A Review of Leyland and Transpower
Security of Supply Projections
Prepared for:
Andrew Smith
Manager Energy Markets Information and Services
Ministry of Economic Development
By
Tom Halliburton
25 September, 2002
Summary and Conclusions
This study reviews aspects of two reports that examine the adequacy of supply in the New
Zealand electricity system. These reports are Transpower’s “System Security Forecast 200102” and a draft of “Electricity Supply and Demand to 2015” to be published by Sinclair,
Knight, Merz and the Centre for Advanced Engineering (the Leyland Report). Transpower’s
report gives two sets of results:
 An overall generation shortfall assuming a “1 in 20”1 dry year with a probability of
shortfalls from 2006-07 assuming demand growth of 2.2% per annum, or from 200405 for high demand growth of 2.6% per annum.
 A probability of small shortfalls in supply (less than 0.5%) from 2001-02 onwards in
some parts of the country.
Leyland’s study, based on what he considers to be an approximately “1 in 20”2 dry year,
gives significant generation shortfall from 2003-04.
Both reports use years ending 30 June. A statement that shortfall might occur in 2003-04
indicates that shortfall is possible at any time from 1 July 2003 to 30 June 2004.
Modeling Approaches
Transpower’s two sets of results are from two different modeling approaches - the first is a
simple stack model using one year time steps which compares current capacity against
demand. Results are presented as figure 2.12 in its report. The second approach outlined in
an appendix to its report gives results from a stochastic optimization model (OCCAM) used
in conjunction with a transmission system model (SCNPD) for more detailed studies.
Leyland uses a more complex stack model, also with one year time steps, implemented on a
spreadsheet. This compares total demand over the year with the generation resources
available, accounts for some fuel constraints, and includes the HVDC link. Rules are
incorporated to estimate how individual plants would be loaded and what fuels would be
used. The Leyland model is difficult to review due to its complexity, especially as data are
not always separated from the model’s calculations.
Drawbacks for the New Zealand system for stack models in general include inability to
model the transmission grid, and no modeling of inflow variability, either within or between
years (other than by scaling the year’s total inflows), or of hydro storage, or of seasonal
patterns in load.
Use of Hydro Data
Leyland obtains mean hydro data by taking the average of actual outputs over the period
1984 - 2001, whereas better results would be obtained using the full record back to 1931.
Transpower inflow data for its stack model are thought to be derived from 72 years of
records, but the exact origin of some of the data is unknown within Transpower and therefore
the data cannot be readily audited. Transpower believe the Waikato data may be incorrect.
There are some weaknesses in inflow data used in both reports.
1
Transpower obtain the 1 in 20 dry year reduction by fitting a normal distribution to data and taking the mean
minus one standard deviation, which gives a 16.6% reduction in flows.
2 Leyland considers a 1 in 20 dry year to be a 30% reduction over six months.
314055-1
2
Differences in input assumptions
There are four major differences in the assumptions used by the reports reviewed:
 Dry year inflows - Transpower reduces total hydro energy by 4047 GWh, or 16.6% as
representing the lower 5 percentile on a normal distribution. From Ministry records,
the March 2002 year generation was about 3300 GWh below mean. Leyland models
the effect of a 30% reduction over six months only, by applying this reduction over
the full year3. This is not unreasonable given the limitations of the model, but is a
much more severe condition than that used by Transpower.
 Plant factors - Transpower uses a New Plymouth plant factor of 95% which is clearly
too high. Its assumed Huntly plant factor of 85% is perhaps reasonable for a dry
period, but not for the full 12 months as it has assumed - bearing in mind that even in
a dry year, much of the year will consist of apparently normal conditions. Leyland’s
maximum plant factor of 66% for Huntly for a normal year is somewhat pessimistic,
but he uses 90% plant factor for four months during a dry period. This performance
was exceeded in 1992, and accordingly Leyland’s figure seems reasonable.
 Water rights - Transpower assumes no loss of water rights. Leyland assumes hydro
water rights losses of 1030 GWh in a mean year, which would result in approximately
721 GWh loss in a dry year. These are rather high, at least in the short term.
 New plant - Transpower assumes no new plant commissions, whereas Leyland has
three categories of assumptions for new plant (committed, probable and possible).
Both studies used similar forecasts for total New Zealand demand, although Leyland’s
forecast for South Island growth is higher and seems to be too high. For New Zealand as a
whole Leyland uses growth averaging 1.72% per annum, while Transpower uses 2.01% per
annum for its medium growth scenario. Its medium growth scenario is used throughout this
analysis. Actual demand for the year ending 31 March 2002 was less than that for the
preceding year, because the 2001 winter supply situation suppressed demand. The figures
used in both reports for 2001-02 demand are consistent with the growth that would otherwise
have been expected based on 2000-01 data.
Summary of Output Results
The reports estimate shortfalls in supply as follows:
Table1: Year Shortfalls Begin
Mean Year
Dry Year
Leyland
Committed
projects only
2009-10
2003-04(2)
Transpower (1)
No new plant
2011-12
2006-07
(1) Results from Transpower simple stack model
(2) Leyland shows insignificant 4 GWh dry year shortfall 2002-03, 1609 GWh 2003-04.
