Energy Planning and Modelling
Wolfgang Eichhammer
Fraunhofer Institute for Systems and Innovation
Research (ISI)
TUEWAS Energy Efficiency Working Group
Workshop Chongqing/China
17 – 20 December 2012
Working Group Meeting
18 December 2012
Overview
 What should be an ideal energy mix, integrating
energy efficiency and renewable energies?
 Energy modelling at macro level (Overall
economy) as well as at micro level (industrial
setups)
 Importance of energy planning and energy mix
modelling for efficient economies
Part I
What should be an ideal energy mix, integrating energy
efficiency and renewable energies?
 An ideal energy mix is vision with a concrete
policy strategy behind and highly developed
analytical tools to underpin the policy strategy.
How may the energy efficiency and renewables mix look like in
the EU in 2050?
 December 2011: EU Energy Roadmap 2050 published which
sets the scene for the European energy and climate policy up
to 2050
 All sectors need to contribute to the target of 85% GHG
emission reduction compared to 1990 (93-99 % for the power
sector).
 Discussion around the forthcoming Directive for Energy
Efficiency
 Role of energy efficiency insufficiently addressed in detail 
EU-wide study on Energy Efficiency Wedges for the German
Environment Ministry by Fraunhofer ISI up to 2050
(www.isi.fhg.de/isi-en/e/projekte/bmu_eu-energyroadmap_315192_ei.php)
Motivation: The 2020 policy frame for energy efficiency needs to
fit longterm requirements


Electricity in 2050 most likely most important energy carrier
Energy efficiency has a strong impact on how radically the electricity
sector needs to be adapted  EU-wide study on (nearly) 100%
renewables in the power sector in 2050 by Fraunhofer ISI
(www.isi.fhg.de/isi-en/x/projekte/314587_bmu-langfristszenarien.php)
+ 550 TWh net
electricity
demand
Potential presentation via factsheets
 Technical energy saving potential
 Cost curve on EU level
 General information
 Technology information
 Energy efficiency technologies
 Calculation methodology
Sectoral energy savings are assigned to specific end-uses and
efficiency technologies
Exemplary results: final energy saving potential by branch in the transport sector
European final energy demand can be more than halved by the year
2050.
2008
2030
2050
Reference
[Mtoe]
1161
1216
1183
Potential
[Mtoe]
-
502
671
Reduction
[%]
-
-41%
-57%
Overall primary energy saving potential in 2050 equals 118 percent of
all EU’s energy imports in the year 2008
Primary energy saving potential
A stabilisation of the European electricity demand on today’s level is
feasible
Assumptions
Reference demand is based on
PRIMES 2009 + extrapolation
 nearly 70 TWh for electric heat
pumps in the households and
tertiary sector (approx. 12% of all
European households)
60 TWh for about 23 million
electric vehicles (8 percent of the
car stock)
In contrast: the electrification of
66% of all passenger cars would
require approx. 260 TWh
additionally
EU27, [TWh]
2020
2030
2050
PRIMES baseline
3,480
3,803
3,969
Remaining load
3,020
3,102
2,485
Savings
13%
18%
37%
Benefits and costs of economic and
technical energy saving potentials
(ALMOST) 100% RENEWABLE ENERGY
IN THE ELECTRICITY SECTOR BY 2050
© Fraunhofer ISI
Enabling technologies
 Grids
 Flexible generation
 Energy efficiency
Introduction to the scenario
Quick facts
Main goals of the study
1) Detailed picture of possible developments in the electricity sector with low
carbon emissions
2) Analyzing the co-benefits of energy efficiency and RES in one scenario
Main parameters
 Region: EU 27 +2
 CO2 emissions in 2050: 5% of 1990 level
 No new CCS and nuclear
 RES-E share >90%
 Low electricity demand (high efficiency), similar to the one today (~3,500 TWh
in 2050)
Scenario Approach
Step 1
Step 2
Definition of
external parameters
Development of
RES-E
• Hourly electricity
demand
• Fuel prices
• CO2 prices
• Emission reduction
• Model:
PowerACE-ResInvest
• Output:
RES-E capacity
Step 3
Feed-in profiles for
photovoltaic and
wind power
Model:
ISI-Wind, ISI-PV
• Output:
Hourly generation
profiles
Calibration
Step 4
Optimization of the power sector
• Model:
PowerACE-Europe (applying least cost optimization)
• Output:
Capacities and dispatch of: conventional power plants, interconnectors, electricity
storages
Introduction to the scenario
Approach
Photovoltaics
 Hourly satellite weather data for
2006-08 for several hundred points
 Model takes into account module
types, orientation, temperature, ...
•Wind power
– Data for over 3,000 weather stations for
–
2006-08
Model takes into account turbine types,
roughness lengths, air density, ...
Introduction to the scenario
Results
•Scenario reaches a RES-share of 95 %
– ~ 50 % Wind power
– ~ 10 % photovoltaics
Over 60% fluctuating generation
Only a small share of the generation is
dispatchable
How can this system be stable and
reliable?
What are the main enablers?
