Speaker: Mike Wilks, Director, Pöyry Management Consulting

Transcription

1 Parallel Session: Demand data Speaker: Mike Wilks, Director, Pöyry Management Consulting

2 Agenda 1. What do we need to define as demand data to proceed through STEP 1,2 and 3? 2. How? Methodology of the Projections of Annual Electricity Volumes 3. Main input data assumptions & results 4. Next steps: Methodology for providing Time series and DSM figures (step4) 5. Questions session 6. Summary of Q&A Session 2

3 1. Whatdo weneedto defineas demanddata to proceed STEP1,2,3? Geographical Scope: EU28 countries (excluding Malta); Bosnia and Herzegovina; the former Yugoslav Republic of Macedonia; Montenegro; Serbia (including Kosovo); Switzerland; and Norway. Temporal Scope: modelling demand in 2050 only, from a base year of Units provided: all annual volumes in TWh. 3

4 2. How? The demand model uses a mixture of top-down and bottom-up approaches: Top-down approach reflects the future uncertainty of European policy and allows macro-economic scenarios to be applied coherently to all countries. Bottom-up approach allows fundamental differences between countries to be projected out to Country specific inputs were only used if data was available on a consistent basis from international studies. 4

5 Four Step Methodology 5

6 Scenario Definitions Calculation step and Criteria Step 1: Economic and Financial Demand parameter Population (demographic changes) x-5: Large scale RES & no emissions x-7: 100% RES x-10: Big & Market x-13: Big Nuclear and CCS x-16: Small and Local Growth Growth Growth Growth Migration only GDP increase Medium Medium Medium Medium Low Step 2: Technology New use High High High High Low Step 3: Political, sociopolitical and environmental Energy efficiency Low High Medium Low High Step 4: Final Electricity Demand including Network Losses Network Losses Medium Medium Medium Medium Medium 6

7 Step 1: Economic and Financial Projections of GDP/Capita and Population growth out to 2050 for each e- Highway2050 scenario. Projections were based on top-down macro-economic scenarios sourced from international studies that provide data at a country level. The advanced econometric models, pre-developed for our extensive power market modelling, projected 2050 business-as-usual electricity demand based on historical trends between electricity demand and GDP 7

8 Step 2: Technology Projected the increase in electricity demand arising from the electrification of heating and transport for each scenario using a mixture of bottom-up and top-down approaches. Designed to capture policy-driven changes that result in electricity accounting for an increased share of the energy mix for heat and transport. Three possible outcomes are listed for the uncertainty of electrification of transport, heating and industry: Residential, Large Scale and All. 8

9 Step 3: Political, Socio-political and environmental Projection of energy efficiency s effects on the annual electricity volumes of each scenario using a top-down approach. Energy efficiency refers to Passive Demand Side technologies. Active demand side technologies are considered in the scope for changing the hourly time series of demand. Step 3 considers only the incremental improvements in energy efficiency from current levels as historical developments in energy efficiency are accounted for in the projection of demand based on GDP growth in Step 1. 9

10 Step 4: Final electricity demand including losses The project applied network losses to electricity demand volumes to obtain final electricity demand including losses. Network losses are applied using a top-down approach. The losses are calculated individually for each country to reflect the individual electricity volumes and electricity networks of each country. 10

11 3. Main input data assumptions& results Step 1: Economic and Financial GDP scenarios are generated from population and GDP/Capita projections. GDP scenarios influence business as usual demand growth calculated from our econometric modelling as well as the growth of transport and heat in Step 2. Top-down approach used two macro-economic studies to project country specific GDP/Capita with Medium growth or Low growth. These studies were from separate organisations, the Medium growth scenario is from the OECD (1.5% CAGR for Europe) and the Low growth scenario is from CEPII* (1.3% CAGR for Europe). The distribution of GDP/Capita between countries in the OECD study was used to define the distribution in both Low and Medium growth scenarios *French Centre d'etudes Prospectives et d'informations Internationales 11

12 GDP/Capita Scenarios $ (2011) 2010 Value Medium Growth Low Growth Austria Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France FYROM Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Montenegro Netherlands Norway Poland Portugal Romania Serbia Slovakia Slovenia Spain Sweden Switzerland UK Task 9.3 Dissemination report

13 GDP/Capita Scenarios 160, ,000 GDP/Capita Scenarios in , ,000 80,000 60,000 40,000 GDP/Capita ($, 2011) 20,000 0 Austria Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France FYROM Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Montenegro Netherlands Norway Poland Portugal Romania Serbia Slovakia Slovenia Spain Sweden Switzerland UK : Low Growth 2050: Medium Growth Task 9.3 Dissemination report

