Modelling of storage processes in TIMES
-
Upload
iea-etsap -
Category
Data & Analytics
-
view
19 -
download
2
Transcript of Modelling of storage processes in TIMES
Julia Welsch, PD Dr. Markus Blesl
Institute for Energy Economics and the Rational Use of Energy
University of Stuttgart
Germany
Copenhagen, 18.11.2014
Modelling of storage processes in
TIMES-PanEU
Julia Welsch 18.11.2014 2
Structure
Introduction 1
The TIMES-PanEU energy system model 2
Modelling of storage processes in TIMES-PanEU 3
Exemplary results 4
Conclusion and outlook 5
Julia Welsch 18.11.2014 3
Structure
Introduction 1
The TIMES-PanEU energy system model 2
Modelling of storage processes in TIMES-PanEU 3
Exemplary results 4
Conclusion and outlook 5
Julia Welsch 18.11.2014
Motivation and objective
4
Motivation:
● Political included expansion of electricity generation from renewable energies in
Germany
● In consequence increasingly fluctuating feeding of electricity from wind- and
photovoltaic systems
Thus there will be occur to an increasing degree negative and fluctuating
residual loads in the future
Storage of excess electricity or curtailment
Versatile possibilities for using excess electricity
Objective:
● Determination of optimal configuration of storage- and Power-to-X-technologies
(for Germany) under minimization of total system costs
● Analysis of interactions between energy supply and energy demand with use of
Power-to-X
Julia Welsch 18.11.2014 5
Structure
Introduction 1
The TIMES-PanEU energy system model 2
Modelling of storage processes in TIMES-PanEU 3
Exemplary results 4
Conclusion and outlook 5
Julia Welsch 18.11.2014
The TIMES-PanEU energy system model
● Linear optimization model
● 30 regions (EU-28 + Norway, Switzerland)
● Time horizon: 2010 – 2050
● Mapping of the whole energy system:
i. Energy supply (electricity, heat, gas)
ii. Energy demand, divided into sectors:
1. Residential sector
2. Commercial sector
3. Agriculture
4. Industry
5. Transport
6
Julia Welsch 18.11.2014
General structure of TIMES-PanEU
7
Cost and emissions balance
GDP
Process energy
Heating area
Population
Light
Communication
Power
Person
kilometers
Freight
kilometers
Demand services
Coal processing
Refineries
Power plants,
Storage and
Transportation
CHP plants
and district
heat networks
Gas network
Industry
Commercial and
tertiary sector
Households
Transportation
Final energy Primary energy
Domestic
sources
Imports
Dem
an
ds
En
erg
y p
rices,
Reso
urc
e a
vailab
ilit
y
Julia Welsch 18.11.2014
Temporal resolution
Germany
● 224 time segments (One week
per season, three-hourly)
● Continuous temporal resolution
for mapping storage processes
8
RS
1
S F W R
…
RS
56
SS
1
…
SS
56
FS
1
…
FS
56
WS
1
…
WS
56
Milestoneyear
Coupling of timeslice trees for modelling trade processes with parameter IRE_TSCVT
Integral optimization
SD
SN
