Modelling different Thermal Energy Storage (TES) options in a TIMES model
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The document discusses a modeling study of various thermal energy storage (TES) technologies using the TIMES model, focusing on their characteristics and implications for energy systems. It includes a case study of Eskilstuna, Sweden, analyzing existing and potential TES options for district heating, their technical and economic parameters, and preliminary results. The aim is to improve understanding of how to integrate TES technologies into energy models for better forecasting and planning.
![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](https://image.slidesharecdn.com/07-etsapworkshopdmytroromanchenko-220105114408/85/Modelling-different-Thermal-Energy-Storage-TES-options-in-a-TIMES-model-6-320.jpg)
Modelling different Thermal Energy Storage (TES) options in a TIMES model
- 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 Unlut urk PRELIMINARY RESULTS – sub-annual heat deliveries
- 9. Burcu Unlut urk PRELIMINARY RESULTS – import/export balance Total system cost – 765 MlnEUR
- 11. Burcu Unlut urk 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.
- 12. Burcu Unlut urk Extra info – Electricity prices 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 El. price 2018 El. price 2030 El. price 2050
- 13. Burcu Unlut urk Extra info – Demand fractions 0.0000 0.0100 0.0200 0.0300 0.0400 0.0500 0.0600 0.0700 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 SH_FR HWD_FR