ROAM Consulting Pty Ltd A.B.N Submission to. Submission to the Carbon Pollution Reduction Scheme Green Paper

Transcription

1 A.B.N Submission to Submission to the Carbon Pollution Reduction Scheme Green Paper 10 September 2008

2 TABLE OF CONTENTS 1) ABBREVIATIONS ) SUMMARY OF ROAM S RECOMMENDATIONS ) INTRODUCTION TO ROAM CONSULTING ) PREFERRED POSITION 8.2 THE IMPORTANCE OF ENERGY EFFICIENCY ) PREFERRED POSITION GROUPING OF ACTIVITIES TO DETERMINE BASELINES ) PREFERRED POSITION DETERMINING TRADE EXPOSURE ) PREFERRED POSITION ALLOCATION OF PERMITS VS 100% AUCTIONING ) PREFERRED POSITION ALLOCATION OF ASSISTANCE TO EITE INDUSTRIES ) DIMENSIONAL ANALYSIS... 7 Proposed Alternative Methodology: Other Alternative Methodologies: ) DETERMINING THE INCREASE IN THE PRICE OF ELECTRICITY DUE TO THE CPRS ) ROAM s calculation of the price increase Significance of the region Significance of the carbon price ) PREFERRED POSITION FUNDING FOR CCS TECHNOLOGIES ) BASE LOAD POWER ) PICKING WINNERS ) EMISSIONS TRAJECTORIES WITH SOLAR THERMAL ) Scenario 1: Focus on CCS technologies ) Scenario 2: Utilization of solar thermal technology ) PREFERRED POSITION 10.5 DIRECT ASSISTANCE TO COAL-FIRED GENERATION 25 11) PREFERRED POSITION 10.9 ALLOCATION OF ASSISTANCE FOR COAL-FIRED GENERATION ) GENERATION VOLUMES OF COAL PLANTS UNDER THE CPRS ) REVENUES OF COAL PLANTS UNDER THE CPRS ) OTHER COMMENTS FROM CHAPTER Fixed capital costs of coal-fired generators Gas generators under the CPRS Renewable generation under the CPRS Security of energy supply Periodic maintenance Quantum of assistance to coal-fired generators Proportion of assistance to black and brown coal generators The form of assistance ) CONCLUSION APPENDIX A) ABOUT ROAM CONSULTING S MODELLING SUITE... I A. FORECASTING WITH 2-4-C... I B. THE 2-4-C MODEL... II C. MODELLING THE TRANSMISSION SYSTEM... II D. KEY PARAMETERS USED BY THE MODEL... III TABLES OF CONTENTS Page i of iv

3 APPENDIX B) SPECIFIC MODELLING ASSUMPTIONS... IV E. ASSUMPTIONS WITH REGARD TO THE DEMAND SIDE... IV i. Inclusion of Customers... IV ii. iii. iv. Regional Load Profiles... IV Demand-Side Participation... IV New Base Loads... IV v. Hydroelectric Pump Storage Loads... IV F. DEMAND AND ENERGY FORECASTS... IV G. ASSUMPTIONS ON THE SUPPLY SIDE (GENERATION ASSETS)... V i. Existing Projects... V ii. iii. Individual Unit Capacities and Heat rates... V Unit Emissions Intensity Factors... V iv. Unit Operational Constraints... V v. Forecast Station Outage Parameters... V vi. vii. viii. ix. Generation Commercial Data... VI Generator Trading Strategies... VI Energy constraints... VI New market entrants... VII H. CARBON PRICE SENSITIVITIES... VII I. GENERATOR BIDDING... VII i. Applying a carbon price... VIII ii. SRMC vs Historical bidding... VIII J. GENERATION PLANTING SCHEDULE... VIII K. INCLUSION OF THE EXPANDED RENEWABLE ENERGY TARGET... XII L. ASSUMPTIONS WITH REGARD TO THE SUPPLY SYSTEM... XVII i. Transmission Losses... XVII ii. iii. iv. Inter-regional Losses... XVII Intra-regional Losses... XVII Transmission Limits... XVII v. Transmission Asset Development... XVII vi. vii. viii. Terranora (Gold Coast to Armidale Interconnector)... XVIII Murraylink (Melbourne to South Australia Interconnector)... XVIII Basslink (Latrobe Valley to Tasmania Interconnector)... XVIII M. ASSUMPTIONS WITH REGARD TO MARKET DEVELOPMENT... XVIII i. Assumptions of VOLL... XVIII ii. Developments in Regional Configurations... XVIII N. ASSUMPTIONS ABOUT MARKET EXTERNALITIES... XIX i. Inflation... XIX ii. The Impact of the Goods and Services Tax... XIX APPENDIX C) APPENDIX D) INTEGRATED RESOURCE PLANNING MODEL INPUT ASSUMPTIONS... XX ABOUT ROAM CONSULTING... XXII O. SERVICES AVAILABLE... XXII P. INTEGRATED RESOURCE MODELLING... XXII Q. WHOLESALE MARKET MODELLING... XXII R. TRANSMISSION SYSTEM MODELLING... XXIII TABLES OF CONTENTS Page ii of iv

4 S. PRODUCTS AVAILABLE... XXIII T. ROAM INSIGHT... XXIII U. PLAN-IT ENERGY (PIE)... XXIII V. 2-4-C AND 2-4-C LIGHT (2L)...XXIV W. WIND ENERGY SIMULATION TOOL (WEST)...XXIV X. DC LOAD FLOW...XXIV LIST OF TABLES TABLE 8.1 COMPONENTS OF THE CALCULATION OF ALLOCATION... 9 TABLE 8.2 COMPONENTS OF THE CALCULATION OF ALLOCATION FOR INDIRECT EMISSIONS (PROPOSED BY ROAM) TABLE 8.3 COMPONENTS OF THE EMISSIONS INTENSITY BASELINE FOR INDIRECT EMISSIONS TABLE 9.1 ASSUMPTIONS FOR IRP SCENARIO TABLE 9.2 PLANT CAPITAL COSTS FOR IRP SCENARIOS TABLE 9.3 ASSUMPTIONS FOR IRP SCENARIO TABLE B.1 ASSUMPTIONS FOR NETWORK DEVELOPMENT... VI TABLE B.3 NEW GENERATION TO 2020/21... IX TABLE B.4 PLANTING SCHEDULES FOR THE CASES MODELLED... X TABLE B.5 NOTIONAL TRANSMISSION LINE LIMITS... XVII TABLE C.1 ASSUMPTIONS FOR NEW ENTRANT LOW EMISSIONS COAL... XX TABLE C.2 ASSUMPTIONS FOR NEW ENTRANT OPEN CYCLE... XX TABLE C.3 ASSUMPTIONS FOR NEW ENTRANT COMBINED CYCLE... XXI TABLE C.1 ASSUMPTIONS FOR NEW ENTRANT SOLAR THERMAL... XXI LIST OF FIGURES FIGURE 4.1 TOTAL EMISSIONS FROM THE NEM... 4 FIGURE 8.1 IMPACT OF VARYING LEVELS OF CARBON PRICE ON THE WHOLESALE PRICE OF ELECTRICITY (NSW) FIGURE 8.2 SIGNIFICANCE OF LOAD GROWTH IN DETERMINING THE INCREASE IN THE PRICE OF ELECTRICITY (NSW, $40/TCO2 CARBON PRICE) FIGURE 8.3 INCREASE IN PRICE OF ELECTRICITY (LOW LOAD GROWTH) FIGURE 9.1 ONLY CCS AND EXISTING GAS TECHNOLOGIES RESULTING PLANTING MIX FIGURE 9.2 ONLY CCS AND EXISTING GAS TECHNOLOGIES COSTS AND EMISSIONS FIGURE 9.3 UTILIZATION OF SOLAR THERMAL TECHNOLOGY RESULTING PLANTING MIX FIGURE 9.4 UTILIZATION OF SOLAR THERMAL TECHNOLOGY COSTS AND EMISSIONS FIGURE 9.5 POOL PRICES WITH AND WITHOUT SOLAR THERMAL TECHNOLOGY FIGURE 11.1 NSW GENERATION VOLUMES (BLACK COAL) FIGURE 11.2 QLD GENERATION VOLUMES (BLACK COAL) FIGURE 11.3 VIC GENERATION VOLUMES (BROWN COAL) FIGURE 11.4 REVENUES (GROSS) OF VICTORIAN BROWN COAL GENERATORS FIGURE 11.5 REVENUES (NET OF CO 2 COSTS) OF VICTORIAN BROWN COAL GENERATORS IMPACT OF A $60/TCO2 PRICE FIGURE 11.6 REVENUES (NET OF CO 2 COSTS) OF VICTORIAN BROWN COAL GENERATORS IMPACT OF A $20/TCO2 PRICE TABLES OF CONTENTS Page iii of iv

5 FIGURE 11.7 REVENUES (NET OF CO 2 COSTS) OF NSW BLACK COAL GENERATORS IMPACT OF A $40/TCO2 PRICE FIGURE 11.8 REVENUES (NET OF CO 2 COSTS) OF QLD BLACK COAL GENERATORS IMPACT OF A $40/TCO2 PRICE FIGURE 11.9 FORECAST RECS PRICES FIGURE A C NEM REPRESENTATION... II TABLE B.2 SCENARIOS MODELLED... VII FIGURE B.1 RENEWABLE ENERGY PLANTING TO MEET THE RET... XIII FIGURE B.2 LOCATIONS OF BOM WEATHER STATIONS... XIV FIGURE B.3 EXAMPLE SOLAR PV GENERATION PROFILE (BY TIME OF DAY AND DAY OF FINANCIAL YEAR)... XVI TABLES OF CONTENTS Page iv of iv

6 1) ABBREVIATIONS BAU Business as usual COAG Council of Australian Governments CPRS Carbon Pollution Reduction Scheme EITE Emissions-intensive trade-exposed O&M Operations and Maintenance NEM National Electricity Market RECs Renewable Energy Certificates RET Renewable Energy Target SRMC Short run marginal cost 2) SUMMARY OF ROAM S RECOMMENDATIONS Regarding preferred position 8.2 Energy efficiency measures Complimentary schemes to promote energy efficiency are of extreme importance and must receive substantial funding and attention. Regarding preferred position 9.3 Grouping of activities for EITE assistance ROAM recommends that the grouping of activities proposed in the Green Paper be carefully analysed to ensure that only like activities are averaged to determine industry baselines. Regarding preferred position 9.5 Trade exposure of industries ROAM recommends that the level of trade exposure of an industry be determined via a variety of measures and taken into account in the allocation of assistance to EITE industries. Regarding preferred position 9.6 Free allocation of permits vs Auctioning 100% of the CO 2 permits should be competitively auctioned. Where assistance to EITE industries is necessary, this should be limited as much as possible, and done on a cash basis from the auction revenues. Regarding preferred position 9.7 Allocation to EITE industries Regarding the allocation of assistance to EITE industries for indirect emissions, ROAM recommends: 1. The methodology proposed for determining the allocation to EITE industries for increase in the price of electricity is dimensionally inconsistent. ROAM has proposed a more appropriate and consistent methodology in this submission. 2. The electricity factor (EF) could be more simply replaced by the Cost increase for electricity (I p ). The method of calculating I p is outlined in this submission. 3. The allocation to EITE industries for increase in the price of electricity should be done on a cash basis, rather than as an allocation of permits. Page 1 of 42

