the contribution of wind to securing electricity demand

The contribution of wind to securing electricity demand

David Brayshaw NCAS-Climate and Department of MeteorologyUniversity of Reading [email protected]

With Chris Dent, Stan Zachary, Giacomo Masato,Alberto Troccoli, John Methven, Rachael Fordham

IntroductionIncreasing deployment of renewable energy systems in UK (mostly wind)

From the UK government (DECC renewable energy strategy 2009)• 5.5% electricity from renewables in 2008• 30% electricity from renewables by 2020

Weather impact: supply becomes more volatile




Questions:1. How much power can we get from a wind turbine once its installed?2. How much is the output from a wind turbine worth in money terms?3. In times of peak demand, how much wind power can be expected?





Brayshaw et al 2011 (Renewable Energy)

Mean output depends heavily (~10%) on large-scale atmospheric circulation state

Example: Winter 2009/10 saw very low UK winds AND cold temperatures from December – March, associated with persistent atmospheric circulation pattern (NAO-)








PhD project for Oct 2011

Explore the use of climate variability in estimating forward energy contract prices at monthly timescales










This talk

Focus on winter season in UK










This talk

Focus on winter season in UK

Disclaimer: Nowhere suggesting that meteorological concerns will dictate renewable deployment but, once deployed, climate variability will become significant factor.

Wind availability during peak demand

Prevailing view:• The “low wind cold snap”

Conceptual picture tends to describe an anticyclone system sitting over the UK

UKERC 2006

James 2007Based on the “GWL” weather classification system

Peak demand 2006: a low-wind event

Oswald et al 2008Peak demand 2006

Peak demand 2006: a low-wind event

Oswald et al 2008Peak demand 2006

What I hope to do is convince you that:• This is not a particularly “good” representation of the real peak-demand situation• Enhanced meteorological understanding will help in quantifying the relationship between wind and demand

NB: This is a work-in-progress

The good news…

Hourly demand level

Wind output(fraction of maximum)

Sinden (2007)

Quantity of wind power generally increases with demand even at moderately high demand levels (>80% of maximum)

Demand is expressed as rank-within-year

… and the badSinden (2007)



Power Oswald et al (2008)• At the half-hour of annual-peak demand in each year (~0.005% frequency) quantity of wind power available can be very low

Hourly demand level


… and the badSinden (2007)



Power Oswald et al (2008)• At the half-hour of annual-peak demand in each year (~0.005% frequency) quantity of wind power available can be very low

BUT

• The total demand at the peak half-hour appears positively related to wind

Hourly demand level


Demand expected in “low wind cold snap”

Three general points:

• Wind positively related to demand • No real surprise as demand related to “effective” temperature and wind-chill

• Highest demand in any given year frequently occurs in conjunction with low-wind

• Very high demand events generally have higher wind-speeds

Question:

• How can we quantify the wind-resource during peak demand events?

Sinden/Oswald

Direct use of energy system data problematic (analysis by Dent and Zachary)

• Peak demand extremes are rare• Energy system (demand, supply) are:

• short (~10-20 years)• inhomogeneous (system evolves in time)

Estimates dominated by properties of few events

• Recorded wind-supply is function of existing wind-farm deployment

Brayshaw, Dent, Zachary (Submitted to J. Risk & Reliability)

Quantifying wind during peak demand

Many of properties we are concerned about relate to meteorological behaviour:

• Demand = f(temperature, wind-speed, ....) + human “noise” + met-human interactions• Wind-supply = f(wind-speed)

The use of meteorological information

E.g., time of day, day of week, what’s on TV, etc...

E.g., Taylor and Buizza 2000

Many of properties we are concerned about relate to meteorological behaviour:

• Demand = f(temperature, wind-speed, ....) + human “noise” + met-human interactions• Wind-supply = f(wind-speed)

Linking to meteorological properties desirable because:

• Longer, approximately homogeneous datasets (~30-60 years+)• Link to climate model simulations for future changes (months-seasons-decades)

Questions:

• What does a low-wind event look like?• What does a high-demand event look like?• How far do the two event types overlap?• How can we objectively identify these events in meteorological records?

The use of meteorological information

E.g., time of day, day of week, what’s on TV, etc...

E.g., Taylor and Buizza 2000

Extreme peak demand in Jan 2010

Temperature (every 6h)

Metered demand(every 1h)

Metered wind(Solid line: every 1h)

Observed wind (broken blue lines):Northern GB (Dashed: every 6h)Southern GB (Dotted: every 6h)



Metered demand(every 1h)

Metered wind(Solid line: every 1h)


N wind

S wind

Temp

Demand

Metered


Contours – Sea level pressureColours – temperatureHatching – low wind (< -1 s.dev.)Dots – high wind (> +1 s.dev.)

