Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Actors’ behaviour analysis in a decentralised energy system: The case of the transport provider, household and industrial sectors in Germany

34 views

Published on

Actors’ behaviour analysis in a decentralised energy system: The case of the transport
provider, household and industrial sectors in Germany

Published in: Environment
  • Be the first to comment

  • Be the first to like this

Actors’ behaviour analysis in a decentralised energy system: The case of the transport provider, household and industrial sectors in Germany

  1. 1. Actors’ behaviour analysis in a decentralized energy system: The Transport, Industry and Household Sectors Mohammad Ahanchian, Isela Bailey, Audrey Dobbins
  2. 2. 17-Nov-18IER University of Stuttgart 2 Project “Decentral”: TIMES Actors Model (TAM) Introduction Introduction
  3. 3. • Introduction • Transport sector • Actors’ characterizations • Methodology • Modelling • Industry sector • Actor characterization • Methodology • Household sector • Actor characterization • Methodology • Modelling • Outlook 17-Nov-18IER University of Stuttgart 3 Agenda Transport Sector
  4. 4. 17-Nov-18IER University of Stuttgart 4 Actors and their investment options Actors’ characteristics Transport Sector Maximize surplus Minimize costsObjective Households / owner Households / tenant S-Bahn D-Bahn operators Bus U-Bahn operators Long- distance bus operators Medium & small renewables Buy electricity from renewable sources Invest in low-carbon buses/trains Attract more passengers Extend network Building retrofit Storage Small renewable Efficient appliances Uptake of low-carbon vehicles Shift to more sustainable modes Reduce travel demand eg., teleworking Budget restriction (Income) Actors Investment options Technology specific discount rate
  5. 5. 17-Nov-18IER University of Stuttgart 5 Data source (Heterogeneity of transport users) Methodology Transport Sector • The German national travel survey documents the mobility behavior of the Germans since 1994. • A broad database consisting of households’ • socio-economic characteristics, • temporal and special details of trip, • trip purpose, • mode of transport, • technical specifications of vehicle, • weather data of the survey days, • City size class • and many other parameters. • The data survey is aimed at identifying causes of transport demand changes as well as examining the effectiveness of planning and policy measures. • Heterogeneous behavioural stability of different persons by conducting survey over a period of one week and repeated over three years. • The surveyed people are targeted in a way to represent the entire German population and the results are able to reproduce the mobility demand of country by using the extrapolation factors on household and individual level and weighting factor on trip level.
  6. 6. 17-Nov-18IER University of Stuttgart 6 Disaggregation of transport users in the household sector Methodology Transport Sector Other living costs Travel budget 8 Income group Owner/tenant Car ownership Urban/rural 64 Transport user Actor groups Budget restriction for investment Number of persons in household Vehicle technical specification 4 Age 4 Engine size WA Fuel consumption Car stock evolution Availability of infrastructure Average speed
  7. 7. 17-Nov-18IER University of Stuttgart 7 Temporal and spatial characteristics of trips Methodology Transport Sector Urban/rural Weekday/ weekend Peak hour or not Trip length Trip purpose Weather data of the survey days??? To calculate tangible and intangible cost of transport modes
  8. 8. 17-Nov-18IER University of Stuttgart 8 Modal characteristics Methodology Transport Sector • Tangible cost of each mode • Intangible cost of each mode • Waiting time • Access/egress time of public modes • Speed • Availability of infrastructure and capacity
  9. 9. 17-Nov-18IER University of Stuttgart 9 General framework Modeling Transport Sector 64 Transport user Actor groups in household sector Budget restriction Car stock evolution Availability of infrastructure Capacity of infrastructure Travel demand Mode Urban/Rural Investment options of actors (transport suppliers and users) Modal characteristics 3 Transport suppliers Actor groups
  10. 10. • Introduction • Transport sector • Actor characterisation • Methodology • Modelling • Industry sector • Actor characterization • Methodology • Household sector • Actor characterization • Methodology • Modelling • Outlook 17-Nov-18IER University of Stuttgart 10 Agenda Industry Sector
  11. 11. 17-Nov-18IER University of Stuttgart 11 Industry in Germany (Case study: Iron and Steel) Industry sector 20% 80% CO2 Emissions 29% 71% Final Energy Consumption Industry Rest of Energy System 23% 77% Industrial Final Energy Consumption Iron and Steel Industry Rest of Industry 21% 79% Industrial CO2 Emissions Industry Sector
  12. 12. 17-Nov-18IER University of Stuttgart 12 Actors’ Characterization – example in the iron and steel industry Industry sector Industry Sector