7 4. The increase in the price of electricity due to the CPRS is related to the emissions from electricity production via a complex relationship, and can only be determined via sophisticated modelling. ROAM is well placed to provide modelling services of this nature. Regarding preferred position 10.3 Investment in Carbon capture and storage technologies ROAM recommends that the government invest in promising renewable technologies such as solar thermal technology at least as much as is invested in carbon capture and storage technologies. Regarding preferred position 10.5 Direct assistance to coal-fired generators Direct assistance to coal-fired generators is unnecessary, and potentially damaging to the renewable energy investment environment (where maximum investment is required). Regarding preferred position 10.9 Compensation to coal-fired generators Coal-fired generation assets will be individually very differently impacted by the CPRS. It is inappropriate to allocate compensation in direct proportion to the capacity of each asset. Given: 1. The difficulty associated with accurately forecasting the impacts on coal-fired generators, 2. The high probability of windfall gains by some generators, 3. The long timeframe over which climate change policy changes has been a foreseeable issue 4. The potential negative treatment of generators who have responsibly invested in efficiency improvements; ROAM considers it appropriate to not provide compensation to coal-fired generators. 3) INTRODUCTION TO ROAM CONSULTING ROAM Consulting is a leading provider of expert services in electricity market modelling to participants in the Australian National Electricity Market and electricity markets around the world. With a diverse team of consultants and specialists originating from various backgrounds, ROAM Consulting has offered sophisticated modelling services since ROAM s core business entails highly detailed modelling of the whole Australian electricity grid to the level of individual generators, on a half hourly basis. ROAM uses its modelling capability to forecast half hourly: Loads Electricity prices Generator production levels and revenues Transmission congestion Greenhouse gas emissions and the dependency of these upon various input assumptions (including policy such as the renewable energy target, and the carbon pollution reduction scheme). This modelling Page 2 of 42

8 is routinely done on timescales from one week ahead, to 30 years ahead. A key feature of ROAM s modelling is the inclusion of transmission limitations for each individual transmission line in the grid. This is unique to ROAM, and allows precise forecasting of transmission issues arising from the integration of renewable energy in high levels, or the change of dispatch order due to emissions trading. These transmission limitations are key in accurate forecasting of electricity market outcomes. As such, ROAM has unique and detailed expertise in the electricity industry that is essential for facilitating the correct design of the Carbon Pollution Reduction Scheme. ROAM s significant clients include: Electricity generators, and generation investors (in the coal, gas, wind, biomass and other renewable industries), Wholesale electricity purchasers (including retailers, mines and smelters), Transmission and distribution network service providers, The electricity market operator and planner (NEMMCO), and the market regulator (AER / AEMC). Government departments (state and federal levels). 4) PREFERRED POSITION 8.2 THE IMPORTANCE OF ENERGY EFFICIENCY The government preferred position on energy efficiency measures is: The Government is also committed to providing low-income households with increases in assistance through the tax and payment system and all households with other assistance to address the impact on their living standards. It is committed to: Provide additional support through the introduction of energy efficiency measures and consumer information to help households take practical action to reduce energy use and save on energy bills so that all can make a contribution. The importance of complimentary measures to promote energy efficiency cannot be overstated. Energy efficiency is central to cost effective emissions reduction, and has the potential to be as important to Australian emissions reduction as all renewable energy technologies combined. It must be recognised that there are substantial non-economic barriers to energy efficiency measures, and these must be identified and addressed. To illustrate the immense benefit of energy efficiency, ROAM has modelled the total emissions from the National Electricity Market (NEM) under a variety of carbon prices, and with two alternative demand growth scenarios: 1. Business as usual (BAU) demand growth (as defined by the NEMMCO medium economic growth forecasts) 2. Low demand growth (as defined by the NEMMCO low economic growth forecasts). Page 3 of 42

9 Total Emissions (Mt CO 2 -e) All other assumptions were the same 1. The resulting total emissions from the NEM under these different levels of load growth are illustrated in the figure below. Figure 4.1 Total emissions from the NEM BAU Load Growth ($0 CO2) BAU Load Growth ($40 CO2) BAU Load Growth ($60 CO2) Low Load Growth ($0 CO2) Low Load Growth ($40 CO2) Low Load Growth ($60 CO2) At any particular carbon price, reducing the load growth substantially reduces the emissions from stationary energy. This illustrates the extreme importance of energy efficiency measures in cost effective reduction of emissions from electricity. ROAM s Recommendation: Complimentary schemes to promote energy efficiency are of extreme importance and must receive substantial funding and attention. 5) PREFERRED POSITION GROUPING OF ACTIVITIES TO DETERMINE BASELINES The Green paper outlines the following preferred position on the grouping of activities for EITE assistance: The proposed emissions-intensive trade-exposed assistance would be provided on the basis of the industry-wide emissions from a process or activity to ensure that assistance is well targeted and is equitable both within and between industries. 1 The modelling method and input assumptions are detailed in Appendices A and B. Page 4 of 42

10 However, the suggested grouping (shown in Appendix D of the Green paper) puts together many industries with very diverse qualities. For example, all non-ferrous metals and products apart from aluminium and alumina are grouped together 2. This puts the zinc smelting industry (which has extremely high electricity usage, and almost no direct emissions) with the copper industry (which has much lower associated total emissions, and almost no electricity usage). It is clearly inappropriate to group these industries together. Care must be taken to investigate the properties of all industries included in each category, to ensure they are similar enough to be appropriately grouped together. ROAM s Recommendation: ROAM recommends that the grouping of activities proposed in the Green Paper be carefully analysed to ensure that only like activities are averaged to determine industry baselines. 6) PREFERRED POSITION DETERMINING TRADE EXPOSURE The Green paper states the following preferred position regarding the level of trade exposure of an industry: All industries, other than those for which there exists a physical barrier to trade, would be considered for emissions-intensive trade-exposed assistance. The argument for this methodology is that the most emissions intensive industries will be more vulnerable to carbon leakage 3. However, carbon leakage is likely to be more closely related to how trade exposed an industry is (rather than its emissions intensiveness). The domestic airline industry is a clear example of a highly emissions intensive industry that is not highly trade exposed, and therefore suffers little risk of carbon leakage. On the other hand, industries such as the zinc smelting are highly trade exposed (due to the very large proportion of product that is exported, up to 80%), are likely to be far more vulnerable to carbon leakage. Despite the difficulties inherent in its calculation (as acknowledged in the Green Paper), the degree of trade exposure should be taken into account when determining the amount of assistance provided, as far as possible. It may be appropriate for each industry to provide information regarding their degree of trade-exposure, and be considered on a case-by-case basis. It is important that the limited amount of assistance available goes as much as possible to those who require it, so that it can be of a sufficient level to cover those industries that are truly trade exposed. ROAM s Recommendation: ROAM recommends that the level of trade exposure of an industry be determined via a variety of measures and taken into account in the allocation of assistance to EITE industries. 2 Appendix D. 3 Page 319, paragraph 3. Page 5 of 42

11 7) PREFERRED POSITION ALLOCATION OF PERMITS VS 100% AUCTIONING The Green paper states the following preferred position: Up to around 30 per cent of Australian carbon pollution permits would be freely allocated to emissions-intensive trade-exposed (EITE) activities. At the outset of the scheme, if agricultural emissions are excluded from scheme coverage, this would be up to around 20 per cent of permits. It is also stated that additional compensation will be given to coal-fired generators, although it is not stated if this will be in the form of a free allocation of permits, and what percentage of permits this might be. The Green paper states 4 : The Government seeks stakeholder feedback on the relative merits of providing direct assistance to coal-fired electricity generators through allocations of carbon pollution permits or cash payments. This means that a maximum of 70% of permits will be competitively auctioned, and perhaps much less. Free allocation of high proportions of permits has caused severe problems in the European emissions trading scheme, for the following reasons 5 : 1. Free allocation of permits as a function of past emissions leads to perverse dynamic effects, where firms have an incentive to emit more now in order to receive a larger free allocation in future. 2. Allocating permits for free results in rent-seeking behaviour by firms as they invest valuable resources in lobbying to obtain a higher allocation. These problems can be resolved by auctioning the permits 6. High levels of auctioning also has the following additional benefits 7 : 1. Auctions induce the private sector to reveal their expected abatement costs to government, reducing problems of asymmetric information 2. Auctioning allowances promotes greater managerial focus on emissions trading, which is likely to increase cost effective abatement effort. 3. Free allocation is a regressive transfer of wealth from (relatively poor) citizens to (relatively wealthy) shareholders. 4 Page 386, paragraph Hepburn C, 2007, Carbon Trading: A Review of the Kyoto Mechanisms. Annu. Rev. Environ. Resour. 32: Hepburn C, Neuhoff K, Grubb M, Matthes F, Tse M Auctioning of EU ETS phase II allowances: Why and how? Clim. Policy 6(1): Hepburn C, 2007, Carbon Trading: A Review of the Kyoto Mechanisms. Annu. Rev. Environ. Resour. 32: Page 6 of 42

12 Utilities in the United Kingdom are estimated to have made 800 million in windfall profits from emissions trading allocations 8, and it has been found that a significant proportion of freely allocated permits could have been auctioned with firm profits unchanged before and after the introduction of the scheme 9. In addition, when high levels of permits are freely allocated, the auction process is ineffective at producing a liquid market (due to the lesser number of permits that are auctioned). Uncertainty at the beginning of the scheme makes market participants with an excess of freely allocated permits reluctant to trade, resulting in inflated permit prices, and a later resulting price crash (when it becomes clear that the allocation was too generous) 10, 11. ROAM s Recommendation: 100% of the CO 2 permits should be competitively auctioned. Where assistance to industries is necessary, this should be limited as much as possible, and done on a cash basis from the auction revenues. 8) PREFERRED POSITION ALLOCATION OF ASSISTANCE TO EITE INDUSTRIES 8.1) DIMENSIONAL ANALYSIS The following is stated in the Green Paper as the preferred method for calculating the allocation of assistance to EITE industries 12 : Allocations of assistance for indirect electricity emissions of new and existing EITE entities would be calculated on the basis of an Australian historical industry-average electricity-intensity baseline for each EITE activity an electricity factor, where the electricity factor is determined to reflect the likely average electricity price impact of the scheme the output of the EITE activity for each entity the assistance rate for that EITE activity This is clarified in more detail with the following formula 13 : 8 IPA Energy Consult Implications of the EU Emissions Trading Scheme for the UK power generation sector. Rep. for UK Dep. Trade Ind., London 9 Hepburn C, Quah J, Ritz R Emissions trading and profit-neutral grandfathering. Oxf. Econ. Dep.Work. Pap. 295, Univ. Oxford 10 Grubb M, Ferrario F False confidences: forecasting errors and emission caps in CO2 trading systems. Clim. Policy 6(4): Ellerman AD, Buchner B Over-allocation or abatement? A preliminary analysis of the EU Emissions Trading Scheme based on the 2006 emissions data. MIT Jt. Program Sci. Policy Glob. Change, Rep Page 49, point 9.7. Page 7 of 42

