Market Performance: a
Hindcasting Perspective
EPOC Jul 2014
Version 2.1
Grant Telfar, Meridian Energy
July 2014
2
Introduction & Background
•
•
Over recent years the NZ electricity industry has
been buffeted by unprecedented levels of
dissatisfaction from the wider public, policy
makers, consumer groups, and regulators.
Dissatisfaction has been particularly focused on
market and regulatory structures and intensified
over the 2008-13 period due to:
−
−
−
−
−
−
•
Winter 2008: the 4th ‘dry winter’ since 2001.
Retail tariffs: up 70+% since 2001.
ComCom: the Wolak report alleging $4.3B of
excess profits since 2001.
2009 Ministerial Review: suggestion of poor
retail competition.
2010 Electricity Industry Act: suggestion of overreliance on public conservation campaigns and
mis-management of reservoir operation.
2012-3 the emergence of NZ Power, growing
awareness of fuel poverty, and equity issues.
Three years on from the implementation of the
EIA there is a natural question as to how the
‘new’ wholesale market is performing:
−
Especially in the light of the extreme dry of 2012
•
With market listing complete additional scrutiny
of industry and company performance is likely:
–
•
Hindcasting is an analytical approach that can
be applied to examine how decision support
tools, analysis, and key assumptions performed
after the fact:
–
–
•
A priori we forecast a range of future outcomes.
Ex post we observe actual market outcomes.
Meridian has undertaken five hindcast exercises
in recent years – the first in 2008 and most
recently in early 2013:
–
•
As well as a desire to understand and analyse key
industry drivers and their impact.
Hindcasting is seen by Meridian as part of good
internal management.
In early 2013, Meridian undertook an updated
hindcasting exercise:
–
–
–
In particular an examination of the 2012 year.
Seeking to isolate individual forecast assumptions,
their impacts, and to test their relevance.
Seeking to embed the hindcasting discipline within
Meridian to compliment its existing selfexamination process and metrics.
3
Updating Meridian’s Hindcasting Perspective
•
•
Meridian has undertaken five hindcasting
exercises in recent years
Some common conclusions were reached:
−
−
−
•
–
•
•
•
This is the type of view that regulators typically
apply on behalf of consumers and tax-payers.
Once outcomes have been assessed against this
metric then additional questions of appropriate
commercial performance may be posed.
A range of potential benchmarks can be
suggested for examining the NZ power system.
Traditionally in the NZ context stochastic
reservoir and power system models are applied:
–
–
–
–
Focusing on wholesale market outcomes.
Covering the Jul-2009 to Dec-2012 period.
As with previous hindcast exercises, we begin to
answer the question of market performance (and
Meridian’s ability to assess it) by first addressing
the question of what is an appropriate metric to
use in measuring the actions of the market.
It is cleanest to start with a metric that judges
behaviour from the perspective of what is best
for NZ:
–
We focus here on the most recent of these – the
2012 hindcast exercise:
−
−
•
Market outcomes are largely determined by
environmental factors – inflows and the unavoidable
costs of generating and managing the system.
The market is not a perfect reflection of a centrally
controlled benchmark – but it is surprisingly close to
one with reservoirs being managed in the best
interests of NZ.
A range of decision support models demonstrate a
good ability to reflect market outcomes.
•
Spectra
SDDP
DOASA
EMarket
From a NZ inc perspective most of these
models seek to balance the costs of excess
thermal fuel burn against the costs of excess
system shortage:
–
–
In the face of significantly uncertain hydro inflows.
To minimise the overall NZ fuel (offer) supply cost.
4
2012 Hindcasting
•
We configure a number of decision support
models to reflect the fundamental underlying costs
of electricity supply and demand in NZ:
−
−
•
Spectra: legacy ECNZ stochastic DP – 2 node
LPcon: Meridian internal stochastic DP – 22 node
Emarket: Energy Link market simulation tool – 2
node & 22 node configuration
Implicit in the above assumptions, we are now
only examining a key residual problem: the NZ
hydro-thermal reservoir management problem:
–
Approach intended to be broadly consistent with the
system short-run marginal cost (SRMC).