  13. 13. Standard Level of Disaggregation 17-Nov-18IER University of Stuttgart 13 Actors’ Characterization – example in the iron and steel industry Industry sector Industry Sector
  14. 14. Standard Level of Disaggregation 17-Nov-18IER University of Stuttgart 14 Actors’ Characterization – example in the iron and steel industry Industry sector • The first steps consists of a bottom-up characterization of actors in the iron and steel industry with the goal of defining 'Actors Groups' that better represent their decision-making behaviour regarding operation and investments in various technologies, especially decentralised technologies. Industry Sector
  15. 15. Standard Level of Disaggregation Data Collection (Plants) 17-Nov-18IER University of Stuttgart 15 Actors’ Characterization – example in the iron and steel industry Industry sector • The first steps consists of a bottom-up characterization of actors in the iron and steel industry with the goal of defining 'Actors Groups' that better represent their decision-making behaviour regarding operation and investments in various technologies, especially decentralised technologies. • Production data is collected for every plant. Industry Sector
  16. 16. Standard Level of Disaggregation Companies (Actors) Data Collection (Plants) 17-Nov-18IER University of Stuttgart 16 Actors’ Characterization – example in the iron and steel industry Industry sector • The first steps consists of a bottom-up characterization of actors in the iron and steel industry with the goal of defining 'Actors Groups' that better represent their decision-making behaviour regarding operation and investments in various technologies, especially decentralised technologies. • Production data is collected for every plant. • Plants belonging to the same company are added together and considered as an 'Actor'. Industry Sector
  17. 17. New Level of Disaggregation Standard Level of Disaggregation Companies (Actors) Data Collection (Plants) 17-Nov-18IER University of Stuttgart 17 Actors’ Characterization – example in the iron and steel industry Industry sector • The first steps consists of a bottom-up characterization of actors in the iron and steel industry with the goal of defining 'Actors Groups' that better represent their decision-making behaviour regarding operation and investments in various technologies, especially decentralised technologies. • Production data is collected for every plant. • Plants belonging to the same company are added together and considered as an 'Actor'. • Then, according to production technology and capacity, similar actors are grouped together for a total of four 'Actor Groups' to be modelled in the next step. Industry Sector
  18. 18. 146 Plants 20 Companies 14 EAF 6 BOS 2 Large 4 Small 9 Large 5 Small Production Technology Production CapacityActors Data Collection 17-Nov-18IER University of Stuttgart 32 Actors’ Characterization – example in the iron and steel industry Industry sector Industry Sector
  19. 19. StandardRepresentation ofIndustrialBranches: Electricity Heat OtherFuels Iron and Steel Demand Emissions AP = Autoproduction hr = Hurdle Rate 17-Nov-18IER University of Stuttgart 24 Methodology Industry sector Industry Sector
  20. 20. Actor Group 1 Actor Group 2 Actor Group 3 Actor Group 4 Demand RepresentationofIronandSteel IndustryinthisWork: Emissions StandardRepresentation ofIndustrialBranches: Electricity Heat OtherFuels Iron and Steel Demand Emissions AP = Autoproduction hr = Hurdle Rate 17-Nov-18IER University of Stuttgart 24 Methodology Industry sector Industry Sector
  21. 21. Actor Group 1 Actor Group 2 Actor Group 3 Actor Group 4 Demand RepresentationofIronandSteel IndustryinthisWork: Emissions StandardRepresentation ofIndustrialBranches: Electricity Heat OtherFuels Iron and Steel Demand Emissions AP = Autoproduction hr = Hurdle Rate 17-Nov-18IER University of Stuttgart 24 Methodology Industry sector Industry Sector
  22. 22. hr1 ElectricityGrid OtherFuels APHeat Actor Group 1 Actor Group 2 Actor Group 3 Actor Group 4 hr2 hr3 hr4 Demand RepresentationofIronandSteel IndustryinthisWork: Heat Electricity DistrictHeat APElectricity CO2Prices Emissions Heat Electricity Heat Electricity Heat Electricity StandardRepresentation ofIndustrialBranches: Electricity Heat OtherFuels Iron and Steel Demand Emissions AP = Autoproduction hr = Hurdle Rate Decentralized Technologies 17-Nov-18IER University of Stuttgart 24 Methodology Industry sector Industry Sector
  23. 23. • Introduction • Transport sector • Actor characterisation • Methodology • Modelling • Industry sector • Actor characterization • Methodology • Household sector • Actor characterization • Methodology • Modelling • Outlook 17-Nov-18IER University of Stuttgart 23 Agenda Household Sector
