3353 Energydemandincity-regions-methods - - PowerPoint PPT Presentation

EUROPEAN INSTITUTE FOR ENERGY RESEARCH EUROPÄISCHES INSTITUT FÜR ENERGIEFORSCHUNG INSTITUT EUROPEEN DE RECHERCHE SUR L’ENERGIE EUROPEAN INSTITUTE FOR ENERGY RESEARCH

3353
 Energy
demand
in
city-regions
-
methods
 to
model
dynamics
of
spatial
energy
 consumption


Sébastien
GIRARD,
Christian
KEIM,
Markus
PETER



EUROPÄISCHES INSTITUT FÜR ENERGIEFORSCHUNG INSTITUT EUROPEEN DE RECHERCHE SUR L’ENERGIE EUROPEAN INSTITUTE FOR ENERGY RESEARCH

ECEEE
Summer
Study
 1-6
June
2009


2

Agenda
 Agenda
 Introduction to urbanisation and its dynamics Urbanisation and energy – scale and interaction

– Scales of assessment and localisation – Localisation of energy demand – Dynamics of urban development – Modelling and simulating the time and spatial evolution

• f urban spaces

Conclusion

3

Urbanisation
 Urbanisation


4

Challenging
situation
 Challenging
situation


5

Growing
urban
population
 Growing
urban
population


National Bureau of Statistics of China (BVSC), 2006 United Nations Population Division (UNPD), 2006

6

7

Building
density

in
Ile
de
France
-
Paris
 Building
density

in
Ile
de
France
-
Paris


8

Patterns
of
former
greenfield
development
 Patterns
of
former
greenfield
development


9

10

Building
stock
 Building
stock


11

Building
typologies
 Building
typologies


Institut für Wohnen und Umwelt (IWU), Darmstadt

12

Energy
standards
 Energy
standards


13

Life
cycle
analysis
of
buildings
 Life
cycle
analysis
of
buildings


Use + maintenace Use + maintenace Use + maintenace

time (years) ressource consumption & impacts energetic refurbishment new construction partial refurbishment total refurbishment deconstruction

evolution of standards

time (years) use value

end of life time initial value lost resources

Kohler, N. (2003). Lebenszyklusanalyse im Planungsprozess von Gebäuden. Darmstadt.

14

Demolition
rates
 Demolition
rates


Hassler, U. and N. Kohler (2004). Das Verschwinden der Bauten des Industriezeitalters: Lebenszyklen industrieller Baubesta¨nde und Methoden transdisziplinärer Forschung. Berlin, Wasmuth.

15

Survival
function
 Survival
function


Bradley, P., E. K. N. Buergel-Goodwin, et al. (2005). "Survival functions of building stocks and components."

16

Localisation
/
Simulation
 Localisation
/
Simulation


17

Modelling
and
simulation
 Modelling
and
simulation
 Demand and supply depend on various local characteristics Time and space simulation: land use evolution, interaction between components of the urban system Geographical Information Systems (GIS) Cellular automata Agent-based modelling (ABM)

18

CORINE
Land
Cover
(100
m*100
m)
 CORINE
Land
Cover
(100
m*100
m)


18

19 19

Infoterra
LaND
25
(25
m*25m)
 Infoterra
LaND
25
(25
m*25m)


20

Basics of spatial simulation

21

Energy
demand
mapping
 Energy
demand
mapping


21

22

Energy
demand
mapping
 Energy
demand
mapping


22

23 23

Energy
demand
mapping
 Energy
demand
mapping


24

Localisation
 Localisation


25

Sectoral
dynamics
 Sectoral
dynamics


2 Industry 3 Tertiary 4 Infrastructure 1 Habitation 0 City energy consumption about 30% about 30% about 10% about 30% 500 – 1000 m 2 - 4 km 500 – 1000 m appropriate spatial scale adaption process time scale 30-60 years 5-30 years 5-30 years 50-100 years slow based on political decisions fast based on global decisions “unified” ownership slow based on private decisions split private ownership fast based on global decisions “unified” ownership

26

Energy
typology
of
urban
space
 Energy
typology
of
urban
space


Initial situation: complex urban system Classification / Typology Energy demand Breakdown of demand per end-uses Breakdown of end- uses per energy carriers

kWh/yr Objects Objects kWh/yr Objects kWh/yr Objects kWh/yr

27

Buildings
and
processes
 Buildings
and
processes


Proc. 0% 100% Bldg. 100% 0% Energy demand Residential Tertiary Industry

28

Constitution
and
sources
of
energy
typology
 Constitution
and
sources
of
energy
typology


Developed energy typology Building typologies Existing energy typologies Economic classifi- cations Specific classifi- cations

29

Tool
development
 Tool
development


Software resources:

• AnyLogic: ABM, SD, DE,

GIS impl.

• NetLogo: ABM, GIS impl.

30

Conclusion Localisation of energy demand Methods of simulation on different scales Prognosis of urban development and resulting energy needs Integration to existing methods Communication to local authorities and spatial and energy planners

31

Thank you for your attention !

EIFER 2009 / Infas

Contact: peter@eifer.org girard@eifer.org keim@eifer.org

32

33

Cell
fuzziness
–
250x250
aggregation
 Cell
fuzziness
–
250x250
aggregation


34

35

Building City

Integrated
urban
planning
 Integrated
urban
planning


Local Planning Authority City Planner Architect Builder Energy Supply Company Energy Services Engineer HVAC Planner

Source: EIFER 2008

36

Dichteparameter als Grundlage für die Energienachfrage

Dichte

37

CONSENS - Cooperative Networks for Sustainable Energy Solutions

´

Outlook

• Simulation to predict urban development
• Moduls of Visualisation
• Complementary knowledge to urban development (EnyCity)
• Communication to planners and local authorities
• Basics for local energy planning

38

Life
cycle
model
of
urban
fragments
 Life
cycle
model
of
urban
fragments


39

The infrastructure and building stock is one of the largest physical, economical and cultural capital of European societies.

