Factors Affecting CO 2 Emission Intensity of Power Sector in Australia

Transcription

1 Factors Affecting CO 2 Emission Intensity of Power Sector in Australia Marjan Nazifi Faculty of Business and Economics Macquarie University The 41 st IAEE International Conference Transforming Energy Markets JUNE 2018, GRONINGEN 1

2 Background Under the Paris Agreement, Australia has an emissions reduction target of 26 to 28 per cent below 2005 levels by This would require the intensity of carbon emissions for the economy to fall by around 65 per cent, and the emissions per capita being halved. Australian emission per unit of GDP have been extremely high: 0.78kg of carbon dioxide equivalent in comparison to 0.43kg of carbon dioxide equivalent in OECD (IEA, 2009). Electricity generation accounts for about 35% of all CO2 emissions in Australia. The average emission intensity of the NEM in Australia has been around 0.90 tco2/ MWh over the period investigated in comparison to around 0.4 tco2 / MWh in EU. Intensities vary significantly across states, e.g tco2 / MWh for Victoria, less than 0.07 tco2 / MWh in Tasmania. 2

3 Figure 1: Emission Intensity of Electricity Sector In Australia and Several Countries (source: vivid Economics, 2013) 3

4 Emission Intensity Emission intensity Emission intensity Figure 2: Emission Intensities of Power Sector by State 1,1 NEM emission intensity 1 SA 0,9 0,5 0, ,9 0,8 0,7 Emission intensity 1,1 NSW 0,2 0,1 0 Emission intensity0,3 TAS 0,95 0,85 0,75 QLD 1,3 1,1 0,9 emission intensity 1,5 VIC 4

5 Table 1: Average Emission Intensities by State Entire Period Pre Carbon Tax Period Carbon Tax Period Post Carbon Tax Period NEM NSW QLD SA TAS VIC

6 Figure 3: NEM Generation by Fuel Type 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Average of NEM_Coal Average of NEM_Gas Average of NEM_Renewable Average of NEM_Others 6

7 Figure 4: NEM Generation by State and Fuel Type 100% 80% 60% 40% 20% 0% NEM % 95% 90% 85% 80% 75% NSW % QLD 100% SA 80% 80% 60% 60% 40% 40% 20% 20% 0% % % TAS 100% VIC 80% 80% 60% 40% 20% 0% % 40% 20% 0%

8 Figure 5: Changes in Electricity Generation by Fuel Source 70 Queensland 80 NSW Terawatt hours Terawatt hours Victoria South Australia Terawatt hours Terawatt hours Tasmania 250 NEM Terawatt hours Terawatt hours Black coal Brown coal Gas Hydro Wind Solar farm Other dispatched Rooftop solar Sources: AEMO; AER. 8

9 Background (cont.) Only a few studies have been conducted on analysing changes in CO 2 emission intensity from the power sector, mainly using decomposition analysis techniques (Shrestha et al., 1996; Shrestha et al., 2009; Malla, 2009; Steenhof et al., 2011; Zhang et al., 2013; Fernandez Gonzalez et al., 2014, Karmellos et al., 2016). In most countries change in fuel intensity was the main responsible factor for the decrease in emission intensity, indicating improvements in efficiency of power generation, although the generation mix effect also contributed to emissions reduction. To the knowledge of the authors, no study so far, investigates factors affecting the evolution of CO 2 emission intensity from power sector in NEM and its states using daily data in a comprehensive manner (O Gorman and Jotzo, 2014; Vivid Economics, 2013). 9

10 Motivation This paper extends the relatively sparse literature on factors affecting the evolution of CO 2 emission intensity of the power sector. This paper adopts an empirical approach to analyse the evolution of the CO2 emissions intensity of power sector in NEM and four selected states in Australia using daily data: The roles of changes in demand, generation structure (fossil fuel mix), the renewable energy target (RET), the carbon pricing mechanism (CPM), and the closure of some fossil fuel plants are examined in the evolution of emissions from the power sector of selected states during 1 Jan 2010 to 30 Dec 2017 by using daily data. It develops a framework that examines the impact over a long sample. This includes the period before the carbon tax became effective, the period of its lifetime from July 2012 to June 2014, as well as the period after the tax was repealed from July 2014 to June It improves the estimates of the impact by relaxing some of the assumption made by previous studies (O Gorman and Jotzo, 2014). 10

11 The Australian National Electricity Market (NEM) NEM is an interconnected grid comprising several regional networks (NSW, QLD, SA, TAS, and VIC). It operates as a mandatory wholesale pool under the management of the Australasian Energy Market Operator (AEMO). The wholesale market of the NEM is a real time energy market and it is settled at a 30- minute period. The NEM weighted average emissions intensity is estimated 0.90 tco2/mwh. Limited amounts of electricity can also be transferred across regions through so-called interconnectors, e.g. between QLD-NSW, NSW-VIC, VIC-TAS, etc. 11

