Risk managing cost-effective decarbonisation of the power sector in Germany

Transcription

1 Risk managing costeffective decarbonisation of the power sector in Germany FINAL RESULTS April 2013 This project is funded by the European Climate Foundation 1

2 Contents Objectives and the methodology Baseline analysis results Sensitivity analysis results New baselines based on Increased Ambition Annex Assumptions and modelling 2

3 Objectives of the analysis 3

4 The Modelling A new approach is needed to move the debate from least cost decarbonisation to cost effective risk managed delivery of policy objectives. This model puts greater emphasis on uncertainty and risks than traditional equilibrium models which tend to focus only on finding a least cost solution to a given set of constraints The Investment Decision Model developed by Redpoint is an agent based investment model. It realistically captures investor behaviour by assuming no perfect foresight: Investors are considered to take decisions based on their expectations of returns according to their knowledge of the future at a given time, assuming a five year market foresight. Focuses on the resilience and robustness of the decarbonisation pathways against unforeseen changes in key market drivers, electricity demand, natural gas prices, RES and CCS deployment. A similar analysis was carried out for Great Britain and Poland to represent different Member States circumstances and reflect European wide issues 4

5 Overview of the baseline scenarios Technology Support Scenario RES E subsidy continues post 2020 Carbon price trajectory of the EC s Energy Roadmap 2050 Carbon Prices in the baseline scenarios Carbon Price Scenario Carbon price is the single driver of decarbonisation RES E subsidy stops in 2015 no further development of supply chains Two baseline policy scenarios reflect competing approaches to delivering power sector decarbonisation in line with the power sector carbon target of 95 MtCo2 pa in 2030 based on the Leitstudie 2011A scenario. 5

6 Technology Support and Carbon Price baseline scenarios were stress tested against a range of uncertainties N.B. Further details of the underlying assumptions can be found in Annex 6

7 Germany high level conclusions (1/2) German policy must simultaneously deliver decarbonisation, security, and affordability objectives. Our analysis shows that policies designed to manage future risks and uncertainties are able to meet this requirement. There remains significant on going potential for coal to gas switching and steady deployment of renewables which increase resilience against failures to deliver electricity efficiency and CCS capacity. The carbon price is an effective driver in increasing or reducing power sector carbon emissions. However, the analysis assumed that lignite and hard coal plants can continue to operate at low load factors and this might not be technically feasible without significant investment. Technical constraints of this nature will make the impact of carbon price on emissions less predictable and introduce discontinuities. 7

8 Germany high level conclusions (2/2) If the carbon price was the single driver of decarbonisation, i.e. renewable subsidies stopped in 2015, the market would deliver gas as the least cost option with the exception that CCS could be fitted at some point in the future. However, failures to deploy CCS would mean that very high carbon prices would be required to quickly attract significant level of renewable energy. German decarbonisation policy places high significance on improved energy efficiency, especially in buildings. As a consequence, if Germany fails to deliver electrical efficiency, an early phase out of lignite and hard coal plants and replacement with gas will be required to compensate for increased power sector emissions. At the same time, the ability for the power sector to compensate for efficiency failures in other sectors will require more extensive deployment of low carbon technologies. Low energy demand scenarios provide higher system resilience against the risk of high power sector costs and failures to deploy CCS capacity. Thus managing energy demand is central to deal with uncertainties and secure the delivery of decarbonisation targets. 8

9 Contents Objectives and the methodology Baseline analysis results Sensitivity analysis results New baselines based on Increased Ambition Annex Assumptions and modelling 9

10 Where technology are supported low carbon capacity is deployed continuously, while in the Carbon Price Scenario decarbonisation is driven by gas in 2020s Technology Support Scenario baseline Cumulative new build (GW) Carbon Price Scenario baseline Cumulative new build (GW) Cumulative N ew Build GW Solar Offshore Wind Onshore Wind Biomass GT Oil Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear Cumulative N ew Build GW Solar Offshore Wind Onshore Wind Biomass GT Oil Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear In the Carbon Price Scenario, there is no renewables build beyond New build consists of unabated gas plant and lignite CCS coming online from

11 Unabated gas capacity remains a significant part of the capacity mix in the Carbon Price Scenario 250 Technology Support Scenario baseline Generation capacity (GW) 250 Carbon Price Scenario baseline Generation capacity (GW) Capacity GW Solar Offshore Wind Onshore Wind Biomass GT Oil Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear Capacity GW Solar Offshore Wind Onshore Wind Biomass GT Oil Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear While coal, lignite and nuclear capacity is replaced by solar and offshore wind, gas capacity does not change and CCS capacity is not required Unabated gas capacity increases while hard coal is squeezed out of the market due to the higher carbon price 11