RD
S F W R
RN
RP
SP
FD
FN
FP
WD
WN
WP
Season
Annual
Daynite
Milestoneyear
Weekly
Rest of Europe
● 12 time segments
● Discontinuous temporal
resolution
Julia Welsch 18.11.2014 9
Examplary demand services RCA
Residential
Space Heat
Single Rural
Single Urban
Multi
Space Cool
Single Rural
Single Urban
Multi
Water Heat
Single Rural
Single Urban
Multi
Other
Lighting
Cooking
Refrigeration
Cloth Washing
Cloth Drying
Dish Washing
Other Electric
Other Energy
Commercial
Space Heat
Small
Large
Space Cool
Small
Large
Water Heat
Small
Large
Other
Lighting
Cooking
Refrigeration
Public Lighting
Other Electric
Other Energy
Agriculture
Julia Welsch 18.11.2014 10
Residential Other RCA
Residential
Space Heat
Single Rural
Single Urban
Multi
Space Cool
Single Rural
Single Urban
Multi
Water Heat
Single Rural
Single Urban
Multi
Other
Lighting
Cooking
Refrigeration
Cloth Washing
Cloth Drying
Dish Washing
Other Electric
Other Energy
Commercial
Space Heat
Small
Large
Space Cool
Small
Large
Water Heat
Small
Large
Other
Lighting
Cooking
Refrigeration
Public Lighting
Other Electric
Other Energy
Agriculture
Julia Welsch 18.11.2014 11
0
5
10
15
20
25
30
Other
Lighting
Cooking
Dish Washing
Cloth Drying
Cloth Washing
Refrigeration
Residential Other
Power
GW
Power
0
5
10
15
20
25
Other
Lighting
Cooking
Dish Washing
Cloth Drying
Cloth Washing
Refrigeration
GW
Julia Welsch 16.11.2014 12
Structure
Introduction 1
The TIMES-PanEU energy system model 2
Modelling of storage processes in TIMES-PanEU 3
Exemplary results 4
Conclusion and outlook 5
Julia Welsch 18.11.2014
Modelling of storage processes in TIMES-PanEU
13
Storage power and storage capacity are endogenous results of modeling
𝑃𝑅𝐶_𝐴𝐶𝑇𝑈𝑁𝑇 𝑃𝐽 𝑃𝐽 𝑃𝐽
𝑃𝑅𝐶_𝐶𝐴𝑃𝑈𝑁𝑇 𝐺𝑊 𝑃𝐽 𝐺𝑊
𝑃𝑅𝐶_𝐶𝐴𝑃𝐴𝐶𝑇
8760 𝐺𝑊ℎ
𝐺𝑊
=8760 ℎ ∙ 3600
𝑠ℎ∙ 10−6
𝑃𝑊𝐺𝑊 ∙ 𝐺𝑊
𝐺𝑊
= 31,536 𝑃𝐽
𝐺𝑊
1 𝑃𝐽
𝑃𝐽 31,536
𝑃𝐽
𝐺𝑊
Storage IN Storage Storage OUT
𝑃𝑅𝐶−𝐴𝐶𝑇𝑈𝑁𝑇: 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑜𝑓 𝑎 𝑝𝑟𝑜𝑐𝑒𝑠𝑠
𝑃𝑅𝐶−𝐶𝐴𝑃𝑈𝑁𝑇: 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝑎 𝑝𝑟𝑜𝑐𝑒𝑠𝑠
𝑃𝑅𝐶−𝐶𝐴𝑃𝐴𝐶𝑇: 𝑅𝑎𝑡𝑖𝑜 𝑓𝑟𝑜𝑚 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝑎𝑛𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦
Julia Welsch 18.11.2014 14
Two different types of storage processes in TIMES:
● Inter-Period Storage: Storage between periods (Store in and store out with
constant power over the whole period)
● Timeslice Storage: Storage between time segments within a period (in
accordance with the definition of the storage level)
● Generalized timeslice storage: Combination of timeslice storages with different
timeslice levels, STS or STK
Modelling of storage processes in TIMES
IN
Storage
OUT
Weekday
F W S
Milestoneyear
Weekend Weekday Weekend Weekday Weekend Weekday Weekend
R
Day
Nig
ht
Day
Nig
ht
Day
Nig
ht
Day
Nig
ht
Day
Nig
ht
Day
Nig
ht
Day
Nig
ht
Day
Nig
ht
Julia Welsch 18.11.2014 15
