Can the decentralized CHP generation provide the flexibility required to integrate intermittent RES in the electricity system?

1. Wir schaffen Wissen – heute für morgen
24. Juni 2015PSI, 24. Juni 2015PSI,
Paul Scherrer Institut
Can the decentralised CHP generation provide the
flexibility required to integrate intermittent RES in the
electricity system?
Evangelos Panos , Kannan Ramachandran
67th Semi-Annual ETSAP Workshop, Abou Dhabi

2.  Swiss energy system & Swiss energy strategy to 2050 objectives
 The concept of dispatchable biogenic CHPs (electricity-driven)
 Extensions on Swiss Times Electricity Model (STEM-E)
 Challenges in modelling
 Preliminary results from model testing
 Conclusions
Introduction
24.06.2015PSI, Seite 2

3. Net Imports Hydro Nuclear
Thermal Renewables
Electricity sector
Industry
Transport
Residential
Services
-2
40
25
3 1
 Electricity production: 66 TWh
Swiss energy system in 2013
24.06.2015PSI, Seite 3
 Final energy consumption: 896 PJ
 CO2 emissions 41 Mtn:
0
50
100
150
200
250
300
350 Renewables
Heat
Electricity
Gas
Oil
Wastes
Coal
Wood
4
5
17
10
5
 Energy Strategy 2050 key objectives:
 Enhancement of energy efficiency
 Unlocking new RES (wind, solar, biomass)
 Withdrawal from nuclear energy
 Imports and fossil to meet residual
electricity demand
 Extension of electricity grid

4. Dams south, Nuclear north
24.06.2015PSI, Seite 4

5. Swiss grid congested lines
24.06.2015PSI, Seite 5
Source: Swissgrid
2/3 of the grid was built between 1950 and 60s with focus on
ensuring regional supplies from nearby plants

6. Gaseous fuel from waste biomass
Wood
Air
Heat storageEnergy conversion
with thermal
machine Electricity grid
 Combined Heat and Power plant
 Specifications for grid stabilisation:
 Fast response power generation
 Temporal independent production of
electricity and heat
 A contractor (electricity utility)
could operate a CHP swarm by
remote:
 Dispatchable, scalable and
decentralised power plant
 Balancing power is sold at high price
levels on the market
The concept of biogenic CHP Swarm
24.06.2015PSI, Seite 6
Running project ETH/PSI sponsored by BFE and Swisselectric Research

7.  A biogenic flexible CHP can participate in the following markets:
a) Electricity supply, on-site or distributed
 competition with power plants
 competition with electricity grid price
b) Heat supply for space and water heating, on-site or distributed
 competition with boilers and heat pumps
 competition with district heating networks
c) Provision of grid balancing services at a grid distribution level:
 competition with on-site solutions e.g. batteries
 competition with services provided by pump hydro, PtG, etc.
 However, the technology is resource-constrained:
 it uses biogas or upgraded biogas injected to gas grid
 needs access to gas pipelines
 competition with other biogas uses
Assessing the potential of CHP Swarms
24.06.2015PSI, Seite 7

8.  To implement biogenic flexible CHPs in TIMES, to assess its
potential and to identify barriers and competitors we need in the
model at least:
 Representation of decentralised power generation
 High time resolution to account for demand and resource fluctuations
 Introduction of dispatchability features (e.g. ramp-up constraints)
 Representation of heat supply and demand sectors
 Representation of electricity & heat storage technologies
 Representation of alternatives, such as power-to-gas pathways
 Representation of bio-methane production options
 Representation of demand for balancing services
Challenges in TIMES modelling
24.06.2015PSI, Seite 8

9. STEM-E:
 Swiss Electricity Model
 288 time slices:
4 seasons X 3 days X 24h
 Exogenous electricity
demand linked to economic
activity
 Electricity load profiles
 Different types of power
plants
 Resource potentials
Swiss Times Electricity Model
24.06.2015PSI, Seite 9
STEM-HE:
 All STEM-E plus:
 Decentralised generation
 Heat demands & load profiles
 Heat supply options
 Storage for electricity & heat
 Power-to-gas / gas-to-power
 Upgraded biogas production
 Ramping constraints
 Balancing services
FROM STEM-E to STEM-HE2012 2014

