generating mix increases (Energeia forecasts a five-fold increase in large-scale renewables to 2040), the larger forecasting error associated with that generation increases the overall need for FCAS correction in the market.

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4.2.2.2 Energy Markets


From previous work with ASTRI7, Energeia and the CST Roadmap team knew that describing the value of CST in large-scale grid-connected applications requires an understanding of the role of competing storage technologies (conventional lithium batteries and PHES) in the integration of intermittent renewable energy generators into the NEM.


Based on the modelling of the Base Case, Energeia modelled the average spot price of CST in half-hour intervals for CST systems with 2-hour dispatch intervals, shown in Figure 11. Each CST system optimises its dispatch to maximise operating profits, with different configurations of CST targeting different levels of storage (as measured in hours of discharge). Each CST plant will maximise their profit, by only entering the market when their costs ($81/MWh in 2032) are covered by the available revenue.

Figure 11 – CST Discharge Opportunities vs. Spot Price (2032)


Figure 12 unpacks how different CST plants maximise the value of their configuration – all configurations except for short discharge (<4hr) CST are uneconomic and will not enter the market – referring back to Figure 11, it can be seen by inspection that this restricts the opportunity of CST to enter the market until costs fall to a more price competitive level.

7 Energeia (2017), ‘Concentrated Solar Power Cost Targets: Prepared by Energeia for the Australian Solar Thermal Research Initiative’

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5 Scenario Development

Energeia applied an industry best-practice scenario development approach to identify the set of key factors driving key customer, market and network outcomes, and their potential interplay over the next twenty years.

5.1 Scenario Framework

5.1.1 Best Practice Approaches

Scenario based modelling approaches have been in common use since first deployed in a large-scale by Shell in the 1970s. They are now typically applied when businesses are facing a complex business environment combined with an uncertain future. In such an environment, single point solutions with a range of sensitivities are not sufficient to capture the impact of multiple and interrelated factors.

Figure 13 outlines how individual scenarios can be used to define the area of plausible futures, normally bookended by a series of wildcard scenarios, and within that strategic space, a scenario or a set of scenarios can be used to form the basis for understanding the range of possible futures.

Figure 13 – Scenario Modelling to define the Strategic Space

Source: IHS CERA

The benefit of scenario modelling approaches is that a range of different scenarios deliver a broader range of possible futures than a single forecast with a range of sensitivities. The investigation of a range of scenarios built up on key factors or strategic drivers enables the impacts and likelihood of each scenario to be well understood, before moving on to model potential real-world cases with multiple factors defining the scenario.

5.1.2 Strategic Drivers

The scenarios are designed based on differential CST deployment rates. Energeia has adopted large scale capital CST costs for a lower and higher cost scenario relative to the Base Case scenario, as provided by ITP. Lower costs are assumed to drive higher deployment case and higher cost case conversely reduce deployment.

Further details are found in Appendix 3: Key Scenario Factors.

5.1.3 Key Performance Indicators

The KPIs of reporting used for each scenario result include:

• Generation Mix Entry and Exit – The mix of capacity of generation evolve over time with the exit of high cost or end-of-lifetime units and the entry of lower cost new units (measured on a net basis over the 2018 to 2040 period, in GW of generation capacity).


• Volume Weighted Average Pricing (VWA) – The cost of supply of electricity into the wholesale market on an average basis across the generation mix dispatched within a given period (measured in the average annualised price, in $/MWh).

• Wholesale Sector Customer Bills – The amount that consumers pay for energy sourced from the wholesale energy market (measured on a cumulative basis in 2040, in $ bn).

• Emissions Pathway – Emissions on a carbon dioxide equivalent measure from generators in the wholesale energy market (measured in MT CO2e).

5.2 Scenario Design

A set of scenarios were developed that considered the impact on CST uptake of falling global CST costs, relative to the global rates of deployment of CST and the targeted intervention into small-scale sub-markets for CST in Australia, and the resultant impact of CST uptake on the Australian energy system.

An additional set of market risk scenarios (detailed in Appendix 2: Base Case Market Scenarios) were developed to understand the impact on CST uptake, and other key market performance indicators, of different market risk related strategic drivers (including energy decentralisation, falling renewable energy costs, large-scale PHES and the impact of the NEG).