Temp

N wind

S wind

Demand

Metered



TempHigh pressure to north, low to south: “Blocking”Easterly wind moderate in south, weak in northVery cold temperature ~ -2oC

N wind

S wind

Demand

Metered

Peak demand in Feb 2006 (as Oswald 2006)



Peak demand in Feb 2006 (as Oswald 2006)

N wind

S wind

Temp


Low wind everywhereHigh pressure over GBModerate temperature ~ +2oC

Circulation typing (GWL)

Figure from Gerstengarbe et al 1999

“Prevailing” weather type

Time-filtered daily-mean circulation fields

Objective correlation to 29 canonical weather typesJames (2006) following Hess and Brezowsky (1952)

Easterly flow into GB (N-S pressure dipole) is keyOne possible approach to identify this is “circulation typing”

Circulation types of most extreme demands

Red = Dipole “Blocking” typesBlue = High-over-Britain

Les

s ex

trem

e p

eak

dem

and

One “event”

Circulation types: GB Wind vs Temperature

High-over-BritainLow wind

Moderate temperature




NE Atlantic high patternsModerate windVery low temperatureExpect much higher demand

Caution: the classification of blocks vs troughs is somewhat ambiguous, especially for TM and HNZ





Blocking patternsAfter Hess et al 1951 Caution: the classification of blocks vs troughs is

somewhat ambiguous, especially for TM and HNZ





Blocking patternsAfter Hess et al 1951 Caution: the classification of blocks vs troughs is

somewhat ambiguous, especially for TM and HNZ

Trough patternsMany seem to be associated with passage of a low-pressure system across mid-Europe

One possible interpretation of Sinden and OswaldSinden: in general wind increases with increasing demand

• As wind increases, wind-chill (and therefore demand) increases• Thus demand and supply positively related over all



Sinden: at fairly high demands (>80%), wind still increases with demand• As above, but in Met terms moving right to left along line




Oswald: at peak demand in year, wind may be very low• Corresponds to years where the lowest T is quite moderate => picks out HB-like types




Oswald: at peak demand in year, wind may be very low• Corresponds to years where the lowest T is quite moderate => picks out HB-like types

Oswald: in years with very high peak demand, wind is quite good• Corresponds to years where the lowest T is extreme => move left on line

ConclusionsCharacteristics of (at least) three types of features need to be understood:

• High-over-Britain (low-wind cold-snap)• Benchmark scenario with high-ish demand and no wind• Demand must be met by some other means

• Blocked types bringing cold continental air into UK from East• Most extreme demand levels likely but some wind• Dry continental air, stable system

• Trough types bringing cold martime air into UK from North• Very high demand levels possible but some wind• Moist maritime air, transient system

Brayshaw, Dent, Zachary (Submitted to J. Risk & Reliability)

Next steps

Simply the meteorological detection method (with Giacomo Masato):• Less GWL types• Blocking indices

Quantitative analysis of wind & temperature distributions within each type• NCAS, Leeds University and UK Met Office downscaling (to 4km)• MSc student #1 to start in April 2011

Relationships to changing climate system:• C21 climate simulations from IPCC-type models (Giacomo)• Climate variability at seasonal-to-decadal timescales• Implications for finance (weather derivatives, energy futures contracts)• MSc student #2 to start April 2011• PhD student to start October 2011

Papers and contact:• [email protected]• Brayshaw, Dent, Zachary, J. Risk & Reliability (submitted)• Brayshaw, Troccoli, Fordham, Methven, Renewable Energy (2011)

Positive Negative

37

Storm tracks and the NAO

Figs: http://www.ldeo.columbia.edu/res/pi/NAO/

Positive Negative

38

Storm tracks and the NAO

Figs: http://www.ldeo.columbia.edu/res/pi/NAO/

Path of weather systems affecting GB influenced by “slow” climate variations

Correlation strength: January surface temperature vs NAO

Left: from NOAA CPC website

NAO, surface temperature and wind

High NAO = warm windy wintersLow NAO = cold still winters

Correlation strength: January surface temperature vs NAO

Left: from NOAA CPC website

NAO, surface temperature and wind

High NAO = warm windy wintersLow NAO = cold still winters

Brayshaw et al 2011 (Renewable Energy) demonstrates:

Prevailing NAO state affects wind-speed distribution at timescales hours – years

This information can be used to improve forecasts of wind-energy output at monthly-timescales

Implications for finance: initial resource assessmentweather derivativeslonger-term energy futures contracts

Winter 2010: a strong NAO-

Surface wind

Surface temperature

Climatology Winter 2010 anomaly

Less wind

Cold

More wind

Warm

the contribution of wind to securing electricity demand

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