3
The aim of the model is to determine when supply / demand mismatches may occur. Generally the effect of
such mismatches will be more severe in winter months. Given that the model uses one year time steps, it
applies inflow reductions over full years so as to avoid under representing potential low hydro situations.
314055-1
3
Table 2 below quantifies factors causing the differences in Table1 dates, for data applying to
a “1 in 20” dry year using parameters applying in 2001-02.
Table 2: Main Contributions to
Lower Leyland Supply
& Demand in a Dry Year
Lower thermal plant factors
Approximate hydro inflow water right loss
in dry year
Approximate hydro potential energy loss,
other causes
GWh(1)
2155
804 (2)
3057
(1) GWh differences apply to the 2001-02 year.
(2) In a mean year the lower hydro inflow potential attributable to water rights loss is 1030 GWh.
The above draws on results from the Transpower stack model. The results from the OCCAM
and SCNPD network modeling system indicate potential supply difficulties from now on,
although it is difficult to interpret these results and to compare them with those of its stack
model. It is unclear whether the short term supply difficulties stem solely from transmission
capacity limits or whether generation capacity limits contribute. The differences in results
from the OCCAM / SCNPD system are due to its more detailed representation of the actual
power system. Additional features represented include inflow uncertainty, reservoir
operations, the transmission system and decision making without perfect foresight.
Conclusions
Transpower’s simple stack model suggests a limited security risk until 2006-07 (or 2004-05
for the high demand growth scenario). There are, however, indications that Transpower’s
model is overly optimistic in respect of plant factors, and as a result it may understate the
risk.
Leyland’s more complex stack model shows significant security risk as early as 2003-04.
Leyland does, however, make rather pessimistic assumptions about loss of water rights.
Without the loss of water rights Leyland would generate a reduced shortfall in 2003-04 which
disappears in 2005-06 (due to new plant commissioning) but still exists in the following
years.
There remain significant differences between the Transpower and Leyland models in the way
they treat dry year hydro inflows. It is not possible to form an opinion as to which is a more
accurate representation of reality in respect of these inflows. Therefore, it is not possible to
form a firm judgment as to the real security risk situation.
Both Transpower and Leyland concede the limitations of their models. Further modeling,
using a more comprehensive model is needed to provide a sound assessment of the security
risk. Given the modeling that has already been done and the data available, a much sounder
assessment of the situation could probably be produced within a time-span of 2-3 months.
314055-1
4
Contents
Objective .................................................................................................................................... 6
Modeling Methodologies and Conclusions of Reports .............................................................. 6
Demand Forecasts ...................................................................................................................... 8
Generation ................................................................................................................................ 11
Large Hydro Schemes .................................................................................................. 11
Large Thermal Stations ................................................................................................ 12
Fuel Supplies ............................................................................................................................ 14
Dry Year Shortfall Sensitivity in Leyland Report ................................................................... 15
Summary of Differences .......................................................................................................... 15
Appendix 1: Power System Issues .......................................................................................... 17
Hydro System Features ................................................................................................ 17
Defining a Drought ...................................................................................................... 18
System Operation During Droughts............................................................................. 18
Ministry of Energy Planning Criteria .......................................................................... 19
Appendix 2: Power System Models ........................................................................................ 20
The OCCAM Model .................................................................................................... 20
SPECTRA .................................................................................................................... 20
SDDP ........................................................................................................................... 21
References ................................................................................................................................ 22
About the Author ..................................................................................................................... 22
314055-1
5
Objective
The objective of this report is to compare some aspects of two reports prepared to assess
security of supply of electricity in New Zealand.
Sinclair Knight Merz in conjunction with the Centre for Advanced Engineering is producing
the sixth edition of “Electricity Supply and Demand to 2015”. A draft copy of this was made
available for review, by its principal author, Bryan Leyland. Transpower has recently
published its “System Security Forecast 2001/02” which has a transmission focus, but
analyses the demand and supply balance on the basis of existing plant.
The objective of this study is to:
 review the projections and highlight the main sources of difference in the assessments
of security of supply risks
 quantify the relative effects on security of supply of these main sources
 comment on the future security of supply risk, in particular the next two to three years
Factors to be considered include:
 demand growth
 dry inflows
 thermal generation plant factors
 potential loss of water rights
Modeling Methodologies and Conclusions of Reports
Both reports perform simple adequacy tests on the electricity system using stack models these compare the maximum output over a year of the various generation resources with the
load. Both models use a one year time step. They do not attempt to simulate operation of the
system in performing this analysis. For a dry year, some measure of low inflows is applied to
reduce the output of hydro plants, while an assumed maximum utilization factor for thermal
stations takes account of the need for maintenance, breakdowns and the effect of demand
patterns on the ability of the system to absorb that station’s generation. Neither analysis is a
capable of considering the effect of:
 annual load pattern - greater energy consumption in winter
 seasonal inflow variability
 the decision making process - real decisions are made with imperfect foresight
 initial storage levels in lakes
 random plant failures
 transmission constraints - (Leyland considers HVDC constraints and losses)
After presenting its overall view of generation adequacy using as above using a stack model,
Transpower perform further studies using the OCCAM model. It is described in Appendix C
of the System Security Forecast and some model details are given in Appendix 2 of this
report. OCCAM takes some account of inflow variability (using high, mean and low inflow
scenarios), reservoir operation and transmission constraints. OCCAM uses weekly time
steps. In conjunction with a detailed transmission system model, SCNPD, it is used to
generate plots of average prices and unserved energy for various regions. These are
314055-1
6
presented in Appendix D of the System Security Forecast. Some examples of more detailed
power system models are given in Appendix 2 of this report.