  24. 24. 17-Nov-18IER University of Stuttgart 24 Households in Germany Household sector Significant consumers of energy: Households consumed ~28% of the final energy consumption in 2013. Together with personal transport, households are responsible for almost 44% of final energy consumption. The majority of the household‘s energy consumption is for space heating (43%) followed by transport (37%) Households represented homogenouslyHouseholds Personal transport, 37% Space heating, 43% Warm water, 10% Cooking, 4% Cooling, 3% ICT, 2% Lighting, 1% Final Energy Consumption by sector, 2013 Final Energy Consumption for households by end-use, 2013 Households 28% Personal transport 15%Other transport 13% Commerce 16% Industry 28% HH Energy Transition targets • +14% heating with renewables • +10% renewables in transport • -10% electricity demand (compared to 2008) • -20% heating demand (compared to 2008) • -10% transport demand (compared to 2005) Household Sector
  25. 25. Characterisation of actors by investment and consumption decision-making behaviour Household sector Household Sector 0% 5% 10% 15% 20% 25% 30% 35% owners tenants owners tenants owners tenants owners tenants SFH MFH SFH MFH Urban Rural Shareofhouseholds 5000-18000 3600-5000 2600-3600 2000-2600 1500-2000 1300-1500 900-1300 <900 17-Nov-18IER University of Stuttgart 25
  26. 26. 17-Nov-18IER University of Stuttgart 26 Characterisation of actors by investment and consumption decision-making behaviour Household sector 0% 2% 4% 6% 8% 10% 12% 14% 0 200 400 600 800 1000 1200 ALL <900 900-1300 1300-1500 1500-2000 2000-2600 2600-3600 3600-5000 5000-18000 Shareofexpenditureondirect andindirectenergy Monthlyexpenditure€ Income groups by monthy household income € Energy (home+mobility) Energy (home) Energy (mobility) Appliances Mobility (materials) Total (direct + indirect) share of expenditure on energy (home) share of expenditure on energy (mobility) share of expenditure on energy (home+mobility) Share of expenditure on indirect energy expenses (home+mobility) • Direct and indirect energy expenditure • Share of expenditure on direct energy (home+mobility) averages 10% across all income groups • Variation in home and mobility split and technology investments • As income increases, so does the indirect energy expenditure (e.g., investment in appliances, home improvements) Household Sector
  27. 27. 17-Nov-18IER University of Stuttgart 27 Characterisation of actors by investment and consumption decision-making behaviour Household sector -35% -25% -15% -5% 5% 15% 25% 35% 45% -400 -200 0 200 400 600 800 1000 1200 1400 1600 1800 Totalshareofhouseholdsper incomegroup Monthlysavings(€) Household income groups by monthly income (€) Potential to afford high upfront investment costs by income group and household composition Average household Total share of households Total share of homeowners • 45% of all households have higher than average savings(~238€ monthly) available for potential investments • 23% of all households have higher than average savings available and are home owners • The majority of households (have insufficient funds or do not have the decision-making power to invest in energy efficient and renewable upgrades and technologies (i.e., not homeowners) Household Sector
  28. 28. 17-Nov-18IER University of Stuttgart 28 TIMES energy model: simplified RES Household sector 1. Population / Income / location Energy demand 3. Income specific technologies / energy services/ measures Energy supply 4. Energy carriers • Electricity • Gas • Wood • Biomass • Solar • District heating • Petrol • Diesel • Biofuels • End-uses specific to building and income: • Lighting, cooking, refrigeration, other appliances, warm water, space heating, cooling • Additional measures: • e.g., Stromsparcheck (energy efficient appliances, behaviour) • Mobility • Population by income groups • urban/rural classification 2. Building types / tenureship • Single-family home (SFH) and Multi-family home (MFH) • Existing, renovated and new • Objective: to explore a least cost solution with maximum utility to meeting end-use energy service demand within a given framework (e.g. Energy and emissions targets) and enable policy recommendations while limiting the available budget Household Sector
  29. 29. • This methodology lays the groundwork for future research into actors’ rational behavior analysis in the energy systems. • A greater focus on designing individual policy instruments for different actors in different sectors could produce interesting findings regarding the least cost energy system transition pathways. • The TIMES Actor Model (TAM) compared in aggregated and disaggregated form with each other in order to represent the advantages of enhanced representation of the behavioral aspects. 17-Nov-18IER University of Stuttgart 29 Outlook
  30. 30. e-mail phone +49 (0) 711 685- fax +49 (0) 711 685- Universität Stuttgart Thank you! IER Institute for Energy Economics and Rational Energy Use Mohammad Ahanchian, Isela Bailey, Audrey Dobbins 87842 87873 Institute of Energy Economics and Rational Energy Use (IER) Department of Energy Economics and Social Analysis (ESA) Heßbrühlstraße 49a, 70565 Stuttgart mohammad.ahanchian@ier.uni-stuttgart.de; isela.bailey@ier.uni-stuttgart.de; audrey.dobbins@ier.uni-stuttgart.de

×