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A simplistic method for representingrenewable gasses and fuels in an energysystems optimisation modelIda Græsted Jensen, Frauke Wiese, Rasmus BramstoftDepartment of Management Engineering, Systems Analysis
e-mail: [email protected]
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Motivation for using renewable gas and fuels
Enhancing the usage of gas inthe Danish energy system
Flexibility for VariableRenewable Energy Integrationin the Nordic Energy System
www.Flex4RES.org
2 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Motivation for this study
High share of wind energy ⇒ need of alternative, renewable anddispatchable electricity sources
How can renewable gas and fuels be integrated in an energysystems model without increasing the running time of themodel too much?
3 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Motivation for this study
High share of wind energy ⇒ need of alternative, renewable anddispatchable electricity sources
How can renewable gas and fuels be integrated in an energysystems model without increasing the running time of themodel too much?
3 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The energy systems model BalmorelI An energy systems model considering existing capacity and
including possibility to investI Focusing on the energy system. Here defined as the heat
and power sectorI Can be extended by inclusion of several add-ons
4 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Why is RE-gas a problem in Balmorel?
I Only modelling ofconversion of fuels toelectricity and heat
I No representation of gasinfrastructure
I Limited representation ofgeography to reducerunning time:
I 4 countries (DK, NO,SE, DE)
I 15 regions (DK: 2, NO:5, SE: 4, DE: 4)
I 35 areas (DK: 14, NO:7, SE: 10, DE: 4)
13
5 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
OptiFlow (FutureGas)
I Generalised spatio-temporal network optimisation model
I Can be connected to Balmorel to model production of REgas and fuels
15 May 2017DTU Management Engineering, Technical University of Denmark
Methodology
1
Balmorel OptiFlow
Electricity balance
District heat balance
Optimisation min. z (Money)
Natural gas market
Background system
Available resources, fuel prices, etc.
Biofuel market
Electricitydemand
Bi-products
District heat demand
6 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
OptiFlow: production of RE gas and fuels
Electrolysis
Storage
Methanation
Transport of
resourcesResources
Storage
Anaerobic digestion
El
Engine using biogas
El
Biogas upgrading
TG ‐FT biodiesel
Transport of
resourcesFT biodiesel
Resources
Engine using syngas
MethanolMethanol synthesis
Thermal gasification
Transport of
resources
Water‐gas‐shift
Gas
Heat
Methanation
2nd generation fermentation
TG ‐ FT‐SPK, Bio‐jet
Ethanol
Bio‐jet
Biogasoline
Heat
ResourcesElectricityHeatProduct gasBiogasHydrogenBio natural gasMethanolBiodieselEthanolBiogasolineBio‐jet
Transport of
resources
Transport of
resources
7 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
OptiFlow: drawbacks
I More data needed:I Higher resolution of
geographical data togenerate meaningfulresults
I Location of resourcesalong withtransportation costs
I Data for all processesincluded
I Size of the model explodes(more on this later)
8 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: overview (Flex4RES)
Using the Balmorel model without Optiflow but includingadd-ons that allow:
I Combining natural gas with upgraded biogas and upgradedthermal gas in existing and new natural gas technologies
I Including production of hydrogen and use of it formethanation of biogas and thermal gas
I Including a common limit of the gasses to reflect theoverall potential of biogas and thermal gas, respectively
9 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: CombTechI Allowing natural gas technologies to combine:
I Natural gasI Biogas (anaerobic digestion) or gas from thermal
gasification upgraded by CO2-removal (BIOGAS UP andTHERMGAS UP)
I Biogas or gas from thermal gasification upgraded byhydrogen addition (BIOGAS H2 and THERMGAS H2)
CHP
CHP
CHP
CHP
CHP
Natural gas
Biogas up
Biogas H2
Thermal gas up
Thermal gas H2
El
Heat
10 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: CombTechI Allowing natural gas technologies to combine:
I Natural gasI Biogas (anaerobic digestion) or gas from thermal
gasification upgraded by CO2-removal (BIOGAS UP andTHERMGAS UP)
I Biogas or gas from thermal gasification upgraded byhydrogen addition (BIOGAS H2 and THERMGAS H2)
CHP
Natural gas
Biogas up
Biogas H2
Thermal gas up
Thermal gas H2
El
Heat
10 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: Hydrogen add-on
Electrolysis
Storage
“Biogas H2"
El
El
“Thermal gas H2”
Thermal gas H2
Heat
Heat
Hydrogen
Fuel cell
Biogas H2
Methanation
Conversion factor
SOEC Methanation
Conversion factor
Bio natural gas
Product gasBiogasHydrogen
ElectricityHeat
Inclusion of a hydrogen demand constraint (simplified):∑i∈IH2prod
pH2i ,t + pH2−unload
t − pH2−loadt
= dH2t +
∑i∈IH2cons
pfueli +∑
i∈IH2upgr
γi pfueli
11 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: Hydrogen add-on