The following table shows when generation shortfalls first occur in the Leyland analysis:
Year in Which Demand Exceeds Supply
Leyland Model
Scenario
Mean Year
Dry Year
Water rights
New Plant
loss?
Yes
Committed
835 GWh, 2009-10
1609 GWh, 2003-04
No
Committed
1218 GWh, 2011-12
805 GWh, 2003-04
Yes
All options (1)
821 GWh, 2019-20 291 GWh, 2003-04 (1)
No
All options
312 GWh, 20019-20
1141 GWh, 2014-15
(1) “All options” implies the commissioning of committed, probable, and possible stations.
(2) Shortfalls do not exceed 622 GWh until 20014-15, and are zero in several years before then.
Figure 2.12 in Transpower’s report gives a less pessimistic view from its stack model of
overall system adequacy than the Leyland Report. The table below presents results for
Transpower’s medium and high growth scenarios. The medium growth scenario is used
throughout this report unless otherwise stated.
Year in Which Demand Exceeds Supply
Transpower Stack Model
Inflows
Load Growth
Scenario
Mean Year
Dry year
Medium
2011-12
2006-07
High
2009-09
2004-05
In Appendix D of Transpower’s report, OCCAM model results are given. This model is used
to generate offer stacks for generators which are fed into the SCNPD model which simulates
transmission system operation. OCCAM shows small amounts for expected shortfalls in
OCCAM Model Results
Year Shortfalls
Region
Begin
North Isthmus
2001-02
Auckland
2008-09
Waikato
2001-02
Bay of Plenty
2001-02
Central North Island
2009-10
Taranaki
2008-09
Hawke’s Bay
2006-07
Wellington
Nelson2006-07
Marlborough
West Coast
2007-08
Canterbury
2001-02
South Canterbury
2008-09
Southland - Otago
-
314055-1
7
Energy supply, beginning in 2001-02 in four of the thirteen regions with most others
following at later dates. It is not known whether these shortfalls are due only to transmission
system effects, or whether generation shortages contribute.
Data for the Leyland model were adjusted until results similar to the Transpower stack model
were obtained.
 Assumption of water rights losses was removed, and Huntly’s maximum plant factor
increased to 80%, with committed plant only - mean year shortfall is delayed for 3
years to 2012-13, as for Transpower
 Assumption of water rights losses was removed, dry year definition changed from
30% loss of inflows to 15% loss, 80% Huntly plant factor - dry year shortfall is
delayed from 2003-04 until 2009-10, later than in Transpower’s model.
 Assumption of water rights losses was removed, dry year definition remaining at 30%
loss of inflows, with committed plants, 85% Huntly plant factor, 95% New Plymouth
plant factor, and operation of both these stations unconstrained by fuel supplies - dry
year shortfall delayed one year to 2004-05.
The additional energy due to the Manapouri second tunnel received special treatment by
Leyland to account for dry year effects. He considers that the second tunnel has no benefit in
a dry year when the station would be operating at low outputs. For this comparative analysis,
Leyland’s special treatment of Manapouri has been ignored when apportioning differences in
the models to various causes.
Leyland’s model effectively shows dry year problems are unavoidable, if his definition of a
dry year is accepted. He defines a dry year to have total New Zealand inflows 15% below
mean, but models the effect that this would have if the low flows occurred over a six month
period, with mean flows over the remainder of the year.
Appendix 1 shows hydro that total hydro storage is less than 20% of total annual inflow.
Hence a dry period of even less than six months can have a serious impact. Appendix 1 also
shows how North and South Island inflows are not strongly correlated. A low flow period in
one island alone might cause supply problems if the HVDC link becomes constrained
Demand Forecasts
Preparation of Transpower’s demand forecasts involved separately considering three
electricity use segments: domestic, commercial and direct supply industrial. The first two are
forecast using a time series model, while the third is treated as a constant, with some likely
growth increments added. Growth is allocated to regions in proportion to forecast population
changes. The forecast represents generation, and so includes transmission system losses. No
specific allowance is made for HVDC transmission losses. As this is a function of
generation, it can not be predicted at the forecasting stage. Transpower bases its forecasts on
the historical metered injections data, so it does not include embedded generation that does
not enter the grid. These small embedded generators simply reduce off-takes from the grid.
To produce the table below, embedded generation estimates from the Leyland report have
been added to Transpower’s data. The addition comprises Leyland’s total cogeneration
output minus those cogeneration resources specifically itemized in the Transpower report.
314055-1
8
This effectively adds to the forecast those cogeneration resources treated by Transpower as
embedded.
The Leyland report specifically forecasts load in each island, but excludes demand met by
cogeneration resources, adds 5% for AC transmission system losses, determines where
generation will be provided, and then calculates HVDC transfer. 9% loss is assigned to the
HVDC transfers, and finally North Island cogeneration plants outputs are added to get the
total generation requirement.
