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The contribution of wind to securing electricity demand

The contribution of wind to securing electricity demand. David Brayshaw NCAS-Climate and Department of Meteorology University of Reading. d.j.brayshaw@reading.ac.uk. With Chris Dent, Stan Zachary, Giacomo Masato, Alberto Troccoli, John Methven, Rachael Fordham. Introduction.

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The contribution of wind to securing electricity demand

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  1. The contribution of wind to securing electricity demand David Brayshaw NCAS-Climate and Department of Meteorology University of Reading d.j.brayshaw@reading.ac.uk With Chris Dent, Stan Zachary, Giacomo Masato, Alberto Troccoli, John Methven, Rachael Fordham

  2. Introduction • Increasing 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

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

  4. Introduction 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-) • Increasing 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: • How much power can we get from a wind turbine once its installed? • How much is the output from a wind turbine worth in money terms? • In times of peak demand, how much wind power can be expected?

  5. Introduction 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-) • Increasing 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: • How much power can we get from a wind turbine once its installed? • How much is the output from a wind turbine worth in money terms? • In times of peak demand, how much wind power can be expected? PhD project for Oct 2011 Explore the use of climate variability in estimating forward energy contract prices at monthly timescales

  6. Introduction 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-) • Increasing 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: • How much power can we get from a wind turbine once its installed? • How much is the output from a wind turbine worth in money terms? • In times of peak demand, how much wind power can be expected? 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

  7. Introduction Brayshaw et al 2010 (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-) • Increasing 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: • How much power can we get from a wind turbine once its installed? • How much is the output from a wind turbine worth in money terms? • In times of peak demand, how much wind power can be expected? 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 Disclaimer: Nowhere suggesting that meteorological concerns will dictate renewable deployment but, once deployed, climate variability will become significant factor.

  8. 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 2007 Based on the “GWL” weather classification system

  9. Peak demand 2006: a low-wind event Oswald et al 2008 Peak demand 2006

  10. Peak demand 2006: a low-wind event • 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 Oswald et al 2008 Peak demand 2006

  11. Wind output (fraction of maximum) The good news… 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 Hourly demand level

  12. Wind output (fraction of maximum) The good news… 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 Hourly demand level

  13. Wind output (fraction of maximum) … and the bad 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 Hourly demand level 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

  14. Wind output (fraction of maximum) … and the bad 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 Hourly demand level Demand expected in “low wind cold snap” 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

  15. Sinden/Oswald • 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?

  16. Quantifying wind during peak demand • 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)

  17. The use of meteorological information • 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) E.g., Taylor and Buizza 2000 E.g., time of day, day of week, what’s on TV, etc...

  18. The use of meteorological information • 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? E.g., Taylor and Buizza 2000 E.g., time of day, day of week, what’s on TV, etc...

  19. 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)

  20. Extreme peak demand in Jan 2010 Temperature (every 6h) Temp Metered demand (every 1h) Demand S wind Metered wind (Solid line: every 1h) N wind Metered • Observed wind (broken blue lines): • Northern GB (Dashed: every 6h) • Southern GB (Dotted: every 6h)

  21. Extreme peak demand in Jan 2010 Temp Demand S wind N wind Metered Contours – Sea level pressure Colours – temperature Hatching – low wind (< -1 s.dev.) Dots – high wind (> +1 s.dev.)

  22. Extreme peak demand in Jan 2010 High pressure to north, low to south: “Blocking” Easterly wind moderate in south, weak in north Very cold temperature ~ -2oC Temp Demand S wind N wind Metered Contours – Sea level pressure Colours – temperature Hatching – low wind (< -1 s.dev.) Dots – high wind (> +1 s.dev.)

  23. Peak demand in Feb 2006 (as Oswald 2006) Temperature (every 6h) • Observed wind (broken blue lines): • Northern GB (Dashed: every 6h) • Southern GB (Dotted: every 6h)

  24. Peak demand in Feb 2006 (as Oswald 2006) Low wind everywhere High pressure over GB Moderate temperature ~ +2oC Temp N wind S wind Contours – Sea level pressure Colours – temperature Hatching – low wind (< -1 s.dev.) Dots – high wind (> +1 s.dev.)

  25. Circulation typing (GWL) Easterly flow into GB (N-S pressure dipole) is key One possible approach to identify this is “circulation typing” “Prevailing” weather type Time-filtered daily-mean circulation fields Figure from Gerstengarbe et al 1999 Objective correlation to 29 canonical weather types James (2006) following Hess and Brezowsky (1952)

  26. Circulation types of most extreme demands One “event” Less extreme peak demand Red = Dipole “Blocking” types Blue = High-over-Britain

  27. Circulation types: GB Wind vs Temperature High-over-Britain Low wind Moderate temperature

  28. Circulation types: GB Wind vs Temperature NE Atlantic high patterns Moderate wind Very low temperature Expect much higher demand High-over-Britain Low wind Moderate temperature Caution: the classification of blocks vs troughs is somewhat ambiguous, especially for TM and HNZ

  29. Circulation types: GB Wind vs Temperature NE Atlantic high patterns Moderate wind Very low temperature Expect much higher demand High-over-Britain Low wind Moderate temperature Blocking patterns After Hess et al 1951 Caution: the classification of blocks vs troughs is somewhat ambiguous, especially for TM and HNZ

  30. Circulation types: GB Wind vs Temperature Trough patterns Many seem to be associated with passage of a low-pressure system across mid-Europe NE Atlantic high patterns Moderate wind Very low temperature Expect much higher demand High-over-Britain Low wind Moderate temperature Blocking patterns After Hess et al 1951 Caution: the classification of blocks vs troughs is somewhat ambiguous, especially for TM and HNZ

  31. One possible interpretation of Sinden and Oswald • Sinden: in general wind increases with increasing demand • As wind increases, wind-chill (and therefore demand) increases • Thus demand and supply positively related over all

  32. One possible interpretation of Sinden and Oswald • Sinden: 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

  33. One possible interpretation of Sinden and Oswald • Sinden: 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

  34. One possible interpretation of Sinden and Oswald • Sinden: 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: in years with very high peak demand, wind is quite good • Corresponds to years where the lowest T is extreme => move left on line

  35. Conclusions • Characteristics 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)

  36. 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: • d.j.brayshaw@reading.ac.uk • Brayshaw, Dent, Zachary, J. Risk & Reliability (submitted) • Brayshaw, Troccoli, Fordham, Methven, Renewable Energy (2011)

  37. Storm tracks and the NAO Positive Negative 37 Figs: http://www.ldeo.columbia.edu/res/pi/NAO/

  38. Storm tracks and the NAO Positive Negative Path of weather systems affecting GB influenced by “slow” climate variations 38 Figs: http://www.ldeo.columbia.edu/res/pi/NAO/

  39. NAO, surface temperature and wind Correlation strength: January surface temperature vs NAO High NAO = warm windy winters Low NAO = cold still winters Left: from NOAA CPC website

  40. NAO, surface temperature and wind Correlation strength: January surface temperature vs NAO 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 assessment weather derivatives longer-term energy futures contracts High NAO = warm windy winters Low NAO = cold still winters Left: from NOAA CPC website

  41. Winter 2010: a strong NAO- Climatology Winter 2010 anomaly Less wind Surface wind More wind Cold Surface temperature Warm

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