12 Unabated hardcoal and lignite is replaced with offshore wind and gas Technology Support Scenario baseline Generation Mix (TWh) Carbon Price Scenario baseline Generation Mix (TWh) Generation TW h Solar Offshore Wind Onshore Wind Biomass GT Oil Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear Demand Generation TW h Solar Offshore Wind Onshore Wind Biomass GT Oil Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear Demand Despite significant reductions, lignite and, to a certain extent, unabated hard coal remain in the mix up to 2030 and CCS capacity is not required Unabated gas increases its share of the generation mix significantly, as a result of the higher carbon price and CCS capacity is required 12

13 Carbon emissions reduction follow a fairly linear path in both scenarios Technology Support Scenario baseline Emissions by fuel (mn tonnes CO2) Carbon Price Scenario baseline Emissions by fuel (mn tonnes CO2) mn tonnes CO Solar Offshore Wind Onshore Wind Biomass GT Oil Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear Target Line mn tonnes CO Solar Offshore Wind Onshore Wind Biomass GT Oil Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear Target Line Cumulative emissions are slightly higher in the Carbon Price Scenario as most low carbon capacity is only commissioned in the 2020s. 13

14 Power sector costs are higher in Technology Support Scenariobaseline Technology Support Scenario baseline Breakdown of power sector costs, bn cumulative Carbon Price Scenario baseline Breakdown of power sector costs, bn cumulative 14

15 Wholesale costs are lower where technologies were supported compared to where carbon price was the only driver for investment Technology Support Scenario Wholesale costs, bn Carbon Price Scenario Wholesale costs, bn

16 Contents Objectives and the methodology Baseline analysis results Sensitivity analysis results New baselines based on Increased Ambition Annex Assumptions and modelling 16

17 Coal to gas switching option allows carbon price to be an effective instrument, but failure to deploy CCS and high demand almost double required carbon price Carbon Price Scenario Required carbon price ( /tco2) Ranges of carbon prices (52 to 150 /tco2) High demand and failure to deploy CCS (due to cost or policy/technology failure) requires much higher carbon prices to achieve policy objectives 17

18 Required carbon price in Carbon Price Scenario to meet power sector decarbonisation target Carbon Price Scenario Required carbon price ( /tco2) High demand and failure to deploy CCS (due to cost or policy/technology failure) requires much higher carbon prices to achieve policy objectives 18

19 Renewables deployment produces steady abatement with more predictable delivery Technology Support Scenario Annual CO2 Emissions (mn tonnes CO2) Carbon Price Scenario Annual CO2 Emissions (mn tonnes CO2) 19

20 Renewables deployment produces steady abatement with more predictable delivery Technology Support Scenario Carbon intensity (g/kwh) Carbon Price Scenario Carbon intensity (g/kwh) 20

21 Total power sector costs Impact of sensitivities (1/2) Technology Support Scenario Power sector costs, bn , cumulative Carbon Price Scenario Power sector costs, bn , cumulative Costs are more resilient to uncertainty especially to higher electricity demand and higher gas price volatility Costs increase significantly if electricity demand is higher than expected. Costs increase even further when unexpected higher electricity demand is combined with failure of CCS deployment 21

22 Total power sector costs are more predictable where technologies are supported (2/2) Technology Support Scenario Power sector costs, bn , cumulative Carbon Price Scenario Power sector costs, bn , cumulative Cost range +11% to 5% Cost range +18% to 5% Overall power sector costs can go up by 11% in the case that electricity demand is higher than expected Overall powers sector costs can go up by 18% when electricity demand is higher than expected and CCS fails to be deployed 22

23 Wholesale costs are more resilient to uncertainties and show lower cost risks where technologies are supported Technology Support Scenario Wholesale costs, bn , cumulative Carbon Price Scenario Wholesale costs, bn , cumulative Cost range +48% to 9% Overall wholesale costs can go up by 20% e.g. when electricity demand is higher than expected Overall wholesale costs go up by 48% e.g. when electricity demand is higher than expected and CCS fails to be deployed 23

24 Large gas demand uncertainties, in particular in Carbon Price Scenario, raise questions as to the level of new investment required in gas infrastructure (1/2) Technology Support Scenario Power sector gas consumption (bcm) Carbon Price Scenario Power sector gas consumption (bcm) Future value of new gas investments remains uncertain 24

