Outline. Wind Energy in the National Electricity Market. Stochastic renewable energy resources. Wind farms as intermittent generation

Transcription

1 Outline Wind Energy in the National Electricity Market CEEM 2006 Stochastic renewable energy resources Intermittent generation- definition & issues Trends in wind farm installations in Australia Planning issues Network-related issues Power variability issues Forecasting; spot & derivative markets Commercial viability of wind farms in the NEM 2 Stochastic renewable energy resources Wind farms as intermittent generation Renewable energy (RE) fluxes are time-varying: Solar, wind, hydro (tidal), biomass, geothermal, wave They can be be partially predicted RE resources are often non-storable & publicly owned: There may be a public role in their prediction Generators transform RE resources to electrical energy: Generators are often privately owned & operated Risk issues associated with stochastic RE: Traditional electricity industry relies on storable primary energy Non-storable stochastic RE introduces a new set of risks due to: Spatial distribution of RE fluxes (shared risks) RE generator performance (individual & shared risks) Security & market impacts (shared risks) National Electricity Rules (NER) define intermittent generation as: A generating unit whose output is not readily predictable, including, without limitation, solar generators, wave turbine generators, wind turbine generators and hydro generators without any material storage capability Issues identified by NEMMCO: Forecasting; Frequency Control Ancillary Services (FCAS); voltage control; management of network flows 3 4

2 The power in the wind Australian wind resource (Estimate of background wind (m/s) AGO) Doubling the wind speed increases the power eightfold but doubling the turbine area only doubles the power. 5 6 The drought risk: rainfall in Australia, 2002 What about wind? (WMO Annual Report 2002) Comparing AusWEA forecast ( & readily acceptable (RA) wind capacity for Australia Qld NSW Vic SA Tas WA Aus Inst MW App MW Antarctic vortex strengthening & shrinking, taking rainfall south (ABC TV, 18/9/03) 7 Total MW RA MW

3 Australian wind farm planning AusWEA best practice guidelines: Australian wind farm planning experience to date Limited experience to date: Some strong support, some strong opposition Stages in the process (AusWEA guidelines): Site selection; feasibility; detailed assessment, development application; construction; operation; decommissioning State handbooks & planning protocols: Project-based, some variations between states Australian conditions favour large wind farms connected at high voltage Mixed federal, state & local government approvals process lacks coherence: Project based - may not manage cumulative issues & interactions well Other industries have a comprehensive planning framework, eg: Strong, state-based planning framework for the minerals industry 9 10 Network issues for wind farms #1 Networks are shared, centrally planned resources: Must limit network disturbances caused by wind farms Wind farms must survive disturbances from the network Renewable resources are often distributed differently from fossil fuel resources: Weak network conditions likely to be more common in Australia & New Zealand than Europe or North America Network must be built to carry peak flows: Want good estimates of aggregation & seasonal effects Benefits of staged development of wind resources: Network savings; reduced voltage & frequency impacts 11 Network issues for wind farms #2 Wind turbine starting & stopping transients: Severity can be alleviated by: Soft-start High wind-speed power-management Some wind turbine designs: May cause voltage distortions: Harmonics &/or transients May have poor power factor, eg: Uncompensated induction generator May not ride-through system disturbances Temporary voltage or frequency excursions However, wind turbine technology is rapidly improving 12

4 Wind turbine type comparison (Slootweg & Kling, 2003, Size of wind turbines used by Western Power ( Network connection issues & examples Approximate ability of a transmission line to accept a wind farm: 66kV 20MVA 132kV 100MVA 330kV 200MVA Constraints may be determined by several factors: Thermal, voltage, fault clearance, quality of supply Thermal ratings depend on line temperature & wind speed Relevant wind farm rating is its maximum output, not the sum of turbine rated powers: Coincident output of the connected wind turbines Connection costs to 330kV (Transgrid, 2002) Wind farm number Total wind MW Conn. cost $M Conn.cost $/kw 2, Important to capture economies of scale of grid connection 15 16

5 SA wind regions with existing transmission access (green) Still potential impacts on existing generation & interconnector rating SA regions with limited transmission access (yellow) Eyre Peninsula Backbone network upgrade to support 500MW wind Estimated cost of 275kV backbone upgrade: $140M or $280/MW assuming equally shared by 500MW of wind. Wind may not have to pay full cost of backbone upgrade. Approx. 500 km (Meritec, 2002) Managing supply-demand balance in the electricity industry Generator input power Thermal power stations Hydro generators Wind farms + Frequency is a measure of supply-demand balance: always varying due to fluctuations in the power flows associated with particular devices Wind energy is only one of many fluctuating power flows _ Load electrical power plus network losses Industrial Commercial Residential 19 20