13 A ia k a d e EI O k EI EF O ia ia a ia ia The first half of this equation relates to allocations with respect to direct emissions, and the second half relates to allocations with respect to indirect electricity emissions. For a business that only has indirect emissions in the form of electricity usage, this equation can therefore be simplified: A ia k a EI e ia EF O ia The components in this equation are detailed in the table below, with units and meanings as ROAM understands them from the text in the Green Paper. 13 Page 324. Page 8 of 42

14 Table 8.1 Components of the calculation of allocation Description from the Green Paper Interpretation by ROAM Units of measurement, as understood by ROAM A ia allocation of permits to entity i for emissions associated with activity a Number of permits tco 2 -e k a assistance rate for activity a, representing the degree of assistance provided to entities for this activity both initially, and over time Percentage of emissions covered for assistance (proposed as 90% or 60% depending on emissions per unit of revenue for that activity) Unitless EI ia e electricity-intensity baseline for indirect electricity emissions for entity i conducting activity a (that is, baseline level of electricity per unit of output for the activity) 14 Electricity used per unit of output for the activity MWh / unit of output EF electricity factor, which reflects the impact of the carbon price on the price of electricity A unitless number around 1. An electricity factor greater than 1 suggests that activity will suffer a larger than average increase in price due to the CPRS. An electricity factor less than 1 suggests that activity will suffer a smaller than average increase in price due to the CPRS 15. Unitless O ia output of activity a by entity i Units of output (number of items produced) Unit of output Using these definitions, a dimensional analysis of the equation above reveals that it is dimensionally inconsistent. To illustrate this: 1. Take the above equation: A ia k a EI e ia EF O ia 14 ROAM believes that this is an ambiguous and misleading statement for consistency EI ia e must be measured in units of tco 2 -e per unit of output, rather than tco 2 /kwh as this statement seems to suggest. This is very unclear, and ROAM has contacted the Department of Climate Change for clarification. 15 This is ROAM s understanding upon reading the Green Paper. It is not detailed in a clear way, and should be confirmed. Page 9 of 42

15 2. Write the units of each component: tco 2 MWh unitless unitless unit of unit of output output 3. Remove unitless components: tco 2 MWh unit of unit of output output 4. Then cancel like units: tco 2 MWh This is clearly nonsensical and inconsistent. ROAM proposes an appropriate alternative below. Proposed Alternative Methodology: Businesses do not have to surrender permits for indirect emissions, and will instead simply experience a cost increase due to the increased purchase price of electricity. It is therefore far more sensible and straightforward to make the allocation for indirect emissions as a cash payment, rather than an allocation of permits. This is for two reasons: 1. For businesses with only indirect emissions and no direct emissions, this will remove the need to trade permits (this removes transaction costs for these businesses, and simplifies the process). 2. The allocation for indirect emissions is directly related to the cost increase experienced by businesses, rather than the emissions due to their activity. Although related, these are different and have a complex relationship. Ultimately, the purpose of the allocation is to protect EITE industries from cost increases, so the methodology should be directly related to this, rather than emissions. 3. The methodology for calculation of the allocation is more transparent and straightforward (as outlined below). ROAM proposes the following methodology. The allocation should be made in cash, rather than in permits. It will be k a, multiplied by the total anticipated increase in cost to a business per annum. The total increase in cost to a business will be given by: The electricity used per unit of output in the year (MWh/unit of output), multiplied by The output in the year (unit of output), multiplied by The increase in the price of electricity ($/MWh) This is summarised in the equation below: A ia k a EL o O ia I p Page 10 of 42

16 The components of this equation are defined in the table below. Table 8.2 Components of the calculation of allocation for indirect emissions (proposed by ROAM) A ia Description Allocation of money to entity i for emissions associated with activity a (cash payment per annum to business for indirect emissions) Units of measurement, as understood by ROAM $ k a Assistance rate for activity a, representing the degree of assistance provided to entities for this activity both initially, and over time (percentage of emissions covered for assistance, proposed as 90% or 60% depending on emissions per unit of revenue for that activity) Unitless EL o Electricity used by the activity per unit of output MWh / Unit of output O ia output of activity a by entity i (number of items produced) Unit of output I p Increase in cost of electricity due to the CPRS $ / MWh Defining I p, rather than using an electricity factor, is far more transparent and clear in meaning, and greatly simplifies its calculation. In the following section, ROAM outlines the calculation of I p. Other Alternative Methodologies: If the government wished to use an alternative methodology closer to that proposed in the Green Paper, ROAM outlines two possibilities below. These are self consistent, but ROAM believes them to be less transparent and more complicated than the methodology already proposed above. 1. Define EI e ia in units of emissions If EI e ia is instead measured in units of emissions (tco 2 -e per unit of output), this corrects the above described inconsistency. This would make EI e ia the the emissions-intensity baseline for indirect electricity emissions (rather than the electricity-intensity baseline), very similar in meaning to EI d ia (the emissions-intensity baseline for direct emissions, also measured in units of tco 2 -e per unit of output). EI ia e could then be further broken down into components for calculation as follows: These new components are defined in the table below. EI e ia EL o EM e Page 11 of 42

17 Table 8.3 Components of the emissions intensity baseline for indirect emissions EI ia e Description Emissions-intensity baseline for indirect electricity emissions for entity i conducting activity a (emissions from electricity per unit of output for the activity) Units of measurement tco 2 -e / unit of output EL o Electricity used by the activity per unit of output MWh / unit of output EM e Emissions-intensity of the electricity used (calculated on a region-by-region basis) tco 2 -e / MWh This is now consistent internally, and with the methodology already outlined in the Green Paper for direct emissions. However, the exact meaning of the electricity factor (EF) remains unclear. The determination and scaling of EF will not be simple and transparent to determine. 2. Define EF in units of emissions per kwh e If it was desirable to keep EI ia in units of kwh per unit of output, dimensional consistency could be achieved by measuring EF in units of tco 2 e per kwh. If the price uplift in the wholesale price of electricity is directly related to the emissions associated with that electricity, then this would be appropriate. However, as is discussed later, this is not the case. Additionally, under this methodology, EF is not a factor, and has no relation to the price increase due to the CPRS (which is the stated purpose of the electricity factor). ROAM s Recommendations: 1. The methodology proposed for determining the allocation to EITE industries for increase in the price of electricity is dimensionally inconsistent. ROAM has proposed a more appropriate and consistent methodology above. 2. The electricity factor (EF) could be more simply replaced by the Increase in the price of electricity (I p ). The method of calculating I p is outlined in the following section. 3. The allocation to EITE industries for increase in the price of electricity should be done on a cash basis, rather than as an allocation of permits. 8.2) DETERMINING THE INCREASE IN THE PRICE OF ELECTRICITY DUE TO THE CPRS The Green Paper states that it will be necessary to determine the likely price increase in electricity due to the CPRS, and determine the electricity factor based upon this 16 : The Government s preferred position is that the electricity emissions factor would be determined to reflect the likely average electricity price impact of the scheme. 16 Page 328, paragraph 5. Page 12 of 42

18 The government acknowledges that it will be very challenging to accurately determine the increase in the price of electricity due to the CPRS, since it will depend on many unknown factors 17 : Increases in electricity prices will vary by location in Australia and from hour to hour, depending on: the demand for electricity the average emissions intensity of electricity generators the emissions intensity of the marginal electricity generator (the generator that sets the price at a particular point in time) supply constraints (that is, constraints between different regions in the National Electricity Market that prevent importation of electricity from lower cost regions) new investment in the electricity market and the resource costs of new entrants. ROAM agrees that all of these factors are important in the calculation of the cost increase in electricity, due to the CPRS. The green paper proposes that the electricity factor will not be a dollar amount that electricity prices have increased, but will be some factor related to this, that gives a relative weighting of the increase in price in different regions, or for different industries. This is not clarified well in the Green Paper, and ROAM proposes that the alternative methodology outlined above will be a far more transparent way of determining the allocation for indirect emissions. ROAM proposes the calculation of I p, the cost increase in electricity due to the CPRS, rather than an electricity factor (EF). Although relying on modelling is not ideal, due to a lack of alternatives I p can be best determined via the most sophisticated modelling available. ROAM s modelling capabilities are uniquely well placed to offer this kind of detailed calculation. ROAM is regularly employed by the electricity market operator (NEMMCO) to calculate market data. For example, ROAM has performed the highly computationally intensive calculations of minimum reserve levels for NEMMCO since Minimum reserve levels are the key determining factor for setting the plant requirement in the National Electricity Market (NEM) to meet the Reliability Standard. ROAM s calculations have been endorsed by NEMMCO and at the Reliability Panel Board level, and been in practice in the working NEM since In addition, ROAM assists NEMMCO by providing benchmarking services for the annual calculation of the Marginal Loss Factors for the NEM. The Marginal Loss Factors take account of the grid losses at every location, and are directly applied to the price of electricity paid to generators and by loads. 17 Page Page 13 of 42

19 As specialists in electricity market forecasting, ROAM is well placed to determine the cost increase due to the CPRS under a range of sensitivities. ROAM has already performed a wide range of calculations of this nature (with the results presented below), and could offer services to the Department of Climate Change for a broad, detailed, and definitive study of the determination of I p (or EF, if preferred) ) ROAM s calculation of the price increase ROAM routinely includes carbon pricing in its 2-4-C forecasting model. A description of this model is provided in Appendix A. Detailed input assumptions used for the modelling are detailed in Appendix B. The figure below shows the wholesale pool price outcomes from ROAM s modelling for the NSW region as an example (note that all regions are calculated). In the absence of a carbon price, the average pool price in NSW is expected to be around $30/MWh for the duration until The amount of load growth over the period is significant; business as usual (BAU) load growth has been compared with low load growth forecasts, to account for impacts of energy efficiency measures under the CPRS. The results presented in this report relate to modelling in the NEM. ROAM also has equivalent expertise in modelling the electricity grids in Western Australia and the Northern Territory though these are not discussed specifically in this submission. Page 14 of 42

