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IntERACT
MODELWORKING PAPER NO. 03 8. January 2014
Page 1
IntERACT TIMES-DK phase I
Abstract:
The TIMES-DK model implemented into the IntERACT model setup is documented in the following presentation. The documentation co- vers the phase 1 version of the model covering energy supply of elec- tricity and district heat as well as use of energy from the household sector. The model does not include industry, services and agricultural use of energy nor any part of the transport sector.
The development of the TIMES-DK phase 1 has been carried out by DTU Management Engineering in close cooperation with the Danish Energy Agency.
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IntERACT TIMES-DK phase I
DTU Management Engineering E4SMA
IntERACT Team, Danish Energy Agency
Project consultancy team
• DTU Management Engineering
Kenneth Karlsson – Head of Energy System Analysis Helge Larsen – Senior scientist
Stefan Petrovic – PhD candidate
Olexandr Balyk – research assistant
• E4SMA
Maurizio Gargiulo – president E4SMA Rocco De Miglio
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TIMES-DK Vision
• Detailed bottom up model including all sectors in the Danish energy system
• Linked to a macro-economic CGE model of the Danish economy
• Finding least cost pathways to political RE or emission reduction targets
• Through the CGE model showing impact on Danish economy from energy policy and changed global framework
• A new tool for optimizing GHG reduction scenarios horizontal across all sectors
• Taking part in the international collaboration under IEA,
ETSAP to develop and use the TIMES tool
TIMES-DK Phase I
• Phase I has been running from December 2012 to December 2013
• DTU Management Engineering and e4sma has been consultancies on the development of TIMES-DK
• Focus has been on the power and district heating sector and the household sector
• Linking to the CGE-model has also been an important part
• 5 full week workshops and 2 half week workshops has been essential for developing the structure of the model
• The work has been presented at two international workshops; one in Lisbon and one in Paris
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Deliverables from phase I
1. This presentation
2. Working and tested TIMES-DK model (databases, model files etc.)
3. Documentation of Power and District Heating Sector model 4. Documentation of Household Sector model
5. Short introduction paper to TIMES models and a tutorial
6. Excel result sheets for single scenarios and for comparison of scenarios
7. Interface to the RAMSES model (so RAMSES data can be used in TIMES-DK)
8. TimeSliceTool for aggregating hourly data to model time slices 9. Heat Atlas data for buildings (calculated heat loss, cost of heat
savings, cost of expansion of district heating grid)
TIMES-DK Model (1/2)
• Before using the model the software VEDA and GAMS has to be installed on the computer
• Copy the model folder VEDA Models under VEDA (c:\veda\veda_models\TIMES DK_HOU_v14)
• Copy the database folder “Denmark” to VEDA_BE\databases
• Result master files (can be found in \Results sheet
masters\) should be placed at same level as the TIMES DK model folder
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TIMES-DK Model (2/2)
• A TIMES model can optimize across all sectors
• Modules developed so fare are in green
Resources
Power & DH
plants Refineries
Households Industry Trade&service
Transport
Agriculture Other
Fuels
electricity district heat
Power and District Heating Model (1/3)
• Represent all existing non-industrial power and district heat producing plants in DK based on RAMSES database
• Include data for new power and district heat producing technologies from the Danish Technology Catalogue
• Two model regions: DK-W and DK-E
• Two heating areas in each region (central and de-central)
• Transmission lines to Norway, Sweden, Germany and Netherlands modelled as a price interface and capacity
• Model runs until 2050 with 32 time steps in a year
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Power and District Heating Model (2/3)
• Simplified illustration of the models
Reference Energy System (RES)
• The power and DH sector delivers
electricity and DH to all the sectors in the model (household sector is also
modelled, while the other sectors are represented by an exogenous demand
Power and District Heating Model (3/3)
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• As existing plants gets old or un-
economic they can be replaced by new
• The endogenous response of the optimization is in size (GW), type of plant, utilization
(operating hours) of the new capacities and in the calculation of the correspondent prices for electricity and heat.