5.2.1 Summary

Energeia developed included a section of three different market sub-segments in its final scenario design:

• Off-Grid Small-Scale Customers – Off-Grid mines, representative of large, remote customers, which are looking to reduce their electricity costs, which are currently based on diesel, or diesel and solar PV

• Off-Grid Small-scale Communities – Fringe-of-Grid communities, representative of remote, grid connected communities, where governments and networks are looking to reduce cost-to-serve by potentially by taking them off grid during peak demand or permanently

• Grid-Connected Large-Scale – Grid-connected CST acting as large-scale renewable energy generation and storage, offering wholesale energy and ancillary services (primarily frequency regulation) to the Australian Energy Market Operator (AEMO)

These three market sub-segments were combined into four different scenarios that considered the interaction between small-scale market development opportunities and different rates of deployment of large-scale CST based on global rates of cost decline (as shown in Table 2).

Table 2 – Scenario Summary – Relationship between Market Sub-Segments and Scenario Cases

CST Market Sub-Segments Scenarios


Off-Grid Mines ✓ ✓

Fringe-of-Grid/Off-Grid Communities ✓ ✓

Large-Scale ✓ ✓ ✓ ✓

Source: Energeia Analysis

The four different scenarios addressed the following questions:

• Low Case – what is the uptake of large-scale CST in Australia if global deployment is slower than forecast, and learning rates are slower than expected?

• Base Case – what is the uptake of large-scale CST in Australia given the most likely case for global deployment, learning rates and therefore capital costs?


• No Regrets Case – how would the Base Case be improved if targeted interventions were made to encourage the development of small-scale CST in various sub-segments, in addition to the uptake of large-scale systems in the transmission network?

• High Case – what would be the net uptake of CST systems if the targeted interventions in small-scale market sub-segments were complemented by a faster global deployment of CST, and resultant fast learning rates and steeper cost declines?

5.2.2 Detailed Design

The final scenario design, showing the scenario settings for each of the strategic drivers considered in each of the scenarios, is detailed in Table 3.

Table 3 – Detailed Final CST Scenario Design

Description Low Case

Base Case

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Snowy Intervention 2024 2024 2024 2024

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Reliability Policy NEG NEG NEG NEG

Bidding No Change No Change No Change No Change

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Storage -7.5%, PV -5%




CST Costs ITP Provided (2.8% CAGR)

ITP Provided (5.0% CAGR)



Large Scale Storage Li-ion + CST +

PHES Li-ion + PHES +

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PHES

Resource Zones Yes Yes Yes Yes

New Interconnectors No No No No

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Off-Site Communities No No Yes Yes

Source: Energeia. Note: (i) No Regrets and High Cases also include small-scale market sub-segments.


6 Modelling Results

Modelling results were reported firstly with respect to the outcomes for CST under each of the final scenarios, and then the impact of that level of CST uptake on the remainder of the energy market.

6.1 CST Outcomes

Modelling completed by Energeia showed that there was uptake in three of the four scenarios considered (upwards from 3.94 to 12.08 GW), and that significant economic value (ranging from $2.023 to 4.844 bn) was delivered under these scenarios.

6.1.1 Uptake

Figure 14 shows the cumulative CST uptake capacity across the modelled period. Key observations include:

• No CST uptake in any state in the Low Case due to an uneconomic cost structure for CST relative to alternative technologies.

• The Base Case contains CST uptake in South Australia, Queensland and Victoria.

• The No Regrets Case has additional CST uptake in New South Wales in addition to the States in the Base Case

• Increased uptake of CST in the High Case, with a significant uplift in capacity in NSW and Victoria.

Figure 14 – Cumulative CST Uptake (GW)


Additional analysis of the modelling of uptake by market segment and sub-segment reveals that:

• Market Sub-Segment Timing – Deployment of CST commences at a small-scale with fringe-of-grid microgrids (2022) and off-grid mines (2025, before grid-connected applications (i.e. CST bidding into the wholesale energy market) becomes viable from the 2031.

• Market Segment Share – Small-Scale markets are of greatest importance for CST uptake in the No Regrets Case, accounting for 22% of deployment, as shown in Figure 15.

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6.1.2 Economic Benefit

The economic benefits delivered by CST uptake, net of interventions, is shown in Figure 16. The benefits for the three cases where CST is deployed – Base, No Regrets and High Cases – are highly dependent on the CST deployment rate and on the targeted small-scale market segment interventions:

• CST Deployment Rate – An additional $2.8 bn (in present value) can be achieved if deployment of CST follows the High Case (relative to the Base Case), whereas $2 bn (in present value), relative to the Base Case is lost if CST deployment follows the Low Case. In other words, the rate of deployment can either reduce the NPV by 100% or increase it by 240%, relative to the Base Case.

• Small-Scale Markets – The difference between the No Regrets Case and the Base Case is of $1.353 bn, representing an uplift of 67% over the Base Case, a significant economic benefit.