20 Wholesale price of electricity ($/MWh) Figure 8.1 Impact of varying levels of carbon price on the wholesale price of electricity (NSW) $100 $90 $80 $70 $60 $50 $40 $30 $20 $10 $ Financial Year Low load growth, $0/tCO2 Low load growth, $20/tCO2 Low load growth, $40/tCO2 Low load growth, $60/tCO2 BAU load growth, $0/tCO2 BAU load growth, $20/tCO2 BAU load growth, $40/tCO2 BAU load growth, $60/tCO2 When a carbon price is applied ($20, $40 and $60 per tonne of CO2 are used as indicative examples), the wholesale pool price is uplifted by corresponding amounts. This uplift has been calculated using ROAM s sophisticated 2-4-C modelling software, taking into account the emissions factors of all generators in the NEM, their other physical properties and limitations, and their typical bidding strategies. In performing these simulations, ROAM used a variety of load forecasts to determine the sensitivity of the results to end-user consumption. The figure below illustrates the uplift in the wholesale pool price of electricity in NSW under a $40 /tco 2 carbon price. Three load scenarios were analysed in this study: Business as usual (BAU) load growth, with extreme weather conditions (weather conditions expected to be exceeded 1 year in 10); Business as usual (BAU) load growth, with mild weather conditions (weather conditions expected to be exceeded 1 year in 2). Due to the high impact of weather conditions on the price of electricity this would be expected to be a minimum price case, rather than an average price case, and; Low load growth, with extreme weather conditions. Page 15 of 42

21 Uplift in wholesale price of electricity ($/MWh) Figure 8.2 Significance of load growth in determining the increase in the price of electricity (NSW, $40/tCO2 carbon price) $50 $48 $46 $44 $42 $40 $38 $36 $34 $32 $ Financial Year BAU load growth, extreme weather conditions BAU load growth, mild weather conditions Low load growth, extreme weather conditions In the early years of the study, the load scenario has a minimal impact on the price uplift under the CPRS, but the difference is more significant in later years of the study. This is consistent with expectations, since the greatest difference in electricity consumption between the load growth scenarios is seen in the later years. However, ROAM concludes that the specific load growth scenario has a relatively smaller impact on the increase in the average price of electricity forecast under the CPRS than other factors. The figure below illustrates the two criteria that do have a significant impact on the price increase forecast under a CPRS, being: 1. The region of the NEM, and; 2. The carbon price. These are addressed separately below. Page 16 of 42

22 Uplift in wholesale price of electricity ($/MWh) Figure 8.3 Increase in price of electricity (Low load growth) $70 $60 $50 $40 $30 $20 $10 $0 $20 $40 $60 Carbon price ($/tco2) NSW QLD SA TAS VIC Significance of the region ROAM s modelling consistently shows that the amount of uplift in the wholesale electricity price due to the CPRS is dependent upon the region. Victoria, with its high proportion of electricity produced from emissions intensive brown coal, shows a relatively larger uplift. Tasmania however shows a lower uplift in the price, largely due to the high proportion of energy sourced from emissions free hydro generation in the region. The price uplift in Tasmania has nonetheless been found to be substantial, which is not reflected in the National Greenhouse Accounts factors workbook. The Department of Climate Change NGA factors correctly indicate that electricity in Tasmania is only responsible for a fraction of the indirect emissions of electricity in other states (the EF for scope 2 emissions is only 0.12 kg CO 2 -e/kwh, compared with factors of between 0.84 kg CO 2 -e/kwh and 1.22 kg CO 2 -e/kwh for other regions of the NEM) 18. However, the price of electricity in Tasmania is closely correlated with the price in Victoria, due to normal market behaviour 19. Therefore, despite being responsible for low emissions compared to the rest of the NEM, Tasmania electricity users will see a substantial and disproportionate price increase. This is a critically important consideration when calculating the allocation of assistance to EITE industries. 18 Department of Climate Change, National Greenhouse Accounts (NGA) Factors, January Prices in the NEM are set by one region only, modified by the Inter-Regional Loss Factors, except in the event of transmission congestion which causes price separation between regions. Page 17 of 42

23 Significance of the carbon price The level of the carbon price itself dictates how much of the increase in cost is passed on to consumers. This is because the changes in generator merit order (for example, changing the order of dispatch of coal and gas generation) occur at different prices in different regions, depending upon the properties of the generators and fuels in those regions. At lower carbon prices, lower cost abatement opportunities can be utilised. ROAM s Recommendation: The increase in the price of electricity due to the CPRS is related to the emissions from electricity production via a complex relationship, and can only be determined via sophisticated modelling. Due to extensive experience using modelling to determine similar market parameters, ROAM is well placed to provide services of this nature. 9) PREFERRED POSITION FUNDING FOR CCS TECHNOLOGIES The government has the following stated position on funding for carbon capture and storage (CCS) technologies: The Australian Government has made significant contributions to progress the commercial deployment of carbon capture and storage (CCS). These contributions, and any further support, should recognise the technical and institutional hurdles to the development and deployment of carbon capture and storage technologies, and reflect Australia s significant domestic and international interests in the development of this technology. ROAM notes that carbon capture and storage is only one of a multitude of low emissions technologies that are available to reduce emissions from the NEM. Solar thermal technology, in particular, can very likely provide large scale base-load power, and has many advantages over CCS, including: 1. It can be developed more rapidly, and will very likely be available on a large scale sooner than CCS. CCS is widely acknowledged to be at least ten years from commercial deployment, whereas solar thermal plants with continuous storage and supply are available now. A 10MW plant with storage is planned for commissioning in Cloncurry in 2009, and Worley Parsons proposes the construction of 8,500 MW of solar thermal plant by 2020 (beginning in 2011). This substantial sum is more than sufficient to meet the 20% by 2020 renewable energy target. 2. Solar thermal technologies will very likely will have a lower capital cost than CCS. The present capital cost of these planned projects is quoted to be between $3,100/kW and $4,000/kW, and is expected to decrease over time. This is less than the capital costs often quoted for future CCS technologies (which are not yet available). 3. Solar thermal technologies are mechanically relatively simple and do not require fuel. This means that they will have very low ongoing costs, compared to much higher short run marginal costs for CCS (which will include the rising cost of fuel, operations and maintenance for complex machinery, transport and storage costs for CO 2, and emissions permits for the CO 2 not captured). Page 18 of 42

24 4. Solar thermal energy has no associated carbon emissions (CCS technologies still emit at least 10% of the associated CO 2, and often emit far more than this amount). 5. It is not dependant on CO 2 transport infrastructure. Developing this infrastructure will be expensive. 6. It does not have the regulatory and leakage risk issues associated with long term CO 2 storage (which is yet to be proven viable). 7. It is renewable, and not continuing dependency on finite fossil fuel reserves. One of the reasons stated for favouring CCS over renewable technologies is the belief that existing coal generation facilities could be retrofitted with CCS technology. However, proponents of CCS technologies agree that this is unlikely to be a cost effective option under any carbon price 20. Carbon capture and storage involves an increased auxiliary load at the power station to extract and compress the gas. Old coal-fired stations are inefficient and therefore produce more CO 2 per MWh of electricity. This means that there will be an even higher auxiliary load associated with capturing and sequestering the carbon from an inefficient station, further decreasing the (already low) efficiency. This decreases the cost effectiveness of the station. In order for CCS to be competitive with renewable technologies in a carbon constrained future it will be necessary to build the newest and most efficient possible coal-fired plants (rather than retrofitting existing inefficient power stations). 9.1) BASE LOAD POWER ROAM proposes that there is an unwarranted focus on the importance of base-load power. While the current system relies upon base-load, intermediate and peaking plant, there is very realistic potential to move towards an electricity system that instead incorporates a mixture of: 1. Intermittent renewable technologies such as wind and solar PV; 2. Schedulable renewable technologies such as solar thermal, geothermal and waste biomass, and; 3. Intelligent grids utilizing wide-spread demand side management. This would be a very different system from that in use today, but should be considered a feasible alternative. 9.2) PICKING WINNERS By investing the majority of research funds into CCS technologies, Australia is picking winners. Given the level of uncertainty regarding the development of CCS technology, this exposes Australia to a significant risk if this technology should not succeed. 20 Climate Change Policies: international and Australian Trends and impacts on the National Electricity Market. A report for the National Electricity Market Management Company, Prepared by the National Institute of Economic and Industry Research, June Page 19 of 42

25 9.3) EMISSIONS TRAJECTORIES WITH SOLAR THERMAL ROAM has used an integrated resource planning (IRP) model to determine the optimal forward planting in the NEM to continue to meet reliability requirements at least cost, under various assumptions. The integrated resource planning model works by simulating every possible planting outcome (on a half hourly basis, using ROAM s 2-4-C model), and then calculating the least cost outcome for each set of input assumptions ) Scenario 1: Focus on CCS technologies In the first scenario examined, ROAM used the assumptions listed in the table below. Table 9.1 Assumptions for IRP Scenario 1 Demand Renewable energy Emissions caps Retirements CCS technology Continues to grow at a business as usual rate (as defined by NEMMCO s medium economic growth forecast) Planted to meet the 20% by 2020 renewable energy target, and then maintained at 20% of the supply to 2035 (with growing demand this entails a constant small growth in renewable energy to 2035). Further development in renewable energy beyond the 20% by 2020 target was not included. Applied. A linear trajectory that decreases from today s emissions in the NEM, to a 10% reduction from 2000 levels was used. The model was allowed to retire the most polluting plants in each region, and replace them with CCS technologies (Collinsville, Hazelwood, Morwell, Munmorah and Swanbank B). Was not allowed before 2018 (CCS is widely acknowledged to be a minimum of 10 years from wide scale commercial deployment). This also meant that retirements were not possible before CCS technology was assumed to be capable of 75% carbon capture (25% is released to the atmosphere, and must be covered by emissions permits). New coal New gas New coal plants were not allowed without CCS technology applied The model could choose to install either Open Cycle Gas Turbines (OCGT) or Combined Cycle Gas Turbines (CCGT) in each region The capital costs used for this study are show in the table below, with more detailed assumptions listed in Appendix C. Table 9.2 Plant capital costs for IRP Scenarios Plant type Region Capital costs ($/kw) CCS coal QLD & NSW 4114 Page 20 of 42

26 Installed Capacity (MW) CO2 Price ($/tco2) VIC 4896 Open cycle gas turbine (OCGT) All 734 Combined cycle gas turbine (CCGT) All 1071 With these input assumptions, the planting scenario shown below is the optimal way to meet the required emissions reductions; a 10% reduction from 2000 levels by The model retires plant as soon as possible, and must apply a very high carbon price ($45/tCO 2 from , and rising to $90/tCO 2 by 2035) to meet the emissions reductions. This high carbon price significantly changes the dispatch order of the existing plants in the NEM, reducing the dispatch of the more polluting plants. Figure 9.1 Only CCS and existing gas technologies resulting planting mix $ $ $ $ $40 $20 0 $0 Replace with CCS QLD CCS coal NSW CCS coal VIC CCS coal QLD OCGT NSW OCGT VIC OCGT SA OCGT QLD CCGT NSW CCGT VIC CCGT SA CCGT CO2 price Without further retirements, the model has very few planting alternatives that result in emissions below the required cap. This means that using only CCS and existing gas technologies, the carbon price must be high to reduce emissions sufficiently via forcing existing coal plant to reduce output markedly. The emissions cap applied is show in the figure below, in light green. The dark green shows the actual resulting emissions trajectory, which is somewhat below the emissions cap. The red line shows the cumulative cost of this scenario, which reaches M$100,000 by The cumulative cost shown consists of fixed and variable costs for new generation, but variable costs only for existing generation. That is, it does not include capital repayments for existing infrastructure. Page 21 of 42