12 Figure 6: Investment in New Generation, and Plant Retirements Megawatts Coal Gas Wind Other Note: Retirements exclude mothballed plant. Source: AEMO; AER. 12

13 Table 2: Generation Investment in the NEM OWNER POWER STATION TECHNOLOGY QUEENSLAND SUMMER CAPACITY (MW) DATE COMMISSIONED EDL OCI Oaky Creek 2 Waste coal mine gas NEW SOUTH WALES AGL PV Solar Development Broken Hill Solar Moree Solar Farm Moree Solar Elementus Energy Williamsdale Solar VICTORIA PacificHydroPortlandWindFarm Portland Stage 4 Wind Coonooer Bridge Wind Farm Coonooer Bridge Wind SOUTH AUSTRALIA Hornsdale Wind Farm Hornsdale (stage 1) Wind Waterloo Windfarm Waterloo Expansion Wind

14 Table 3: Generation Withdrawals from YEAR POWER STATION REGION WITHDRAWN GENERATION TECHNOLOGY CAPACITY (MW) STATUS Swanbank B Qld CCGT 480 Decommissioned progressively between April 2010 and May Munmorah NSW Coal 600 Retired Tarong Qld Coal 700 Closed 2012 to Collinsville Qld Coal 180 Retired Morwell, Brix Vic Coal 205 Retired Wallerawang C NSW Coal 1000 Retired Redbank NSW Coal 144 Retired Pelican Point SA CCGT 249 Half capacity withdrawn. Announced return to full capacity in June quarter Swanbank E Qld CCGT 385 Placed into cold storage. Expected to return December Northern SA Coal 540 Retired Playford B SA Coal 200 Retired Anglesea Vic Coal 150 Retired Hazelwood Vic Coal 1600 Retired ANNOUNCED WITHDRAWAL 2017 Smithfield NSW Gas 171 Retirement 2017 Tamar Valley Tas CCGT 208 Mothballing 2021 Mackay Qld OCGT 34 Retirement 2022 Daandine Qld CCGT 33 Retirement 2022 Liddell NSW Coal 2000 Retirement 14

15 Econometric Specification An OLS regression model is developed as follows : E t = γ 0 + γ 1 D t + γ 2 D t 2 + γ 3 S t + γ 4 R t + γ 5 d tax + 16 i=6 γ i d clo,i + ε t for the NEM and each of the four major regional markets NSW, QLD, SA, and VIC separately. Average daily data for period investigated from 1 July 2010 to 30 Dec Newey-West (HAC) estimator is applied to address autocorrelation and heteroscedasticity in errors. 15

16 Econometric Specification (cont.) E t denotes the average emission intensity of electricity sector. D t denotes the average daily demand for electricity. D 2 t denotes the demand square. S t denotes the shift of the fuel mixture. It is defined as the share of fuel i in the total energy input of the power sector. R t denotes the Renewable Energy Target. d tax denotes a dummy variable for carbon tax, which is equal to 1 from 1 July 2012 to 30 June 2014, and 0 otherwise to statistically evaluate the potential impact of the carbon tax. A dummy for the closure of the coal-fired plants, d clos,i is added to investigate the potential impact of the generation withdrawals on emissions from the power sector. 16

17 Table 4: Regression Results (using share of resources, without coal) E t = γ 0 + γ 1 D t + γ 2 D 2 t + γ 3 S t + γ 4 R t + γ 5 d tax + γ i d clo,i + ε t 9 i=6 Market γ 0 γ 1 γ 2 γ 3 γ 4 γ 5 γ 6 γ 7 γ 8 γ 9 Adj R 2 NSW 0.99*** -0.03* *** -0.71*** * ** *** -0.02** 88% QLD 0.96** ** ** * ** ** 78% SA 0.90*** -0.20* ** -0.97*** *** -0.01** 95% VIC 0.98*** -0.21* 0.18* -0.58** -0.79** * -0.07*** -0.06*** -0.02*** -0.02** 95% 17

18 Table 5: Regression Results (using share of resources with coal) E t = γ 0 + γ 1 D t + γ 2 D t 2 + γ 31 S tg + γ 32 S tc +γ 4 R t + γ 5 d tax + Market γ 0 γ 1 γ 2 γ 31 γ 32 γ 4 γ 5 γ 6 γ 7 γ 8 γ 9 Adj R 2 9 i=6 γ i d clo,i + ε t NSW 0.89*** -0.03* 0.04* -0.40*** 0.15** -0.69** * *** *** ** 88% QLD 0.88** * -0.61** 0.38** -0.94** * ** *** 79% SA 0.90*** -0.20* *** 0.15** -0.96*** *** -0.04** 96% VIC 0.98*** -0.21* ** 0.96*** -0.72** * -0.08*** -0.05*** -0.01*** -0.02** 95% 18