As can be seen from the tables below, the two reports use generally similar forecasts for total
New Zealand demand, when Transpower’s mean growth scenario is considered. Leyland
forecasts significantly more growth in the South Island. This higher growth must be
questioned because a significant part of the South Island load is accounted for by the Tiwai
aluminium smelter, so for the remaining smaller base the growth is effectively occurring at an
even higher rate than Leyland’s 2%. In a mean hydrological year, the higher South Island
growth results in lower HVDC losses, but in a dry year, it could cause problems through
constrained southward transfer on the HVDC link.
Forecast Demand, GWh - Including Transmission Losses
Leyland (1)
Transpower (Medium Scenario)
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
South
Island
North
Island
New
Zealand
South
Island
North
Island
New
Zealand
14322
14608
14900
15198
15502
15812
16129
16451
16780
17116
24225
24604
24990
25370
25769
26175
26588
27007
27434
27867
38843
39484
40119
40771
41432
42122
42853
43711
44501
45313
13923
14120
14323
14542
14758
14948
15132
15305
15474
15638
25477
26142
26826
27556
28263
28932
29579
30217
30849
31473
39400
40262
41149
42098
43021
43880
44710
45523
46322
47112
%
Transpower
exceeds
Leyland
1.41%
1.93%
2.50%
3.15%
3.69%
4.01%
4.15%
3.98%
3.93%
3.82%
(1) Leyland’s New Zealand total includes HVDC link loss. Individual island forecasts do not.
Forecast Demand Growth, % - Including Transmission Losses
Leyland
Transpower (Medium Scenario)
South
North
New
South
North
New
Island
Island
Zealand
Island
Island
Zealand
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
2.00%
2.00%
2.00%
2.00%
2.00%
2.00%
2.00%
2.00%
2.00%
1.57%
1.57%
1.52%
1.57%
1.57%
1.58%
1.58%
1.58%
1.58%
1.65%
1.61%
1.63%
1.62%
1.66%
1.74%
2.00%
1.81%
1.83%
1.42%
1.44%
1.53%
1.48%
1.29%
1.23%
1.15%
1.10%
1.06%
2.61%
2.62%
2.72%
2.57%
2.37%
2.23%
2.16%
2.09%
2.03%
2.19%
2.20%
2.31%
2.19%
2.00%
1.89%
1.82%
1.76%
1.70%
Ministry data for years ending 31 March 2001 and 2002 show total generation requirements
of 38646 GWh and 38148 GWh respectively. The drop for the 2002 year was due to the high
prices experienced during the winter of 2001. Transpower’s 2001-02 forecast represents a
1.95% increase on the Ministry’s actual 2000-01 data. Therefore it is a reasonable estimate
of the generation requirement that would have occurred in the absence of the unusually high
prices produced by low inflows in the winter of 2001.
314055-1
9
Leyland and Transpower demand forecasts for 2001-02 are very similar, with Transpower
1.41% higher. Transpower’s growth forecasts for its medium scenario are higher, giving
3.82% higher demand than Leyland by 2010-11. Demand forecasts are not a significant
factor in the different conclusions of the two reports.
314055-1
10
Generation
Transpower’s analysis assumes no new generation, to illustrate the need for new plant. The
analysis of Leyland’s report presented here uses the option giving the largest amount of new
plant - committed, probable, and possible options are all included.
Large Hydro Schemes
Leyland obtains mean hydro data by taking the average of actual outputs over the period
1984 - 2001. Transpower data is from an unknown source - data used for a previous study
was simply re-used. It is thought to represent a seventy two year average. Transpower staff
believe the Waikato figure to be too low, and Waitaki to be too high, but the New Zealand
total to be reasonable.
Comparison of Mean Hydro GWh
Aniwhenua
Mangahao
Matahina
Patea
Tongariro Scheme
Waikaremoana
Waikato River
Wheao
North Island total
2000-01
1931 - 1989
Leyland
Transpower CAE 1993 (1)
126.5
146
123.9
130
230.9
303
311
117.5
138
1288.2
1437
2624 (2)
439.6
540
504
4608.6
3173
3173
130.4
123
7065.6
5990
Clutha
Cobb
Coleridge
Highbank
Manapouri (3)
Waipori
Waitaki
South Island total
2673.6
190.6
243.9
82.6
4967.0
181.7
8194.2
16533.7
3905
195
295
101
5168
220
8554
18438
New Zealand total
23599
24428
4064
243
216
5062 (4)
8378
(1) This column’s data is from ref [1]. The data refers to inflows, not specifically to generation, ie
this suggests that no allowance has been made for high flow periods when spill is unavoidable.
(2) Tongariro resource consents may have changed this number. Improvements in modeling of
diversions prior to their commencement have been made since this data was prepared.
(3) Leyland and Transpower data for Manapouri includes the effect of the second tailrace tunnel
throughout this report, for ease of comparisons
(4) CAE data does not include the second tailrace tunnel at Manapouri. It is likely that the CAE
number includes the potential energy contained in water that must be spilled from Manapouri and at
the Mararoa diversion.