NOT a re-litigation of the market’s operational
decisions – rather we are using the models in the
role of a proxy ‘regulatory benchmark’.
–
The decision support models used are:
−
−
−
•
•
–
•
Beginning in Jul09:
1.
We update each model for events of Jul09-Dec12:
−
−
−
−
−
−
−
−
Storage conditions as at Jul2009.
Actual inflows and wind conditions.
Market geothermal and co-generation output.
Observed thermal and geothermal availability
(planned & unplanned outages).
Observed hydro and wind availability (planned &
unplanned outages).
Observed market HVDC configuration/outages.
Observed market NI and SI demand.
Assumed thermal SRMC offers and fuel costs.
2.
3.
•
All demand conditions, geothermal output, and
plant outages are assumed to be known with
perfect foresight – rather than via the application of
generic planning assumptions.
Whereas hydro and wind conditions are assumed
to be unknowable.
Thermal and hydro offer behaviours are assumed
to be broadly cost reflective (a modified SRMC).
Optimise the use of water in storage in the face of
historical hydrological uncertainty – ie all weekly
flows over the 1931-2012 period.
Run the model to simulate the 42 month Jul09 to
Dec12 period for all hydrological sequences.
Examine the single inflow ‘sequence’
corresponding to the Jul09 to Dec12 period.
Having done this we can now compare
modelled benchmark results to actual market
outcomes.
5
The Full Forecast Distribution
1,000
800
600
400
Dec12
Sep12
Apr12
Jun12
Jan12
Oct11
Jul11
Feb11
May11
Nov10
Aug10
May10
Mar10
Dec09
Sep09
-
SI Hindcast SRMC Prices
$250
$250
$200
$200
$150
$150
$100
$100
Dec12
Sep12
Jun12
Apr12
Jan12
Jul11
Oct11
May11
Feb11
Nov10
$Aug10
$May10
$50
Mar10
$50
Dec09
This holds true for both the ‘dry’ period of 2012 and the ‘wet’
period of 2009-11
Now we examine the single hydrological forecast
consistent with what occurred on the day …
1,200
200
We can observe that prices and storage have oscillated
within the extreme bounds of the feasible forecast
produced (in this case by Spectra) beginning in Jul2009.
–
•
1,400
Jun09
•
1,600
Jun09
–
We can view this via a familiar distributional perspective.
These price and storage charts show a wide range of
possible outcomes (in blue).
Superimposed over the top of these charts are the actual
market outcomes (in red) as occurred in reality.
1,800
Sep09
–
–
Pukaki Hindcast Storage
stored energyy [GWh]
Beginning in Jul09 each model forecasts a wide range
of outcomes over the following 42 month period
corresponding to 82 different historical hydrological
sequences:
baseload price [$/MWh]
•
6
Comparison: Storage
Sep12
Jun12
Mar12
Dec11
Sep11
Jun11
Mar11
Sep10
Jun10
Mar10
Dec09
Dec10
3,750
3,500
3,250
3,000
2,750
2,500
2,250
2,000
1,750
1,500
1,250
1,000
750
500
250
0
Sep12
Jun12
Mar12
Dec11
Sep11
Jun11
Mar11
Dec10
This broadly implies that there are no gross differences
in the use of water between the models.
Sep10
Of Hawea storage is not great.
Of Taupo storage is adequate.
Of the split between Tekapo and Pukaki is mixed but never
fully satisfactory.
PKI+TEK Storage LPcon
PKI+TEK Storage EMarket-LP
PKI+TEK Historical Average
Hyd Seq Beginning: 2009-2010
Jun10
•
•
•
3,750
3,500
3,250
3,000
2,750
2,500
2,250
2,000
1,750
1,500
1,250
1,000
750
500
250
-
Mar10
There are divergences between modelled outcomes but
these are small (in general).