27 CO2 emissions (MT per year) Cumulative Cost (M$AU) Figure 9.2 Only CCS and existing gas technologies costs and emissions CO2 Emissions (Mt/a) [LHS] Emissions trajectory Cumulative Cost (excl CO2 cost $M) 9.3.2) Scenario 2: Utilization of solar thermal technology An alternative scenario that utilizes solar thermal technology was also modelled. In this scenario, solar thermal plants were included in place of CCS technologies, with similar properties except for an emissions factor of zero, and a capital cost of $3,100/kW. This is the present cost of this technology with thermal storage (it is the cost of the 10MW plant planned for Cloncurry, commissioning in 2009). It is anticipated that the cost of solar thermal technologies will reduce below this over time, but this has not been modelled here to produce an accurate upper bound. The other input assumptions used for scenario 2 are detailed in the table below. Table 9.3 Assumptions for IRP Scenario 2 Demand As for Scenario 1. Continues to grow at a business as usual rate (as defined by NEMMCO s medium economic growth forecast). Renewable energy Planted to meet the 20% by 2020 renewable energy target, and then maintained at 20% of the supply to 2035 (with growing demand this entails a constant small growth in renewable energy to 2035). Page 22 of 42

28 Installed Capacity (MW) CO2 Price ($/tco2) Emissions caps Retirements Solar thermal technology As for Scenario 1. A linear trajectory that decreases from today s emissions in the NEM, to a 10% reduction from 2000 levels was used. As for Scenario 1. The model could choose to retire the most polluting plants in each region, and replace them with CCS technologies (Collinsville, Hazelwood, Morwell, Munmorah and Swanbank B). Since CCS was not allowed before 2018, retirements were also not allowed before The model could choose to install solar thermal plants in all regions, with a capital cost of $3,100/kW (and other parameters as detailed in Appendix C). New coal As for Scenario 1. technology applied. New coal plants were not allowed without CCS New gas As for Scenario 1. The model could choose to install either Open Cycle Gas Turbines (OCGT) or Combined Cycle Gas Turbines (CCGT) in each region. The resulting optimal least cost planting is shown in Figure 9.3. The model chooses to extensively plant with solar thermal technology, and does not install any CCS technology at all. Some combined cycle gas turbines are installed in the later years. The carbon price rises to $30/tCO2 quickly, but goes no higher over the entire simulation period. This is because with the construction of zero emissions plants to meet the load growth, it is not necessary to dramatically change the dispatch of existing plants via carbon price uplift in order to meet the emissions reduction target (as it was in scenario 1). Additionally, retirement of existing plants is not required to meet the emissions reduction target. Figure 9.3 Utilization of Solar thermal technology resulting planting mix $ $ $ $ $40 $20 0 $0 Replace with CCS QLD Solar thermal NSW Solar thermal VIC Solar Thermal QLD OCGT NSW OCGT VIC OCGT SA OCGT QLD CCGT NSW CCGT VIC CCGT SA CCGT CO2 price This scenario results in a much lower cumulative cost of less than M$80,000 by 2035, to produce the same emissions reduction, as illustrated in the figure below. Page 23 of 42

29 CO2 emissions (MT per year) Cumulative Cost (M$AU) Figure 9.4 Utilization of Solar thermal technology costs and emissions CO2 Emissions (Mt/a) [LHS] Emissions trajectory Cumulative Cost (excl CO2 cost $M) Since the cost of solar thermal technologies is expected to reduce over time, the costs for scenario 2 should be lower than those modelled here. The lower carbon price in the second scenario results in substantially lower wholesale pool prices for electricity, as illustrated in the figure below. Average prices are around $40/MWh when there is investment in solar thermal technologies. If CCS technologies are used exclusively, the pool price rises to $55/MWh by 2014, and to $75/MWh by This will be a substantial additional cost to electricity intensive industries, exacerbating the problem of assistance to EITE industries for increased electricity prices. The downward trend over time in all pool prices is due to the progressive planting of renewable energy under the 20% by 2020 renewable energy target (complimentary scheme). Due to their low short run marginal costs, and zero carbon emissions, renewable technologies have the effect of lowering the pool price. Page 24 of 42

30 Wholesale pool price of electricity ($/MWh) Figure 9.5 Pool prices with and without solar thermal technology Year NSW - CCS QLD - CCS VIC - CCS NSW - Solar thermal QLD - solar thermal VIC - Solar thermal These results illustrate the reduced costs and great benefits to the Australian economy that could be realized by considering renewable technologies such as solar thermal energy to be worthy of substantial research and commercialisation investment. ROAM s Recommendation: ROAM recommends that the government invest in solar thermal technology and other promising renewable technologies at least as much as is invested in carbon capture and storage technologies. 10) PREFERRED POSITION 10.5 DIRECT ASSISTANCE TO COAL-FIRED GENERATION The Green Paper states 22 : To ameliorate the risk of adversely affecting the investment environment, the Government proposes to provide a limited amount of direct assistance to existing coal-fired electricity generators. ROAM considers that the onset of greenhouse policy such as the CPRS has been foreseeable for a significant period of time, and its introduction is unlikely to adversely affect the investment environment in the electricity environment in Australia. 22 Preferred position Page 25 of 42

31 Given that future energy investment is required largely in the renewable sector, it is most important to provide policy assurance to these industries. To encourage maximum development in the renewable energy industry, the Government should indicate strong support of renewable technologies. Direct assistance to coal-fired generators does not support this position. ROAM s Recommendation: Direct assistance to coal-fired generators is unnecessary and potentially damaging to the renewable energy investment environment (where maximum investment is required). 11) PREFERRED POSITION 10.9 ALLOCATION OF ASSISTANCE FOR COAL-FIRED GENERATION The Green paper outlines the following preferred position on the allocation of assistance to coal-fired generation: The proposed direct assistance for coal-fired electricity generators would be allocated to individual recipients using a simple asset-by-asset method that involves: the available assistance being split into separate pools, with one pool being made available to brown coal-fired assets and the other to black coal-fired assets assistance in each pool being allocated to individual assets in direct proportion to the capacity of each asset. ROAM s modelling consistently shows that individual coal-fired generators will be affected very differently by the CPRS. Some will show severe reductions in volume (and therefore revenue). Others, however, are likely to show increased volumes (compensating in part for the reduced volumes from the most emissions intensive plants), and will therefore have very moderate revenue impacts. The impacts on generators cannot be determined from their emissions factors alone, since the outcomes are heavily dependent on transmission limitations between regions. It is therefore inappropriate to assume that brown coal generators will be more severely impacted than black coal generators (since brown coal generation is exclusively located in Victoria). Some highly emitting coal generators are likely to show increased volumes under the CPRS, to compensate for the reduced volumes of other plants in that region. A selection of ROAM s latest modelling results is illustrated in the sections below. 11.1) GENERATION VOLUMES OF COAL PLANTS UNDER THE CPRS Figure 11.1 shows the generation volumes of significant coal plants in NSW. For the major black coal generators in NSW, very little impact on generation volumes is observed Page 26 of 42

32 even with a substantial $60/tCO2 price 23. Whilst some generators such as Liddell, Wallerawang and Redbank show a small decrease in volume (compared with the BAU case, with no carbon price applied), the majority of plants in NSW show an increase in volume. In particular, Bayswater and Eraring show substantial increases in volume. This is because the volume lost by less efficient plants must be replaced. ROAM s modelling consistently shows that rather than gas generation making up the difference, the more efficient coal plants in each region run at higher levels. This suggests that the more efficient coal plants will not lose volume, and moreover, will be able to fully pass on their increased costs to consumers. 23 These results are for the BAU load growth case, weighted by 70% mild weather outcomes and 30% extreme weather outcomes. Varying load growth has little impact, since lower levels of load growth discourage the installation of new generation. Page 27 of 42

33 Generation (GWh) Generation (GWh) Figure 11.1 NSW generation volumes (black coal) No carbon price Bayswater Eraring Liddell Mt Piper Redbank Vales Pt Wallerawang $60/tCO Bayswater Eraring Liddell Mt Piper Redbank Vales Pt Wallerawang The impacts of a high $60/tCO2 price on significant QLD black coal plants are illustrated in Figure Similar to NSW, the older, higher emissions plants (Stanwell, Gladstone, 24 The increase in Liddell s output in is due to the reinstatement of a unit in that year. Page 28 of 42

34 Callide B and Swanbank B) show a small to moderate decrease in volume, whilst the difference is made up by increasing volumes at the lower emissions coal plants (Tarong, Tarong North and Millmerran). Several plants show almost no change in volume at all, despite the high carbon price (Callide C, Kogan Creek). As for NSW, this suggests that the more efficient coal plants will not lose volume, and moreover, will be able to fully pass on their increased costs to consumers. Figure 11.3 illustrates the impacts of the CPRS on Victorian brown coal plants. Yallourn and Hazelwood, the two most emissions intensive plants in the NEM, show substantial decreases in volume upon implementation of a $60/tCO2 carbon price. Modelling consistently shows that these two plants can expect a substantial loss of asset value under the CPRS. Morwell (another emissions intensive plant) is also negatively affected. Despite their relatively high emissions intensity, the other Victorian brown coal plants show very stable volumes with the application of a $60/tCO2 price. This is due to transmission issues limitating imports into Victoria, requiring that substantial proportions of Victoria s electricity are sourced from within the region. With the significant loss of generation volume from Hazelwood and Yallourn, the other plants in that region must continue to operate. These plants would be expected to maintain sufficient market power to pass on their increased costs to electricity consumers. Page 29 of 42

35 Generation (GWh) Generation (GWh) Figure 11.2 QLD generation volumes (black coal) No carbon price Callide B Callide C Gladstone Kogan Creek Millmerran Stanwell Swanbank B Tarong Tarong North ZeroGen $60/tCO Callide B Callide C Gladstone Kogan Creek Millmerran Stanwell Swanbank B Tarong Tarong North ZeroGen 25 Tarong s energy output is halved in due to two units being shut down during the drought. It does not fully recover previous generation volumes due to the commissioning of Kogan Creek. Page 30 of 42