314055-1
11
South
Island
2000-01
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
16534
16021
16038
15844
15861
15698
15714
16057
17673
18374
19190
Mean Year Hydro Generation, GWh
Leyland
Transpower
New
South
New
North Island
North Island
Zealand
Island
Zealand
7066
6572
6590
6602
6608
6615
6622
6628
6635
6642
6648
23599
22593
22628
22446
22469
22313
22336
22685
24309
25016
25839
18438
18438
18438
18438
18438
18438
18438
18438
18438
18438
18438
5990
5990
5990
5990
5990
5990
5990
5990
5990
5990
5990
24428
24428
24428
24428
24428
24428
24428
24428
24428
24428
24428
%
Transpower
exceeds
Leyland
3.39%
7.51%
7.37%
8.11%
8.02%
8.66%
8.56%
7.13%
0.49%
-2.41%
-5.78%
Leyland assumes a loss of energy when resource consents are renewed. 500 GWh per year is
assumed to be lost in the North Island from 2001-02. Assumed total losses in the South
Island are 530, 740 and 920 GWh beginning in years 2001-02, 2003-04 and 2005-06
respectively. This estimate is based on that given by RJ Aspden in a paper written in
November 1991, revised in 1994, and with the comment made in 1996 by Aspden that he still
considered this to be reasonable - now six years ago. This estimate is too old to be reliable.
Outstanding consents to be renewed in the near future include Clyde, Roxburgh, the Waikato
River stations, and an appeal has been lodged regarding the Tongariro scheme. In the case of
the Waikato and Clutha systems, it is unlikely that there will be significant drops in the total
energy output of the hydro systems as no diversions are involved.
Transpower estimates of total mean hydro potential are 829 GWh (3.39%) higher than
Leyland’s in 2000-01, although differences for each island are significantly larger.
Transpower’s estimate becomes increasingly higher until 2006-07 due to the increasing
losses in water rights assumed by Leyland. This is a contributor to the greater shortfalls
reported by Leyland.
Large Thermal Stations
One of the most critical aspects of this type of study is the selection of maximum plant
factors for thermal stations. The plant factor determines the maximum total annual output of
the station, and represents the proportion of the year that the station could operate at full load.
Plant factor must generally be reduced from 100% to allow for forced outages (outages
caused by breakdowns), routine maintenance outages, planned overhauls, and the reduction in
output occurring during startups and shutdowns. Constraints on plant operation due to fuel
supplies, transmission system capacity etc would further reduce practical output capability.
Due to load variation over the year, and within each day, there is not always sufficient
demand to absorb the output of all thermal stations. At low load times of the year, in the
early hours of the morning in particular, it may be necessary to shut down the higher cost
thermal plants due to lack of load requirements, even during a dry period.
314055-1
12
Transpower and Leyland make different assumptions for Huntly and new Plymouth plant
factors:
Comparison of Plant Factors (%)
Huntly mean year
Huntly dry year
New Plymouth
Stratford Power CC
Otahuhu A
Otahuhu B
Southdown
Leyland
66.3
82 (1)
67.0
90.0
0.0
90.8
95.0
Transpower
85.6
85.6
95.0
93.5
0.0
93.9
100.1
(1) Applies a 90% plant factor for 8 months but during a dry year only, giving an effective plant
factor of 82% for the year.
Transpower’s figure of 95% plant factor for a sustained period at New Plymouth is unrealistic
for a station that is now approximately 27 years old and is one of the most inefficient of the
large thermal stations. Furthermore, Transpower does not assume that this station
decommissions, whereas Leyland assumes it closes at the end of 2008-09. Leyland assumes
New Plymouth capacity drops from 400 to 300 MW in 2002-03, whereas Transpower assume
it drops from 440 MW to 330 MW in 2003-04.
Huntly is now approximately 18 years old, which may affect its ability to run for prolonged
periods at high output. A further constraint on Huntly is imposed by restrictions on cooling
water supplies from the Waikato River - at periods of low flows and higher water
temperatures, the station output is limited. The possibility of prolonged operation at high
loads was demonstrated over the four months of the 1992 electricity shortage when Huntly
was run at plant factors exceeding 80% with an average of 96.2% from March to June.
Plant Factors (%) During 1992
Shortage from ref [2]
New
Month
Huntly
Plymouth
Nov 91
47.8
3.4
Dec 91
48.4
9.4
Jan 92
58.8
17
Feb 92
63.2
17.2
Mar 92
86.5
16.6
Apr 92
96.5
33.6
May 92
99.3
94.6
June 92
102.5
103.5
Comparison of Mean Maximum Annual GWh, 2001-02
Station
Huntly - mean year
Huntly - dry year
New Plymouth
Stratford Power CC
Otahuhu A
Otahuhu B
Southdown
Total - mean year
Total - dry year
314055-1
Leyland
Transpower
% Transpower
exceeds Leyland
5808
7183
2348
2791
0
2903
982
14832
16207
7500
7500
3662
2950
0
3250
1000
18362
18362
22.6
4.2
35.9
5.4
0.0
10.7
1.8
19.2
11.7
13
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
Mean Year Thermal Generation Capability, GWh
% Transpower
Leyland
Transpower
exceeds
Fossil
Geothermal
Fossil Geothermal
Leyland
14832
2442
18362
2436
16.9%
14245
2482
18362
2436
19.6%
14245
2482
17446
2436
15.9%
14245
2947
17446
2436
13.5%
17554 (1)
3342
17446
2436
-5.1%
17554
3342
17446
2436
-5.1%
17554
4235
17446
2436
-9.6%
17554
4651
17446
2436
-11.7%
15793 (2)
4851
17446
2436
-3.8%
15793
4851
17446
2436
-3.8%
(1) A new gas fired thermal plant is assumed to be commissioned for 2005-06 (e.g. Huntly combined
cycle).
