The Effect of Wind Power on Nodal Prices in New Zealandeneken.ieej.or.jp/data/6633.pdf · • New...

The Effect of Wind Power on Nodal Prices in New Zealand

Le Wen Research Associate

Energy Centre The University of Auckland

[email protected]

Basil Sharp Professor of Economics

Chair in Energy Economics The University of Auckland [email protected]

IEEJ：April 2016 © IEEJ2016

mailto:[email protected]



Outline

• Background – NZ renewable supply profile – NZ electricity market

• Impact of wind power on price – European experience – Evidence from NZ

• Cross sectional results • Spatial analysis

• Brief observations on solar


New Zealand’s Generation Profile


Hydropower Technology: Mature renewable generation source Storage ~ 6 weeks, efficiency ~ 85-95% Fast response to changes in demand Significant impact on environment Potential: Modest pumped hydro storage Run-of-river Integration with other renewables

0

100

200

300

400

500

600

700

800

900

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

MW

Hydro legacy


Geothermal Energy

Currently ~ 14% & in 2016 > gas


Wind Power

• Contributes around 5-6 % of electricity in NZ

• Wind & water negatively correlated

• Mature technology • Technology:

– Rotor efficiency – Low acoustic noise – Active wind speed pitch

variation • Return on investment also

depends on load factor – EU ~ 20%, NZ ~45%


New Zealand Electricity Market

Competitive generation State Owned Transmission

Local line companies

Regulated monopolies


Why does wind power matter?

• Wind power offers positive environmental and economic impacts.

• Wind contributes to the reduction of carbon emission from fossil fuel combustion and to sustaining a clean environment.

• Adding wind into existing power supply has led to lower electricity prices.


Balancing Supply & Demand

MWh Supply = Demand

A

B J

B

Demand

$/MWh

Bid Price

Wholesale/retail Also hedge market

Economic Rent to A


Economic impact of wind energy

ΔP

$/MWh

MW

D

SHW SLW

A

B C

E

D

G

F

PLW

PHW

Notes: D = demand SLW = supply with low wind SHW = supply with high wind PLW = Price with low wind PHW = Price with high wind


Motivation • The impact of wind power on prices has been

examined in Europe, heterogeneous support for RES & the electricity markets are to varying regulated.

• In contrast, the NZ market is open, no subsidies and all supply technologies compete in electricity whole sale market.

• System operator dispatches on basis of least cost.

• There are no subsidies to RES & market data provide ideal opportunity to examine impact of wind generation on wholesale prices

• .


Questions • What is the impact of wind power on nodal

price? • Does the wind penetration have a significant

effect on nodal price? • Does the impact of wind generation at one node

have an impact on prices at other nodes?


Previous Literature

Summary of estimated reduction in electricity price

Authors Country Model MOE Price Effect

(% Δ Price)

Jonsson et al. (2009) West Denmark Non-parametric 17.5 (penetration > 4%)

Morthorst (2007) West Denmark No model At >150 MW (5 – 14) At > 500 MW (12-14)

de Miera et al. (2008) Spain Simulation 11.7-25.1

Munksgaard & Morthorst (2008)

West Denmark East Denmark

No model 12 – 14 2-5

Weigt (2008) Germany Optimisation -

Sensfuss et al. (2008) Germany PowerACE -


Previous Literature

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17

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8

11

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4

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10

12

14

16

18

Sensfuss et al. (2008) Jonsson et al. (2009) Munksgaard & Morthorst (2008)

de Miera et al. (2008) Weigt (2008)

Figure 3. MOE Price Effect (EUR/Mwh) in 2009 Prices Source: Poyry


Data • New Zealand Electricity Authority’s Centralised Dataset (CDS) 2012

• Reasons for choosing 2012 data

1. Wind capacity increased over time and reached 623 MW in 2011. No wind plants were commissioned over the next three years.

2. In 2014, an additional 66 MW wind capacity added.

3. In 2012 wind accounted for 5% of energy generation in 2012 slightly higher than the 4% in 2011.


Accumulated Wind Capacity (MW)

0.23 4.13 35.85 36.33

168.23 168.33

319.33

464.18 465.39

622.94

682.74 689.54

0

100

200

300

400

500

600

700

800

1993 1996 1999 2003 2004 2005 2007 2009 2010 2011 2014 2015


Simplified Nodes

There are in excess of 240 grid injection points


Location of wind farms IEEJ：April 2016 © IEEJ2016

Negative relationship between spot prices and wind generation

(Node BPE)

(1) High wind generation

(2) Low wind generation

22

024

026

028

0

Win

d g

en

era

tio

n (

MW

h)

020

40

60

Price (

$/M

Wh

)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day

Price Wind generation

Wind generation is greater than average 114 MWhSpot price and high wind generation at BPE on 5 Jan, Thursday 2012

020

40

60

80

10

0

Win

d g

en

era

tio

n (

MW

h)

050

10

015

020

0P

rice (

$/M

Wh

)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day


Wind generation is less than average 114 MWhSpot price and low wind generation at BPE on 23 Oct, Tuesday 2012