Introduction
to
urban
spaces
 Introduction
to
urban
spaces


39

40

Urban
dynamics
 Urban
dynamics


• Main
questions


Main
questions


– Which
are
the
driving
forces
for
urban
transformation?
 – Which
are
the
resulting
energy
demands?
 – Which
data
are
usable
for
the
simulations?


• Methods


Methods


– Regional
spatial
analysis
 – Model
of
the
transformation
of
building
stock
 – Possible
influences
of
transportation
 – Case-study
Metropolregion
Stuttgart
 – Definition
of
criteria
for
adaptation
and
classification
of
 existing
tools


• Simulation
methods


Simulation
methods


– Develop
an
allocation
methodology
of
energy
demand
 (data
management,
spatial
statistics)
 – Create
a
simulation
and
visualization
methodology
in
a
 forecasting
approach
(Geosimulation)
 – Develop
a
tool
in
a
regional
energetic
and
urban
 planning
framework


41

Agenda
 Agenda


• Auswirkungen
der
Stadtentwicklung
auf
die
Energienachfrage


42

Agenda
 Agenda


• Auswirkungen
der
Stadtentwicklung
auf
die
Energienachfrage


43

Research
scheme
 Research
scheme


Views Content Methods Scope of Building stocks Composition of building stocks Dynamic of Building stocks Localisation of Building stocks

buildings, job market, urban structure Land use, fallow ground, infrastructure statistics, random sample, cluster analysis statistics, random sample, ALK, aerial view Type of buildings, infrastructures Construction technique, morphology Product-modelling, building research, building history Building history, statistics, production of building material Dynamic of one building Dynamic of building stocks Alternative model, history of buildings, renovation of buildings, survival functions Building history, urban geography, survival functions, pattern analysis GIS, maps, plans, land register, ALK, aerial view Urban fragments, towns Building stocks cluster analysis, satellit views, historical research

44

Adressaten:
Levels
and

actors
 Adressaten:
Levels
and

actors
 Buildings Groups of buildings Infrastructures Towns, Neighbourhoods Actual and virtual systems

• wners, developers, users, facility managers,

planning professions, construction industry.

• wners, users, facility managers, planners,

construction industry. utility companies, state administration and technical service, planning authorities.

• wners,developers, planers, users, state

administration, political parties utility companies, IT companies, state admnistration, users

45

Demographie Urbanisierung Anforderungen Überalterung Energieversorgung Umwandlungsgeschwindigkeit Institutionelle Regimes Finanzierungsmodelle Zustand Bestand Ökosystem Altersvorsorge Kulturerbe

Kritische Bestands Parameter

46

Technologien
auf
Quartiersebene
 Technologien
auf
Quartiersebene


47

Fields
of
acting
–
what
should
we
know
 Fields
of
acting
–
what
should
we
know


48 48

Multiples
scales
of
decision
and
action
 Multiples
scales
of
decision
and
action


• Various
dimensions:


– Structural
characteristics
 – Infrastructures
 – Demographic
aspects
 – Social
aspects
 – Decision-making
and
actors,
...


• Interactions
between
components
of
the
urban
system

• Energy
balance

• Scale
effect


European Union Country Region Agglomeration City Settlement Building Individual

Action (“act local”) Decision (“think global”)

49 49

Assessing
and
implementing
energy
efficiency
in
urban
 Assessing
and
implementing
energy
efficiency
in
urban
 spaces
 spaces


• 1. Multiple
scales
of
decision
and
action

• 2. Localization
of
energy
demand

• 3. New
supply
systems
and
new
potentials

• 4. Modelling
 and
 simulation
 of
 time
 and
 spatial


dynamics
of
the
urban
space


50

Agenda
 Agenda


• Auswirkungen
der
Stadtentwicklung
auf


die
Energienachfrage


• Levels
of
implementation
of
energy


efficient
technologies
and
strategies


51

Urban dynamics and energy demand - 2009

• Prototyp Agend based modeling: urban development and influences

to the local energy demand

• Local economic drivers - integration of building stock model
• Evaluation and verification of developed simulation model
• Implementation based on DB GIS models – platform
• Scenarios for urban transformation
• Simulation modules and standards
• Energy models of economical segments and building stock
• Data mining region Stuttgart
• Contact to local authorities
• Transferability - fingerprint and indicators

52

Definition
-
Content
 Definition
-
Content


Energy efficiency is closely connected to buildings technology and urbanisation The urbanisation processes are related to possible future energy demand Understanding urbanisation processes leads to a better knowledge and possible quantification of Energy Efficiency potentials A consistent building stock model + its dynamics acts as a basis for predicting future energy demand Hereafter some examples: Simulation of urbanisation processes Localisation of the energy demand Urban morphology vs energy performances Building density vs energy efficiency

53 53

New
supply
systems
and
new
potentials
 New
supply
systems
and
new
potentials


• More
efficient
demand
impacts
energy
supply

• Implantation
 of
 traditional
 solutions
 e.g.
 natural


gas
networks
less
profitable


• Increasing
fossil
fuel
prices

• Rapid
development
of
decentralized
generation


54 54

Dynamics
of
urban
spaces
 Dynamics
of
urban
spaces


• Diffusion
of
innovations
(decentralized


generation)


• Life
cycle
analysis
on
several
scales

• Land
use
evolution