10. Representation of decentralised sector
24.06.2015PSI, Seite 10
Very High Voltage
Grid Level 1
Nuclear
Hydro Dams
Pump Storage
Imports
Exports
Distributed
Generation
Run-of-river hydro
Gas Turbines CC
Gas Turbines OC
Geothermal
High Voltage
Grid Level 2
Large Scale
Power Generation
Medium Voltage
Grid Level 3
Wind Farms
Solar Parks
Oil ICE
Waste Incineration
District Heating
CHPs
Wastes, Biomass
Oil
Gas
Biogas
H2
Low Voltage
Distribution Grid
Large Industries &
Commercial
CHP oil
CHP biomass
CHP gas
CHP wastes
CHP H2
Solar PV
Wind turbines
Commercial/
Residential
Generation
CHP gas
CHP biomass
CHP H2
Solar PV
Wind turbines
 4 different grid voltage levels are represented
 Allows for compensation of RES generation that is fed into grid
 Similar structure with TIMES-PET (and perhaps with other models)
 Not always straightforward assignment of power plants to each level

11.  To reduce complexity the focus is on heat that can be supplied by
CHPs
 Space and water heating in buildings and commercial sectors
 Differentiation between different types of houses
 Two classes of heat in industrial sectors: <500 oC, >500 oC
Heat demand sector
24.06.2015PSI, Seite 11
Residential Sector
Single Family Multi Family
Space
Heating
Existing
buildings
Space
Heating
New
buildings
Space
Heating
Existing
buildings
Space
Heating
New
buildings
Water
Heating
Industry
Heat
>500 o
C
(CHPs NO)
Services
Space
Heating &
Water
Heating
Heat Supply Technologies
(boilers, resistors, heat pumps, night storage devices, Decentralised CHP)
Heat
<500 o
C
(CHPs YES )

12.  Statistical estimation of consumer behaviour from surveys [1]
 Data reconciliation to minimise the deviation between actual and
calculated annual heat demand from the profiles:
Heat demands and load profiles
24.06.2015PSI, Seite 12
HDD
Annual
Demand
Outside
temperature
Typical Daily
Profile
% of seasonal
demand
% of demand per
typical day
% demand
for 288 typical hours
Adjustment of
electricity load curve
min 𝑤1,𝑡 𝐷𝑡 − 𝐹𝑡
2
+ 𝑤2,𝑡 𝑥 𝑡𝑠 − 𝑦𝑡𝑠
2
𝑡𝑠𝑡
𝐹𝑡 𝑥1 … . 𝑥 𝑛 = 0
𝐶 𝑘 𝑥1 … . 𝑥 𝑛 = 0
𝒘 weights, user defined
𝒚 Initial hourly profiles
𝒙 Adjusted hourly profiles
𝑭 Calculated annual heat demand
𝑫 Actual annual heat demand
𝑪 Other constraints that must hold
Single family houses
Multi family houses
Commercial
Industry

13.  Space heating: morning peak
followed by a long day-time
plateau and a smaller evening
peak
 Water heating: sharp variations
depending on use
Examples of obtained heat profiles
24.06.2015PSI, Seite 13
Existing single family houses – Space heating in PJ
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
H01
H02
H03
H04
H05
H06
H07
H08
H09
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20
H21
H22
H23
H24
SPR-WK
SUM-WK
FAL-WK
WIN-WK
Existing multi family houses – Space heating in PJ
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
H01
H02
H03
H04
H05
H06
H07
H08
H09
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20
H21
H22
H23
H24
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
H01
H02
H03
H04
H05
H06
H07
H08
H09
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20
H21
H22
H23
H24SPR-WK
SUM-WK
FAL-WK
WIN-WK
All households– Water heating in PJ