Negative relationship between spot prices and wind generation

(Node HAY)



95

10

010

511

011

5

Win

d g

en

era

tio

n (

MW

h)

020

40

60

Price (

$/M

Wh

)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day


Wind generation is greater than average 56 MWhSpot price and high wind generation at HAY on 5 Jan, Thursday 2012

020

40

60

Win

d g

en

era

tio

n (

MW

h)

050

10

015

020

0pri

ce

($/M

Wh)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day


Wind generation is less than average 56 MWhSpot price and low wind generation at HAY on 23 Oct, Tuesday 2012


Positive relationship between spot prices and load (Node BPE)



80

10

012

014

016

0Lo

ad

(M

Wh

)

020

40

60

Price (

$/M

Wh

)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day

Price Load

Wind generation is greater than average 114 MWhSpot prices and load when wind generation is high at BPE on 5 Jan, Thursday 2012

10

015

020

025

030

0

Lo

ad

050

10

015

020

0P

rice (

$/M

Wh

)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day

Price Load

Wind generation is less than average 114 MWhSpot prices and load when wind generation is low at BPE on 23 Oct, Tuesday 2012


Positive relationship between spot prices and load (Node HAY)



15

020

025

030

035

0

Lo

ad

(M

Wh

)

020

40

60

Price($

/MW

h)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day

Price Load

Wind generation is greater than average 56 MWhSpot prices and load when wind generation is high at HAY on 5 Jan, Thursday 2012

20

025

030

035

040

045

0Lo

ad

(M

Wh

)

050

10

015

020

0P

rice (

$/M

Wh

)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day

Price Load

Wind generation is less than average 56 MWhSpot prices and load when wind generation is low at HAY on 23 Oct, Tuesday 2012


Table 5. Summary Statistics of Main Variables Used in the Analysis VARIABLES Mean Standard Deviation Price ($/MWh) 83.54 59.79 Lagged dependent variable Price at t-1 83.55 59.80 Generation type (MWh) wind 21.11 46.66 hydro 192.20 277.73 thermal 84.45 215.31 geothermal 48.92 155.32 load 285.30 298.55 Wind/load 0.29 0.80 Weekday 0.71 0.45 Normal season 0.50 0.50 Wet season 0.17 0.38 Dry season 0.33 0.47 Observations/nodes without considering load 96624/11 96624/11 Observations/nodes when considering load 87276/10 87276/10


Econometric Model IEEJ：April 2016 © IEEJ2016

Panel Estimations of Wind Generation on Node Prices Dependent variables: electricity price at t

Model 1* Model 2η Model 3* VARIABLES Fixed effects Random effects Fixed effects Lagged dependent variable YES YES YES Generation type wind -0.0232998*** -0.0140759*** -0.0224703*** [0.003] [0.002] [0.004] hydro -0.0088215*** 0.0003030 -0.0038882*** [0.001] [0.000] [0.001] thermal 0.0096993*** 0.0001010 0.0103440*** [0.001] [0.000] [0.001] geothermal 0.0009495 -0.0046425*** 0.0062111 [0.007] [0.001] [0.007] load / 0.0025091*** / / [0.000] / Wind/load / / -0.3492378* / / [0.208] Weekday 2.4044351*** 2.4049730*** 2.4821574*** [0.217] [0.232] [0.233] Seasonal controls Wet season -0.9239011*** -0.8097038*** -0.8740708*** [0.274] [0.293] [0.293] Dry season 0.4443342** 0.6172464*** 0.5324236** [0.218] [0.234] [0.234] R2 0.741 0.720 0.718 Observations 96613 87266 87266


Results • Without controlling the demand side (load), a marginal

increase of 1 GWh of wind generation is associated with a reduction of 23$ per MWh in nodal prices in New Zealand.

• A marginal increase of 1 GWh of thermal generation rises 10$ per MWh in nodal price.

• A 10% increase in wind penetration is found to reduce the nodal price by 3.4%.


Spatial Model for North Island


Nodes on google map


Spatial Coordinates & Plant Type

X-coordinate Y-coordinate BPE – Bunnythorpe -40.2809 175.6396 HAY – Haywards -41.150278 174.981389 HLY – Huntly -37.543889 175.152778 OTA – Otahuhu -36.9512 174.865383 TKU – Tokaanu -38.98113 175.768282 WKM - Whakamaru -38.419633 175.808217

North Island nodes Plant type OTA Thermal HLY Thermal, Wind WKM Geothermal, Hydro TKU Hydro BPE Wind HAY Wind


Spatial Weight matrix

BPE HAY HLY OTA TKU WKM BPE 0 0.319532 0.125337 0.101925 0.266768 0.186438 HAY 0.413842 0 0.124990 0.107428 0.195570 0.158170 HLY 0.096994 0.074683 0 0.409360 0.172460 0.246503 OTA 0.095225 0.077495 0.494210 0 0.146525 0.186545 TKU 0.188337 0.106607 0.157335 0.110724 0 0.436997

