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PLEASE READ BEFORE ACCCESSING PRESENTATION
Please note that this presentation gives a snapshot of the current, ongoing research on the Zero Carbon Britain project. Details may change before the publication of the report, therefore please contact me ([email protected] ) before you use or cite the material in this presentation.
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Future Energy Networks
Modelling Supply And Demand in a Renewable Energy Future
Tobi KellnerCAT
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Quick Introduction• I am a renewable
energy consultant & researcher at the Centre for Alternative Technology (CAT) in Machynlleth, Wales
• CAT is an education & research centre established 1973
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About ZCB 2030Aims:• Show that a future with
100% renewable energy & zero (net) GHG emissions is physically possible
• Stimulate debate, shift goal posts
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Modelling: Why?
Modelling Future Energy Systems –Why?
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Modelling: Why?
DECC UK Energy Flow Chart 2011
Today’s Energy System
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Modelling: Why?
Gas
Coal
Oil
Transport
Industry
Domestic
DECC UK Energy Flow Chart 2011
Today’s Energy System
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Modelling: Why?
Gas
Coal
Oil
Today’s Energy System
Today‘s energy system
• Is heavily dependent on finite fossil fuels with high GHG emissions
• Has grown & evolved over many decades
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Modelling: Why?
Gas
Coal
Oil
Today’s Energy System
Tomorrow‘s energy system
• Radical changes in supply:Uncontrollable renewables(and/or inflexible nuclear)
• Radical changes in demand:Electrification of heating & transport
• No time for trial & error evolution!
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Modelling: How?
Modelling Future Energy Systems –What?
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Modelling: What?
Parameter Options ZCB choice
Spatial system boundaries
Single region? UK? Europe?
UK(not “Britain”...)
Interaction with outside
Interconnectors?Imports/exports?
None(island system)
Spatial resolution Model individual regions & flows between them?
Treat UK grid as a single point, “copper plate UK”
Temporal resolution Year? Day? Hour? Millisecond?
1 hour
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Modelling: How?
Modelling Future Energy Systems –How?
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Modelling: How?
SupplyModelSupplyModel
Demand Model
Demand Model
wind speeds
solar radiation
wave height
...
Heat demand
Appliances hourly demand
Transport demand model
Hourly supply
Hourly demand
BackupBackup
StorageStorage
Weather
ZCB energy model
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Modelling: How?ZCB energy model
For the ten years 2002-2011 (87,648 hours), we have
• Hourly data on offshore & onshore wind speeds, solar radiation, wave heights
• Hourly electricity consumptionfrom National Grid
• Daily weighted temperatures from National Grid
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Modelling: How?
wind speeds
solar radiation
wave height
...
Heat demand
Appliances hourly demand
Transport demand model
Weather
ZCB energy model
• Use real historic data or synthesise from statistical model?
• Potentially complex interactions synthetic model would be very complex
• Is historic data plausible basis for future model?
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Offshore Wind
Example: Hourly model for
offshore wind power
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Offshore Wind
Offshore wind: Strongest UK renewable energy source
Need to model output of widely distributed future wind farm fleet
Problem: Almost no historic measured offshore wind speed data
Offshore wind
Heat pumps
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Offshore Wind
Solution: NASA‘s MERRA (Modern-Era Retrospective Analysis for Research and Applications), a kind of „weather back-cast“
Hourly data for past decades, 0.5° spatial resolution
MERRA
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Offshore WindValidation
Validation: compare MERRA to real offshore wind data, e.g. half-hourly readings from helipad at Ekofisk oil field
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Offshore WindValidation
0.0
5.0
10.0
15.0
20.0
25.0
30.0
15 Dec 09 17 Dec 09 19 Dec 09 21 Dec 09 23 Dec 09 25 Dec 09 27 Dec 09 29 Dec 09 31 Dec 09
measured
measured (hourly)
MERRA
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Offshore WindValidation
y = 0.7986x + 0.8908R² = 0.8535
0
5
10
15
20
25
30
0 5 10 15 20 25 30
MER
RA
mod
el w
ind
spee
d (m
/s, h
ourl
y)
Ekofisk oil platform measured wind speed (m/s, hourly)
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Offshore Wind
Approach: Define regions for fixed & floating offshore wind farms
• Assign capacity (in GW) for each region
• Get hourly wind speeds & calculate hourly power output for each region
Methodology
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0%
20%
40%
60%
80%
100%
19/5/11 21/5/11 23/5/11 25/5/11 27/5/11
Pow
er o
utpu
t (%
)
region A
region B
Offshore WindMethodology
0%
20%
40%
60%
80%
100%
19/5/11 21/5/11 23/5/11 25/5/11 27/5/11
Pow
er o
utpu
t (%
)
all UK regions
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Complete model
Bringing it all together:The Hourly Energy Model
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-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
Hourly energy model
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
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Hourly energy model
>90GW excess supply available
>60GW dispatchable backup required
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
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Hourly energy model
-100
-50
0
50
100
150
200
0% 25% 50% 75% 100%
Exce
ss /
shor
tfal
l (GW
)
% of time level is exceeded
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Short term variation
• Large hour-to-hour fluctuations, dominated by heat demand
• Demand Side Management (DSM) can help, e.g. „smart charging“ of electric cars
• Pumped hydro storage and heat storage can provide short term storage (a few 100GWh)-120
-90
-60
-30
0
30
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14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
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Hourly energy model
-100
-50
0
50
100
150
200
0% 25% 50% 75% 100%Exce
ss /
shor
tfal
l (G
W) no Demand Side Management
with Demand Side Management
no DSM: 1% of the time>35GW backup needed
with DSM: 1% of the time>49GW backup needed
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-90
-60
-30
0
30
60
90
120
150
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
GW
Weather base date
Long term variation
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport
-120
-90
-60
-30
0
30
60
90
120
150
180
14/12/2010 19/12/2010 24/12/2010 29/12/2010 03/01/2011
GW
Weather base date
geothermal
hydro
tidal
wave
onshore wind
offshore wind
solar PV
industrial
appliances
heating
transport
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Long term variation
• Significant longer-term variation between months & years
• Ideally many TWh of storage
-90
-60
-30
0
30
60
90
120
150
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
GW
Weather base date
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Backup & storage
• Flexible dispatchable storage & backup is still required
• Gas allows storage of large quantities of energy (100s of TWh)
• Gas turbines allow flexible dispatch, proven technology
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Backup & storage
• Hydrogen can easily be created from renewable electricity (electrolysis)
• But natural gas (methane) is easier to store and we have vast existing infrastructure
• The Sabatier reaction allows „methanation“ of hydrogen
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Sabatier reaction
Sabatier: CO2 + 4H2 → CH4 + 2H2O
source: Sterner (2010)
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Long term gas storage
-90
-60
-30
0
30
60
90
120
150
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
GW
pow
er /
TW
h m
etha
ne in
sto
rage
Weather base date
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The Future: Integrated Energy Networks
Dispatchable generation
Variablegeneration
Synthetic
H2 / CH4
Production
Gas Storage
Central Heat
Pumps
Heat Storage
CHP (maybe?)
ElectricityElectricityGridGrid
Heat Heat NetworksNetworks
GasGasGridGrid
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The easy part:• Hourly model of
energy supply
The tricky part:• Model of interaction
between price, demand, storage and backup
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Thank You Very Much
[email protected]