14. Representation of heat systems [2]
24.06.2015PSI, Seite 14
Heat supply option 1
Heat supply option 2
Heat supply option N
Heat storage
Heat System
𝑎 𝑝,𝑡 ∙ 𝑋 𝑝,𝑡
𝑁𝐶𝐴𝑃
+𝑏 𝑝𝑝,𝑡 ∙ 𝑋 𝑝𝑝,𝑡
𝑁𝐶𝐴𝑃
= 𝑐1,𝑡
∀𝑝 ∈ 𝑃𝑃𝑅𝐼 ; 𝑝𝑝 ∈ 𝑃𝑆𝐸𝐶∪𝑆𝑇𝐺 ; 𝑡 ∈ 𝑇 ; 𝑡𝑠_𝑝𝑒𝑎𝑘 ∈ 𝑇𝑆 ; 𝑎 𝑝,𝑡, 𝑏 𝑝,𝑡, 𝑐1,𝑡, 𝑐2,𝑡, 𝑐3,𝑡 const
𝑎 𝑝,𝑡 ∙ 𝑋 𝑝,𝑡
𝑁𝐶𝐴𝑃
+𝑏 𝑝𝑝,𝑡
𝑀𝐴𝑋
∙ 𝑋 𝑝𝑝,𝑡
𝑁𝐶𝐴𝑃
≤ 𝑐2,𝑡
𝑎 𝑝,𝑡 ∙ 𝑋 𝑝,𝑡
𝑁𝐶𝐴𝑃
+𝑏 𝑝𝑝,𝑡
𝑀𝐼𝑁
∙ 𝑋 𝑝𝑝,𝑡
𝑁𝐶𝐴𝑃
≥ 𝑐3,𝑡
Fixed capacity ratio: e.g. solar thermal and gas
boiler
Upper and lower bounds on capacity ratios: e.g.
micro CHP and gas boiler
𝑋 𝑝,𝑡
𝐶𝐴𝑃
𝑝∈𝑃 𝑃𝑅𝐼
≥ 𝑑𝑒𝑚𝑎𝑛𝑑 𝑡,𝑡𝑠_𝑝𝑒𝑎𝑘
Match demand capacity requirement with the
capacity of the primary heat supply systems
 Combinations of primary and secondary heating systems is possible via
user constraints:
Heat demand

15. Avoiding technology mix in heat supply
24.06.2015PSI, Seite 15
H01 H03 H05 H07 H09 H11 H13 H15 H17 H19 H21 H23
Wood boiler
Solar thermal
Gas boiler
District heating
Heat from CHP on-site
Oil boiler
Heat Pump
Electric boiler
Coal boiler
H01 H03 H05 H07 H09 H11 H13 H15 H17 H19 H21 H23
Wood boiler
Solar thermal
Gas boiler
District heating
Heat from CHP on-site
Oil boiler
Heat Pump
Electric boiler
Coal boiler
 No individual technology
optimisation is applicable for
heat supply systems:
a) Go for MIP by ensuring that only
one heat system will supply a
heat demand class [2] (not
directly supported in TIMES)
OR:
b) Introduce a utilisation curve for
each heat supply system with
NCAP_AF(UP) and ACT_UPS(FX)
in accordance with the demand
curve

16.  Primary reserves react to frequency deviations within 30 seconds
 Secondary reserves are activated just slightly after the primary reserves
and maintain a balance between generation and demand within each
balancing area (duration from 1 to 60 minutes)
 Tertiary reserves are called only after secondary control has been used
for a certain duration, to free the secondary reserves for other purposes
Introducing balancing services
24.06.2015PSI, Seite 16
Secondary reserves
Secondary reserves
Tertiary reserves
Source: Swissgrid

17.  Each power plant is producing 4 additional commodities related
to the primary & secondary upward and downward reserves
 The set of the attributes of a power plant is augmented by:
 Its minimum stable operation
 The % of total capacity available for primary and secondary upward
and downward reserves
 The ramp-up and ramp-down rates
 We can also provide the % of upward reserve met by online
plants to avoid unrealistically provisions of upward reserves from
offline technologies
 The additional equations for balancing services:
 Can be introduced as UC (takes time to enter the constraints in EXCEL)
 Can be implemented as TIMES extension in GAMS (prone to errors)
Porting OSEMOSYS methodology [3]
24.06.2015PSI, Seite 17

