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Analysis of the role of energy storages in Germany with TIMES PanEU – methodology and results
1. IER
Analysis of the role of
energy storages in
Germany with TIMES
PanEU – methodology
and results
ETSAP Workshop Madrid
Julia Welsch, Markus Blesl
Julia
Welsch
4. Motivation:
• Political induced increase of share of renewables
• Increasingly feed in of electricity from variable renewables (wind and pv)
Occurance of a strongly fluctuating negative residual load
Increased need for flexibility options to balance supply and demand of electricity
Objective:
• Methodological improvements of the energy system model TIMES PanEU
regarding to the temporal resolution and the modelling of energy storages
• Determination of the optimal configuration of energy storages and power-to-x
under minimizing the overall system cost
• Including ESTMAP database for storages
• Analysis of the role of electricity storages within the ESTMAP project
17.11.2016 4
Introduction
5. Energy system model TIMES PanEU:
• Linear optimization model
• 30 regions (EU-28, NO, CH)
• Horizon: 2010 to 2050
• Whole energy system, from
energy supply to energy service
demand
17.11.2016 5
TIMES PanEU
Model improvement for modelling storages:
• Creation of methodologial extensions for modelling and analysis of energy storages
• Increase of the temporal resolution for Germany
• The high temporal resolution is based on representative, coherent and successive time segments
• Modelling of energy storages and power-to-x for Germany and Europe in TIMES PanEUResearch focus:
Integrated consideration of options in the sectors of electricity, heat and mobility over the
optimization period taking into account sector coupling through the use of Power-to-Heat, Power-to-
Gas and electric mobility
Supply of district
heat and electricity
Power-to-Heat
Power-to-Gas
Electricity Storage
Heat Storage
Gas Storage
Supply of
energy sources
Industry
Autoproducer
Commercial
Autoproducer
Households
Battery
Night Storage
Power-to-Heat
Heat Storage
Transport
Electric mobility
Energyservicedemand
Energysourceprices,
ressourceavailability
Agriculture
Demand
Other energy
conversion
7. 17.11.2016 7
Increase of the temporal resolution for Germany
Methodology: Temporal resolution in TIMES PanEU
RD
R
RP
RN
Milestoneyear
S F W
SD
SP
SN
FD
FP
FN
WD
WP
WN
Season
Weekly
Annual
Daynite
RS101
R
…
RS156
Milestoneyear
S F W
SS101
…
SS156
FS101
…
FS156
WS101
…
WS156FP
FPS101
…
FPS156
Coupling of timeslice trees for modelling trade
Integral optimization over all regions and modelling periods
Germany:
• 280 time segments:
• One typical week per season with three-
hourly-resolution (224 time segments)
• One additional week for representing high
feed of variable renewable energies (56 time
segments)
• Representative, coherent times segments
TIMES PanEU
• 12 time segments per year (one typical day per season with
three time steps)
• No coherent temporal resolution
8. 17.11.2016 8
Demand structure exemplary for households
Methodology: Temporal resolution in TIMES PanEU
0
10
20
30
Refrigeration Washing machine
Cloth drying Dishwasher
Cooking Lighting
Other
Gas Cooker
Electric Hot
Water
Elektric
Heating
Electric Cooker
…
0
5
Demand for Hot Water
0
5
10
Demand for Space Heat
0
10
20
Demand for Cooking
Demand for electricity of households
GW
Electricity demand and electricity
load profile are results of the
optimization and change depending
on the used application technologies
9. Sector couplingElectricity production
17.11.2016 9
Methodology: Flexibility options in TIMES PanEU
Power-to-Gas
Electric vehicles
H2,
Biogas
Electricity storage
Pump storage
Diabatic CAES
Adiabatic CAES
Stationary battery storage
Grid
Heat market
Power-to-Heat
Mobility
Mobile battery storage
Gas
Power-to-Gas
Flexibility options in the
electricty sector
DSM
Curtailment
Changed electricity
demand
Conventional
power plants
Coal
Gas
Oil
Nuclear
Renewable
power plants
Wind
PV
Geothermal
Solarthermal
Biomas
Hydro
CHP
renewable/
conventional
Grid
Power-to-Heat
Electricity trade
between countries
Changed electricity
demand in Europe
Heat storage
Natural gas storage
Hydrogen storage
Gas grid as storage
10. 17.11.2016 10
Modelling of storages as a sequence of three processes
Methodology: Modelling of energy storages in TIMES PanEU
Storage content and storage power are endogenous optimziation results
Input
Process
Capacity: Power
(GW)
Storage
process
Capacity: Energy
amount (PJ)
Output
process
Capacity: Power
(GW)
11. 17.11.2016 11
Interface ESTMAP storage database – TIMES PanEU/TIMES regional models
EXCEL database analysis input deck Creation of Pivot Tables in EXCEL
Integration of data in TIMES
Database and GIS mapping
Technical/economical characterization
Potentials
12. 17.11.2016 12
Interface TIMES regional models – PowerFys
TIMES
Energy system
modelling
PowerFys
Electrcity market
modelling
• Capacities of power and CHP plants
• Electricity and heat production of power and CHP plants
• Fuel input in power and CHP plants
• Emissions
• Electricity trade (capacities and amounts)
• Capacities of electrcity storages
• Electrcity demand by sector
14. 17.11.2016 14
Scenario definition
Scenario analysis from the ESTMAP project
Scenario assumptions
2030 2050
Base MorePV
Battery
Cost
Base MorePV
Battery
Cost
GHG reduction in the EU
(vs. 1990)
40 % 90 %
Renewables at electricity
consumption in the EU
30 % 80 %
Renewables at gross final
energy consumption in the EU
27 % 75 %
PV Capacity in Europe 120 GW 180 GW 120 GW 364 GW 546 GW 364 GW
PV Capacity in Germany 58 GW 87 GW 58 GW 70 GW 105 GW 70 GW
15. 17.11.2016 15
Electricity production by energy carrier in Germany
Scenario analysis from the ESTMAP project
-100
0
100
200
300
400
500
600
700
Statsitics
Base
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
2010 2015 2020 2025 2030 2035 2040 2045 2050
Electricity storage
(excl. pump storage)
Net Imports
Others / Waste non-
ren.
