Electricity production from non-storable renewable energy resources

Transcription

1 Electricity production from non-storable renewable energy resources Renewable energy and the National Electricity Market: Issues & Challenges CEEM, 23 November 2005

2 Outline Hydro energy (storable for up to a few years) Wind energy (non-storable) Solar energy (non-storable) 2

3 Woolnorth wind farm 65 (+75) MW Tasmania s power stations (Tas Govt 2000) Pieman Mersey Bell Bay 240MW Musselroe wind farm (130 MW) Great Lake Rainfall (mm) King Derwent 500 Gordon 3

4 Hydro scheme ratings & average yields: total ave. yield = 1180 MW (HydroTas. & Tas Govt, 2000) Ave yield Pieman Bell Bay Derwent King Gordon Mersey Forth Great Lake 4

5 Distribution of 64 annual yields (Tas Govt 2000) Overall Hydro Tasmania s storages are currently 30% full which is 15% lower than this time last year. The Northern Headwaters are 77% full, down from 99% full last year ( 25/8/05) (105MW GT s purchased & storages at 40% at 7/11/05) 5

6 The drought risk: rainfall in Australia, 2002 What about wind? (WMO Annual Report 2002) Antarctic vortex strengthening & shrinking, taking rainfall south (ABC TV, 18/9/03) 6

7 Variability of wind energy Wind farm output varies due to uncertainty in the wind resource: Sub-hourly Hourly Daily Seasonal Annual (Giebel (2000) Riso National Lab, Denmark) 7

8 Variability in wind energy: estimate for SA ( 8

9 Spectral analysis of Danish long-term wind data (17 years of data) Spectral gap between weather and local turbulence phenomena (Sorensen, 2001, Fig 2.110, p194) 9

10 Forecasting the output of wind farms 30 minute horizon (FCAS & spot market): Turbulence spectrum - likely to be uncorrelated for turbines spaced > 20 km: Then % power fluctuations ~ N -0.5 eg for 100 identical wind farms spaced >20 km apart, %fluctuation in total power ~ 0.1x%fluctuation for 1 farm 30 minutes to ~3 hours: ARMA model best predictor of future output > 3 hours - NWP model best predictor: Key issue: predicting large changes in output of appropriate groups of wind farms 10

11 2-hour prediction for Lake Benton wind farm, USA 138 turbines, 103.5MW, hourly data (Hirst, 2001) Two-hour ahead prediction of wind power: MW Pred (T+2) = xMW(T) + [MW(T) - MW(T-1)] 11

12 Combined output of 2 wind farms 80 km apart (Gardner et al, 2003) 12

13 Cross-correlation function between the output powers of 2 wind farms 80 km apart (Gardner et al, 2003) 13

14 Cross-correlations between measured power outputs of German wind farms (Giebel (2000) Riso National Lab, Denmark) 14

15 Cross-correlations between 34 years of 12-hourly data for grid points in Northern Europe (Giebel (2000) Riso National Lab, Denmark) 15

16 Wind energy duration curve for Northern Europe (normalised to average) (Giebel (2000) Riso National Lab, Denmark) 16

17 3000 km 17

18 Predicting the output of a wind turbine 6, 12, 18, 24, 36 & 48 hours ahead (Focken et al, 2002) 48 & 36 hr predictions: Front timing later than actual 48 & 36 hr predictions: Front timing ok but not magnitude 18

19 Wind power scenario forecasting (Jende, 2005) Actual: ---- Aust Govt is spending $15m on a wind power forecasting system to facilitate high levels of wind power penetration 19

20 CSIRO Windscape TM model ( Windscape derives location-specific wind forecasts from a Numerical Weather Prediction model (Steggle et al, CSIRO, March 2002) 20

21 (Steggle et al, CSIRO, March 2002) Windscape predictions of annual mean wind speed at 65 m, showing nested model results More rapid changes in colour Electricity probably from non-storable imply renewable higher energy local resources turbulence CEEM

22 SEDA NSW Wind atlas ( 22

23 SA wind production - average diurnal ( - quite variable day to day 23

24 SA wind generation from daily wind data ( 24

25 Output of Lake Bonney 80 MW wind farm, SA (NEMMCO, 2005) Lake Bonney MW out MW Feb-05 00:00:00 01-Feb-05 02:00:00 01-Feb-05 04:00:00 01-Feb-05 06:00:00 01-Feb-05 08:00:00 01-Feb-05 10:00:00 01-Feb-05 12:00:00 01-Feb-05 14:00:00 01-Feb-05 16:00:00 01-Feb-05 18:00:00 01-Feb-05 20:00:00 01-Feb-05 22:00:00 02-Feb-05 00:00:00 25

26 Solar thermal electricity generation: SCEGS VI Plant, Kramer Junction, Cal, USA on a clear day ( 26

27 PV output & load in Sydney (Watt et al, 2005) 27

28 PV output & load in SA & Vic (Watt et al, 2005) 28

29 Conclusions Stochastic renewable energy resources: Each has its own particular characteristics Each brings new challenges for electricity industry restructuring (technical, economic, policy, regulation) Hydro energy: Storable for up to a few years in some cases Wind energy: Non-storable but potential for diversity between sites Solar energy: Non-storable but potential for diversity between sites Predictability is a key unresolved issue 29