36 Generation (GWh) Generation (GWh) Figure 11.3 VIC generation volumes (brown coal) No carbon price Anglesea Hazelwood Latrobe Valley IDGCC Loy Yang A Loy Yang B Morwell Yallourn $60/tCO Anglesea Hazelwood Latrobe Valley IDGCC Loy Yang A Loy Yang B Morwell Yallourn Page 31 of 42

37 11.2) REVENUES OF COAL PLANTS UNDER THE CPRS Revenues for each plant were calculated in each scenario, using the half hourly generation of each plant, and the pool price in that half hour. All revenues are adjusted for the effects of auxiliary loads and Marginal Loss Factors. Figure 11.4 shows the gross revenues for the major coal plants in Victoria with no carbon price, and with a $60/tCO2 carbon price. Revenues were highly inflated in and due to drought conditions causing decreased output of hydro generation, and the significant reduction in output of some major thermal generators such as Tarong Power Station. Under the $60/tCO2 carbon price the revenues of all generators increase dramatically. This is because their increased bids (reflecting their carbon costs) have increased the pool price, but volumes have not changed substantially (due to the lack of replacement capacity). However, the $60/tCO2 carbon price case does not reflect the increased costs to generators of emissions permits. Figure 11.5 shows the revenues net of the cost of permits to each generator calculated from the annual volumes of each generator, the emissions factor of each generator, and the carbon price in that case. This is a better comparative case with the no carbon price case. Figure 11.5 shows that even with a substantial $60/tCO2 price applied, the brown coal generators in Victoria show only a moderate reduction in revenue. Figure 11.6 shows the impact of a lower carbon price ($20/tCO2). Unexpectedly, in this case Victorian brown coal generators show an increase in revenues when a $20/tCO2 price is applied. This is due to increased volumes offsetting the additional cost of emissions permits (as the more emissions intensive Hazelwood has lost substantial volume). Although the magnitude of the difference is within the sensitivity of ROAM s model, it does highlight that coal generators will not necessarily experience a large loss of revenue with the onset of the CRPS, and some may benefit. Similar moderate revenue impacts are observed for black coal generators in NSW and QLD with the application of a $40/tCO 2 price (Figure 11.7 and Figure 11.8). Significantly, for most generators, revenues are similar to those in historical years, even under high levels of carbon price. This indicates that generators will continue to be profitable, and energy security will not be threatened by the CPRS. Page 32 of 42

38 Gross Revenue ($ millions) Gross Revenue ($ millions) Figure 11.4 Revenues (Gross) of Victorian brown coal generators 900 No carbon price Anglesea Hazelwood Latrobe Valley IDGCC Loy Yang A Loy Yang B Morwell Yallourn 1800 $60/tCO Anglesea Hazelwood Latrobe Valley IDGCC Loy Yang A Loy Yang B Morwell Yallourn Page 33 of 42

39 Net Revenue ($ millions) Net Revenue ($ millions) Figure 11.5 Revenues (net of CO 2 costs) of Victorian brown coal generators impact of a $60/tCO2 price 900 No carbon price Anglesea Hazelwood Latrobe Valley IDGCC Loy Yang A Loy Yang B Morwell Yallourn 900 $60/tCO Anglesea Hazelwood Latrobe Valley IDGCC Loy Yang A Loy Yang B Morwell Yallourn Page 34 of 42

40 Net Revenue ($ millions) Net Revenue ($ millions) Figure 11.6 Revenues (net of CO 2 costs) of Victorian brown coal generators impact of a $20/tCO2 price 900 No carbon price Anglesea Hazelwood Latrobe Valley IDGCC Loy Yang A Loy Yang B Morwell Yallourn 900 $20/tCO Anglesea Hazelwood Latrobe Valley IDGCC Loy Yang A Loy Yang B Morwell Yallourn Similar results are observed for other regions, as illustrated in the figures below. Page 35 of 42

41 Net Revenue ($ millions) Net Revenue ($ millions) Figure 11.7 Revenues (net of CO 2 costs) of NSW black coal generators impact of a $40/tCO2 price 1200 No Carbon Price Bayswater Eraring Liddell Mt Piper Redbank Vales Pt Wallerawang 1200 $40/tCO Bayswater Eraring Liddell Mt Piper Redbank Vales Pt Wallerawang Page 36 of 42

42 Net Revenue ($ millions) Net Revenue ($ millions) Figure 11.8 Revenues (net of CO 2 costs) of QLD black coal generators impact of a $40/tCO2 price 1000 No carbon price Callide B Callide C Gladstone Kogan Creek Millmerran Stanwell Swanbank B Tarong Tarong North ZeroGen 1000 $40/tCO Callide B Callide C Gladstone Kogan Creek Millmerran Stanwell Swanbank B Tarong Tarong North ZeroGen Page 37 of 42

43 ROAM s Recommendation: Coal-fired generation assets will be individually very differently impacted by the CPRS. It is inappropriate to allocate compensation in direct proportion to the capacity of each asset. Given: 5. The difficulty associated with accurately forecasting the impacts on coal-fired generators, 6. The high probability of windfall gains by some generators, 7. The long timeframe over which climate change policy changes has been a foreseeable issue 8. The potential negative treatment of generators who have responsibly invested in efficiency improvements; ROAM considers it appropriate to not provide compensation to coal-fired generators. 11.3) OTHER COMMENTS FROM CHAPTER 10 Fixed capital costs of coal-fired generators The Green Paper states 26 : The profitability of emissions-intensive generators could be reduced in two ways. First, generators could lose market share to generators with lower emissions intensity. A reduction in volume is particularly significant for coal-fired generators, because they need to sell significant quantities of electricity to cover their high fixed capital and maintenance costs. While coal-fired generators do have high capital costs, many of the coal-fired generators in the NEM are old. This is particularly the case for the most inefficient and emissions intensive coal-fired generators that are expected to lose volume under the CPRS. These generators have been in the NEM for more than 30 years, and could be expected to have earned sufficient return to cover their capital costs. Therefore, to remain profitable, these generators need only earn their short run marginal costs (which are extremely low for coal-fired generators), and their fixed maintenance costs. Gas generators under the CPRS The Green Paper states 27 : Gas-fired generators are likely to benefit from the scheme. ROAM s modelling has shown that while some gas generators are likely to benefit, others are likely to experience reduced volumes and revenues. For some, the impacts is as severe (in percentage terms) as for coal-fired generators. 26 Page 348, paragraph Page 350, paragraph 5. Page 38 of 42

44 RECs price ($/MWh) Renewable generation under the CPRS The Green Paper states 28 : Zero-emissions renewable generators are likely to benefit from the scheme, as the scheme will impose no increase in their operating costs but wholesale electricity prices will rise. While this is broadly the case, under low carbon prices, renewable generators will remain supported by the Renewable Energy Target scheme. It is expected that as the pool price increases under the CPRS, the price of Renewable Energy Certificates (RECs) will decrease by a corresponding amount, due to competitive sale of the certificates. This means that renewable generators will not experience a net benefit under the CPRS until the carbon price is sufficiently high that the RECs price reaches zero (indicating that the Renewable Energy Target scheme is no longer necessary to support renewable generation). ROAM s modelling indicates that this may occur at a carbon price between $40/tCO 2 and $60/tCO 2 depending upon the degree of load growth (as shown in the figure below) Figure 11.9 Forecast RECs prices Year Low load growth, $20/tCO2 Low load growth, $40/tCO2 Low load growth, $60/tCO2 Medium load growth (10% POE), $20/tCO2 Medium load growth (10% POE), $40/tCO2 Medium load growth (10% POE), $60/tCO2 Security of energy supply The Green Paper states 29 : 28 Page 351, paragraph Page 366, paragraph 1 Page 39 of 42

45 The Government seeks stakeholder feedback on the effect on the security of energy supply of: measures specific to the energy market the medium-term national target range direct assistance to coal-fired electricity generators. ROAM agrees with the assessment in the Green Paper that direct assistance to coal-fired generators will not impact on energy security. Since ROAM s 2-4-C model has been used on behalf of NEMMCO since 2004 to estimate the level of reliability in the NEM and consequently set the official Minimum Reserve Levels for all regions of the NEM, ROAM could capably offer detailed studies into the impacts of the CPRS on reliability to confirm this. Periodic maintenance The Green Paper states 30 : The Government seeks information from stakeholders on the role of periodic maintenance costs in affecting the timing of the retirement of existing emissions intensive generation units, and the associated energy security implications. ROAM agrees with the assessment detailed in the Green paper, suggesting that generators are likely to be able to retire single units at a time, reducing impacts to the NEM. Quantum of assistance to coal-fired generators Preferred position 10.6 states: Final decisions on an appropriate quantum of the proposed direct assistance for coal-fired electricity generators would be made after the medium-term national target range is established. ROAM supports this approach, since the level of the emissions targets will directly determine the carbon price, and is therefore an essential parameter for determining the loss of asset value from coal-fired generators. Proportion of assistance to black and brown coal generators The Green Paper states 31 : 30 Page 366, paragraph Page 383, paragraph 3. Page 40 of 42

46 The Government seeks stakeholder views on: whether the relative proportion of the black coal and brown coal pools of assistance should be determined by estimating the relative impact of the scheme on these two asset classes using the broad results of a bottom-up electricity market modelling exercise the appropriate definition of brown and black coal for the purposes of allocating direct assistance between assets in the two classes whether it is appropriate to limit allocations of direct assistance to generation assets that are exclusively coal-fired. Once the emissions trajectories are announced, ROAM will be able to use integrated resource planning models to determine the likely carbon price trajectory, and therefore the likely impact of the CPRS on coal-fired generators. This could provide a basis for determining the relative pools of assistance to black and brown coal-fired generators. However, as discussed above, different generators within each of these pools will be affected very differently. Interstate transmission limitations will mean that while the most emissions intensive inefficient brown coal plants in Victoria lose substantial volumes, the remaining more efficient (but still highly emissions intensive) brown coal generators in Victoria will increase volumes to compensate. A similar effect will occur amongst black coal generators in NSW and QLD. This means that a simple categorisation as black or brown coal has very little bearing on the anticipated loss of asset value and profitability of each generator. ROAM agrees that bottom-up modelling has inherent uncertainties, and that small changes in input assumptions can result in substantial changes in the detailed outcomes of the model (such as the revenues of individual generators). Therefore, while bottom-up electricity market modelling could be used to determine the relative proportion of black and brown coal pools of assistance (and ROAM would be appropriately capable of conducting this modelling), ROAM does not recommend bottom-up modelling to determine the assistance to individual generators. ROAM s Recommendation: Due to the difficulty of allocation of assistance between coal-fired generators, and its low importance to the CPRS (relative to the other competing demands on CPRS revenues), ROAM recommends that the Government not provide direct assistance to coal-fired generators. The form of assistance The Green paper states 32 : The Government seeks stakeholder feedback on the relative merits of providing direct assistance to coal-fired electricity generators through allocations of carbon pollution permits or cash payments. 32 Page 386, paragraph 11. Page 41 of 42