Other Renewables
Biomass / Waste
ren.
Solar
Wind offshore
Wind onshore
Hydro incl. pump
storage
Nuclear
Gas CCS
Gas w/o CCS
Oil
Lignite CCS
Lignite w/o CCS
Coal CCS
Coal w/o CCS
Net electricity
TWh
• Electrification of the energy
system until the year 2050
(achieve GHG emission targets)
• Electrification enables
integration of variable renewable
energies
• Other energy carriers can be
substituted
• With nuclear phase-out in the
year 2025 wind turbines are
increasingly used to achieve the
share of renewables
• From the year 2035 additional
lignite CCS power plants enter
the market (reduction of GHG
emissions)
16. 17.11.2016 16
Capacities in Germany
Scenario analysis from the ESTMAP project
0
50
100
150
200
250
300
350
Statsitics
Base
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
2010 2015 2020 2025 2030 2035 2040 2045 2050
Electricity storage
(excl. pump
storage)
Others / Waste
non-ren.
Other Renewables
Biomass / Waste
ren.
Solar
Wind
Hydro incl. pump
storage
Nuclear
Natural gas
Oil
Lignite
Coal
Capacity
GW
• Increase of the
capacities is higher
than the increase of
the overall electricity
production
• Lower full load hours
of pv and wind plants
17. 17.11.2016 17
Electricity storages in Germany
Scenario analysis from the ESTMAP project
0
5
10
15
20
25
Statistics
Base
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
2010 2015 2020 2025 2030 2035 2040 2045 2050
Battery stationary Redox
Flow
Battery stationary Lead
Acid
Battery stationary
Lithium Ion
CAES adiabatic cavern
CAES adiabatic tank
CAES diabatic cavern
Pump Storage
Storage input capacity
GW
0
5
10
15
20
25
30
Statistics
Base
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
20102015 2020 2025 2030 2035 2040 2045 2050
Battery stationary Redox
Flow
Battery stationary Lead Acid
Battery stationary Lithium
Ion
CAES adiabatic cavern
CAES adiabatic tank
CAES diabatic cavern
Pump Storage
Storage output capacity
GW
• The increased
fluctuating supply of
electricity from wind
and pv systems leads to
a higher flexibility need
in the whole energy
system
• Therefore new
electricity storages
(especially stationary
lithium ion batteries in
combination with pv)
are built from the year
2045 onwards in
Germany
18. 17.11.2016 18
Electricity storages in Germany
Scenario analysis from the ESTMAP project
0
30
60
90
120
150
180
Statistics
Base
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
Base
MorePV
BatteryCost
20102015 2020 2025 2030 2035 2040 2045 2050
Battery stationary Redox
Flow
Battery stationary Lead
Acid
Battery stationary Lithium
Ion
CAES adiabatic cavern
CAES adiabatic tank
CAES diabatic cavern
Pump Storage
Storage content
GWh
It can be seen, that the ratio of storage content and power is lower for the battery
storage than for the pump storage. This is due to the lower content-specific
investment costs with higher power-specific investments of pumped storage.
19. 0
20
40
60
80
100
120
140
160
180
200
2010 2015 2020 2025 2030 2035 2040 2045 2050
Stored electricity
Electricity electric cars
Increased electricity demand
Decreased electricity demand
Load shifting in time segment with negative
residual load
Curtailment in time segments with negative
residual load
Net export in time segments with negative
residual load
Electricity Power-to-Gas in time segments
with negative residual load
Electricity Power-to-Heat in time segments
with negative residual load
17.11.2016 19
Flexibilisation of electricity in Germany for the MorePV scenario
Scenario analysis from the ESTMAP project
TWh
Flexibilisation
• In order to achieve a high share of renewables in electricity consumption in Germany, less curtailment is
used in 2050
• The amount of electricity which is not curtailed is instead stored in electricity storage or used by Power-
to-Heat or Power-to-Gas installations (sector coupling)
21. 17.11.2016 21
Conclusions and outlook
Conclusions:
• Comparing the results for Germany to the ones from the TIMES PanEU model it can
be seen, that a big part of new energy storages in Europe are built in Germany.
• This results are not only attributable to the amount of potential storage sites from
the ESTMAP database, but also due to the model design with a higher temporal
resolution only of the German region.
• With a lower temporal resolution the storage need in the energy system is generally
underestimated. The high flexibility need, which results from peaks and valleys of
PV and wind production, cannot be mapped adequately in a model with a temporal
resolution of only a few typical days.
• New storages after 2040, other flexibility options are important model components
Outlook:
• Outcomes of energy system analysis depend on storage technology parameters,
such as cost projections, so that a sensitivity analysis could be useful
• Higher temporal resolution for other countries – model handling
22. Thank you!
E-Mail
Phone +49 (0) 711 685-
Fax +49 (0) 711 685-
University of Stuttgart
Heßbrühlstraße 49a
70565 Stuttgart
Germany
Julia Welsch
87848
87873
Institute for Energy Economics and the
Rational Use of Energy (IER)
julia.welsch@ier.uni-stuttgart.de