6 Managing system security in the NEM Unreachable or unacceptable futures Emergency control Present state 5 min Possible futures managed Future state space managed by decentralised by decentralised decisions (market-based) decisions Possible futures managed by centralised decisions Time Growing uncertainty Simulated dispatch with 500MW wind in SA (Oakeshott, 2005) 3,000 2,500 2,000 1,500 1, NEMMCO concerns about wind energy (NEMMCO, 2003) Frequency control in normal operation: Frequency regulating service costs ~5 $/MWH Security control - largest single contingency Will wind farms ride-through disturbances? Interconnection flow fluctuations: Exceeding flow limit may cause high spot price Forecast errors due to wind resource uncertainty: Five minute dispatch forecast (spot price) Pre-dispatch & longer term (PASA & SOO) forecasts 22 19/12/ :30 20/12/ :30 20/12/ :30 20/12/ :30 20/12/ :30 21/12/ :30 21/12/ :30 21/12/ :30 21/12/ :30 22/12/ :30 22/12/ :30 22/12/ :30 22/12/ :30 23/12/ :30 23/12/ :30 23/12/ :30 23/12/ :30 24/12/ :30 24/12/ :30 24/12/ :30 24/12/ :30 25/12/ :30 25/12/ :30 25/12/ :30 25/12/ :30 26/12/ :30 26/12/ :30 26/12/ :30 26/12/ :30 27/12/ :30 27/12/ :30 27/12/ :30 27/12/ :30 28/12/ :30 28/12/ :30 28/12/ :30 28/12/ :30 29/12/ :30 29/12/ :30 29/12/ :30 29/12/ :30 Ladbroke OSB-AG Northern PLAYB-AG PPCCGT Torrens B Torrens A Quarantine Total Wind Load Total Wind 1000 MW wind, SA: 1% likelihood variation (ESIPC, 2005) 1 per month 1 per week 1 per day 24

7 Western Power s proposed wind penalty charge (c/kwh) (Western Power, 2002) Demand forecast errors South Australia,2004 Q4 (NECA, 04Q4 Stats, 2005) Spectral analysis of Danish long-term wind data (17 years of data) Spectral gap between weather and local turbulence phenomena 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 may be best predictor of future output > 3 hours - NWP model may be best predictor: Key issue: predicting large changes in output of appropriate groups of wind farms (Sorensen, 2001, Fig 2.110, p194) 27 28

8 SOO & ANTS (to 10 years) NEMMCO AWEFS spec. ( Purpose Dispatch POE 50% Frequency 5min Resolution 5min No. of intervals 1 Time to produce <10sec Leadtime 40sec Predisp 50% 5min 5 min 24x 5min <30sec 90sec Predisp 10%, 50% & 90% 30min 30min 2-3days <3min 60sec ST PASA 10%, 50% & 90% 30min 30min 8days <3min 60sec MT PASA 10%, 50% & 90% Weekly Daily 735days 10min 60sec 29 Wind energy in Forecasts the NEM CEEM are 2006 for individual wind farms & specified groups of wind farms 30 2-hour prediction for Lake Benton wind farm, USA 138 turbines, 103.5MW, hourly data (Hirst, 2001) Combined output of 2 wind farms 80 km apart (Gardner et al, 2003) Two-hour ahead prediction of wind power: MW Pred (T+2) = xMW(T) + [MW(T) - MW(T-1)] 31 32

9 Cross-correlation function between the output powers of 2 wind farms 80 km apart (Gardner et al, 2003) Cross-correlations between measured power outputs of German wind farms (Giebel (2000) Riso National Lab, Denmark) 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 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 35 36

10 S1: Infra-red satellite map (BoM Aust,1125 UTC 24/4/05) CSIRO Windscape TM model ( Windscape derives location-specific wind forecasts from a Numerical Weather Prediction model (Steggle et al, CSIRO, March 2002) SEDA NSW Wind atlas ( (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 Wind probably energy in the NEM imply CEEM higher 2006 local turbulence 39 40

11 Hampton Wind Farm, NSW (2x660 kw Vestas, connected to different 11 kv feeders) Issues for NEM spot market Wind farms will operate as price takers : Generate whenever wind is blowing 3 Second data being collected Turbulence probably fairly high at this site Induction generators may not ride through voltage dips well. NEM spot market prices are volatile with a rectangular price distribution: Prices are usually low, sometimes high Timing of high prices not easily predicted Value of wind energy in the spot market: Will depend on how regularly wind farms are producing when spot prices are high Price-demand plots for NEM regions NSW (top) & SA (bottom) Jan-Mar 2004 ($/MWH vs MW) (NECA, 04Q1 Stats, 2004) Smoothed NEM Regional Ref Prices (RRPs) since market inception (NECA, 04Q4 Stats, 2005) 43 44

12 Annual average RRP flat contract prices (NECA, 04Q4 Stats, 2005) Forward prices for wind energy Wind farms may have to accept a lower price than flat contract due to uncertainty in production: Daily Seasonal, Annual (Giebel (2000) Riso National Lab, Denmark) Renewable Energy Certificate Prices (A$/MWH) (Offer, 2003) Wind farms marginal at $70/MWH (PWC, 2002)

13 Conclusions on wind energy in the NEM Brings new challenges for electricity industry restructuring (technical, market design, regulation) Network connection issues: Wind energy distributed differently from fossil fuels Planning issues - visual & bird impacts: Regional, rather than project specific Forecasting & system security issues: Regional, rather than project specific Wind not competitive on NEM electricity price: Requires additional income from selling Renewable Energy Certificates (RECs) but now fully subscribed 49 Many of our publications are available at: 50