Household Model (1/7)
• Two type of services are modelled in the Household model:
Services from electric appliances and heat service
Appliances - model structure (2/7)
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The mix of appliances are modelled in two type of dwellings – multi-storey and detached based dwellings
Seven different types of appliances are included and represent all electricity consumption in dwellings except from heating
Appliances in the model (3/7)
Appliances in detached dwellings Stock in
1000
Electricity consump.
Lifetime before
replacement
Appliances in multi-storey dwellings Stock in
1000
Electricity consump.
Lifetime before
replacement
Heat Services (4/7)
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Heat services are measured as Mm2 in the model
The building are split in before and after 1972 and in multi-story (multi
storey+non-detached) and detached (detached+farm houses)
Household heat (5/7)
Building age
Energy Service Demand [Mm2] <72 >72 New Decentralised Detached Buildings 19.74 16.45
Centralised Detached Buildings 15.04 10.63
Indivdual Detached Buildings 32.19 9.98
Decentralised Multi S. Buildings 8.48 8.35
Centralised Multi S. Buildings 16.82 10.33
Individual Multi S. Buildings 1.06 1.63
93.34 57.37 150.71
Building age
Energy Demand [PJ] <72 >72 New
Decentralised Detached Buildings 9.68 5.22
Centralised Detached Buildings 7.27 3.38
Indivdual Detached Buildings 17.12 3.11
Decentralised Multi S. Buildings 4.86 2.13
Centralised Multi S. Buildings 9.58 2.93
Individual Multi S. Buildings 0.57 0.34
Building age
Energy Unit Demand [PJ/Mm2] <72 >72 <2020 >2020 Decentralised Detached Buildings 0.49 0.32 0.23 0.07
Centralised Detached Buildings 0.48 0.32 0.23 0.07
Indivdual Detached Buildings 0.53 0.31 0.23 0.07
Decentralised Multi S. Buildings 0.57 0.26 0.22 0.07
Centralised Multi S. Buildings 0.57 0.28 0.21 0.07
Individual Multi S. Buildings 0.54 0.21 0.23 0.07
• For each heating area (central, de- central and
individual) building area and heat
demand are
retrieved from the heat atlas divided on DKW, DKE, the two building types and two construction periods.
Buildings DKE
Projection of heated area (6/7)
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• Based on a
projection of heated area within the
building groups; the model keeps track on the change in heat demand due to replacement of old buildings with new and the increase in heated area.
Heat supply technologies (7/7)
• The heat demand
from the building can be met by three
different types of technologies: Indiv.
boilers, district
heating and energy savings. The latter will always be in
combination with one of the other two
The TIMES tool (1/4)
• TIMES (The Integrated MARKAL-EFOM System) model generator was developed as part of the IEA-ETSAP (Energy Technology
Systems Analysis Program), an international community which uses long term energy scenarios conduct in-depth energy and environmental analyses
• TIMES is a technology rich, bottom-up model generator, which uses linear-programming to produce a least-cost energy system, optimized according to a number of user constraints, over
medium to long-term time horizons in-depth energy and environmental analyses
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Commodity Process Commodity
flow Process
Commodity
coal, oil, wind etc.
power plant, windturbine, etc.