14 Population Scenarios For population, a top-down approach used Eurostat s Convergence scenario to project country specific populations in the Population Growth scenario. The Migration Only scenario did not grow in total population, but the population distribution changed to match the distribution of the Eurostat Convergence scenario. Combining both GDP/Capita and Population scenarios provided GDP projections specific to each country. 14

15 Population Scenarios Millions of people 2010 Value Population Growth Migration Only Austria Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France FYROM Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Montenegro Netherlands Norway Poland Portugal Romania Serbia Slovakia Slovenia Spain Sweden Switzerland UK Task 9.3 Dissemination report

16 Population Scenarios Population Scenarios in 2050 Task 9.3 Dissemination report Population (millions) Austria Belgium Bosnia and Herzegovina Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France FYROM Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Montenegro Netherlands Norway Poland Portugal Romania Serbia Slovakia Slovenia Spain Sweden Switzerland UK : Migration Only 2050: Population Growth

17 GDP Scenarios Combining GDP/Capita scenarios and population scenarios allows the development of GDP scenarios, shown here. CAGR(unless stated 2010 Value Population GrowthScenario; Migration Only Scenario; otherwise) ($billion 2011) Medium GDP/Capita Growth Low GDP/Capita Growth Austria % 1.1% Belgium % 1.7% Bosnia and Herzegovina % 1.2% Bulgaria % 3.2% Croatia % 1.2% Cyprus % 1.9% Czech Republic % 1.8% Denmark % 1.4% Estonia % 2.0% Finland % 1.4% France % 1.2% FYROM 8 1.6% 1.2% Germany % 0.7% Greece % 1.1% Hungary % 1.7% Ireland % 1.5% Italy % 1.1% Latvia % 5.1% Lithuania % 4.9% Luxembourg % 0.8% Montenegro 3 1.6% 1.2% Netherlands % 1.3% Norway % 1.9% Poland % 1.3% Portugal % 1.1% Romania % 3.6% Serbia % 1.2% Slovakia % 1.7% Slovenia % 1.5% Spain % 1.4% Sweden % 1.7% Switzerland % 1.8% UK % 1.7% Task 9.3 Dissemination report

18 GDP Scenarios Task 9.3 Dissemination report

19 GDP driven Electricity Demand Growth Scenarios 6000 Scenario dependent'business as Usual' electricity demand growth against GDP growth Electricity Deman nd (excluding losses) (TWh) GDP ($bn 2011) X-5 to X-13 'Business as Usual' Electricity Demand in 2050 (TWh) Historical Electricity Demand (TWh) X-16 'Business as Usual' Electricity Demand in 2050 (TWh) X-5 to X-13 GDP in 2050 ($bn 2011) Historical GDP ($bn, 2011) X-16 GDP in 2050 ($bn 2011)

20 Step 2: Technology Analysed qualitative scenario descriptions produced in WP1. Identified that in addition to varying scenarios by level of new use technologies, scenarios could also be split three outcomes: Residential, Large Scale, and All, that define the size and scale of the new use. This lead to the splitting of the scenarios as follows: x-5 and x-7 x-10 and x-13 x-16 Electrification of Technology (heating and Transport) All Large scale Domestic Level of new use High High Low 20

21 Transport Two transport types that would have a significant impact on new electricity demand in 2050: Road Freight and Passenger Cars. Road freight is considered Large Scale whilst Passenger Cars are considered Domestic. Passenger Cars Modelled 2050 Passenger Cars per capita using historical relationship with GDP per capita. Applied top-down scenarios of the proportions of cars that are electric vehicles or plug-in hybrids dependent on the scale of new use and if this new use is domestic or large scale. Modelled annual kilometres per car by country Average kwh/km then allows the calculation of total power demand Passenger Cars (milli ions) Scenario dependent passenger car numbers and types 2010 X-5 X-7 x-10 x-13 x-16 Other PHEVs Electric Vehicles 21

22 Road Freight As well as Passenger Cars Pöyry modelled Road Freight. This required Pöyry calculating: Tonne kilometres by road freight vehicles in 2050 the current European figure for tonne km was uplifted by GDP growth and split between countries. Share of road freight tonne kilometres by size of vehicle Share of tonne kilometres by Electric Vehicles or Plug-in Hybrid Vehicles Electricity demand per tonne kilometre by vehicle type In general, Road Freight electricity demand will be far less than Passenger Car demand. 22

23 Heating Two key components in additional heat electricity demand projections: Estimation of additional end-use energy demand met by electricity by country in each scenario (i.e. additional electrification of heat from current levels); and Conversion efficiency, which is assumed to be the same for all countries and in all scenarios (i.e. kwh of electricity required to produce a kwh of heat). The Project considers heat demand in three sectors residential, commercial and industrial which fit into our scenario definitions as follows: Large scale industry and commercial; and Domestic residential (e.g. houses using heat pumps or direct electric heating as a means to heat water or directly heat rooms). 23