Figure 16 – Value Stack by Scenario (Present Value, $ bn, real 2018)


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Figure 17 – Value Share by Scenario (%)


6.2 Market Impacts

6.2.1 Summary Results

The summarised results are shown in Table 4, which shows net entry, annual VWA NEM prices in 2040, the

cumulative wholesale component of customer bills of all states in 2040, and emissions on a 2040 snapshot basis.

The most strongly positive values for each reported variable are highlighted in shades of green, and the most

undesirable values are shown in shades of red.

Table 4 – Results Summary

Scenario Type CST Uptake

Capacity (GW, 2040)

Net Entry (GW, cum. 2018-40)

VWA Pricing ($/MWh, 2040)

Wholesale Bills ($B, cum. 2040)

Carbon Emissions

(Mt CO2-e, 2040)

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Base Case 3.94 27.73 68.67 13.62 79.14

No Regrets Case 5.08 28.87 68.67 13.62 79.14

High Case 12.08 29.18 64.05 12.56 76.91

Source: Energeia Modelling. Note: (i) All prices and costs reported in 2018 real.

Energeia drew the following key conclusions from the results of the deployment rates scenarios (shown in

graphically Figure 18):

• Low Case – uniformly worse scenario across the board, with higher wholesale market prices, customer bills and emissions relative to the Base Case.

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• No Regrets Case – 29% increase in CST deployment relative to the Base Case

• High Case – 307% increase in CST deployment relative to the Base Case, with strong market impacts in lower prices, bills and emissions.

Figure 18 – Results Variance to the Base Case


Turning to the KPIs themselves in Figure 18, Energeia’s key observations are as follows:

• Significantly higher fleet growth in the High Case, whereas the opposite occurs in the Low Case

• Lower VWA pricing is associated with the High Case, whereas higher prices are associated with the Low Case

• The High Case leads to the cheapest customer bills whereas the Low Case leads to the most expensive customer bills

• Lowest carbon emissions in 2040 occur in the High Case, followed by the Base Case and the Low Case.

Each of the different reporting dimensions is considered at greater length in the following sections.

6.2.2 Detailed Results

6.2.2.1 Generation Capacity Entry/Exit

The Base Case in Figure 19 shows a net increase in the buildout of mainly solar PV and CCGT with some wind and PHES. Relative to the Base Case, a few key observations can be made which shows the results for the cumulative entry and exit of generation capacity in the 2018 to 2040 period:

• Over 12GW of coal exits, combined with price declines in large scale renewable technologies, contribute to over 40GW of new generator entry in all scenarios.

• No CST generators entry in the Low Case, instead there is an increase in the entry of solar PV and CCGT generators in the market relative to the baseline.

• The High Case shows the highest buildout of CST energy out of the three scenarios. As a result, there is significantly less buildout of CCGT generators compared to the baseline.

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Figure 19 – Net Entry of Generation and Storage (Cum. 2018-40)


6.2.2.2 Volume Weighted Average Pricing

The VWA pricing trends in 5-yearly increments are shown in Figure 20. VWA prices falling by almost $30/MWh

relative to today by 2025 due to a significant increase in renewable generation built throughout this period.

During this period, the increase in solar PV generation and the use of gas peaking plants to meet evening

demand, serve to both increase the differential between daily peak and off-peak pricing.

Figure 20 – Volume Weighted Average Pricing (5-Yearly 2020-40)


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• The VWA for both all scenarios are consistent with the Base Case in the first 10 years of the period,

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• Post 2030, VWA in the Low Case increases at a faster rate than all other cases. The opposite occurs for

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6.2.2.3 Customer Bill Impacts

Figure 21 – Customer Bills (Cum. Wholesale Bills in 2040 by State, Real $ bn)


The cumulative wholesale component of customer bills from Figure 21 show that:

• If the learning rate for CST exceeds the Base Case, customers in the High Case could cumulatively save $1 bn (in present value terms) from now until 2040 on top of the Base Case.

• A slower transition into CST deployment, if technology cost curve reductions slow down and stall over the period, will cost customers up to an additional $0.7 bn (in present value terms) in the period to 2040 (comparing the Low Case to the Base Case).

6.2.2.4 Emissions Pathway

As shown in Figure 22, Energeia modelling showed that carbon emissions in all scenarios decline over time. This

is due to ongoing exits of coal-fired power generation, declining costs of rooftop solar PV and storage over time

and increasing focus on achieving renewable energy targets from each state.

From 2030, renewable and low emissions gas generation fill the gap left by coal exits, resulting in a declining

emissions trajectory. Between the scenarios, there are marginal differences in grid emissions. These include:

• Further decreases in carbon emissions in the High compared to the Base Case, as more renewable

energy generation is included in the generator fleet mix.