Electricity Electricity for household sector
Electric appliances
Example of how energy systems are described in TIMES:
The TIMES tool (2/4)
• VEDA Front End is the user interface for
TIMES-DK from where input files are organised and updated
• When the files to
include in a model run is checked, then the problem is send to GAMS for solving
• VEDA is checking all files for syntax and
definition errors before executing GAMS
VEDA FE
The TIMES tool (3/4)
• VEDA Back End collects all model output from several scenarios
• Scenarios can be compared and all processes and
commodities can be inspected
• Standard tables are defined for the most interesting results
• Some of the tables are linking to the Excel
Result sheets
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VEDA BE
The TIMES tool (4/4)
• A 12 step tutorial is delivered as a part of the documentation,
which will be beneficial for new users of TIMES before running TIMES- DK
• E4SMA can provide
training courses after
agreement
Tools - Excel Result Sheets (1/2)
• Standard tables has been created in VEDA BE which are linked to Excel-sheets so result data easily are exported and presented in prepared tables and graphs
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TIMES-DK_Results_TIMES-DK.XLS
TIMES_DK_Graphs_TIMES-DK.xlsb
Tools - Excel Result Sheets (2/2)
• Up to 3 scenarios can be compared in the Compare
Scenarios Tool
Tools – RAMSES Interface
• The RAMSES database holds data on almost all power and district heating producing plants in Denmark. In TIMES these around
1200 existing and new units are aggregated to around 60 plants using the RAMSES interface. The interface directly prepare input files for TIMES
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Tools – TimeSliceTool (1/2)
• This tool is developed to aggregate hourly data for wind
production, PV production, power demand, heat demand
etc. into the 32 time steps of TIMES DK
Tools – TimeSliceTool (2/2)
The time slices are defined to catch critical situations in the power system:
• A: Wind High, Power demand low
• B: Power demand High, Wind Low
• C: Peak PV
• D: Rest
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Tools – Heat Atlas (1/2)
• Danish heat atlas represents a collection of spatially referenced data about nearly 2.5 mill. buildings in Denmark
• From BBR information on type, construction year and heat supply is together with weather data from DMI used for heat loss
calculation for each building
• SBI and DTU Civil Engineering reports are used to define
standards of the existing buildings and heat saving potentials
• Energy Producer Count (Energiproducenttællingen) is used to link buildings with district heating grids
• This gives a dataset which can be aggregated to area specific building types used in the TIMES DK model with information on heat loss from the existing buildings, the cost of introducing heat savings in the different building and the cost of connecting them to the nearest district heating network
Tools – Heat Atlas (2/2)
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Heating areas in the model
Dark blue – central
Light blue – next to central Dark green – de-central Grey + rest - individual
Marginal costs of heat savings
Costs of expanding district heating
Tools – Process diagram tool
• To illustrate the structure of TIMES-DK model a freeware
yEd is used to create flow diagrams – the result of this can
be seen in the flow diagrams following this slide
Flow diagram Power Sector
Heat only plants
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Flow diagram Power Sector
Condensing
power plants
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Flow diagram Power Sector
CHP plants
de-central
Flow diagram Power Sector
CHP plants
central
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Flow diagram Household
Sector
Appliances
Flow diagram Household
Sector
Heating of
buildings
Scenarios
• For testing TIMES-DK throughout the development three
scenarios has been run every time a new version on the model was ready. The Power sector model alone reached version 15 while starting up the combined power sector and household
sector version of TIMES-DK. The combined version delivered here has also reached version 15.
• The three scenarios:
1. Base – only a few constraints are added, such as limits of how fast wind can be implemented, heat savings can be introduced and all waste have to be used
2. WLP - as “Base” but including the target that 50% of the electricity production in Denmark has to be covered by wind power
3. WLP+NFE – as “WLP” but including facing out fossil fuels for power and district heat production by 2035
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Scenario disclaimer
• No taxes are included in the presented model runs
• Fixed price profiles are used for import and export of electricity
• The house hold sector is the only demand sector modelled, which mean demand for power and district heat from
industry, service, transport and others are kept constant throughout the scenario period
• Base is called “A” in the graphs
• WLP is called “B”
• and WLP+NFE is called “C”
Power production on fuel - compared
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In the first years there is export to Sweden.
In 2020 the onshore wind
potential is reached and offshore is
growing.
