The Effect of Wind Power on Nodal Prices in New Zealand
Le Wen Research Associate
Energy Centre The University of Auckland
Basil Sharp Professor of Economics
Chair in Energy Economics The University of Auckland [email protected]
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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
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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
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1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
MW
Hydro legacy
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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%
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New Zealand Electricity Market
Competitive generation State Owned Transmission
Local line companies
Regulated monopolies
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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.
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Balancing Supply & Demand
MWh Supply = Demand
A
B J
B
Demand
$/MWh
Bid Price
Wholesale/retail Also hedge market
Economic Rent to A
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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
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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
• .
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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?
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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 -
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Previous Literature
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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
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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.
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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
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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
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10
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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
Price Wind generation
Wind generation is less than average 114 MWhSpot price and low wind generation at BPE on 23 Oct, Tuesday 2012
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Negative relationship between spot prices and wind generation
(Node HAY)
(1) High wind generation
(2) Low wind generation
95
10
010
511
011
5
Win
d g
en
era
tio
n (
MW
h)
020
40
60
Price (
$/M
Wh
)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day
Price Wind generation
Wind generation is greater than average 56 MWhSpot price and high wind generation at HAY on 5 Jan, Thursday 2012
020
40
60
Win
d g
en
era
tio
n (
MW
h)
050
10
015
020
0pri
ce
($/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
Price Wind generation
Wind generation is less than average 56 MWhSpot price and low wind generation at HAY on 23 Oct, Tuesday 2012
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Positive relationship between spot prices and load (Node BPE)
(1) High wind generation
(2) Low wind generation
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10
012
014
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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 (
$/M
Wh
)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day
Price Load
Wind generation is less than average 114 MWhSpot prices and load when wind generation is low at BPE on 23 Oct, Tuesday 2012
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Positive relationship between spot prices and load (Node HAY)
(1) High wind generation
(2) Low wind generation
15
020
025
030
035
0
Lo
ad
(M
Wh
)
020
40
60
Price($
/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 (
$/M
Wh
)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24Time of day
Price Load
Wind generation is less than average 56 MWhSpot prices and load when wind generation is low at HAY on 23 Oct, Tuesday 2012
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Table 5. Summary Statistics of Main Variables Used in the Analysis VARIABLES Mean Standard Deviation Price ($/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
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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
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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%.
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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
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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
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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
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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.
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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
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Solar power in New Zealand
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Auckland
Christchurch
Hamilton
Tauranga
Dunedin
Wellington
Distributed solar power
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Solar potential of Auckland’s residential rooftops based on LiDAR data
Acknowledgements: Basil Sharp, Vincent Wang
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LiDAR data LiDAR (Light Detection And Ranging) uses laser light to sample the surface of the earth
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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
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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%
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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
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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
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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
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Contact :[email protected]