18.  Each power plant eligible for participating in the balancing
markets is divided into two parts
 A capacity transfer UC ensures that the capacity-related costs are
paid only once:
 Demand for balancing services: 3 ∗ 𝜎 𝐷
2
+ 𝜎 𝐺
2
+ 𝐾
Porting OSEMOSYS methodology
24.06.2015PSI, Seite 18
𝑋 𝑝,𝑡
𝐶𝐴𝑃
= 𝑋 𝑝𝑝,𝑡
𝐶𝐴𝑃
Power Plant p
(participation in the
electricity market)
Power Plant pp
(participation in the
balancing markets)
ELC
Electricity demand
Primary upward reserve
demand
Primary downward
reserve demand
Secondary upward
reserve demand
Secondary downward
reserve demand
PU
PD
SU
SD
PU_DEM
PD_DEM
SU_DEM
SD_DEM
ELC_DEM

19. 𝑋 𝑝,𝑡,𝑡𝑠
𝐶𝐴𝑃𝑂𝑁
= 𝑋 𝑝,𝑡,𝑡𝑠
𝐸𝐿𝐶
/(𝑐𝑎𝑝𝑎𝑐𝑡 𝑝 ∙ 𝑦𝑟𝑓𝑟𝑡,𝑡𝑠) :online capacity of process 𝑝
Downward reserve:
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑘
≤ 𝑋 𝑝,𝑡,𝑡𝑠
𝐶𝐴𝑃𝑂𝑁
∙ 𝑚𝑎𝑥 𝑘,𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝𝑝 : 𝑘 ∈ 𝑃𝐷, 𝑆𝐷
Upward reserve for a fast ramping plant: (𝐦𝒂𝒙 𝒌,𝒑𝒑 ≥ 𝒔𝒕𝒂𝒃𝒍𝒆𝒐𝒑 𝒑𝒑)
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑘
≤ 𝑋 𝑝𝑝,𝑡
𝐶𝐴𝑃
∙ 𝑎𝑓𝑝𝑝,𝑡,𝑡𝑠 ∙ 𝑚𝑎𝑥 𝑘,𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝𝑝: 𝑘 ∈ 𝑃𝑈, 𝑆𝑈
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑃𝐷
+ 𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑆𝑈
≤ 𝑋 𝑝,𝑡,𝑡𝑠
𝐸𝐿𝐶
𝑋 𝑝,𝑡,𝑡𝑠
𝐸𝐿𝐶
≤ 𝑋 𝑝,𝑡,𝑡𝑠
𝐶𝐴𝑃𝑂𝑁
∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝
Upward reserve for a slow ramping plant: ( 𝒎𝒂𝒙 𝒌,𝒑𝒑 ≤ 𝒔𝒕𝒂𝒃𝒍𝒆𝒐𝒑 𝒑𝒑)
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑘
≤ 𝑋 𝑝,𝑡
𝐶𝐴𝑃𝑂𝑁
∙ 𝑎𝑓𝑝𝑝,𝑡,𝑡𝑠 ∙ 𝑚𝑎𝑥 𝑘,𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝𝑝: 𝑘 ∈ 𝑃𝑈, 𝑆𝑈
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑃𝐷
+ 𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑆𝑈
+ 𝑋 𝑝,𝑡
𝐶𝐴𝑃𝑂𝑁
∙ 𝑠𝑡𝑎𝑏𝑙𝑒𝑜𝑝 𝑝𝑝 ∙ 𝑐𝑎𝑝𝑎𝑐𝑡_𝑝 ≤ 𝑋 𝑝,𝑡,𝑡𝑠
𝐸𝐿𝐶
𝑋 𝑝,𝑡,𝑡𝑠
𝐸𝐿𝐶
+ 𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑃𝐷
+ 𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑆𝑈
≤ 𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝐶𝐴𝑃𝑂𝑁
∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝𝑝
Key equations for balancing services [3]
24.06.2015PSI, Seite 19