Note that in all models (to differing degrees) the handling:
2,750
2,500
2,250
2,000
1,750
1,500
1,250
1,000
750
500
250
0
NZ Hydro Storage:
LPcon, Spectra & EMarket
Dec09
–
•
PKI+TEK Storage Market
PKI+TEK Storage Spectra
PKI+TEK Storage EMarket-SP
Total NZ storage levels have show a very good level of
alignment between the all modelled results:
–
Sep09
To some extent in terms of practical use storage outcomes
in all models are configurable and if storage/risks implied
here are considered unacceptable then outcomes can be
altered.
Sep09
–
In the 2012 situation this is closer to market outcomes.
Jun09
•
Hyd Seq Beginning: 2009-2010
2,750
2,500
2,250
2,000
1,750
1,500
1,250
1,000
750
500
250
-
Jun09
–
There are some divergences between modelled outcomes –
but these are not large.
However late 2011 and winter 2012 show notable
differences with LPcon holding the Southern lakes first
lower and then higher than Spectra and Emarket:
storage [GWh]
–
•
Pukaki & Tekapo Hydro Storage:
LPcon, Spectra & EMarket
The storage levels from all models show good
alignment over an extraordinary 42 month period that
has traversed both an extreme dry period and an
extended extreme wet period:
storage [GWh]
•
ALL Storage Market
ALL Storage LPcon
ALL Storage Spectra
ALL Storage EMarket-LP
ALL Storage EMarket-SP
NZ Historical Average
7
Comparison: Generation
Meridian Hydro Generation:
LPcon, Spectra & EMarket
Meridian total market generation:
150
100
50
Eg higher levels of spill encountered in LPcon – at both
Manapouri and Pukaki/Tekapo.
Weekly Thermal Generation:
LPcon, Spectra & EMarket
-
Market
Huntly
Average
-
50
100
150
200
week generation [GWh]
250
CCGT
OCGT
-
50
100
150
200
week generation [GWh]
250
Market
LPcon
Dec12
Sep12
Jun12
Apr12
Jan12
Oct11
Jul11
May11
Feb11
Nov10
Aug10
Spectra
EMarket-LPcon
Sep12
Market
50
Jun12
Lpcon
100
Mar12
Lpcon
150
Dec11
Spectra
200
Sep11
Spectra
250
Jun11
Emarket-Lpcon
300
Mar11
Emarket-Lpcon
350
Dec10
Emarket-Spectra
Hyd Seq Beginning: 2009-2010
Sep10
Emarket-Spectra
LPcon
EMarket-Spectra
400
generation [GWh]
Meridian Weekly Generation:
LPcon, Spectra & EMarket
Market
EMarket-LPcon
Thermal Generation:
LPcon, Spectra & EMarket
Thermal volumes are high in LPcon & EMarket-LPcon
reflecting higher levels of spill in all Southern catchments.
Thermal volumes are particularly high in EMarket-Spectra
due to Taupo spill effects (static reserves representation)
–
StdDev
Man+Wind
Spectra
Jun10
–
May10
Thermal generation trends are very similar between the
models:
Mar10
-
Dec09
•
200
Jun09
•
250
Dec09
There are minor differences between modelled generation
levels (highest in LPcon and all higher than market) driven
by spill differences:
300
Sep09
–
Reflected in the standard deviations of weekly generation.
350
Jun09
•
Hyd Seq Beginning: 2009-2010
400
Sep09
Is delivered in a more volatile manner by LPcon and
EMarket at the weekly level than suggested by Spectra –
perhaps reflecting the LP nature of the underlying analytical
engines, differences in tributary flow modelling, or both:
generation [GWh]
–
Mar10
•
EMarket-Spectra
8
Comparison: Market Prices
$175
$150
$150
$125
$125
$100
Sep12
Jun12
Mar12
Dec11
Sep11
Jun11
Mar11
Dec10
Hyd Seq Beginning: 2009-2010
$225
$225
$200
$200
$175
$175
$150
$150
$125
$125
$100
$100
BEN LPcon
BEN EMarket-LP
BEN2201 Market
BEN EMarket-SP
Sep12
Jun12
$Mar12
$25
$-
Dec11
$50
$25
Sep11
$50
Jun11
$75
Mar11
$75
Dec10
A range of reasonable sensitivities can drive a movement in
average prices of +/30%.