47 As discussed in section 7) above, ROAM recommends that any direct assistance is given in the form of cash payments, rather than a free allocation of permits. 12) CONCLUSION Given the overwhelming importance of the correct design of the Carbon Pollution Reduction Scheme, and the wide range of vested interests in this issue, ROAM urges the Department of Climate Change to widely seek information from impartial sources. ROAM prides itself on presenting information and modelling results free from the pressure of vested interests. ROAM is available and happy to discuss the issues outlined in this submission further with the Department of Climate Change. ROAM is also happy to provide further information about the electricity industry in any area. Page 42 of 42

48 Appendix A) About ROAM Consulting s modelling suite a. Forecasting with 2-4-C 2-4-C was the primary tool used to produce the highly detailed modelling presented in this submission. The modelling presented here focuses on the NEM, although ROAM also has the capability to perform similarly detailed modelling simulations for the SWIS (in Western Australia) and the Northern Territory. 2-4-C is ROAM s flagship product, a complete proprietary electricity market forecasting package. While capable of modelling any electricity network, with it in use in small systems such as the North-West Interconnected System (NWIS) of Western Australia, and the enormous 4000 bus CalISO system of California, 2-4-C was built first and foremost to match as closely as possible the operation of the NEMMCO Market Dispatch Engine (NEMDE) used for real day-to-day dispatch in the NEM. 2-4-C implements the highest level of detail, and bases dispatch decisions on generator bidding patterns and availabilities in the same way that the real NEM operates. The model includes modelling of forced full and partial and planned outages for each generator, including renewable energy generators and inter-regional transmission capabilities and constraints. ROAM continually monitors real generator bid profiles and operational behaviours, and with this information constructs realistic market bids for all generators of the NEM. Then any known factors that may influence existing or new generation are taken into account. These might include for example water availability, changes in regulatory measures, or fuel availability. The process of doing this is central to delivering high quality, realistic operational profiles that translate into sound wholesale price forecasts. 2-4-C has been used on behalf of NEMMCO since 2004 to estimate the level of reliability in the NEM and consequently set the official Minimum Reserve Levels for all regions of the NEM. APPENDICES Page I of XXIV

49 b. The 2-4-C Model The multi-node model used to produce the forecasts in this report is shown in Figure A.1. This nodal arrangement features a single node per region of the NEM is the same as that used in NEMDE. Figure A C NEM Representation Queensl and This network representation means that no visibility of intra-regional network capabilities exists directly. In order therefore to model these important aspects of the physical system, NEMMCO employs the use of Constraint Equations that in effect transpose intra-regional network issues to the visible parts of the network; that is, the interconnectors joining the regions of the NEM. These Constraint Equations consist of several hundred mathematical expressions which define the interconnector limits in terms of generation, demand and flow relationships. 2-4-C implements these Constraint Equations within its LP engine in fully co-optimised form. More detail on ROAM s modelling of the Constraint Equations is given in Section c. South Austral i a Vi ctori a NewSouth Wal es Modelling major transmission lines and Constraint Equations delivers an outcome consistent with the real operation of the NEM under normal system conditions. Additionally, the occurrence of congestion in the network is the primary factor that drives out-of-merit dispatch outcomes and hence price volatility. These important aspects of the NEM would not be seen in a more simplistic model. Tasmani a Blue bi-directional arrows signify the AC interconnectors between the regions of the NEM, while the red arrows signify High-Voltage DC Links. c. Modelling the Transmission System ROAM s 2-4-C dispatch model implements the full set of NEMMCO 2007 ANTS Constraints as supplied by NEMMCO with the 2007 Statement of Opportunities. These Constraint Equations define interconnector flow limits in terms of generation, demands and flows. A Constraint Equation for an interconnector is defined in a particular direction and will be of the following form: X * Flow InterconnectorA DirectionB Constant Z * Demand where : X, Y, Z, P, Q are constants Y * Output RegionA GenA P* Output GenA Q* Output GenB R*Flow InterconnectorB DirectionA APPENDICES Page II of XXIV

50 In this formulation, there are variables (called dispatchable terms in this context) on both the left and right sides of the equation. Linear Programming (LP) engines, which are used to evaluate the bids of generators and dispatch the NEM at least cost, are not able to fully optimise dispatch outcomes with constraints in this form. Instead they require that all variables be on the left side of the equation only. Therefore, this re-formulation is performed prior to submitting the constraints to the LP. This linear formulation is known as co-optimised format. Therefore, prior to entering these Constraint Equations into 2-4-C, they are converted into co-optimised form. d. Key Parameters used by the Model Data contained within the 2-4-C model is a combination of the best information sources within the public domain including: All released NEMMCO Statements of Opportunity through to 2007, together with half-hourly historical load profiles by region; Annual Planning Statements by Network Service Providers: o All published Powerlink statements through to 2008, together with half hourly historical load profiles by zone; o All published TransGrid statements through to 2008; o All published Vencorp statements through to 2008; o All published ESIPC statements through to 2008, and; o All published Transend statements through to Corporate Annual Reports up to for many market participants (generators, retailers and network service providers), and; General reports from market participants. More specifically focussing on the assumptions used in the modelling presented in this submission is given in the following section. APPENDICES Page III of XXIV

51 Appendix B) Specific modelling assumptions e. Assumptions with regard to the Demand Side i. Inclusion of Customers At each region, a bulk load consumption facility has been included to represent the cumulative, time-sequential, load consumption profile anticipated at each of the six regions used in the study. ii. Regional Load Profiles Load data for each bulk consumption facility has been derived directly from historical load profiles for each region, and grown to meet the energy and demand forecasts published in the NEMMCO 2008 Energy and Demand Projections. iii. Demand-Side Participation The vast majority of demand in the wholesale market currently operates as a series of aggregated loads for the purposes of schedule and dispatch. Though some individual customers may be responsive to price, the majority of end-consumers are shielded from short-term price fluctuations through retail contracts. Thus, incentives to reduce demand during high-price periods are dissipated. In this study, as detailed in the NEMMCO 2008 Energy and Demand Projections, DSP is captured as part of the actual measured demand and therefore inherently part of the demand forecast. iv. New Base Loads No new base loads are included in this study. v. Hydroelectric Pump Storage Loads The 2-4-C version used for this study includes a detailed hydroelectric model, including pump storage loads. The pumping loads for the following hydroelectric facilities have been included in the load profile: Wivenhoe power station; Shoalhaven power station Snowy Mountains Scheme: Tumut 3 power station. f. Demand and energy forecasts To account for sensitivities to the load, ROAM has considered three load forecasts an M10 case, an M50 case and a L10 case. These are: M10 case - Medium load growth, 10% P.O.E. M50 case - Medium load growth, 50% P.O.E. L10 cast Low load growth, 10% P.O.E. where P.O.E. is the probability of exceedence. APPENDICES Page IV of XXIV

52 The 10% P.O.E. case represents an extreme weather year resulting in demand levels exceeded only 1 year in 10. The 50% P.O.E. case represents a reasonably mild weather year (exceeded 1 year in 2). These 10% and 50% P.O.E. cases represent upper and lower bounds. To show the likely case, ROAM has calculated a weighted value for all properties. This weighted value is calculated as 30% of the 10% P.O.E. value and 70% of the 50% P.O.E. value. The regional load trace forecasts (that is, the half-hourly load data) have been developed using the actual recorded financial year load traces for each region as the reference year. g. Assumptions on the Supply Side (Generation assets) i. Existing Projects The market forecasts take into account all existing market scheduled generation facilities. In this study, the likely commissioning schedule (beginning typically three months prior to commercial operation) for new generators has been taken into account. ii. Individual Unit Capacities and Heat rates Details of unit capacities and heat rates (for thermal plants) have been collated and included on the basis of information available from the public domain. iii. Unit Emissions Intensity Factors Emissions Intensity Factors have been collated from public sources and along with heat rates are the basis for determining the uplift in Short Run Marginal Cost under the Carbon Pollution Reduction Scheme. iv. Unit Operational Constraints Information on unit minimum load and ramp rate constraints is included in the 2-4-C database. This database has been developed based on pre-market information, moderated with information being currently supplied to the market. Such information is taken into consideration in the simulation of market operation (to ensure that an infeasible solution is not simulated). v. Forecast Station Outage Parameters As noted previously, the Advanced Mode of modelling unit availability is used. This mode utilises independent schedules for each unit of: Planned maintenance, and Randomised forced outage (both full and partial outage) distribution. APPENDICES Page V of XXIV

53 These schedules have been constructed based on information in the public domain - in particular, the following six key parameters are used in the development of outage schedules and are detailed in the table below. Table B.1 Assumptions for Network Development Full Forced Outage Rate: Partial Forced Outage Rate: Number of Full Outages: Number of Partial Outages: Derated Value: Full Maintenance Schedule: Proportion of time per year the unit will experience full forced outages. Proportion of time per year the unit will experience partial forced outages. The frequency of full outages per year. The frequency of partial outages per year. Proportion of the unit s maximum capacity that the unit will be derated by in the event of a partial outage. Maintenance schedule of planned outages (each planned outage has a start and end date between which the unit will be unavailable). Historical generator availability data has been used in conjunction with the Advanced Mode of outage modelling, as this represents the best available (comprehensive) source of information at the time of the study. vi. Generation Commercial Data In the development of the chosen trading strategy for each generator across the NEM, some key commercial data has been required. Such data is assembled in the 2-4-C database and includes the following: vii. The intra-regional Marginal Loss Factor (MLF); Operations and maintenance cost; Fuel cost, which has been computed with reference to: o Unit heat rate; o Fuel heating value, and; o Fuel unit price; Emission factors for greenhouse gas production. Generator Trading Strategies For this study, assumed bid profiles are compiled for all existing generators on the basis of historical trading and dispatch patterns. viii. Energy constraints APPENDICES Page VI of XXIV

54 Time-varying bid profiles for all hydro power stations including Hydro Tasmania, Snowy Hydro, Southern Hydro, Kareeya and Barron Gorge have been engineered to deliver production patterns corresponding to historical patterns whilst maintaining appropriate price signals. Competitive bidding strategies for pumped storage hydro plant have been developed to maintain high revenues whilst ensuring energy limitations are not violated. ix. New market entrants New plant (committed, announced and market entry) is assumed to bid the majority of its capacity at SRMC values. h. Carbon price sensitivities To account for sensitivities to the carbon trading price, ROAM has considered three carbon price values: Carbon price = $20/tCO 2 Carbon price = $40/tCO 2 Carbon price = $60/tCO 2 Combined with the load sensitivities, this makes a total of 12 scenarios that were analysed by ROAM in this particular study: Table B.2 Scenarios modelled Case name Load Carbon price M10, 0 $0 /tco 2 M10, 20 $20 /tco 2 Medium growth, 10% POE M10, 40 $40 /tco 2 M10, 60 $60 /tco 2 M50, 0 $0 /tco 2 M50, 20 $20 /tco 2 Medium growth, 50% POE M50, 40 $40 /tco 2 M50, 60 $60 /tco 2 L10, 0 $0 /tco 2 L10, 20 $20 /tco 2 Low growth, 10% POE L10, 40 $40 /tco 2 L10, 60 $60 /tco 2 i. Generator bidding Generator bids are based on analysing past bid profiles for all generators across the NEM and taking into account any known factors that may influence existing or new generation, for instance in response to water availability. In the case of base load generators, these are generally bid at negative price levels up to their minimum operating levels and then at APPENDICES Page VII of XXIV