• Slower decline in carbon emissions in the Low Case compared to the Base Case, as a lower proportion

of the new generation entering the market is renewable energy (wind or solar).

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Appendix 1: Small-Scale Market Sub-Segments

Three small-scale sub-segments which between them accounted for $1.3 bn NPV of consumer benefit, split between:

• Fringe-of-Grid/Off-Grid Microgrids – The largest opportunity in terms of value, totalling $942 m, or more than double the opportunity of large off-grid users (such as mines).

• Off-grid Mines – These represent the a significant off-grid opportunity for CST deployment in the medium term (2020-2030) with $411 m NPV.

• Commercial and Industrial Customers – Energeia reviewed this market segment, but without consideration of the economic value of bypass heat, was unable to identify use cases above 10MW in scale, which were regarded as unfeasible for CST.

Off-Grid Mines

Using historical trends of large new mines in each state across the NEM, Energeia forecast the growth of new mines as shown in Figure 23 with Queensland opening the most mines over the period of 2018 to 2040.

Figure 23 – New Mines – Forecast to 2040


Three mine sizes (based on per annum energy use) were considered in a simplified segmentation:

• 40 GWh – operating consistently at 2.2GWh during the day

• 125 GWh – with a load profile ranging from 7GWh to 7.5GWh during the day

• 300 GWh – with a load profile ranging from 16.5GWh to 17.5GWh during the day

Energeia modelled the optimal8 configuration in a typical CST system as shown in Figure 24, based on the three mine sizes.

8 The optimal storage molten salt storage size is significantly greater than the optimal size for power blocks and heliostats across all mining load profiles. This is to ensure the reliability of the CST system, especially during periods lacking in sunlight. Also note that the configuration sizes for each component increases with the mine sizes.

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Energeia forecast the off-grid market for new mines emerging from 2025 and rising steadily over time in line with underlying growth in mining (refer to Figure 25).

Figure 25 – Off-Grid CST Mines – Cumulative to 2040


By comparing Figure 25 and Figure 23, from 2018 to 2040, 19 of the 27 new mines are able to run completely off-grid with CST as their main source of energy. As shown in Figure 26, CST capacity of 603MW is installed in off-grid mines from 2018 to 2040.

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Figure 26 – Off-Grid CST Mines – Cumulative Generation Capacity to 2040


Fringe-of-Grid/Off-Grid Microgrids

Energeia assessed the potential to take towns or parts of the network at the edge of the grid off the network using a microgrid, by assessing the opportunity for that microgrid to extract value through reduced customer bills, avoided losses and reduced transmission costs. As shown in Figure 27, most zone substations are in urban zones where environment and land use considerations will restrict the deployment of CST. Short rural and long rural zone substations are ideal for CST, most of which are located within the Ergon and Essential networks in Queensland and New South Wales respectively.

Figure 27 – Fringe-of-Grid Market Potential – Zone Substation Count by DNSP

Source: Energeia Analysis.

The results of Energeia’s modelling of CST uptake in microgrids located in areas on the edge of the distribution networks are shown in Figure 28. Over 1GW of CST is taken up throughout the modelling period in the fringe-of-grid/off-grid microgrid application. Regionally, uptake is dominated by inland Queensland, which comes on first (in 2022), followed by inland New South Wales (in 2026). Ergon and Essential networks, in Queensland and NSW respectively, contain almost all the microgrid uptake in the NEM through to 2040.

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Commercial and Industrial Customers

Energeia reviewed the industrial behind the meter potential of CST9, examining the potential to reduce industrial customer electricity bills through self-generation and consumption. Energeia firstly segmented the commercial and industrial customer base in the electricity market based on consumption and load shape, identifying the following segments:

• ‘Industrial’ Customers – Just 21% of all large customers had a 24-hour, 7 day a week flat load shape

• ‘Commercial’ Customers – The remaining almost 80% of the customer base had a 9am-5pm workday shape, with different load requirements at different times during the day, particularly across the midday period

Commercial customers have a load shape amenable to rooftop solar PV solutions (potentially paired with lithium-ion battery storage to extend the benefits of the midday peak) and were not identified as a suitable target market for CST. The smaller segment of industrial customers, with a flatter load shape, were amenable to CST applications as the would gain economic benefit from CSTs ability to provide late afternoon, overnight and early morning time-shifting of generation away from the solar trough in the middle of the day. However, the large customer base used in the segmentation averaged an electrical load of between 1 to 2 MW, well below the absolute operating minimum of CST technology (10MW). Given the average load of customers in the industrial customer base, Energeia recommends that:

• Large Customers – Only the largest of industrial consumers would have sufficient load suitable for CST applications, with most customers unable to obtain economic benefit through a behind-the-meter located CST plant solely focused on lowering electricity bills.