25 Power sector gas consumption Impact of sensitivities (2/2) Technology Support Scenario Power sector gas consumption (bcm) Carbon Price Scenario Power sector gas consumption (bcm) 25

26 New gas capacity Impact of sensitivities (1/2) Technology Support Scenario New gas capacity (GW) Carbon Price Scenario New gas capacity (GW) Range of new gas build Similar to results regarding gas consumption in the power sector, new built gas capacity shows large variation under different scenarios and sensitivities 26

27 New gas capacity Impact of sensitivities (2/2) Technology Support Scenario New gas capacity (GW) Carbon Price Scenario New gas capacity (GW) 27

28 Generation in Carbon Price Scenario Impact of sensitivities (1/2) Carbon Price baseline Shifting Momentum SM High Demand SM Low Demand Generation TW h Generation TW h Generation TW h SM H igh Gas SM H igh Demand Low CCS SM Low Demand Low CCS Generation TW h Generation TW h Generation TW h SM Low Gas SM High Demand EFF SM Low CCS Generation T W h Generation T W h Generation T W h 28

29 Generation in Carbon Price Scenario Impact of sensitivities (2/2) SM HighCCS SM Expensive CCS SM Lignite Load Factor Generation TW h Generation TW h Generation TW h SM Increased Ambition SM H igh OffW ind SM Low OffW ind Generation TW h Generation TW h Generation TW h 29

30 Generation in Technology Support Scenario Impact of sensitivities (1/2) Technology Policy Support Momentum baseline PM High OffW Ind PM Low OffW ind Generation TW h Generation TW h Generation TW h PM High Gas PM High Demand PM Low Demand Generation TW h Generation TW h Generation TW h PM Low Gas PM High Demand EFF PM Low CCS Generation TW h Generation TW h Generation TW h 30

31 Generation in Technology Support Scenario Impact of sensitivities (2/2) PM High CCS PM Lignite Load Factor PM Increased Ambition Generation TW h Generation TW h Generation TW h PM High OffW ind PM Low OffW ind Policy Momentum Generation TW h Generation TW h Generation TW h 31

32 Contents Objectives and the methodology Baseline analysis results Sensitivity analysis results New baselines based on Increased Ambition Annex Assumptions and modelling 32

33 Increased Ambition Carbon intensity of g/kwh and 50g/kWh // Carbon Price Scenario Required carbon price ( /tco2) Driving Increased Ambition through only carbon price would require unsustainably high carbon prices 33

34 Delivery of doubling ambition to g/kwh would require higher deployment of RES and CCS gas Technology Support Increased Ambition ( g) Generation mix (TWh) Carbon Price Increased Ambition ( g) Generation mix (TWh) Generation TW h Solar Offshore wind Onshore Wind Biomass Oil GT Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear Solar Offshore wind Onshore Wind Biomass Oil GT Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear CCS gas becomes part of the mix Lignite and coal are significantly pushed out Higher deployment of renewables Coal and lignite phased out and replaced by a significant increase in unabated and CCSgas instead of CCS lignite 34

35 Matching UK ambition would require near phase out of coal/lignite, deployment of CCS and offshore wind Technology Support Increased Ambition (50 g) Generation mix (TWh) Carbon Price Increased Ambition (50 g) Generation mix (TWh) Generation TW h Solar Offshore wind Onshore Wind Biomass Oil GT Hydro / PS Gas CCS Coal CCS Lignite CCS Gas Coal Lignite Nuclear Very high deployment of renewables would be needed to meet the target Need both CCS gas and lignite Very high deployment of renewables would be needed to meet the target Higher ambition and very high carbon prices push out most fossil fuel based generation CCS gas instead of CCS lignite becomes economical 35

36 Increasing ambition costs more, but additional power sector costs are less significant where RES was supported Technology Support Scenario baselines Power sector costs, bn annual Carbon Price Scenario baselines Power sector costs, bn annual Overall, additional cost of higher ambition is 20 to 49bn between and is cheaper than the CPS Overall, additional cost of higher ambition is 121 to 391bn between

37 Wholesale costs soar under Carbon Price when ambitions are increased Technology Support Scenario baselines Wholesale costs, bn annual Carbon Price Scenario baselines Wholesale costs, bn annual 37