Gas turbine
1%
Storage facilities
2%
CCGT
5%
Other RES
4%
Wind (onshore)
33%
Hydro
16%
Biomass, biogas &
waste
12%
Photovoltaics
10%
Wind (Offshore)
17%
Enabling technologies
Hourly dispatch, Germany 2050, week 42
Enabling technologies
Electricity grids
NTC capacity increases from currently
56 GW to 252 GW in 2050.
 increase by factor 4.5
Enabling technologies
Electricity grids
High RES-E shares call for
higher European integration
– Energy surplus will have to
be transported over larges
distances.
– Demand will often be met
by generation from nonnational sources (RES-E
and conventional power).
Open questions
– Can the grids be developed
fast enough?
– Is this higher level of cooperation politically
possible?
Power flows per year between countries
700 TWh/a
600
500
400
300
200
100
0
2008
2020
2030
2040
2050
Enabling technologies
Dispatchable generation
Enabling technologies
Dispatchable generation
Flexible generation has to be
increased
– Biomass, biogas and
–
hydropower are needed to
provide flexible, carbon neutral
generation
The utilization of gas power
plants will decrease despite the
growth of installed capacity
Open questions
– Which market design generates
–
the necessary security for
investors?
How much biomass will really
be available in the power
sector?
Installed capacity of
dispatchable generation units
700 GW
Storages
600
Natural gas
44
44
Hydro
500
400
49
Biogas
Biomass
44
311
314
236
37
183
300
180
200
178
179
179
11
32
17
43
21
50
22
56
2020
2030
2040
2050
172
100
153
2
13
2008
Enabling technologies
Energy efficiency
Energy efficiency is a key measure as it:
– Decreases total system costs
– Decreases costs per MWh
– For meeting a higher demand , the supply
Gas turbine
1%
portfolio becomes more difficult to handle.
Higher demand  higher shares of
fluctuating generation  higher system
integration costs
Storage facilities
2%
CCGT
5%
Other RES
4%
Wind (onshore)
33%
Hydro
16%
Open questions
– How can energy efficiency (finally) be
achieved?
Biomass, biogas &
waste
12%
Photovoltaics
10%
Wind (Offshore)
17%
Part II
Energy modelling at macro level (Overall economy) as well as
at micro level (industrial setups)
Modell Families at Fraunhofer ISI: Technology-based Analyses,
sectoral and Macro-economic Analyses
Macro-economic Models - General Equilibrium Model (DYE-CLIP)
- Econometric Model(ASTRA (N))
Micro-Macro-Bridge
Energy demand/
Projections (FORECAST
platform: ISI-Industry,
Tertiary,
Households)/Transport
E-Mobily
Models
Energy system models
- Optimisation (Balmorel)
CO2Certificatemodel
- Multi-Agent-Simulation
(PowerACE, ResInvest)
(ClimStrat, Shortterm prognosis)
Windsimulationmodel
GIS
Part III
Importance of energy planning and energy mix modelling for
efficient economies
Scenario Analysis
Electricity System EUMENA 2050
 Optimal path for the electricity sector in the EUMENA region up to 2050
 Recommendation of suitable policy instruments for the implementation of
the path to 2050
 Mutual advantages of an interlinked system of the EUMENA region based
on renewable energy sources:
 CO2-Target of 95% in the electricity
sector and savings of €30/MWh imported
electricity.
 MENA countries can build up in parallel
their own electricity supply system with
renewables and lower CO2-emissions
from the electricity sector by 50% despite
a massive increase in demand.
 MENA countries can build up an expert
industry in the order of 63 Billion €.
Structure of the analysis
Modelling
Case studies
Current and
potential future
RES policy
development in
MENA
Extended GIS Analysis
Cost-potential data (incl.
geographic reference)
Possible European
RES policy
pathways
Power ACE
Reference
electricity
prices
Green-X
RES
deployment
& cost
Support
design
Support
cost
Assessment of future regulatory
compatibility
Policy recommendations
Design elements
General
Flow of
support
Eligibility of
technologies
Eligibility
of plants
Premium
Type of
premium
Duration of
support
Cost burden
of support
Support
differentiation
Cap /
Floor
Support
scheme
Support level
adjustment
Cost
containment
mechanism
Banking/
Borrowing
Minimum/
Maximum prices
Complementary
Instruments
Quota
Obliged Party
Modelling Approach
 Optimization of the electricity sector:
– Powerplant construction and dispatch
– Interconnectors between countries
– RES-E generation
–
–
Additional features after first scenario study:
• PV, Wind onshore, Wind offshore, CSP
Construction and dispatch of storages •High temporal resolution for entire year
•High level of detail in RES-Potential Curves
Limited CO2 emissions
 Year: 2050, Time period: 8760 h
 Meterological Reference dataset: 2007
 Hydro, Biomass, “Others” are fixed:
•Improved datasets
•Additional cost parameters can be
added
– “Energy Road Map”
– National Renewable Energy Action Plans are taken into account
– Dii-Hydro data
 Additional Gasturbines: 10% of national peak load
3/295
Grid Length
18/295
Morocco, Calender Week 32 (2050)
Saudi Arabia, Calender Week 48 (2050)