• Co-Location – Average sized industrial customers may obtain economic benefit from a behind-the-meter CST installation if they were co-located and able to share the facility (i.e. if their aggregate load was sufficient to make CST economically attractive).

• Combined Heat-Power Opportunities – Further work would need to be completed to understand the impact of low-cost process heat for industrial customers who currently use a mix of electricity for energy and gas for heating in their production processes.

9 Exclusive of the economic benefit of combined-heat-and-power (CHP) applications for CST where surplus or by-product heat from the CST plant is used in industrial processes.

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Appendix 2: Base Case Market Scenarios

Energeia defined a series of scenarios to understand the impact on the Base Case of various risks and uncertainties, as defined below and in Appendix 3: Key Scenario Factors.

Market Scenario Development

To validate the developed Base Case, Energeia developed a range of scenarios in consultation with JHA and the CST Roadmap team, to address the following questions:

• What are the behind-the-meter changes, in customer adoption of both DER technology and cost-reflective tariffs, that might impact on demand?

• Who would be impacted by continuing and accelerating falls in the cost of renewable energy generation (solar PV and wind) and large-scale storage?

• How will construction of Battery of the Nation and the second Bass Link interconnector impact on need for new generation in the NEM?

• When would the proposed NEG emissions and reliability measures be triggered (if at all)?

• What impact will the accelerating power sector decarbonisation have on the wholesale market over the medium and longer term?

Strategic Drivers

The scenarios are designed around the following strategic drivers:

• DER Optimisation – This factor assumes key DER technology, tariff reform and asset optimisation measures that unlock the full potential of DER to maximise net benefits.

o DER Costs – rates of decline in behind-the-meter DER (rooftop solar PV and battery energy storage systems) technology costs over time.

o Default Tariff – the tariff assumed that all customers are currently on within a given Distribution Service Network Provider (DNSP) area.

o Alternative Tariff – the tariff option that customers can move to from the default tariff, within a given DNSP area.

o Tariff Assignment – assignment of customers from the default to the alternative tariff can either take place on an opt-in or opt-out basis, or it can be assumed that the customers do not change from the default tariff.

o Orchestration (VPP) – optimal aggregation, optimisation and control of DER to deliver services to the network as required unlock the most value for DER owners and networks.

• Large Scale Cost – The Base Case design assumed an average technology cost decline rate of 7.5% and 5% p.a. for battery energy storage and rooftop solar PV respectively. A scenario was developed with a faster learning rate (15% and 10% declining rates for storage and solar PV) to understand the impact of accelerated uptake of intermittent renewable energy generators in the wholesale market.

• State Government Interventions – All scenarios included the Queensland and Victorian Renewables Energy Targets in the base design, but the announcement of the Tasmanian Government’s Battery of the Nation proposal was considered a material risk to CST (being a competing form of storage) and was therefore selected as a scenario factor.


• National Energy Guarantee10 – The Energy Security Board11 developed reliability policy and emissions policy measure, to replace the Finkel Report recommended Clean Energy Target12.

Further details are found in Appendix 3: Key Scenario Factors.

Scenario Design

The detailed scenario design used to consider the various risks and uncertainties impacting on the Base Case is detailed in Table 5.

10 ESB (November 2017), ‘National Energy Guarantee’. Accessed from: http://www.coagenergycouncil.gov.au/publications/energy-security-board-update 11 The Energy Security Board was established by the COAG Energy Council in August 2017 to coordinate the implementation of the Finkel report recommendations. The establishment of the ESB was the third pillar of improved governance identified in the Finkel report. 12 Finkel et al (June 2017), ‘Independent Review into the Future Security of the National Electricity Market’. Accessed from: https://www.energy.gov.au/government-priorities/energy-markets/independent-review-future-security-national-electricity-market


Table 5 – Market Scenario Design

Description Base Case High DER High Large Scale Battery of the Nation Stronger Reliability

Target Stronger CO2e

Target Key

Factor

Dis

trib

uted

Ene

rgy

Mar

ket

DER Costs Energeia (Storage

-7.5%, PV -5%) Energeia (Storage -15%, PV -10%)



Energeia (Storage -7.5%, PV -5%)

Energeia (Storage -7.5%, PV -5%)

Default Tariff Consumption Consumption Consumption Consumption Consumption Consumption