Electrolysis
Storage
“Biogas H2"
El
El
“Thermal gas H2”
Thermal gas H2
Heat
Heat
Hydrogen
Fuel cell
Biogas H2
Methanation
Conversion factor
SOEC Methanation
Conversion factor
Bio natural gas
Product gasBiogasHydrogen
ElectricityHeat
Inclusion of a hydrogen demand constraint (simplified):∑i∈IH2prod
pH2i ,t + pH2−unload
t − pH2−loadt
= dH2t +
∑i∈IH2cons
pfueli +∑
i∈IH2upgr
γi pfueli
11 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: Hydrogen add-on
Electrolysis
Storage
“Biogas H2"
El
El
“Thermal gas H2”
Thermal gas H2
Heat
Heat
Hydrogen
Fuel cell
Biogas H2
Methanation
Conversion factor
SOEC Methanation
Conversion factor
Bio natural gas
Product gasBiogasHydrogen
ElectricityHeat
Inclusion of a hydrogen demand constraint (simplified):∑i∈IH2prod
pH2i ,t + pH2−unload
t − pH2−load
t
= dH2t +
∑i∈IH2cons
pfueli +∑
i∈IH2upgr
γi pfueli
11 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: Hydrogen add-on
Electrolysis
Storage
“Biogas H2"
El
El
“Thermal gas H2”
Thermal gas H2
Heat
Heat
Hydrogen
Fuel cell
Biogas H2
Methanation
Conversion factor
SOEC Methanation
Conversion factor
Bio natural gas
Product gasBiogasHydrogen
ElectricityHeat
Inclusion of a hydrogen demand constraint (simplified):∑i∈IH2prod
pH2i ,t + pH2−unload
t − pH2−loadt
= dH2t +
∑i∈IH2cons
pfueli +∑
i∈IH2upgr
γi pfueli
11 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: Hydrogen add-on
Electrolysis
Storage
“Biogas H2"
El
El
“Thermal gas H2”
Thermal gas H2
Heat
Heat
Hydrogen
Fuel cell
Biogas H2
Methanation
Conversion factor
SOEC Methanation
Conversion factor
Bio natural gas
Product gasBiogasHydrogen
ElectricityHeat
Inclusion of a hydrogen demand constraint (simplified):∑i∈IH2prod
pH2i ,t + pH2−unload
t − pH2−loadt
= dH2t +
∑i∈IH2cons
pfueli +∑
i∈IH2upgr
γi pfueli
11 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: Hydrogen add-on
Electrolysis
Storage
“Biogas H2"
El
El
“Thermal gas H2”
Thermal gas H2
Heat
Heat
Hydrogen
Fuel cell
Biogas H2
Methanation
Conversion factor
SOEC Methanation
Conversion factor
Bio natural gas
Product gasBiogasHydrogen
ElectricityHeat
Inclusion of a hydrogen demand constraint (simplified):∑i∈IH2prod
pH2i ,t + pH2−unload
t − pH2−loadt
= dH2t +
∑i∈IH2cons
pfueli +∑
i∈IH2upgr
γi pfueli
11 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: Common limit
An add-on which can set maximum limits in a flexible fashion:∑f ∈Fmax,rel (l)
∑i∈I(f )
γipfueli ≤
∑f ∈Fmax (l)
CAPf ∀l ∈ L
Where
I L represents the limit under consideration
I Fmax(l) represents the fuels that has an upper bound
I Fmax ,rel(l) represents the fuels that has an upper boundand the related fuels
StrawThermal
gasThermal gas up
Straw
Biogas Biogas up Biogas H2Biogas Biogas up Biogas H2
Thermal gas H2 ≤
≤
12 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
The simplistic method: Common limit
An add-on which can set maximum limits in a flexible fashion:∑f ∈Fmax,rel (l)
∑i∈I(f )
γipfueli ≤
∑f ∈Fmax (l)
CAPf ∀l ∈ L
Where
I L represents the limit under consideration
I Fmax(l) represents the fuels that has an upper bound
I Fmax ,rel(l) represents the fuels that has an upper boundand the related fuels
StrawThermal
gasThermal gas up
Straw
Biogas Biogas up Biogas H2Biogas Biogas up Biogas H2
Thermal gas H2 ≤
≤
12 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Comparison of models
OptiFlow Simple Base
Blocks of equations 149 100 71Blocks of variables 39 24 19Electricity systemHeating systemRE gas and fuel production ÷ ÷Hydrogen demand ( ) ÷Transport fuels demand ( ) ( )Transportation of biomass ÷ ÷
13 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Scenarios
We test:
I Countries: Denmark, Germany, Norway, and Sweden
I Year 2050
I 4 weeks per year
I 24 hours per week
I No CO2-emissions allowed
I Fixed usage of municipal waste
14 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Model runs
We test the simple version by running:
1 Balmorel with no add-ons
2 Balmorel with the simplistic method but with no hydrogendemand
3 Balmorel with the simplistic method with a flat hydrogendemand in Eastern Denmark (20 PJ/year)
3 Balmorel with the simplistic method with a varyinghydrogen demand in Eastern Denmark (20 PJ/year)
15 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Results: objective function
16 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Results: Fuel Consumption, No add-ons
17 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Results: Fuel Consumption, SimpleNoH2demand
18 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Results: Fuel Consumption, SimpleFlat and SimpleVar
19 / 20
DTU Management Engineering, Technical University of Denmark
Introduction Modelling of RE-gas Case study Results
Conclusion
I We have developed a simple method for inclusion ofrenewable gasses in an energy systems model
I The method gives a smaller model than inclusion of theOptiFlow model
I We suggest to:I Run OptiFlow for each specific country with a large number
of areas in the model to obtain hydrogen demandsI Use the hydrogen demands of the countries in Balmorel
with the simplistic method to obtain the optimal solutionwithin a reasonable running time
20 / 20