(2) Leyland assumes New Plymouth decommissions at the end of 2008-09. Transpower does not.
While operation of Huntly and New Plymouth at high plant factors for periods of a few
months might be possible, a full year at 85% and 95% output respectively, as assumed by
Transpower, is not credible.
Leyland allows Huntly to operate at a plant factor of 66% for the full year, which may be
little pessimistic. However, when calculating dry year shortfalls, Leyland allows Huntly to
operate for four months of the six month dry period at 90% output. The intent is to capture
actual likely performance - the existence of a dry period is not likely to be clear for some
time, so Huntly would not begin maximum generation immediately. The adjustment is a
means of representing the lack of perfect foresight that exists in reality. This is a reasonable
assumption, given the limitations of the modeling methodology.
Plant factor assumptions are a major contributor to the different conclusions of the Leyland
and Transpower reports.
Fuel Supplies
Transpower does not model any fuel constraints in its stack model, and the OCCAM model
does not include these.
Leyland goes to considerable lengths to model fuel supplies - this is a major concern of his.
Huntly can burn gas or coal. New Plymouth can run on gas or oil. There is the possibility of
optimizing where gas is burnt to maximize the total output from these two stations but the
trade-off is not made explicitly in the model. Instead, Huntly is constrained until 2003 to a
maximum of 1000 GWh of coal burn, and is unrestricted on coal after that. New Plymouth is
able to generate up to 1000 GWh on oil at any time.
The size of gas reserves data used by Leyland are similar to the Ministry’s information:
314055-1
14
Maui reserves: Leyland models reserves of 767 PJ on 1 October 2001, compared with the
Ministry’s estimate of 740 PJ on 1 January 2002.
Pohokura reserves: Leyland also has the same value for the Ministry (600 PJ) and uses the
same production start date (2006). Leyland ramps production up in successive years,
allowing offtakes of 20 PJ, 40 PJ and then 60 PJ for remaining years. The Ministry offtake
estimate is 55 PJ per year.
Kupe reserves: Leyland uses a total of 285 PJ, while the Ministry estimates 300 PJ.
Dry Year Shortfall Sensitivity in Leyland Report
A study was carried out using the Leyland model to determine the impact of various features.
The base case discussed throughout this report indicates a dry year shortfall of 1609 GWh in
2003-04. If the assumption of water rights loss is removed, i.e. all water rights are retained,
the 2003-04 shortfall drops to 805 GWh. If it is further assumed that New Plymouth oil
firing capability is unrestricted, the shortfall drops a further 174 GWh to 631 GWh in 200304. Unlimited Maui gas and the assumption of a very high plant factor for New Plymouth
drop shortfall to 158 GWh.
Leyland Model - Sensitivity Study
Dry Year Shortfall (GWh)
2003-04
1609
2004-05
2350
2005-06
1300
2006-07
3538
2007-08
4234
2008-09
4521
No water rights loss
805
1546
0
1789
2883
3403
Add: No New Plymouth oil restriction
631
1372
0
1615
2709
3230
Add: Unlimited Maui
158
1372
0
0
80
777
Add: 85% New Plymouth plant factor
158
899
0
0
0
304
Base case - committed plant
Changes:
This sensitivity study shows that to significantly reduce dry year shortfalls in the Leyland
model it is necessary to make some very optimistic assumptions about the performance of
plant and the availability of fuel.
Summary of Differences
Amounts by which Transpower Mean Year Data
Exceed Leyland Data (GWh)
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
314055-1
Load
Hydro
Inflow
261
506
802
1124
1429
1625
1721
1558
1535
1469
1835
1800
1982
1958
2115
2092
1743
119
-588
-1411
Thermal
Maximum Geothermal
Production
3556
4143
3227
3227
-82
-82
-82
-82
1679
1679
-6
-46
-46
-511
-906
-906
-1799
-2215
-2415
-2415
Cogeneration
287
287
287
47
47
47
47
47
47
47
Amount
Transpower
Alternative Surplus Energy
Exceeds
Generation
Leyland (GWh)
115
115
185
185
227
377
377
377
403
403
5526
5793
4833
3782
-27
-96
-1435
-3312
-2409
-3166
15
Amounts by which Transpower Dry Year Data
Exceed Leyland Data (GWh)
2001-02
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
Load
Hydro
Inflow
(Approx.)
261
506
802
1124
1429
1625
1721
1558
1535
1469
3861
4566
4542
4669
4652
4762
4746
4501
3365
2870
Thermal
Maximum Geothermal
Production
2155
2742
1826
1826
-1483
-1483
-1483
-1483
277
277
-6
-46
-46
-511
-906
-906
-1799
-2215
-2415
-2415
Cogeneration
287
287
287
47
47
47
47
47
47
47
Amount
Transpower
Alternative Surplus Energy
Exceeds
Generation
Leyland (GWh)
115
115
185
185
227
377
377
377
403
403
6151
7157
5991
5091
1109
1172
166
-331
142
-286
Note: Above tables include commissioning of Leyland’s committed, probable and possible plants.
