Modelling different Thermal Energy Storage (TES) options in a TIMES model Dmytro Romanchenko, IVL Swedish Environmental Institute, Sweden

1. Modelling different Thermal Energy Storage (TES)
options in a TIMES model
Dmytro Romanchenko
IVL Swedish Environmental Institute
ETSAP Workshop
Oslo, 29-30.11.2021

2. Agenda
Aim and objectives
TIMES_City_heat model
Case study
Modelling TES
Preliminary results

3. Aim
to improve understanding of how to model different types of TES technologies, i.e., existing vs.
potential investments, centralized vs. decentralized, in TIMES-based energy models
Objectives
- to discuss different TES options
- to identify particularities of the TES options
- to learn how to “translate” these particularities into TIMES attributes
- to discuss the values these attributes can/should get

4. BURC
U
UNLU
TURK
REFERENCE ENERGY SYSTEM
Conversion
• District heating:
- CHPs
- HOBs
- HPs
• Individual heating
• Heat storage
- incl. in the network
- incl. in buildings
Transmission/
Distribution
• DH grid
• Fuel
infrastructure
• Electric grid
(cap)
Linear optimization,
Techno-Economic,
Partial-Equilibrium,
Dynamic model:
- 12 (and 72) time-steps/year
- Looking 20-50 years ahead
Model Output
(Technology, Zone, Time)
Energy use by energy
carrier, presented by:
Time/Sector/sub-sector
End-use tech-s:
• Mix of heating/cooling
equipment (cent. vs.
individ.)
• Generation and
storage capacities
Costs
• Total System Costs
• Running Costs
• Investment costs
Environmental
• GHG
• Air pollutants
Demands (heating)
Typical scenario
assumptions
Demand projection
>30 years
Energy prices
• Import prices
Resources
• Renewables
resources (pot)
• Imp restrictions
Policy
• Taxes/subs tec
• Targets
Techno-Economic Parameters (examples)
• Investment cost
• Fixed O&M costs
• Variable O&M costs
• Efficiency
• Availability factors
• Heat-to-Power ratio
Commercial buildings (COM)
Specified per service Type
Environmental
assumption
• High
• Medium
• Low
Environmental Parameters
Emission factors:
• CO2, NOX, SO2,
VOC, PM10,
PM25 …
Sets of
external costs:
High/Medium/
Low
Residential buildings (RSD)
Specified per Building Type
Sure_City_heat model

5. Studied case
• City of Eskilstuna, Sweden
• 70,000 inhabitants
• District Heating (DH) system
• DH provides 700 GWh/yr of heat
• 65% of the total city’s heating demand
• 90% of the heat is from biomass
• a CHP plant and heat only boilers
• Centralized hot water tank (900 MWh)
2021-12-07

6. Modelling of TES – Existing
Ackumulator – storage
name Vattumanen storage
Max charge rate [MW] 60
Max discharge rate [MW] 60
Max capacity [Mwh] 900
Min capacity [Mwh] 200
losses
charging discharging loss
~FI_Process
Sets TechName TechDesc Tact Tcap Tslvl PrimaryCG Vintage
I: Process Set
Membership
Technology Name Technology Description
Activity
Unit
Capacity Unit
Timeslice
Operational
Level
Operational
Commodity Group
Vintage Tracking
*unit
STS STGHCELWT100 Large Water Tanks (LWT) TJ MW DAYNITE NRG
STG STGHCENTES100 Network Thermal Energy Storage (NTES) TJ MW DAYNITE NRG
Existing
Storage
~FI_T
Sets TechName TechDesc Comm-IN Comm-OUT CommGrp STG_EFF STG_LOSS
NCAP_AFC~
DAYNITE
NCAP_AF
~LO
STOCK~2015 STOCK~2050
I: Process Set
Membership
I:Technology Name Technology Description
Input
Commodity
Output Commodity Group Efficiency Storage loss
Instaleld
capacity
Instaleld
capacity
*unit *unit
STS STGHCELWT100 Large Water Tanks (LWT) HETHTHP HETHTHP NRG 0.98 0.68 1 0.14 60 60
HETHTHP ACT 0.63
STG STGHCENTES100
Network Thermal Energy
Storage (NTES) HETHTHP HETHTHP NRG 1 36.50 1 0 5 5
HETHTHP ACT 0.83