20. Minimum online upward reserve ( 𝑘 ∈ 𝑃𝑈, 𝑆𝑈 )
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑘
𝑝𝑝 ≥ 𝐷𝐸𝑀 𝑘,𝑡,𝑡𝑠 ∙ 𝑚𝑖𝑛𝑜𝑛𝑙𝑖𝑛𝑒𝑡
All reserve from online plants when m𝑎𝑥 𝑘,𝑝𝑝 ≤ 𝑠𝑡𝑎𝑏𝑙𝑒𝑜𝑝 𝑝𝑝 :
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑘
= 𝑋 𝑝,𝑡,𝑡𝑠
𝑂𝑁𝐿𝐼𝑁𝐸 𝑘
∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝
Share of reserve from online plants when m𝑎𝑥 𝑘,𝑝𝑝 ≥ 𝑠𝑡𝑎𝑏𝑙𝑒𝑜𝑝 𝑝𝑝 :
𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑘
≥ 𝑋 𝑝,𝑡,𝑡𝑠
𝑂𝑁𝐿𝐼𝑁𝐸 𝑘
∙ 𝑐𝑎𝑝𝑎𝑐𝑡 𝑝
Upward reserve is limited by online capacity minus power output:
𝑋 𝑝,𝑡,𝑡𝑠
𝐶𝐴𝑃𝑂𝑁
− 𝑋 𝑝,𝑡,𝑡𝑠
𝐸𝐿𝐶
≥ 𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑂𝑁𝐿𝐼𝑁𝐸 𝑃𝑈
+ 𝑋 𝑝𝑝,𝑡,𝑡𝑠
𝑂𝑁𝐿𝐼𝑁𝐸 𝑆𝑈
Upward reserve by online plants limited by their max contribution to
upward reserve:
𝑋 𝑝,𝑡,𝑡𝑠
𝐶𝐴𝑃𝑂𝑁
∙ 𝑚𝑎𝑥 𝑘,𝑝𝑝 ≥ 𝑋 𝑝,𝑡,𝑡𝑠
𝑂𝑁𝐿𝐼𝑁𝐸 𝑘
Key equations for balancing services [3]
24.06.2015PSI, Seite 20

21.  The related to this work project is currently running and we are
still integrating information from our partners regarding grid
constraints, balancing services, CHP technology characterisation
and biomass resource potentials
 “Reference” scenario assumptions used to test the model:
 Based on the “POM” scenario of Swiss energy strategy 2050,
implementing strong efficiency measures
 Fuel prices from IEA ETP 2014, translated to Swiss border pre-tax
prices
 Nuclear phase out to be completed by 2034
 No CCS and no coal in electricity generation
 CO2 price rises to 58 CHF/t CO2 in 2050
 Solar potential: ~10 TWh, Wind potential: ~3 TWh, Hydro: ~40 TWh
Preliminary results from model testing
24.06.2015PSI, Seite 21

22. Energy service demands in PJ
Forecast of demands
24.06.2015PSI, Seite 22
0
100
200
300
400
500
600
2010 2012 2015 2020 2030 2040 2050
Residential thermal
Residential electric
specific
Services thermal
Services electric specific
Industry thermal
Industry electric specific

23. (excl. transport)
Final energy consumption in PJ
24.06.2015PSI, Seite 23
0
100
200
300
400
500
600
700
2010 2020 2030 2040 2050
Hydrogen
Biogas
Solar
Wastes
Wood
Heat *
Coal
Natural gas
Heavy fuel oil
Light fuel oil
Electricity

24. Share of technologies in residential heat
24.06.2015PSI, Seite 24
0%
10%
20%
30%
40%
50%
60%
2010 2020 2030 2040 2050
Heat pumps
Heat *
Gas boilers
Oil boilers

25. Operational profiles of heat technologies
24.06.2015PSI, Seite 25
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
H01
H03
H05
H07
H09
H11
H13
H15
H17
H19
H21
H23
Wood/Pelletts
Gas
Heatradiators
Oil
Heatpumps
Electricboilers/resistors
Solar
Singlefamilyhouses
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
H01
H03
H05
H07
H09
H11
H13
H15
H17
H19
H21
H23
Wood/Pelletts
Gas
Heatradiators
Oil
Heatpumps
Electricboilers/resistors
Solar
Multifamilyhouses

