interact times-dk phase i · disclaimer: the views expressed in this working paper series represent...

Disclaimer: The views expressed in this Working Paper Series represent work in progress,

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IntERACTMODEL

WORKING PAPER NO. 03 8. January 2014

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.

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

01/12/2013IntERACT TIMES-DK2

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


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


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

TransportAgriculture 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



• 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




• 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)


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)


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 ageEnergy Service Demand [Mm2] <72 >72 NewDecentralised 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)


• 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


Commodity Process Commodityflow

ProcessCommodity

coal, oil, wind etc.

power plant, windturbine, etc.

Electricity Electricity for household sector

Electric appliances

Example of how energy systems are described in TIMES:


•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


•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


VEDA BE


•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


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


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


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)


Heating areas in the modelDark blue – centralLight blue – next to centralDark green – de-centralGrey + 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




Condensing power plants



CHP plantsde-central



CHP plantscentral


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


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


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


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


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


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


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


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.




In the de-centralheating areas boilers covers one third in the base year.

Heat savings are profitable already from 2012.




In individualheating areas boilers covers 100% and heat savings are profitable already from 2012.




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.




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.



Heat supply divided on fuels in DKW and DKE.

Heat demand drops as a result of implementation of heat savings.

CPW=woodDSL=oilELC=heat pumpsHCE=central DHHDE=decentral DHNGA=natural gasSOL=solar heatSTR=straw

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