24 (New-electrified) Heating Demand Scenarios Electricity Deman nd (excluding losses, before energy eff ficiency reductions) Scenario dependent heat demand in 2050 by type of technology and scale of end-use x-5 x-7 x-10 x-13 x-16 Additional load from heating other than heat pumps - non-residential Additional load from heat pumps - non-residential Additional load from heating other than heat pumps - residential Electricity demand from heat pumps - residential 24

25 Step 3: Political, Socio-political and environmental Only considers the incremental improvements in energy efficiency from current levels This is because historical developments in energy efficiency are accounted for in the projection of demand based on GDP growth (Step 1). x-5: Large scale RES & no emissions x-7: 100% RES x-10: Big & Market x-13: Big Nuclear and CCS x-16: Small and Local Energy efficiency level Annual improvement in energy efficiency ( ) Total reduction in electricity demand in 2050 Low High Medium Low High 1.0%/annum 1.5%/annum 1.25%/annum 1.0%/annum 1.5%/annum 33% 45% 40% 33% 45% 25

26 Step 4: Final electricity demand including losses Scenario independent Network Losses additon on demand in 2050 Currently a wide range of network loss percentages across Europe (1.8% to 15.7%) and an average network loss proportion of 6.8% This range is reduced in 2050 (1.8% to 8.3%) meaning that the expected Network Losses in 2050 for all Europe are 6.4%. Network losses continue to vary between countries due to geography and the state of the country s infrastructure. ENTSO-E ALL UK Switzerland Sweden Spain Slovenia Slovakia Serbia Romania Portugal Poland Norway Netherlands Montenegro Luxembourg Lithuania Latvia Italy Ireland Hungary Greece Germany FYROM France Finland Estonia Denmark Czech Republic Cyprus Croatia Bulgaria Bosnia and Belgium Austria 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 26

27 Total European Results 27

28 GDP effect on 2050 demand Graph to show a comparison of other studies demand vs. GDP relationship and Pöyry s. Comparison of 2050 Demand Scenarios with Pöyry Demand Calculations 5300 X5 X Electricity Demand (TWh) X10 X13 X16 IRENE-40 BAU RoadMap Reference RoadMap High GDP RoadMap Low GDP X-5 GDP Sensitivity X-7 GDP Sensitivity X-10 GDP Sensitivity % 1.35% 1.45% 1.55% 1.65% 1.75% 1.85% 1.95% 2.05% Average GDP Growth Rate X-13 GDP Sensitivity X-16 GDP Sensitivity 28

29 Final Results: All Europe: Large Scale RES & zero emissions 8000 Country: ENTSO-E ALL Scenario x Demand (inc losses) GDP & Population Scenario 'New use' Technologies Scenario: Transport 'New use' Technologies Scenario: Heat Energy Efficiency Scenario Final demand (exc losses) Final demand (inc losses) 29

30 Final Results: All Europe: 100% RES 8000 Country: ENTSO-E ALL Scenario x Demand (inc losses) GDP & Population Scenario 'New use' Technologies Scenario: Transport 'New use' Technologies Scenario: Heat Energy Efficiency Scenario Final demand (exc losses) Final demand (inc losses) 30

31 Final Results: All Europe: Big & Market 8000 Country: ENTSO-E ALL Scenario x Demand (inc losses) GDP & Population Scenario 'New use' Technologies Scenario: Transport 'New use' Technologies Scenario: Heat Energy Efficiency Scenario Final demand (exc losses) Final demand (inc losses) 31

32 Final Results: All Europe: Big Nuclear & CCS 8000 Country: ENTSO-E ALL Scenario x Demand (inc losses) GDP & Population Scenario 'New use' Technologies Scenario: Transport 'New use' Technologies Scenario: Heat Energy Efficiency Scenario Final demand (exc losses) Final demand (inc losses) 32

33 Final Results: All Europe: Small & Local 8000 Country: ENTSO-E ALL Scenario x Demand (inc losses) GDP & Population Scenario 'New use' Technologies Scenario: Transport 'New use' Technologies Scenario: Heat Energy Efficiency Scenario Final demand (exc losses) Final demand (inc losses) 33

34 Overview of Results by Country Part I ransmission and distribution network losses) (TWh) Electricity demand volumes (including tr Electricity Demand Volumes by Country in Electricity Demand including Losses X-5: Large scale RES & no emissions X-7: 100% RES X-10: Big & Market X-13: Big Nuc and CCS X-16: Small and Local 34

35 Overview of Results by Country Part II Electricity Demand Volumes by Country in 2050 Electricity demand volumes (including tran nsmission and distribution network losses) (TWh) Electricity Demand including Losses X-5: Large scale RES & no emissions X-7: 100% RES X-10: Big & Market X-13: Big Nuc and CCS X-16: Small and Local 35