19 Table 6: Regression Results (using energy level, without coal) E t = γ 0 + γ 1 D t + γ 2 D t 2 + γ 3 S t + γ 4 R t + γ 5 d tax + 9 i=6 γ i d clo,i + ε t Market γ 0 γ 1 γ 2 γ 3 γ 4 γ 5 γ 6 γ 7 γ 8 γ 9 Adj R 2 NSW 0.88*** -0.04* *** -3.87*** * *** *** ** 87% QLD 0.89** -0.25* 0.02* -3.93** -3.01** * ** -0.09** 77% SA 0.41*** *** -4.18*** * -0.01** -0.03*** 90% VIC 0.25*** -0.25* 0.03* -3.01** -4.05** *** -0.06*** -0.03*** -0.01** 93% 19

20 Table 7: Regression Results (using energy level, with coal) E t = γ 0 + γ 1 D t + γ 2 D t 2 + γ 31 S tg + γ 32 S tc +γ 4 R t + γ 5 d tax + 9 i=6 γ i d clo,i + ε t Market γ 0 γ 1 γ 2 γ 31 γ 32 γ 4 γ 5 γ 6 γ 7 γ 8 γ 9 Adj R 2 NSW 0.90*** -0.03* 0.04* -2.38*** 0.14*** -4.43*** * *** *** -0.06** 86% QLD 0.89*** * -3.77*** 0.379*** -4.28*** * *** *** 77% SA 0.31*** -0.20* 0.17* -0.99*** 0.99** -6.27*** * -0.04*** -0.06** 93% VIC 0.98*** -0.21* 0.16* -3.15*** 1.08*** -4.64*** * -0.07*** -0.05*** -0.03*** -0.02** 94% 20

21 Table 8: Regression Results (using share of resources, without coal & closure dummies) E t = γ 0 + γ 1 D t + γ 2 D t 2 + γ 3 S t + γ 4 R t + γ 5 d tax + +ε t Market γ 0 γ 1 γ 2 γ 3 γ 4 γ 5 Adj R 2 NSW 0.88*** -0.07* *** -0.71*** ** 87% QLD 0.96** -0.16* 0.22* -0.63*** ** ** 75% SA 0.90*** -0.30* *** -0.99*** *** 94% VIC 0.98*** -0.35* *** -0.67*** ** 91% 21

22 Table 9: Regression Results (using energy level, with coal, without closure dummies) E t = γ 0 + γ 1 D t + γ 2 D t 2 + γ 31 S tg + γ 32 S tc +γ 4 R t + γ 5 d tax + ε t Market γ 0 γ 1 γ 2 γ 31 γ 32 γ 4 γ 5 Adj R 2 NSW 0.90*** * -2.37*** 0.189*** -5.29*** ** 84% QLD 0.88*** * -3.76*** 0.374*** -5.40*** ** 77% SA 0.24*** -0.38** 0.23** -0.96* 2.99* -8.48*** *** 91% VIC 0.99*** -0.45** 0.18** -4.13*** 2.11*** -4.89*** *** 90% 22

23 Table 10: Regression Results (using energy level, without coal & closure dummies) E t = γ 0 + γ 1 D t + γ 2 D t 2 + γ 3 S t + γ 4 R t + γ 5 d tax + ε t Market γ 0 γ 1 γ 2 γ 3 γ 4 γ 5 Adj R 2 NSW 0.87*** -0.06** *** -5.37*** * 86% QLD 0.88** * -3.96*** ** *** 76% SA 0.25*** -0.49** -0.78* -4.28*** -6.18*** *** 86% VIC 0.98*** * -4.40*** -4.93*** *** 85% 23

24 Table 11: NEM_Regression Results E t = γ 0 + γ 1 D t + γ 2 D t 2 + γ 31 S tg + γ 32 S tc +γ 4 R t + γ 5 d tax + 16 i=6 γ i d clo,i + ε t Market γ 0 γ 1 γ 2 γ 31 γ 32 γ 4 γ 5 γ 6 γ 7 γ 8 γ 9 γ 10 γ 11 γ 12 γ 13 γ 14 γ 15 γ 16 Adj R 2 NEM (LEVEL) NEM (No Dum) NEM (No coal) NEM (No D&C) NEM (SHARE) NEM (No Dum) 0.96*** -0.83** 0.34* -1.23*** 0.35** -2.54*** * *** *** ** ** * ** * ** ** ** ** 95% 0.95*** -0.83** 0.34* -1.36*** 0.38** -2.38*** ** 87% 0.97*** -0.43** 0.44* -0.95*** -2.24*** * *** *** ** ** * ** * ** ** ** * 94% 0.93*** -0.45** 0.42* -1.85*** -2.28*** ** 86% 1.15*** -0.93* *** -0.99*** * *** *** ** ** ** * * ** ** * 95% 1.18*** -0.93* *** -0.99*** ** 86% 24