26. Storage technologies for heat in services
24.06.2015PSI, Seite 26
0
1'000
2'000
3'000
4'000
5'000
6'000
1 3 5 7 9 11 13 15 17 19 21 23
Total Heat
Demand
-120
-100
-80
-60
-40
-20
0
20
40
1 3 5 7 9 11 13 15 17 19 21 23
Thermochemical
Storage of CHP heat
Nightboilers

27. Electricity generation sector
24.06.2015PSI, Seite 27
0
2
4
6
8
10
2010 2020 2030 2040 2050
Solar
Wind
Wood &
Biogas
-10
0
10
20
30
40
50
60
70
80
2010 2012 2015 2020 2030 2040 2050
Solar
Wind
Geothermal
Wastes
Wood
Biogas
Gas
Nuclear
Hydro
Pump storage
Net Imports
Electricity production by fuel in TWh Electricity
production from
renewables in TWh

28. CHP Swarms (capacity & operation profile)
24.06.2015PSI, Seite 28
2020 2030 2040 2050
Capacity (MW) 109 185 124 56
Electricity production (GWhe) 585 1200 522 415
% of total electricity production 1% 2% 1% 1%
% of decentralised thermal only electricity production 39% 80% 35% 28%
% of decentralised total electricity production 17% 16% 5% 3%
Heat production (GWhth) 838 1719 748 595
0
20
40
60
80
100
120
140
160
180
200
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
1 3 5 7 9 11 13 15 17 19 21 23
CHP Swarms
MW
Solar PV
(MW)
Solar PV
CHP Swarms
Winter working day
0
20
40
60
80
100
120
140
160
180
200
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2200
1 3 5 7 9 11 13 15 17 19 21 23
CHP Swarms
MW
Solar PV
(MW) Summer Sunday

29.  CHP Swarms seems a promising technology for providing
flexibility to the electric system
 Potential parameters affecting their uptake include:
 Developments in the large-scale generation
 Costs of bio-methane and access competition from other uses
 Feed-in tariffs for biomass
 Competition in heat supply from heat pumps
 Storage costs for storing excess heat from CHP Swarms
 Modelling challenges:
 No satisfactory solution for the technology mix effect in heat sectors
 Improvement of the dispatching of the power plant technologies:
crucial factor for the balancing services as well
Conclusions – Further Challenges
24.06.2015PSI, Seite 29

30. References
24.06.2015PSI, Seite 30
[1] Main Sources used for obtaining the heat demand profiles:
 BFE, “Analyse des schweizerischen Energieverbrauchs 2000 - 2012 nach Verwendungszwecken“, 2013
 Rossi Alessandro, “Modelling and validation of heat sinks for combined heat and power simulation: Industry”, 2013
 Ayer Roman, “Modelling of heat sinks for combined heat and power simulation: Households”, 2013
 Federal office of Meteorology and Climatology MeteoSwiss
 Mark Hellwig "Entwicklung und Anwendung parametrisierter Standard-Lastprofile", 2003
 Ulrike Jordan, Klaus Vajen, “Realistic Domestic Hot-Water Profiles in Different Time Scales”, 2001
[2] Modelling heat systems:
 Merkel E., Fehrenbach D., McKenna R., Fichter W., Modelling decentralised heat supply: An application and methodological extension
in TIMEs, Energy 73 (2014), 592-605
[3] Modelling balancing services:
 Welsch M., Howells M., et al., Supporting security and adequacy in future energy systems: the need to enhance long-term energy
system models to better treat issues related to variability, Int. J. Energy Res. 39 (2015), 377-396

31. 24. Juni 2015PSI, 24. Juni 2015PSI, Seite 31
Thank you for the attention !
Dr. Evangelos Panos
Energy Economics Group in the Laboratory of Energy Analysis
evangelos.panos@psi.ch