WKM 0.128957 0.084472 0.220326 0.138108 0.428138 0

Moran's I = 0.155 , p-value = 0.016 => significant positive spatial autocorrelation


Results (1) (2) (3) (4) (5) (6) (7)

VARIABLES Main Wx Spatial Variance Direct Indirect Total

wind -0.0128*** -0.0317*** -0.159*** -0.756*** -0.915*** (0.000517) (0.00104) (0.00350) (0.0174) (0.0209)

hydro -0.00495*** 0.0109*** 0.0156*** 0.106*** 0.122*** (0.000301) (0.000504) (0.00185) (0.00906) (0.0109)

thermal -0.00588*** 0.0120*** 0.0157*** 0.111*** 0.127*** (0.000175) (0.000328) (0.00116) (0.00572) (0.00687)

geothermal -0.0168*** -0.0696*** -0.274*** -0.343*** (0.00118) (0.00535) (0.0212) (0.0265)

Weekday 0.696*** 2.858*** 11.24*** 14.09*** (0.0486) (0.238) (0.940) (1.178)

Wet Season -1.770*** -7.355*** -28.92*** -36.27*** (0.0562) (0.220) (0.869) (1.088)

Dry Season 0.617*** 2.570*** 10.11*** 12.68*** (0.0560) (0.238) (0.939) (1.177)

rho 0.951*** (0.000425)

lgt_theta -4.193*** (0.298)

sigma_e 24.03*** (0.163)

Constant 5.053*** (1.441)

Observations 52,704 52,704 52,704 52,704 52,704 52,704 52,704 R-squared 0.325 0.325 0.325 0.325 0.325 0.325 0.325

Number of id 6 6 6 6 6 6 6


Discussion • In SDM model, results show that a marginal increase of

1MW in wind generation is associated with a reduction of NZ $0.91 per MWh in nodal price. 1MW increase in neighbourhood wind generation is associated with a price drop by NZ $0.32 per MWh.

• Adding 1MW geothermal supply leads reduction in nodal price by 0.34$.

• On the contrary, a marginal increase of 1 MWh in thermal generation causes a rise of NZ $0.13 per MWh in nodal price. 1MW increase in neighbourhood thermal generation is associated with a price rise by NZ $0.12 per MWh.


Can NZ Meet its Renewables Target?

Operating Typical annual output

Historic capacity factor

Consented Expected annual output

Total annual output

MW GWh MW GWh TWh Geothermal 840.7 6345 0.86 250.0 1886.8 8.2 Wind 623.0 2220 0.41 2725.5 9712.9 11.9 Hydro 5469.5 24900 0.52 244.5 1113.1 26.0 Total 46.2 Total with reduced productivity

41.6


Solar power in New Zealand

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Auckland

Christchurch

Hamilton

Tauranga

Dunedin

Wellington

Distributed solar power


Solar potential of Auckland’s residential rooftops based on LiDAR data

Acknowledgements: Basil Sharp, Vincent Wang


LiDAR data LiDAR (Light Detection And Ranging) uses laser light to sample the surface of the earth


Data divided into subsets for •Data management •Comparison of results per suburb

Census 2006 area divisions •Eliminated

• Only partial LiDAR coverage • Large areas with few residential houses • Areas with no buildings 334 suburbs

Calculate solar potential per rooftop •Annual solar energy per m2

Approach


Mt Eden Rating each rooftop’s best 14 m2

Rating Annual radiation Share in Auckland 1 <1100 kWh/m2 3.7% 2 1100-1200 kWh/m2 4.6% 3 1200-1300 kWh/m2 18.0% 4 1300-1400 kWh/m2 71.8% 5 1400-1500 kWh/m2 1.9%


Mt Eden vs Pukekohe West Comparing ratings for best 14m2


Auckland Council goal:

PV to power equivalent of 176 565 homes

Assume 8000kWh demand each: 1412.5 GWh

Some implications Share of houses

Number of houses

Best m2 Total annual radiation [GWh]

Assumed efficiency

Output [GWh]

1.00 505 598 99 705 0.05 4 985 0.25 126 366 14 2 453 0.12 294 0.10 50 559 36 2 512 0.12 301 0.50 252 799 14 4 862 0.12 583 0.50 252 799 36 12 249 0.12 1 470


Challenge of Solar • Auckland’s potential exceeds Melbourne (AU) &

Germany • Scheduling challenges for SO? • Residential dwellings

– Generation occurs when many families not at home • Marginal rate of return (currently low tariff) • Storage

– Access to lines company network (local monopoly) • Pricing issues for both solar & non-solar homes

• Commercial buildings – Greater potential for use during business day


Conclusions

• NZ well-endowed with renewable sources & wind contributes ~ 5-6%, high capacity factor

• It is a competitive source of electricity

• Results show that the nodal price is reduced by increased wind power penetration which benefits consumers.

• Integrating solar into supply brings new challenges, especially pricing use of distribution network & tariff


Contact :[email protected]

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