38 Increasing ambition would increase gas consumption in general Technology Support Scenario baselines Power sector gas consumption (bcm) Carbon Price Scenario baselines Power sector gas consumption (bcm) Gas consumption increases about 33 50% to deliver increased ambition Gas consumption increases more than 50% to deliver g/kwh policy objective On the contrary, delivering 50 g/kwh would require less gas consumption as significant renewables replace lower emission fossil fuel gas 38

39 Contents Objectives and the methodology Baseline analysis results Sensitivity analysis results New baselines based on Increased Ambition Annex Assumptions and modelling 39

40 Redpoint Investment Decision Model (IDM) The Redpoint IDM constructs detailed market outlooks in the GB power market covering the period of The IDM is based on an agent simulation engine that aims to mimic players decision making with regards to their investment decisions in new plant as well as their decisions to retire existing plants. The model contains a list of potential new build projects according to their size, cost and earliest possible year of operation. Total investment in a particular technology is limited by the technology s maximum annual and cumulative build constraints. If the constraint is binding, the projects with the highest expected returns are built. Technology costs (capex and opex) can be varied over time and if required set endogenously within the model dependent on levels of deployment, which may affect rates of learning and position on the supply curves. For each year, the levelised cost of energy (LCOE) of potential new build projects are compared against their expected revenues (given assumed load factors, future price expectations, capacity payments and support levels) and where costs are less than expected revenues, projects are moved first to a planning stage, and subsequently, if still economic, to a committed development phase. Additionally, retirement decisions for existing plants are also made on the basis of near term profitability expectations. A 5 year forward looking view for investing in a new plant is assumed and a 1 year forward looking view for plant retirement decisions. Where applicable, the model can include full representation of Contracts for Difference (CfDs) and a universal capacity mechanism. 40

41 Investment modelling Non perfect foresight The model has a 5 year forward view of commodity prices and demand supply (1) Rolling through each year, the model estimates power prices and dispatch for the forward view horizon. The resulting expected gross margin is compared to the expected levelised costs (2). On that basis the model decides whether a project should enter the planning stage (3) and then rolls forward to the next year (4). During planning the project can still be cancelled. Once the planning period is over the model will decide whether to move to the construction phase at which point the project is committed. 41

42 Generator decisions: new build and retirement Generator build decisions: For new plants the levelised non fuel cost includes capital costs and annual fixed costs. The gross margin is calculated as the expected margin from power revenues, capacity payments and financial support less fuel and carbon costs and non fuel variable costs. There are two trigger points which a project must pass to progress to construction. If a project is in the money it enters planning. If it continues to be in the money at the end of the planning period, the project is committed to the construction phase, and will become operational after a defined number of years. Generator retirement decisions: The logic for closure decisions of existing generators is analogous to that for new investments. The key difference, however, is that the capital already invested is ignored as this is considered to be a sunk cost. As a result, total annual fixed costs are compared against the expected gross margin and, when these are higher for a pre defined number of years, the plant retires. Capital and fixed O&M costs Expect ed transmission charges Fixed O&M costs Expect ed transmission charges Expect ed levelised nonfuel costs Trigger 1 Trigger 2 Compare Expect ed gross margin Planning Commit Under Construction Operational Expect ed fixed costs Plan closure Close Compare Trigger 1 Trigger 2 Expect ed gross margin For war d looking stack + prices Anticipated low carbon support Anticipated capacity payments For war d looking stack + prices Anticipated capacity payments 42

43 The model allowed policy intervention to correct deviation from the policy objective Carbon Price scenario High demand + low CCS > Increased carbon price to 150/t and push out all lignite and hard coal generation Low CCS; Expensive CCS; High demand; High electricity demand (low efficiency) > Increased carbon price between 90 /t and /t Low demand + low CCS > Increased carbon price only to 75/t Over delivery Low demand; Low gas price; High CCS > Increased carbon price only to 65/t or maintained baseline carbon price Technology Support scenario Over delivery High demand; Low offshore wind > Increased offshore wind (between 30 and 35 GW by 2030) High electricity demand (low efficiency) > Subsidy for lignite CCS Low demand; Low gas price; High offshore wind > Reduced offshore wind deployment rate 43

44 Capital cost assumptions Capital costs ( /kw, real 2011) Nuclear CCGT Gas CCS Coal & Lignite Onshore Wind Biomass Solar PV CCS Offshore Wind (Low) Offshore Wind (Base) Offshore Wind (High) All capital costs except offshore wind are based on the Energy Roadmap 2050 Offshore wind capital costs (Base/High/Low) are based on the study by ARUP for DECC The costs evolve over time reflecting learning curves and economies of scale. In particular solar and CCS are not yet mature technologies and can therefore follow steep learning curves. 44