55 marginal costs for the remainder of the capacity. These base load generators are referred to as price-takers in the market. In the case of intermediate plants, these are bid as pricetakers for the peak periods of the day and may be started at other periods in response to a high price signal. Peaking generators are generally bid at or above their marginal costs and start when prices reach these values due to low generator reserve margins caused by high demand intervals or periods of generator failures. Since prices may be set at different times by base, intermediate and peaking plant, depending on load levels and simulated failures of generating units, the simulation faithfully replicates the price variability in the real market. i. Applying a carbon price The carbon cost for each generator (in $/MWh) is given by each generator s emissions factor (tco 2 /MWh), multiplied by the cost of emissions permits. Since the electricity market in Australia is not internationally trade exposed, it is anticipated that generators will largely increase their bids by the amount of their respective carbon costs. Hence, the effects of a carbon price on the NEM was modelled by adding the carbon cost ($/MWh) to the bids of each generator. Once these uplifts were applied to all bid bands of all generators, the competitive dispatch was recalculated for each half hourly interval. The CPRS is scheduled to begin in 2010, and it is anticipated that the carbon price will rise over time. ROAM has modelled the effects of varying levels of carbon price in all years of the study for completeness. ROAM s modelling can also determine the carbon price trajectory associated with various emissions trajectories, but has not applied this here (due to the uncertainty regarding the chosen emissions trajectory). ii. SRMC vs Historical bidding Many generators do not currently bid their short run marginal costs (SRMCs). When carbon prices are applied, it is expected that more polluting plants will be forced to bid closer to their short run marginal costs in order to remain competitive. This means that applying a carbon price uplift to historical (current) bids is not necessarily an accurate representation of the bidding strategy of plants under an emissions trading regime, particularly for high carbon prices. To account for this, ROAM has used the short run marginal costs (SRMCs) of plants to adjust negative bids (which are clearly not representative of costs). Any bids found to be below the SRMC of a particular plant were lifted to the SRMC (with the impacts of the carbon price applied). This approach takes account of relative changes in the bidding order, by ensuring that gas generation will undercut coal generation (for example) when the carbon price is sufficiently high that the two overlap. j. Generation Planting Schedule Between now and 2020, a great deal of new plant will be required to meet demand growth (if business as usual growth continues). Which new plants come in, and when, is critical in price forecasting. ROAM has conducted an extensive study of committed and announced plants, and assessed likely planting schedules for each of the modelling runs reported here. The plants included in some (or all) modelling runs are listed in the table APPENDICES Page VIII of XXIV

56 below. Some hypothetical plants are listed since these are necessary by the end of the study to meet the continually growing demand. Table B.3 New Generation to 2020/21 Region Station Type Queensland NSW Summer Capacity (MW) Status Braemar 2 OCGT 474 Committed Mt Stuart Upgrade OCGT 126 Committed Condamine CCGT 135 Committed (80MW from 1/02/2009) Darling Downs CCGT 551 Committed Yarwun Alumina Refinery Cogen Braemar 2 extension OCGT 290 Swanbank F CCGT/OCGT 400 Fairview CSM 100 Spring Gully 1 CCGT/OCGT 500 ZeroGen Clean Coal 300 Spring Gully 2 CCGT/OCGT 500 Darling Downs CCGT/OCGT 430 Darling Downs CCGT/OCGT 430 CCGT 145 Committed Publicly announced Publicly announced Publicly announced Publicly announced Publicly announced Publicly announced Publicly announced Publicly announced Tallawarra CCGT 422 Committed Uranquinty OCGT 471 Committed Uranquinty OCGT 157 Committed Eraring Upgrade Coal 240 Publicly announced Colongra OCGT 668 Committed Bega CCGT 120 Bamarang OCGT 300 Marulan OCGT 300 Tomago OCGT 450 Publicly announced Publicly announced Publicly announced Publicly announced NSW Peaker OCGT 1000 Hypothetical NSW Intermediate CCGT 500 Hypothetical APPENDICES Page IX of XXIV

57 Table B.3 New Generation to 2020/21 Region Station Type Victoria Summer Capacity (MW) Status Bogong Hydro 140 Committed Loy Yang A-4 Upgrade Coal 30 Loy Yang A-2 Upgrade Coal 40 Advanced proposal Advanced proposal Mortlake Unit 1 CCGT 520 Committed Mortlake Unit 2 OCGT 500 Publicly announced Latrobe Valley IDGCC 400 Publicly announced New Vic OCGT 1 OCGT 500 Hypothetical South Australia Tasmania New Vic CCGT 1 CCGT 500 Hypothetical Quarantine OCGT 121 Committed Pelican Point 2 CCGT 240 Publicly announced Mallala OCGT 250 Proposed Hallett B CCGT 250 Proposed Tamar Valley OCGT 40 Committed Tamar Valley CCGT 191 Committed Bell Bay (retirement) CCGT -240 Committed Tamar Valley Upgrade OCGT 120 Hypothetical Both the load and the carbon price affect the planting schedules, because: The low load growth case does not need as much new capacity as the medium load growth cases The higher carbon prices incentivise more planting of combined cycle gas turbines. The relative weighting of these factors and their impact on the planting schedule was determined with integrated resource planning modelling. This method utilises hundreds of thousands of 2-4-C modelling runs to determine the lowest cost planting schedule under various conditions. The planting schedules used for each case are outlined in the table below. Wind farms are listed in each zone in the first year that they are included, but grow in capacity over time as more wind farms are added. Table B.4 Planting schedules for the cases modelled L10 M10 M50 $0, $20, $40, $60 $0, $20, $40 $60 $0, $20, $40 $ Solar Vic APPENDICES Page X of XXIV

58 Wind Farms NSW - CAN Wind Farms NSW - NCEN Wind Farms QLD - NQ Wind Farms SA - NSA Wind Farms TAS - TAS Wind Farms VIC - CVIC Wind Farms VIC - MEL Darling Downs - Mortlake Pelican Point 2 Wind Farms SA - ADE Yarwun Cogen Wind Farms SA - SESA Bagasse QLD - SEQ Mortlake - Paralana Geothermal Bagasse QLD - NQ Habanero 1 Geothermal Wind Farms QLD - SWQ Bamarang Bega - QLD_PEAK03 - QLD_PEAK03 - Solar QLD - - Swanbank F - Swanbank F Wind Farms NSW - NNS Fairview CSM and CCS Latrobe Valley IDGCC - Mallala Marulan - QLD_INT01_in_Braemar Collinsville - Retirement Habanero 2 Geothermal APPENDICES Page XI of XXIV

59 - Hallett B - NSW_INT01 - NSW_INT QLD_PEAK01 Tomago OCGT - Wind Farms QLD - SEQ Wind Farms VIC - LV ZeroGen NSW_INT01 - NSW_INT01 - NSW_PEAK02 - VIC OCGT QLD_PEAK02 VIC_SA_INT01 k. Inclusion of the expanded Renewable Energy Target Sufficient renewable generation was planted to meet the expanded 20% by 2020 renewable energy target, as shown in the figure below. APPENDICES Page XII of XXIV

60 Energy supplied by renewable sources in the NEM (GWh pa) Figure B.1 Renewable energy planting to meet the RET Wind Geothermal Solar Bagasse Solar hot water Existing renewable generation (post-1997) Pre-1997 renewables (not elligible for renewable energy certificates) Projection to expanded MRET Existing MRET Financial Year Individual announced wind farm projects were planted in their announced locations around the grid to make up the target, and were included in transmission congestion calculations on a half hourly basis. To model the output of wind farms, historical data was sourced from automatic weather stations around Australia from the Bureau of Meteorology and converted to generator outputs using turbine power curves. The locations of the weather stations in eastern Australia are shown in the figure below. APPENDICES Page XIII of XXIV

61 Figure B.2 Locations of BOM weather stations The wind data from the Bureau of Meteorology (BOM) weather stations was taken at a variety of elevations (from 1m off the ground to 70m above the ground), and elevation strongly affects wind speeds. The wind at the height of a turbine hub (from 50m to 80m) will be much faster than the wind at ground level, and the amount of the increase in speed is strongly dependent upon many factors, including the type of ground cover (rock, grass, shrubs, trees) and the nature of the weather pattern causing the wind. In addition, the local topography affects wind speeds very strongly (winds tend to be focused by flowing up hillsides, for example). Therefore, the wind speed at a weather station perhaps 30km distant from a wind farm is likely to be correlated strongly in time with the wind at the site of the turbines, but the absolute scaling of the speeds is highly uncertain. Without more data about each wind site and weather station s local topography and turbine layout it would not be possible to accurately use the wind data to predict the absolute wind speeds at the turbine hubs (this modelling is also very time consuming). However, it is reasonable to assume that the wind speeds at the weather station will be very highly correlated in time with the wind speeds at the turbine site (analysis of existing wind farm generation profiles compared with the BOM weather station data has shown APPENDICES Page XIV of XXIV

62 this to be the case). Therefore, the BOM data provides an excellent way to determine the daily and seasonal variation of wind at different sites, and also the likely correlation between the output of nearby wind farms (which is highly material for transmission congestion). In order to scale the wind appropriately to get realistic generation outputs for each wind farm it has been assumed that wind sites will have (on average) a capacity factor of around 30%. Based on the capacity factors of existing wind farms, and the known capital costs of wind farms this is a reasonable assumption (with capacity factors much lower than 30% they will not cover their long run marginal costs). The wind is scaled, and then a turbine power curve applied to convert the wind speeds into actual generation (this accounts for the fact that the efficiency of turbines varies strongly with wind speed). There is very good agreement between the results of this method and the known output of existing wind farms. Wind farms were bid into the market at $0, with volumes based upon their unit trace outputs in each half hour period. Solar generators were modelled as a Gaussian output that increased to a peak in the middle of the day, with longer hours during the summer. The profile is shown in the figure below. Solar generators were bid into the market at $0, with volumes based upon their unit trace outputs in each half hour period. APPENDICES Page XV of XXIV

63 Figure B.3 Example Solar PV Generation Profile (by time of day and day of financial year) Geothermal and bagasse generators can be scheduled, and therefore will be assumed to bid into the market in the same way as a conventional generator. Since they have negligible short run marginal costs, and high capital costs, and are therefore incentivised to maximise their volumes, they will bid at $0. APPENDICES Page XVI of XXIV