Alternative Tariff Demand Demand Demand Demand Demand Demand

Tariff Assignment Opt-in Opt-out Opt-in Opt-in Opt-in Opt-in

Orchestration (VPP) No 2018 No No No No

Gov

ernm

ent

Inte

rven

tion

Snowy Intervention 2024 2024 2024 2024 2024 2024

State Government Interventions

VRET VRET VRET VRET + Battery of the

Nation VRET VRET

Emissions Policy NEG NEG NEG NEG NEG 100% Renewables by

2040

Reliability Policy NEG NEG NEG NEG USE based Target None

Bidding No Change No Change No Change No Change No Change SRMC

Res

ourc

e C

ost

Impr

ovem

ent

Wholesale Gas Prices AEMO AEMO AEMO AEMO AEMO AEMO

Large Scale Wind/PV Costs



Energeia (Storage -15%, PV -10%)




CST Costs ITP Provided ITP Provided ITP Provided ITP Provided ITP Provided ITP Provided

Large Scale Storage Li-ion + PHES + CST Li-ion + CST + PHES Li-ion + CST + PHES Li-ion + CST + PHES Li-ion + CST + PHES Li-ion + CST + PHES

Resource Zones No No No No No No

New Interconnectors No No No No No No

Coal Lifetime (Years) 50 50 50 50 50 50



Market Scenario Results

Overview

Energeia drew the following key conclusions from the results of the market scenarios, as summarised in Table 6:

• High DER Case – results in the cheapest consumer bills due to an increase in DER uptake and nearly double the CST uptake capacity relative to the Base Case but increases NEM prices relative to the Base Case.

• High Large Scale – slight improvements in NEM pricing and CO2 emissions relative to the Base Case, but slight increases in consumer bills and less CST entered the market.

• Battery of the Nation – slight improvements on all measures relative to the Base Case, but less uptake of CST.

• Reliability Target – slightly more entry of generators, a slight decrease in carbon emissions, slightly cheaper consumer bills and NEM prices, and less uptake of CST relative to the Base Case.

• CO2e Target – lowest carbon emissions but has the highest customer bills and NEM prices and the highest CST uptake.

Table 6 – Results Summary – Market Scenarios

Scenario Type CST Uptake

Capacity (GW, 2040)

Net Entry (GW, cum. 2018-40)

VWA Pricing ($/MWh, 2040)

Wholesale Bills ($ bn,

cum. 2040)

Carbon Emissions

(Mt CO2-e, 2040)

Base Case 3.94 27.73 68.55 13.62 79.14

High DER 7.25 21.29 96.23 11.90 62.04

High Large Scale 2.48 26.82 68.35 13.72 78.00

Battery of the Nation 2.89 28.01 67.58 13.50 78.83

Reliability Target 2.48 28.71 67.58 13.50 78.83

CO2e Target 21.39 45.84 105.92 20.88 0.37


Turning to the KPIs themselves, as shown in Figure 29, Energeia’s key observations are as follows:

• Only the High DER and CO2 Target cases had an increase in the CST uptake capacity.

• Minimal net change to the generation fleet in the High DER case, and the maximal build out of new capacity in the CO2 Target case.

• Most scenarios either had minimal change in the volume weighted average prices at the end of the period (higher prices are associated with High DER and CO2 Target cases).

• CO2e Target case leads to the highest customer bills out of all the scenarios whereas the High DER case leads to the lowest customer bills.

• Lowest carbon emissions in 2040 in the CO2 Target case, followed by the High DER case whereas all other scenarios show minimal decreases.


Figure 29 – Results Variance to Base Case – Market Scenarios


CST Uptake Capacity

Figure 30 shows the cumulative CST uptake capacity across the modelled period. The Base Case shows CST uptake in Queensland, South Australia and Victoria. In comparison to the Base Case, key observations include:

• CO2e Target case shows significantly more CST uptake than other scenarios, followed by the High DER case. In these two cases, CST enters the market in NSW.

• Other scenarios are shown to have a decrease in CST uptake relative to the Base Case.

Figure 30 – CST Uptake Capacity in GWs installed (Cum. 2018-40, Market Scenarios)


Generation Capacity Entry/Exit

The Base Case in Figure 31 shows a net increase in the buildout of mainly solar PV and CCGT with some wind and PHES. Relative to the Base Case, a few key observations can be made which shows the results for the cumulative entry and exit of generation capacity in the 2018 to 2040 period:

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• CO2e Target case require significant build out of renewable energy and matching storage capacity with no need for the buildout of CCGT.

• The High DER and CO2e Target cases show the highest buildout of CST.

• The High DER shows the least buildout of solar PV out of all cases due to the uptake of small-scale DER in households.