45 Long run marginal cost of electricity assumptions in baseline scenarios Nuclear CCGT CCGT CCS Coal CCS Lignite Lignite CCS Onshore Wind Offshore Wind Nuclear CCGT CCGT CCS Coal CCS Lignite Lignite CCS Onshore Wind Offshore Wind Nuclear CCGT CCGT CCS Coal CCS Lignite Lignite CCS Onshore Wind Offshore Wind LRMC ( /MW h real 2011) The chart on the right shows the development LRMC of various technologies, split into their various components Carbon Fuel VOM Fixed Capital

46 Other cost assumptions Technology Hurdle Rate Variable Operating & Maintenance ( /MWh) Fixed costs (% of capital costs) Gas 8.2% % Coal 9.0% % Lignite 9.0% % Gas CCS 12.0% % Coal CCS 12.0% % Lignite CCS 12.0% % Nuclear 11.5% % Onshore W ind 9.0% % Offshore W ind 11.0% % 46

47 Baseline commodity prices The Base commodity prices are based on the 450 scenario from the IEA World Energy Outlook Where applicable, the lignite fuel price is assumed to be 1.7 /GJ (real 2011) throughout the modelling horizon. curr/unit real ARA Coal ($/t) Brent Oil ($/bbl) Gas ($/mmbtu) EUA Car bon ( /t ) 47

48 Gas price shocks were introduced overnight with no foresight for beginning or ending of the event Baseline gas price is based on the 450 scenario from the IEA World Energy Outlook High and low gas price shocks are 75% higher or lower than the baseline price. Gas price shocks introduced overnight in early 2020s and lasts for 4 5 years a l (re rm e / th p e ric p a s G Base Low Shock High Shock

49 Electricity demand: baseline and sensitivity assumptions HIGH DEMAND BASELINE DEMAND Overall electricity demand is 575 TWh. This was due to a combination of failing to deliver demand reductions in the domestic, SME and industrial sectors (only half of the demand assumed in the Leitstudie 2011A is realised). In addition there is higher demand from electric vehicles compared to baseline (ca. 56 TWh in 2030). The additional EV demand is consistent with the Leitstudie 2011C scenario which specifically looks at higher long term electrification of the transport sector in Germany. Overall electricity demand is 490 TWh. This is based on the Leitstudie 2011A scenario (incl. ca 25 TWh demand for electric vehicles and 10 TWh for hydrogen production). The Leitstudie scenario is consistent with the government s long term target of a 25% reduction in electricity demand by 2050 compared to 8. LOW DEMAND Overall electricity demand is 441 TWh. This is also based on the Leitstudie 2011A demand but with no demand from electric vehicles or for hydrogen production. 49

50 Annual Electricity Demand T W h Investment decisions were taken with the expectation of base electricity demand but then were subject to higher or lower electricity demand Germany Annual electricity demand trajectories 2010 Low Base High BaseLow BaseHigh Investment decisions were made with the expectation of a base demand. The model illustrates how uncertain demand development works. Every five years, investors readjust their expectations in line with a base demand (red) trajectory (green and purple dotted lines); however, the demand remains higher or lower than their expectations (yellow and blue lines). For example: In the High Demand case, the expectation in 2015 follows the green dashed line, although outturn demand follows the yellow line. In 2019 expectations still follow the downward path (smaller green dashed line). In 2020 expectations are reset but again follow the downward gradient as illustrated by the green dashed lines. This 5 year cycle continues throughout the modelling horizon. 48

51 Technology deployment assumptions and maximum levels CCS Nuclear High deployment: 50% higher than the baseline deployment (ca. 30 GW by 2030) Baseline: No CCS built under Technology Support scenario baseline; Max. 15 GW of combined CCS capacity across all fuels (gas, hard coal, lignite) in Carbon Price scenario baseline Low deployment: CCS technology fails a year into construction of the first commercial plant and there is no subsequent CCS deployment. Consistent with current government policy we assume that existing nuclear plants are phased out as planned an there is no new nuclear build Offshore wind High deployment: maximum potential of 35GW by 2030 Baseline: as per government target trajectory of 10GW by 2020 and 25GW by Low deployment: no further offshore wind build beyond the 10 GW in 2020 Onshore wind High deployment: maximum potential of 50 GW by 2030 Baseline: Capacity is currently ca. 30GW. Maximum potential of 45 GW by 2030 Low deployment: maximum potential of 40 GW by