analysis in a decentralized energy system: The Transport, Industry - - PowerPoint PPT Presentation

▶
analysis in a
SMART_READER_LITE
LIVE PREVIEW

analysis in a decentralized energy system: The Transport, Industry - - PowerPoint PPT Presentation

Jan 07, 2023 266 likes •585 views

Actors behaviour analysis in a decentralized energy system: The Transport, Industry and Household Sectors Mohammad Ahanchian, Isela Bailey, Audrey Dobbins Introduction Introduction Project Decentral: TIMES Actors Model (TAM) IER

slide-1
SLIDE 1

Actors’ behaviour analysis in a decentralized energy system:

The Transport, Industry and Household Sectors

Mohammad Ahanchian, Isela Bailey, Audrey Dobbins

slide-2
SLIDE 2

17-Nov-18 IER University of Stuttgart 2

Project “Decentral”: TIMES Actors Model (TAM)

Introduction

Introduction

slide-3
SLIDE 3
  • Introduction
  • Transport sector
  • Actors’ characterizations
  • Methodology
  • Modelling
  • Industry sector
  • Actor characterization
  • Methodology
  • Household sector
  • Actor characterization
  • Methodology
  • Modelling
  • Outlook

17-Nov-18 IER University of Stuttgart 3

Agenda

Transport Sector

slide-4
SLIDE 4

17-Nov-18 IER University of Stuttgart 4

Actors and their investment options

Actors’ characteristics

Transport Sector

Maximize surplus Minimize costs Objective

Households /

  • wner

Households / tenant S-Bahn D-Bahn

  • perators

Bus U-Bahn

  • perators

Long- distance bus

  • perators

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

  • ptions

Technology specific discount rate

slide-5
SLIDE 5

17-Nov-18 IER 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.

slide-6
SLIDE 6

17-Nov-18 IER 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

slide-7
SLIDE 7

17-Nov-18 IER 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

slide-8
SLIDE 8

17-Nov-18 IER 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
slide-9
SLIDE 9

17-Nov-18 IER 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

slide-10
SLIDE 10
  • Introduction
  • Transport sector
  • Actor characterisation
  • Methodology
  • Modelling
  • Industry sector
  • Actor characterization
  • Methodology
  • Household sector
  • Actor characterization
  • Methodology
  • Modelling
  • Outlook

17-Nov-18 IER University of Stuttgart 10

Agenda

Industry Sector

slide-11
SLIDE 11

17-Nov-18 IER 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

slide-12
SLIDE 12

17-Nov-18 IER University of Stuttgart 12

Actors’ Characterization – example in the iron and steel industry

Industry sector

Industry Sector

slide-13
SLIDE 13

Standard Level of Disaggregation 17-Nov-18 IER University of Stuttgart 13

Actors’ Characterization – example in the iron and steel industry

Industry sector

Industry Sector

slide-14
SLIDE 14

Standard Level of Disaggregation 17-Nov-18 IER 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

slide-15
SLIDE 15

Standard Level of Disaggregation Data Collection (Plants) 17-Nov-18 IER 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

slide-16
SLIDE 16

Standard Level of Disaggregation Companies (Actors) Data Collection (Plants) 17-Nov-18 IER 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

slide-17
SLIDE 17

New Level of Disaggregation Standard Level of Disaggregation Companies (Actors) Data Collection (Plants) 17-Nov-18 IER 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

slide-18
SLIDE 18

146 Plants 20 Companies 14 EAF 6 BOS 2 Large 4 Small 9 Large 5 Small

Production Technology Production Capacity Actors Data Collection

17-Nov-18 IER University of Stuttgart 32

Actors’ Characterization – example in the iron and steel industry

Industry sector

Industry Sector

slide-19
SLIDE 19

Standard Representation

  • f Industrial Branches:

Electricity Heat Other Fuels

Iron and Steel

Demand Emissions

AP = Autoproduction hr = Hurdle Rate

17-Nov-18 IER University of Stuttgart 24

Methodology

Industry sector

Industry Sector

slide-20
SLIDE 20

Actor Group 1 Actor Group 2 Actor Group 3 Actor Group 4

Demand

Representation of Iron and Steel Industry in this Work:

Emissions

Standard Representation

  • f Industrial Branches:

Electricity Heat Other Fuels

Iron and Steel

Demand Emissions

AP = Autoproduction hr = Hurdle Rate

17-Nov-18 IER University of Stuttgart 24

Methodology

Industry sector

Industry Sector

slide-21
SLIDE 21

Actor Group 1 Actor Group 2 Actor Group 3 Actor Group 4

Demand

Representation of Iron and Steel Industry in this Work:

Emissions

Standard Representation

  • f Industrial Branches:

Electricity Heat Other Fuels

Iron and Steel

Demand Emissions

AP = Autoproduction hr = Hurdle Rate

17-Nov-18 IER University of Stuttgart 24

Methodology

Industry sector

Industry Sector

slide-22
SLIDE 22

hr1

Electricity Grid Other Fuels AP Heat

Actor Group 1 Actor Group 2 Actor Group 3 Actor Group 4

hr2 hr3 hr4

Demand

Representation of Iron and Steel Industry in this Work:

Heat Electricity District Heat AP Electricity CO2 Prices Emissions Heat Electricity Heat Electricity Heat Electricity

Standard Representation

  • f Industrial Branches:

Electricity Heat Other Fuels

Iron and Steel

Demand Emissions

AP = Autoproduction hr = Hurdle Rate

Decentralized Technologies 17-Nov-18 IER University of Stuttgart 24

Methodology

Industry sector

Industry Sector

slide-23
SLIDE 23
  • Introduction
  • Transport sector
  • Actor characterisation
  • Methodology
  • Modelling
  • Industry sector
  • Actor characterization
  • Methodology
  • Household sector
  • Actor characterization
  • Methodology
  • Modelling
  • Outlook

17-Nov-18 IER University of Stuttgart 23

Agenda

Household Sector

slide-24
SLIDE 24

17-Nov-18 IER 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 homogenously

Households

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

slide-25
SLIDE 25

Characterisation of actors by investment and consumption decision-making behaviour

Household sector

Household Sector 0% 5% 10% 15% 20% 25% 30% 35%

  • wners

tenants

  • wners

tenants

  • wners

tenants

  • wners

tenants SFH MFH SFH MFH Urban Rural Share of households 5000-18000 3600-5000 2600-3600 2000-2600 1500-2000 1300-1500 900-1300 <900

17-Nov-18 IER University of Stuttgart 25

slide-26
SLIDE 26

17-Nov-18 IER University of Stuttgart 26

Characterisation of actors by investment and consumption decision-making behaviour

Household sector

0% 2% 4% 6% 8% 10% 12% 14% 200 400 600 800 1000 1200 ALL <900 900-1300 1300-1500 1500-2000 2000-2600 2600-3600 3600-5000 5000-18000

Share of expenditure on direct and indirect energy Monthly expenditure € 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

slide-27
SLIDE 27

17-Nov-18 IER 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

200 400 600 800 1000 1200 1400 1600 1800

Total share of households per income group Monthly savings (€) 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

slide-28
SLIDE 28

17-Nov-18 IER 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

slide-29
SLIDE 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-18 IER University of Stuttgart 29

Outlook

slide-30
SLIDE 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