25 Summary & Conclusions The emission intensity of electricity generation has decreased by about 8.5 per cent over the period investigated. Based upon this analysis, the decline in emission intensity of power sector in NEM were primarily driven by: an increase in share of renewable generation; an increase in share of gas in the generation mix; and the closure of the coal-fired plant The results demonstrated that changes in renewable deployment could be considered as the principal factor affecting the emission intensity of the NEM. The Renewable Energy Target through both the LRET and SRET could be the main driver behind the increase in the share of renewable generation. The fossil fuel mixture effect (reduction in coal-fired generation combined with the increase in generation from gas) was responsible for reducing the emission intensity of the power sector. The permanent and temporary closure of coal-fired plants across states removed significant capacity from the market leading to the fall of emission intensity. 25

26 Summary & Conclusions (cont.) It seemed that other drivers, such as carbon tax and fuel efficiency, were not as influential. Changes in emission intensity in SA was mainly influenced by the increase in renewable generation. In NSW and QLD, both generation mix effect (which in part might be driven by the QGS, the NSW GGRS, and the CPM) and the closure of coal plants were sympathetic to decrease in emission intensity over the period investigated. In VIC, the coal generation withdrawals (and the flooding of the Yallourn power station) significantly contributed to fall in emission intensity. The finding suggested that the impact of the CPM on investment into greener generation assets was rather limited (given the substantial increase in spot electricity prices). It seemed that the CPM resulted mainly in short-term and minor changes in generation behaviour, since it was not considered to remain in place over relevant investment horizons. Environmental policies can be a significant driver if they are accompanied by a stable and long-term policy framework. 26

27 Thank you! 27

28 Figure 5: NEM Generation by Region and Fuel Type in 2017 a. Generation in the NEM, by fuel source, Per cent of total generation Black coal Brown coal Gas Hydro Wind Solar farms Other dispatched Rooftop solar Capacity Output b. Generation capacity in the NEM, by region and fuel source, 1 January Megawatts Queensland NSW Victoria South Australia Tasmania Black coal Brown coal Gas Hydro Wind Solar farm Other dispatched Rooftop solar 28

29 Figure 7: Basslink Interconnector 29

30 How to Measure Fuel Costs The model assumes that fuel costs are fully and directly passed on to electricity prices. Fuel costs are calculated based on half-hourly electricity demand and short-run marginal costs (SRMC) for each individual power generation instalment in the NEM based on data provided by ACIL Tasman (2009), ACIL Allen Consulting (2014), AEMO (2016) and CO2CRC Limited (2016). Generators are ranked in ascending order of their short-run marginal costs, known as the merit order. Using data on half-hourly demand for each market, we simulate the dispatch of individual power plants to calculate a weighted average of fuel costs for each half-hourly interval. Thus, for the carbon-tax period, we consider the SRMC inclusive of the additional costs of carbon for the merit order. The calculated fuel costs for electricity generation are then subtracted from the volumeweighted wholesale electricity prices to generate the spread as represented on the lefthand side of the equation. 30

31 Background (cont.) In February 2011, the Labour government proposed the so-called Clean Energy Bill: a two-stage carbon policy mechanism commencing with a fixed price carbon period from July 1, 2012 and transitioning to an emissions trading scheme (ETS) on July 1, One carbon permit allowed the discharge of 1 tonne of CO 2 compliance year. in a A fixed price carbon (introductory) phase came into effect on July 1, 2012 with an initial price of $23. The tax was planned to increase to $25.40 for the financial year The carbon tax was abolished with effect of July 2014 by the Liberal- National coalition government that came into power in September The same Coalition Government introduced a Direct Action plan in

32 Figure 7: Merit Order in the NEM (pre-tax) 32

33 Figure 8: Merit Order in the NEM (post-tax) 33

34 Figure 9: Renewable Generation Contribution to the NEM Renewable generation contribution to NEM electricity supply 20 Per cent of total generation Hydro 2013 Wind Solar farm Rooftop solar 34

35 Figure 7: Effect of Plant Closures on Generation Capacity 4000 South Australia Victoria Megawatts Megawatts