Figure 31 – Net Entry/Exit of Generation and Storage (Cum. 2018-40, Market Scenarios)


Volume Weighted Average Pricing

VWA pricing in 5-yearly increments for all scenarios is shown in Figure 30. The Base Case shows VWA prices falling by almost $30/MWh relative to today by 2025 due to large scale renewable uptake. During this period, the increase in solar PV generation and the use of gas peaking plants to meet evening demand, serve to both increase the differential between daily peak and off-peak pricing.

In comparison to the Base Case, it can be seen by inspection that:

• All cases, apart from the High DER and CO2e Target cases, have similar price trajectories as the Base Case with NEM prices falling in 2025 before increasing steadily to $60 to $70/MWh in 2035.

• The High DER and CO2e Target cases secure greater price reductions over the first 10 years of the period (due to lower generation costs of entering renewable energy) but underperform compared to the Base Case in the last 10 years of the forecast as investments in dispatchable renewables are required to maintain system security.

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Figure 32 – Volume Weighted Average Pricing (5-Yearly 2020-40, Market Scenarios)


Customer Bill Impacts

The wholesale component of customer bills in 2040 is shown in Figure 33, which shows that three of the five scenarios are tightly correlated with the Base Case, with a few exceptions:

• Customers in the High DER case could cumulatively save approx. $1.7 bn in 2040 compared to the Base Case.

• An aggressive transition into a 100% renewables energy industry in 2040 could increase customer bills by nearly $7 bn cumulatively in 2040 compared to the Base Case (exclusive of costs of carbon).

Figure 33 – Customer Bills (Cum. Wholesale Bills in 2040 by State, Real $ bn, Market Scenarios)


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Emissions Pathway

As shown in Figure 34, Energeia modelling showed that carbon emissions in all scenarios decline over time. This is due to ongoing exits of coal-fired power generation, declining costs of rooftop solar PV and storage over time and increasing focus on achieving renewable energy targets from each state. Here are key observations of the emissions pathways of each scenario:

• All cases, excepting the CO2e Target case, are shown to follow a decline like the Base Case.

• Carbon emissions in the CO2e Target case are reduced by more than double relative to the Base Case in the last 10 years of the forecast.

• The High DER case results in a decrease in carbon emissions at a faster rate relative to the Base Case due to the increased uptake of small-scale solar PV and storage in households.

Figure 34 – NEM Carbon Dioxide Equivalent Emissions (5-Yearly 2020-40, Market Scenarios)


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Appendix 3: Key Scenario Factors

Energeia’s scenario modelling in Appendix 2: Base Case Market Scenarios is based on the following energy market scenario factors.

Deployment Rates

Energeia has adopted large scale capital CST costs for a lower and higher cost scenario relative to the Base Case scenario, as provided by ITP. As shown in Figure 35, costs decline over time at 3%, 5% and 7% CAGR for the low, medium and high case respectively.

Figure 35 – CST Large-Scale Technology Cost Forecast


Energy Market Factors

Distributed Energy Resources

DER Costs

The increasing application of DER, such as solar and storage, will continue to impact electricity demand from a consumer level and electricity sourced from generators. The uptake of small-scale DER are highly influenced by the changing costs. Energeia forecasts a continued steep decline in solar module and inverter costs, driven by:

• Battery Economies of Scale – further steep decline in battery cell and inverter prices through to 2025 due to increasing economies of scale, as electric vehicle and battery sales grow.

• DER Retail Margins – retail margin compression is forecast to occur for DER as competition increases.

• DER Installation Costs – a combination of productivity increases and cost sharing drive down installation costs over the medium to longer term.

The forecasts of unit costs for rooftop solar PV (per kW) and lithium-ion batteries (per kWh) used in this study are shown in Figure 36 and Figure 37.

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Figure 36 – Rooftop Solar PV Costs per kW installed


Figure 37 – Battery Energy Storage System Costs per kWh installed


DER Orchestration

As technologies like batteries become smarter and cheaper, their deployment will become increasingly common, and aggregators will seek to bring together the capacity of household batteries into Virtual Power Plants (VPPs). These VPPs will be able to offer networks grid services that can ultimately help networks avoid additional augmentation expenditure, avoid cross subsidies and lower network bills13, by managing the response of the consumer’s battery (or other DER technology) to pricing signals set under cost reflective tariffs. Energeia applied its network controlled DER functionality to model the impact of DER orchestration.