36 4.Nextsteps: Methodologyfor providing Time seriesand DSM figures (step4) Time series modelling: Generating fixed demand profiles Fixed demand time series are generated in the Load Vision model from three main sources of electricity demand: current electricity uses; transport; and new heating. Illustrative electricity demand profile for UK winter day New electricity demand sources (transport & heat) have separate demand profiles based upon: within-day pattern of demand; within-week pattern of electricity demand (i.e. working day versus nonworking day); and seasonal pattern of electricity demand (i.e. differences between months). Source: Pudjiantoa et al., Smart control for minimizing distribution network reinforcement cost due to electrification, Energy Policy, Task 9.3 Dissemination report

37 Time series modelling: Modelling demand response Fixed time series will then be modelled against five forms of demand response Predictable demand response (e.g. regular overnight charging of electric vehicles) will be captured by the changes in the static demand profiles produced by the Load Vision model; and Dynamic demand response (driven by real-time prices) to be modelled within Antares. Type Description Modelled in Load Vision (static) Modelled in Antares (dynamic) Energy demand Overall reduction in consumption of energy Yes destruction driven by short-term prices Electricity demand Switching to a cheaper fuel to meet energy Yes destruction consumption requirement, driven by shortterm prices Energy demand shifting Requires strong economic/behavioural Yes Yes incentive to change temporal pattern of energy end use Electricity demand Uses storage to decouples electricity Yes Yes shifting Vehicle to Grid (V2G) storage demand from the energy demand it meets Requires strong economic and behavioural incentive (including large short-term price differentials) and will be limited by technical characteristics. Yes Source: Pöyry Management Consulting Task 9.3 Dissemination report

38 Time series modelling: Pöyry s modelling recommendations Recommendations were made for the parameters required to model demand response within Load Vision (static modelling) and Antares (dynamic modelling) These were based upon input from WP3, discussions between WP2.1 members and Pöyry s experience in modelling demand response Source: Pöyry Management Consulting; WP3 Electricity Demand Type Current Electricity Use Static Modelling % of peak shaving (5% for all scenarios) 2050 electricity demand by country (TWh) Dynamic Modelling Cumulative demand destruction at peak prices (P/Q pairs) Modelling constraints for dynamic dispatch of current demand (e.g. maximum response availability from wet appliances) Transport Load patterns for Fuel switching capability electric vehicles from hybrid vehicles to liquid (hourly) fuels (%) New heat 2050 EV demand (TWh) Annual load for each heating type, including nonresidential (GWh) Recommendations to determine hourly demand pattern Constraints for modelling dynamic dispatch of EV demand (50% of electricity demand e.g. Public infrastructure chargers & home chargers) Fuel switching capability from backup gas boilers (%) Constraints for modelling dynamic dispatch of heat demand (e.g. rate of energy loss, charging rate) Task 9.3 Dissemination report

39 5. Questions session Q1. Are there any comments on the approach that the Project has chosen? Q2. Are there any thoughts on what we ve selected as the key drivers of demand? Are we missing anything significant? Q3. Are there any thoughts on key differences in demand drivers and demand behaviours between countries? Q4. Are there any thoughts on our final demand levels do they make sense, do they fit with other studies and is the range of outcomes reasonable? Q5. Any other comments? 39

40 6. Summary of Q&A Session Questions How captured new non-heat/transport residential demand use? Is GDPgrowth range wide enough? Is 2050 demand range high enough? Do you take into account relativity of gas/electricity prices and their impact on fuel switching to electricity? Howdo you account for potential future different cluster demand relationships? Answers Implicitly captured with Step1 (economic growth variations) which would translate into greater residential use of technology Theyare strongly different in relative terms and 2050 outcomes, but do reflect long term path not short term situations (e.g. recession). Only Roadmap2050 High case higher than ehighways2050 High case. Our range is much wider than other European studies need to balance pushing the boundaries versus credibility of study versus others There is a key assumption that Europe is committed to decarbonisation thus will use markets or policy to ensure this happens. We do include changes in demand price elasticity linked to GDP performance and also have developed methodology to derive demand response scenarios. Should be examined elsewhere within ehighways 2050 project. The translation of our Demand datasets tocluster level is in forthcoming phase of project it will consider how cluster relationships could change under the different scenarios Possible impact of today s discussions on the output on demand data used in ehighways 2050 project Other ehighways WPs are explicitly looking at technology use inc. residential Will consider if should use European Roadmap 2050 High GDP scenario for ehoighways2050 High GDP case Will makes sure demand allocation to clusters recognises different potential future patterns ehighways2050 project will need to identify market/policy intervention which ensure fuel switching in each scenario e.g. if gas prices low relative to electricity but zero emissions sought (Scenario x-5) 40