wind energy in the national electricity market · some wind turbine designs: – may cause voltage...

© CEEM 2006

Wind Energy in the National Electricity Market

2Wind energy in the NEM © CEEM 2006

OutlineStochastic renewable energy resourcesIntermittent generation- definition & issuesTrends in wind farm installations in AustraliaPlanning issuesNetwork-related issues Power variability issues– Forecasting; spot & derivative markets

Commercial viability of wind farms in the NEM


Stochastic renewable energy resourcesRenewable 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)


Wind farms as “intermittent generation”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


The power in the wind

Doubling the wind speed increases the power eightfoldbut doubling the turbine area only doubles the power.


Australian wind resource(Estimate of background wind (m/s) – AGO)


Antarctic vortex strengthening & shrinking, taking

rainfall south(ABC TV, 18/9/03)

The drought risk: rainfall in Australia, 2002 What about wind? (WMO Annual Report 2002)


Comparing AusWEA forecast (www.auswea.com.au) & readily acceptable (RA) wind capacity for Australia

8900500500500220031002100RA MW

193010028012003008013Total MW

132067220800207620App MW

6102967400921713Inst MW

AusWATasSAVicNSWQld


Australian wind farm planning

AusWEA best practice guidelines:– www.auswea.com.au

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


Australian wind farm planning experience to date

Limited experience to date:– Some strong support, some strong opposition

Mixed federal, state & local government approvals process lacks coherence:– Project based - may not manage cumulative issues &

interactions wellOther industries have a comprehensive planning framework, eg:– Strong, state-based planning framework for the

minerals industry


Network issues for wind farms #1Networks 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 AmericaNetwork 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


Network issues for wind farms #2Wind turbine starting & stopping transients:– Severity can be alleviated by:

Soft-startHigh 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 disturbancesTemporary voltage or frequency excursions

– However, wind turbine technology is rapidly improving


Wind turbine type comparison(Slootweg & Kling, 2003, http://local.iee.org/ireland/Senior/Wind%20Event.htm)


Size of wind turbines used by Western Power (www.wpc.com.au)


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 supplyThermal 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)

15028.32004

18017.71002

65012.9201

2,50012.751

Conn.cost$/kW

Conn. cost $MTotal wind MW

Wind farm number

Important to capture economies of scale of grid connection


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

(Meritec, 2002)

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


Managing supply-demand balance in the electricity industry

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

Thermalpower stations

Wind farms

Hydrogenerators

Industrial

Commercial

Residential

Generator input power Load electrical powerplus network losses

+ _


Future state space managedby decentralised decisions

Managing system security in the NEM

Possible futures managedby decentralised(market-based) decisions

Possiblefuturesmanaged bycentraliseddecisions

Presentstate

Growing uncertainty

Time

5 min

Unreachable orunacceptable

futures

Emergency control


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


Simulated dispatch with 500MW wind in SA(Oakeshott, 2005)