Cost Reflective Pricing Tariffs

Network tariff reform, and the introduction of cost reflective tariffs is an ongoing change in the pricing and regulatory environment impacting on customer bills, and therefore on customer energy buying behaviours, and

13 Key finding from Energeia’s work on Energy Network Australia’s National Transformation Roadmap. Accessed from: http://www.energynetworks.com.au/energeia-modelling-unlocking-value-energy-customers

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ultimately on net demand and energy sent out by generators in the wholesale market. To understand the impact of pricing reforms and changes, Energeia developed a fully cost reflective tariff structure to compare with existing pricing practices, as shown in Table 7 and Table 8, for use in each scenario. The basis for these tariffs were:

• Consumption-based Tariff – this was modelled as per current defaults in DNSP pricing proposals.

• Cost Reflective Tariff – Energeia modelled a critical peak pricing (CPP) tariff that targets the top 5 network and retail peaks each year with a strong peak consumption signal. The strong signal encourages storage DER generation/dispatch during these times and therefore reduces demand in peak times.

Table 7 – Consumption Tariffs

Table 8 – Cost Reflective Tariff

Component Type Unit Rate

Network

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Block-1 $/kWh $0.10

Block-2 $/kWh $0.10

Block-3 $/kWh $0.09

Retail

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Block-3 $/kWh $0.20

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Volume $/kWh $0.00

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Retail

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Volume $/kWh $0.10

CPP $/kWh $9.48

Source: DNSPs, Energeia Analysis Source: Energeia Analysis

Energeia modelling have implemented the option for customers to “Opt-in”, or to choose their tariff based on cost-savings, or to “Opt-out”, where customers are unable to change their tariff and must follow regulatory-approved pricing.

Large Scale Renewable Energy

Energeia forecasts the costs of large scale renewable energy, including Solar PV, Wind and Storage, to determine the impacts of CST uptake in various scenarios driven by economic and government factors.

Figure 38 – Large Scale Renewable Capital Cost per kW installed


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As shown in Figure 38, large scale solar PV generation costs fall at a much faster rate than both small-scale solar in Figure 36 and wind. In 2 years, the capital cost of solar PV generation is cheaper than wind, although the costs per MWh for wind also decline over time.

Large scale levelized storage costs are forecast for large scale lithium (Li-ion), pumped hydro energy storage (PHES) and CST as shown in Figure 39. Li-ion storage costs are set to fall faster and further than small-scale storage in Figure 37. In 2034, the cost of Li-ion falls below the cost of PHES, which maintains a current cost base into the future. CST is shown as the cheapest large-scale storage option in the future as costs decline over time with productivity improvement in heliostat design and thermal efficiency.

Figure 39 – Large Scale Levelised Storage Cost per MWh installed


State Interventions

Victoria Renewable Energy Targets

In 2017, the Victorian Government legislated the Renewable Energy (Jobs and Investment) Act 201714 stating its commitment to investing in a strong renewable energy industry. As part of this initiative they announced a renewable energy target of 25% by 2020 and 40% by 2025. The VRET will impact the cost consumers pay for their household electricity and drive the entry and exit of generators to meet the target.

Energeia implemented these mechanisms as a forced component in all scenarios to identify how CST uptake will impact the NEM relative to the Base Case.

Battery of the Nation

The Battery of the Nation initiative was established in 2017 by the Commonwealth of Australia and the Tasmanian State Government. ARENA and Hydro Tasmania have jointly funded feasibility studies into both the expansion and redevelopment of existing hydro-electric power stations and the examination of potential PHES projects in Tasmania.

Energeia modelling has implemented these components of the Battery of the Nation initiative as a forced component in relevant scenarios.

14 Renewable Energy (Jobs and Investment) Act 2017. Accessed from https://www.energy.vic.gov.au/__data/assets/pdf_file/0022/80509/VRET-fact-sheet-Bill.pdf

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The Energy Security Board (ESB) was established in August 2017 following a recommendation in the Finkel Report, and the ESB made its first intervention into the electricity market in November 2017 when it announced the development of the NEG to replace the proposed Clean Energy Target (another Finkel Report measure).

The NEG is still uncertain in its structure, application and enforcement, but based on the initial release of the high-level design, Energeia modelled the impacts on the following basis:

• Emissions Policy – Modelling of the NEG emissions guarantee on a year-by-year basis, to determine the lowest cost fuel mix required to meet the current Commonwealth Government emissions targets for wholesale generation, driving the entry and exit of different generating units to meet the target.

• Reliability Policy – NEG reliability constraints are modelled on a half-hourly basis to determine if supply meets both forecast demand, and demand plus a reliability target (i.e. 105% of demand), where additional firm capacity is required it enters the wholesale market as a reserve generator (i.e. a generator that cannot bid into the wholesale market).

The operation of the NEG was tested using the above mechanisms to determine the impacts relative to the Base Case.

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