The first table above shows how the amount of extra energy assumed by Transpower declines
over time as Leyland’s model commissions additional plant. The dry year table above differs
in two respects from the mean year table. Leyland assumes additional output from Huntly
during a dry year, and he represents the dry year differently. The dry year inflow data above
compares Transpower’s 15% annual reduction with Leyland’s 30%. This is a device used by
Leyland to capture the impact of a six month mean period followed by a six month dry spell
in a single year, and is necessary because the modeling technique allows only one year time
steps to be used.
Key differences are hydro inflows, the definition of dry year inflows, thermal station plant
factors and the commissioning of new stations. This last factor can be discounted as
Transpower’s study is intentionally for existing plant only. Leyland deducts 1030 GWh from
hydro potential from 2001-02 due to loss of water rights which is pessimistic. Transpower’s
study is optimistic in using a plant factor of 95% for New Plymouth, and its 85% plant factor
for Huntly is realistic only for a few months of a shortage period, not for a full year.
The sensitivity study carried out using the Leyland model shows that the occurrence of
shortfall in 2003-04 in the event of low hydro system inflows is not sensitive to a wide range
of parameters. The quantity of shortfall varies, but it was not completely eliminated by
making a number of more optimistic assumptions for plant performance, fuel availability and
water rights renewals.
The critical aspect that cannot be resolved by using either of these stack models is the true
nature of a dry period. A model able to capture the details of hydrology and reservoir
management is essential to advance understanding beyond that possible from either of these
two models.
314055-1
16
Appendix 1: Power System Issues
Hydro System Features
Hydro storage capacity in the New Zealand system is small, apart from Lake Pukaki. The
diagram below from [1] illustrates hydro storage as a percentage of inflows, in energy terms:
Storage as Percentage of Total Inflows
Mean inflows
= 100%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
North Island
South Island
New Zealand
Bottom bar represents amount of storage
The small proportion of inflows that can be stored means that the system is relatively more
vulnerable to short duration droughts than other systems with larger storage.
Ni vs SI Inflow Variances
60.00%
50.00%
SI High / NI High
SI High / NI Low
40.00%
South
Island
Inflow
Variance
30.00%
20.00%
10.00%
0.00%
-10.00%
-20.00%
-30.00% SI
-30.00%
S I Low / NI High
Low / NI Low
-20.00%
-10.00%
0.00%
10.00%
20.00%
30.00%
40.00%
North Island Inflow Variance
On the other hand the lack of any strong correlation between North and South Island inflows
is an advantage, as shown in another diagram taken from [1]. When low flows occur in the
314055-1
17
South Island, there is not an especially high probability of low inflows in the North. Hence
the probability of the extreme situation of inflows being 30% below mean in both islands
over a six month period may be lower than assumed in Leyland’s report.
Defining a Drought
Leyland defines a low inflow event as a year with inflows 30% below mean for six months,
with mean inflows for the balance of the year. The intention is to test the effect of a six
month dry period, followed by six months of mean inflows, giving a 15% reduction over the
full year. Because the model only works in one year time steps, Leyland has to apply the
reduced inflows over the full year to test the ability of the system to handle the six month
drought. Transpower makes only the 16.6% reduction over the full year.
The most severe period of low inflows into the hydro system in recent history is the 1992
event. For the eight month period November 1991 to June 1992, inflows as a percentage of
mean were [2]:
North Island
91%
South Island
71%
New Zealand
76%
System Operation During Droughts
With the benefit of hindsight the 1991-92 drought was clearly a very extreme event, but this
was not apparent until its later stages. Total flows for the New Zealand system, month by
month as percentage of mean were as follows [2]:
October 1991
November
December
January 1992
February
March
April
May
June
July
107%
76%
72%
94%
95%
79%
66%
53%
61%
112%
The consequences of a lack of perfect foresight may be to delay operating all available plant
at maximum capacity. With hindsight this delay would clearly not have been the optimal
strategy. For example, during the 1992 drought Huntly did not begin running on all four
units until April 14th, and on July 15th one unit was taken out of service for repairs. With
perfect foresight, all four Huntly units would have been fully loaded months earlier. To more
accurately predict the consequences of low inflows, the decisions made by system managers
must be modeled using only the information that those managers have available at the time.
This condition is sometimes known as non-anticipative - the model must not anticipate future
events.
314055-1
18
A stochastic optimization model provides a realistic means of representing decision making
by risk neutral decision makers with access to all information. This type of model is aware of
the statistical behaviour of the inflows, and so as lake levels drop to lower levels than normal
for the time of year is likely to increase the dispatch of thermal plant. In 1992 ECNZ
planners were using a model of this type.
Ministry of Energy Planning Criteria
In the absence of reliable system simulation models, an adequacy of supply standard
was applied by the Ministry of Energy when carrying out long term generation system
expansion planning. Least cost analysis was performed to select the best way of
meeting this adequacy standard. See ref [1]. The standard ensured that planned
generating capacity was 7 percent above the central load forecast in a dry year. A dry
year was defined as having hydro inflows 85 percent of mean. The 7% margin was
intended to ensure that even in a dry year supply could be maintained in the face of
delays to the commissioning of new plant, unexpected equipment breakdowns, higher
than expected load growth, etc. The total annual demand only was considered - the
pattern within a year was not considered, so it is quite similar to the Transpower and
Leyland methods.