Timeslice Storage
General simplified equations (∀ 𝑟, 𝑣, 𝑡, 𝑝, 𝑐, 𝑡𝑠) :
1. Time overall equation EQ_STGTSS: 𝑉𝐴𝑅−𝐴𝐶𝑇 𝑟, 𝑣, 𝑡, 𝑝, 𝑡𝑠 = 𝑉𝐴𝑅−𝐴𝐶𝑇 𝑟, 𝑣, 𝑡, 𝑝, 𝑡𝑠 − 1 +
𝑉𝐴𝑅−𝑆𝐼𝑁 𝑟, 𝑣, 𝑡, 𝑝, 𝑐, 𝑡𝑠 − 1 – 𝑉𝐴𝑅−𝑆𝑂𝑈𝑇 𝑟, 𝑣, 𝑡, 𝑝, 𝑐, 𝑡𝑠 − 1
2. Whole storage capacity is available in every timeslice EQL_CAPACT:
𝑉𝐴𝑅−𝐴𝐶𝑇 𝑟, 𝑣, 𝑡, 𝑝, 𝑡𝑠 ∙1
𝑅𝑆−𝑆𝑇𝐺𝑃𝑅𝐷(𝑟,𝑡𝑠) ≤
𝑉𝐴𝑅−𝑁𝐶𝐴𝑃 𝑟, 𝑣, 𝑝𝑡𝑣=𝐵𝑎𝑠𝑒𝑦𝑒𝑎𝑟 + 𝑁𝐶𝐴𝑃−𝑃𝐴𝑆𝑇𝐼(𝑟, 𝑣, 𝑝) ∙ 𝑃𝑅𝐶−𝐶𝐴𝑃𝐴𝐶𝑇(𝑟, 𝑝)
Storage Level Season/
Annual Weekly Daynite
𝑹𝑺−𝑺𝑻𝑮𝑷𝑹𝑫 𝒓, 𝒕𝒔 1
8760
24 ∙ 7∙ 𝐺−𝑌𝑅𝐹𝑅(𝑟, 𝑥)
(x is directly
upstream, defined
node of weekly)
365 ∙ 𝐺−𝑌𝑅𝐹𝑅(𝑟, 𝑥)
(x is directly
upstream, defined
node of daynite)
r: Region
v: Year of commissioning
t: Current period
p: Process
c: Commodity
ts: timeslices of storage level
VAR_ACT: Storage content at the beginning of ts
VAR_NCAP: New installed capacity
NCAP_PASTI: Stock
PRC_CAPACT = 1
Julia Welsch 18.11.2014 16
Structure
Introduction 1
The TIMES-PanEU energy system model 2
Modelling of storage processes in TIMES-PanEU 3
Exemplary results 4
Conclusion and outlook 5
Julia Welsch 18.11.2014 17
Operation of electricity storage in the year
2050 without Curtailment (only Germany)
-20
0
20
40
60Spring 2050
-20
0
20
40
60Summer 2050
-20
0
20
40
60Fall 2050
-20
0
20
40
60
Winter 2050
Residual load
Storage power
Store in times of low or negative residual load (electricity price low)
Store out in times of high residual load (electricity price high)
Power
GW
Julia Welsch 18.11.2014
-20
-10
0
10
20
30
40
50
60
Fall 2050 without curtailment
Operation of electricity storage in the year
2050 with Curtailment (only Germany)
18
Lower storage capacity than in scenario without curtailment
Lower total system costs in scenario with curtailment
Power
GW
-20
-10
0
10
20
30
40
50
60
Fall 2050 with curtailment
Residual load
Storage power
Julia Welsch 18.11.2014 19
Structure
Introduction 1
The TIMES-PanEU energy system model 2
Modelling of storage processes in TIMES-PanEU 3
Exemplary results 4
Conclusion and outlook 5
Julia Welsch 18.11.2014
Conclusion and outlook
20
Conclusion:
● Need of more electricity storage with increasingly power input of fluctuating
renewable energies
● Objective value difference ca. 35 Billions Euro
Possibility of curtailment leads to a lower electricity storage capacity and lower
total system costs
Outlook:
● Analysis of Power-to-Heat, Power-to-Gas and other electricity storages
(compressed air, battery)
● Reserve power
● Differenet scenarios
● Sensitivity analysis
Julia Welsch 18.11.2014
Backup
22
Julia Welsch 18.11.2014
Modeling of storage processes in TIMES-PanEU
Energy storage Transformation processes
Electricity storage
Gas storage
Heat storage
Hot water storage Heater
Power-to-Heat
Power-to-Gas
Battery storage
Compressed air storage
Pumped storage
Hydrogen storage
Natural gas grid
Natural gas storage Fuel cell
Elektrolysis
Methanation
23