0

500

1,000

1,500

2,000

2,500

3,000

19/1

2/20

02 1

9:30

20/1

2/20

02 0

1:30

20/1

2/20

02 0

7:30

20/1

2/20

02 1

3:30

20/1

2/20

02 1

9:30

21/1

2/20

02 0

1:30

21/1

2/20

02 0

7:30

21/1

2/20

02 1

3:30

21/1

2/20

02 1

9:30

22/1

2/20

02 0

1:30

22/1

2/20

02 0

7:30

22/1

2/20

02 1

3:30

22/1

2/20

02 1

9:30

23/1

2/20

02 0

1:30

23/1

2/20

02 0

7:30

23/1

2/20

02 1

3:30

23/1

2/20

02 1

9:30

24/1

2/20

02 0

1:30

24/1

2/20

02 0

7:30

24/1

2/20

02 1

3:30

24/1

2/20

02 1

9:30

25/1

2/20

02 0

1:30

25/1

2/20

02 0

7:30

25/1

2/20

02 1

3:30

25/1

2/20

02 1

9:30

26/1

2/20

02 0

1:30

26/1

2/20

02 0

7:30

26/1

2/20

02 1

3:30

26/1

2/20

02 1

9:30

27/1

2/20

02 0

1:30

27/1

2/20

02 0

7:30

27/1

2/20

02 1

3:30

27/1

2/20

02 1

9:30

28/1

2/20

02 0

1:30

28/1

2/20

02 0

7:30

28/1

2/20

02 1

3:30

28/1

2/20

02 1

9:30

29/1

2/20

02 0

1:30

29/1

2/20

02 0

7:30

29/1

2/20

02 1

3:30

29/1

2/20

02 1

9:30

Ladbroke OSB-AG Northern PLAYB-AG PPCCGT Torrens B Torrens A Quarantine Total Wind Load Total Wind24Wind energy in the NEM © CEEM 2006

1000 MW wind, SA: 1% likelihood variation (ESIPC, 2005)

1 per day

1 per week

1 per month


Western Power’s proposed wind penalty charge (c/kWh) (Western Power, 2002)


Demand forecast errorsSouth Australia,2004 Q4 (NECA, 04Q4 Stats, 2005)


Spectral analysis of Danish long-term wind data (17 years of data)

Spectral gap between weatherand local turbulence phenomena

(Sorensen, 2001, Fig 2.110, p194) 28Wind energy in the NEM © CEEM 2006

Forecasting the output of wind farms30 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


SOO & ANTS (to 10 years)

30Wind energy in the NEM © CEEM 2006Forecasts are for individual wind farms & specified groups of wind farms

NEMMCO AWEFS spec. (www.nemmco.com.au)

60sec10min735daysDailyWeekly10%, 50% & 90%

MT PASA

60sec<3min8days30min30min10%, 50% & 90%

ST PASA

60sec<3min2-3days30min30min10%, 50% & 90%

Predisp

90sec<30sec24x 5min5 min5min50%Predisp

40sec<10sec15min5min50%Dispatch

LeadtimeTime to produce

No. of intervals

ResolutionFrequencyPOEPurpose


2-hour prediction for Lake Benton wind farm, USA138 turbines, 103.5MW, hourly data (Hirst, 2001)

Two-hour ahead prediction of wind power:MWPred(T+2) = 2.7 +0.9xMW(T) + [MW(T) - MW(T-1)]


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


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 ok but not magnitude

48 & 36 hr predictions: Front timing later than actual


Wind power scenario forecasting(Jende, 2005)

Actual: ----

Aust Govt is spending $15m ona wind power forecasting system to facilitate high levels of wind power penetration


S1: Infra-red satellite map (BoM Aust,1125 UTC 24/4/05)


CSIRO WindscapeTM model (www.clw.csiro.au/products/windenergy)

Windscape derives location-specific wind forecasts from a Numerical Weather Prediction model

(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 probably imply higher local turbulence

(Steggle et al, CSIRO, March 2002)


SEDA NSW Wind atlas(www.seda.nsw.gov.au)


Hampton Wind Farm, NSW (2x660 kW Vestas, connected to different 11 kV feeders)

3 Second data being collected

Turbulence probably fairly high at this site

Induction generatorsmay not ride throughvoltage dips well.


Issues for NEM spot marketWind farms will operate as “price takers”:– Generate whenever wind is blowing

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 NEMregionsNSW (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)


Annual average RRP flat contract prices (NECA, 04Q4 Stats, 2005)


Forward prices for wind energyWind 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)

20

25

30

35

40

45

50

55

60

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 202048Wind energy in the NEM © CEEM 2006

Wind farms marginal at $70/MWH(PWC, 2002)


Conclusions on wind energy in the NEMBrings 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 subscribed50Wind energy in the NEM © CEEM 2006

Many of our publications are available at:www.ceem.unsw.edu.au

wind energy in the national electricity market · some wind turbine designs: – may cause voltage...

Documents