For the margins calculation the maximum possible generation at each thermal station
over the year was restricted according to fuel type:





diesel oil fired stations: 5% plant factor
heavy fuel oil fired stations: 15% plant factor
Stratford on Kapuni gas: 50% plant factor
other thermal stations: 70% plant factor
HVDC link: 80% plant factor
where:
Plant factor 
total annual generation per year
installed MW times hours in year
The margin calculation was as follows:
i. South Island dry year generation = total South Island mean year potential
generation times 0.85
ii. HVDC transfer = minimum of (HVDC capacity times hours in year times 0.8,
or South Island dry year generation - South Island Demand).
iii. North Island dry year generation = total North Island mean year hydro
potential generation times 0.85 + thermal station installed MW, times plant
factor appropriate to fuel type times hours in year
iv.
NI DryYear Generation  HVDC Transfer  NI Demand
Margin 
NZ Demand
314055-1
19
Appendix 2: Power System Models
Most optimal planning models have cost minimization as their objective. The prices for
electricity predicted by these models are not applicable to a market situation where prices are
determined by the offer strategies of the market participants. However the short run marginal
prices from the optimization provide a floor for market prices, in most cases. The quantity of
electricity available predicted by these models will be at least that actually available in a
market consisting of rational participants. The global optimization model has all information
available and so can make the truly optimal decisions, whereas market participants only have
a subset of that information available.
A brief summary of some stochastic power planning models follows. All these models
attempt to model inflow variability, reservoir operations, seasonal load variations, make nonanticipative decisions, and make optimal hydro-thermal trade-offs. OCCAM and SDDP
model the transmission system. These aspects can not be handled by stack models such as
those used by Transpower or Leyland.
The OCCAM Model
The OCCAM model is referred to in Appendix C of Transpower’s report. It is a
broad overview model of the New Zealand system, designed to capture many of the
essential features without going into great detail in any of them. It models six hydro
reservoirs with three possible scenarios for inflows (wet, normal and dry), thermal
power stations and major components of the transmission system. The default
transmission system consists of 18 nodes and 22 branches. It uses weekly time steps
with typically three load duration blocks per week. No fuel supply constraints are
modeled. It and is practical to solve for about a three year time horizon - solve time
becomes rather long beyond this. The solution process involves repeated solves of a
linear program.
SPECTRA
This is a two reservoir stochastic dynamic programming model designed for medium
term system operational planning and for long term system expansion studies and is
designed specifically for the New Zealand system. Its origins are the PRISM model
developed by the Ministry of Energy in the early 1980s. SPECTRA amalgamates all
the hydro stations in each island into one equivalent station and storage reservoir and
links them with the HVDC. It uses all the historical data to carry out a stochastic
optimization to determine water values. A detailed simulation phase models each
major hydro catchment separately, using rules to derive water values for each lake
from the island total calculated in the optimization phase. It has a limited ability to
model fuel supply constraints.
314055-1
20
SDDP
Power Systems Research Inc of Rio de Janeiro developed the SDDP model about
twelve years ago for a World Bank project to model Central American power systems.
Since then the model has undergone continual improvement. It is a multi reservoir,
hydro-thermal optimization, with transmission system constraints and losses, with
fuel constraints on thermal power stations. This is the most detailed model available
for long term studies of systems such as New Zealand’s, and it has been used to
analyse the power systems of many countries. The model was tested on the New
Zealand system in 1995 by ECNZ with good results, but was not pursued as the effort
involved in implementing a new cost based global optimization was not justified as
ECNZ moved into a new market environment.
314055-1
21
References
[1] “Reliability of Electricity Supply - Project Report”, Centre for Advanced Engineering,
Dec 1993.
[2] “The Electricity Shortage 1992”, Report of the Electricity Shortage Review Committee 1992, Dec 1992
About the Author
Dr Tom Halliburton holds the degrees of BE (Hons) and PhD in Electrical Engineering from
the University of Canterbury, and is a New Zealand Registered Engineer. His PhD was on
the subject of scheduling hydro-thermal power systems. He has spent most of his career in
the power industry, concentrating on power system optimization model development and
long term power planning studies. He has also worked in hydro power station operations and
maintenance, and high voltage substation design.
From 1996 until late in 2001, Tom worked in the United States, gaining experience in a
variety of electricity markets, and analytical and modeling techniques. Initially he worked on
model development at the US Department of Energy (Bonneville Power Administration) in
Vancouver, Washington. Then he moved to San Francisco where he developed a number of
models for hydro generation planning, scheduling and bidding at the Pacific Gas and Electric
Company. Before returning to New Zealand, Tom spent two years at Enron in Houston,
Texas, where he was responsible for power system and electricity market analysis in a
number of countries. As a member of Enron’s Research Group, he also developed
optimization based decision aid tools for natural gas traders, gas storage valuation models and
models for power marketing transactions.
He now works as a consultant specializing in power system modeling and analysis.
Contact details:
Tom Halliburton
95 Wyndham Road
Upper Hutt
Email:
Tel:
Fax:
314055-1
tom.halliburton@attglobal.net
64 4 972 9138
64 4 972 9139
22