WLP+NFE has a huge import in 2045 until more offshore wind is put up in 2050 Biomass plays a small role as bridging fuel
Fuel consumption power & DH sector
Coal is dominant until 2025, but geothermal kicks in already from 2015 and is faced out when new
improved heat pumps based on ambient heat comes on the market in 2035 (only in
WLP+NFE).
From 2035 wind is the main fuel in the WLP+NFE only
supplemented by waste and ambient heat from 2040
Total cost in power & DH sector
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Total discounted investment costs (INCOST), fixed operational costs (FIXOM), variable costs (VAROM) and fuel costs.
Over time when replacing old
plants, the higher efficiency and less fuel based
technologies switch the main cost from being fuel cost to investment costs.
Power production share on fuels in each time slice - 1. quarter 2030
Shows average capacity activated in each time slice (MW).
The time slice RNWB has a huge import from
Norway and Sweden, while there is export to Sweden in RWDA
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Power production share on fuels in each time slice - 2. quarter 2030
Shows average capacity activated in each time slice (MW).
There is not a large peak in the 2.
quarter such as in 1.
PV delivers more in 2.Q because of more full load hours and coal delivers less as heat demand is lower and the CHP benefit decreases
Zooming in on the scenarios
• Moving down into the result sheets created for each
scenario, many more details are available especially on the household sector
• WLP+NFE is used as example in the following. The same
graphs and more can be found in the result sheets (\model
files\results\)
WLP+NFE – power production on regions
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Electricity
production divided on west and east DK shows a huge import to DKW from Norway from 2035 and from Sweden to DKE.
The import is based on a fixed price profile and only limited by the transmission
capacity.
Therefore, will an ambitious Danish energy policy lead to more import as the Danish
production price increase.
WLP+NFE – District heat production
District heat
production divided on west and east DK.
Waste is used for DH production throughout the period.
Natural gas is
almost faced out in 2015 where
geothermal heat comes in strong to be replaced after 2035 by heat pumps based on ambient air.
Biomass mainly play a role in DKE.
WLP+NFE – District heat production
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District heat
production divided on central and de- central heating areas.
In general the
demand for district heating goes down which is due to implementation of heat savings.
The de-central areas switch to heat pumps already in 2015, while central area has a lot of big thermal CHP’s waits until 2035.
WLP+NFE – CO2 emissions
The CO2 emissions are reduced
dramatically until 2035. After that only CO2 emissions from burning of waste is remaining.
WLP+NFE - Heat supply to buildings
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Heat supply
divided on boilers, exchangers (DH), and heat savings.
In the central heating areas most buildings are
connected to DH grid and the model connects the last buildings in 2012.
In the central areas heat savings does not become
profitable before 2025 where the large CHP’s begins to shut down.
WLP+NFE - Heat supply to buildings
Heat supply
divided on boilers, exchangers (DH), and heat savings.
In the de-central heating areas boilers covers one third in the base year.
Heat savings are profitable already from 2012.
WLP+NFE - Heat supply to buildings
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Heat supply
divided on boilers, exchangers (DH), and heat savings.
In individual heating areas boilers covers 100% and heat savings are
profitable already from 2012.
WLP+NFE - Heat supply to buildings
Heat supply
divided on boilers, exchangers (DH), and heat savings.
Looking at heat supply to the group
“detached”
buildings shows an equal share
between boilers and DH in the beginning.
Heat savings
become profitable from 2012 and are gradually replacing district heat.
WLP+NFE - Heat supply to buildings
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Heat supply
divided on boilers, exchangers (DH), and heat savings.
Heat supply to the group “Multi-
storey” buildings are mainly district heat in central areas as they
mainly are located in larger cities.
Heat savings
becomes profitable from 2020.
WLP+NFE - Heat supply to buildings
Heat supply
divided on fuels in DKW and DKE.
Heat demand drops as a result of
implementation of heat savings.
CPW=wood DSL=oil
ELC=heat pumps HCE=central DH HDE=decentral DH NGA=natural gas SOL=solar heat STR=straw