7. Modelling of TES – Investments
~FI_Process
Sets TechName TechDesc Tact Tcap Tslvl PrimaryCG Vintage
I: Process Set
Membership Technology Name Technology Description Activity Unit Capacity Unit
Timeslice
Operational
Level
Operational
Commodity
Group Vintage Tracking
*unit
STS STGHCELWT101 Large Water Tanks (LWT) TJ TJ_a DAYNITE NRG
STS STGHCESWT101 Small water Tanks (SWT) TJ TJ_a DAYNITE NRG
STG STGHCEUTES101 Underground Thermal Energy Storage (UTES) TJ TJ_a SEASON NRG
STG STGRHABiTES101 Buildings Thermal Energy Storage (BiTES) - RHAPA TJ MW DAYNITE DEM
STGRHABiTES102 Buildings Thermal Energy Storage (BiTES) - RHAPB TJ MW DAYNITE DEM
……………………………………………………. - RHAPC
New Processes ~FI_T
Sets TechName TechDesc Comm-IN Comm-OUT CommGrp START STG_EFF STG_LOSS
I: Process Set
Membership
I:Technology Name Technology Description Input Commodity
Output
Commodity
Group Starting Year Efficiency Storage loss
*unit *unit
STS STGHCELWT101 Large Water Tanks (LWT) HETHTHP HETHTHP 2025 0.98 0.68
STS STGHCESWT101 Small water Tanks (SWT) HETHTHP HETHTHP 2025 0.98 0.34
STS STGHCEUTES101 Underground Thermal Energy Storage (UTES) HETHTHP HETHTHP 2025 0.7 0.53
STG STGRHABiTES101 Buildings Thermal Energy Storage (BiTES) - RHAPA RHAPA RHAPA DEM 2025 1 0
RHAPA ACT
STGRHABiTES102 Buildings Thermal Energy Storage (BiTES) - RHAPB RHAPB RHAPB DEM 2025 1 43.80
RHAPB ACT
NCAP_AFC~DAYNITE NCAP_AF~LO CAP_BND~UP~2025 CAP_BND~0 Life PRC_CAPACT INVCOST~2016 INVCOST~2050
Max capacity bound start
year
Max capacity bound
I/E rule
Lifetime of
Process
Capacity to Activity
Factor
Investment Cost Investment Cost
k€/TJ_a
k€/MW
k€/TJ_a
k€/MW
40 1 823.6 823.6
40 1 113 888.9 113 888.9
20 1 161.0 129.6
0.0 0.0 0.0 50 31.54
0.0
1.0 0.0 25.0 5 50 31.54 1.2 1.2
0.8

8. Burcu
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PRELIMINARY RESULTS – sub-annual heat deliveries

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PRELIMINARY RESULTS – import/export balance
Total system cost – 765 MlnEUR

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Thank you!

11. Burcu
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Thermal Energy Storage in TIMES
Existing TES Investment TES options
Unit
TES in DH
network
Existing
Tank
TES in
apartment
buildings
TES in
single-
family
buildings
Tank Borehole Pit
Cycle efficiency
(inflow/outflow)
% 1 0.98 1 1 0.98 0.95 0.7
Yearly losses % 36.5* 0.68 43.8* 43.8* 0.68 0.5 0.53
Maximum
inflow/outflow
MWh 5* 60 100* 50* - - -
Maximum capacity MWh 100* 900 2000* 1000* - - -
Lifetime Yr - 40 - - 40 40 20
Investment cost kEUR/TJ - - 1.2** 1.2** 823 500 161
Fixed O&M cost kEUR/TJ - - - - 2.4 2 0.8
Table 1. Assumed techno-economic parameters of the investigated TES technologies.

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Extra info – Electricity prices
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El. price 2018 El. price 2030 El. price 2050

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Extra info – Demand fractions
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