NI Spectra
SI Wholesale Market Prices:
LPcon, Spectra & EMarket
Sep10
–
HLY2201 Market
HLY EMarket-SP
Jun10
Note that price outcomes show significantly more
sensitivity to changes in models and/or assumptions
than do physical outcomes:
HLY LPcon
HLY EMarket-LP
Mar10
–
Sep10
$-
Jun10
$25
$-
Mar10
$50
$25
Dec09
$50
Sep09
$75
Dec09
–
Both the SI and the NI show good general alignment.
There are clearly short lived spot market events that
different models reflect quite different: eg Aug-2011
There are market events that none of the models reflect
well: eg Nov-2010
The largest divergence is the second half of 2012 where SI
Spectra and EMarket-Spectra prices increase to high levels.
$100
$75
Sep09
Market
$
64
$
54
$
49
$
91
$200
$175
Jun09
Spectra
$
62
$
57
$
51
$
75
$225
$200
The general shape of modelled prices from all models
track together reasonably tightly through high priced dry
periods and low priced wet periods:
–
–
•
EMarketSpectra
$
68
$
57
$
54
$
75
$225
Jun09
LPcon
Jul09-Dec12 $
64
FY2010
$
63
FY2011
$
53
FY2012
$
80
EMarketLPcon
$
54
$
48
$
37
$
72
Hyd Seq Beginning: 2009-2010
baseload price [$/MWh]
NZ Average Price
•
NI Wholesale Market Prices:
LPcon, Spectra & EMarket
Average modelled NZ prices over the full 42 month
period are similar, all in a $55-$65 range compared to
$64/MWh in the market:
baseload price [$/MWh]
•
SI Spectra
9
LPcon, Spectra, and EMarket Hindcast
Jul2009 to Dec2012
HydroSeq:2009-2012
Comparison: Market Outcomes
•
High level outcomes over the Jul-2009 to Dec-2012
period are broadly similar between all of the models:
–
–
–
•
•
•
–
Generation
There are variations in prices, spill, and thermal generation
with physical outcomes in general being better aligned than
pricing outcomes.
Storage levels are similar overall - but over the crucial 2012
period LPcon held Southern reservoirs at higher levels than
delivered by the other models (but lower during late 2011).
Modelled generation revenues are similar – driven by
modelled prices.
Through a period of starkly different market conditions
the consistency between the models is good.
In broad terms this means that all the models are
reflecting very similar inputs, market drivers, and
underlying economic rationales.
While the consistency of model outcomes – both
between different models and within the same model
but using different input assumptions is broadly good:
–
Prices
Primal outcomes (physical – eg storage) are significantly
more robust than dual (eg price, revenue) outcomes.
Eg, changes to thermal offer/fuel price assumptions or
shortage assumptions or ... may yield storage outcomes
that are only modestly different but present price outcomes
that vary much more significantly.
Dem Res
HVDC
Spill
Storage
SI
NI
SI-S/FIR
NI-S/FIR
Pukaki
Manapouri
MEL Wind
Taupo
Waikaremoana
TPD
Tekapo
Clutha
TPL NI Hydro
TPL SI Hydro
Todd Hydro
Other NI Hydro
Other SI Hydro
Geothermal
Co-Gen
Other Aux
Huntly
CCGTs
OCGTs
TOTAL Hydro
TOTAL Thermal
TOTAL Other
NI
SI
S->N
Pukaki
Tekapo
Clutha
Manapouri
Taupo
Waitaki Avg
Tekapo Avg
Hawea Avg
Taupo Avg
LPcon
$ 60.1
$ 66.7
$
2.9
$ 16.2
22,821
16,563
3,837
14,591
2,049
4,733
3,260
12,541
4,941
3,662
1,077
531
841
14,295
4,899
10,015
10,047
20,755
2,062
87,611
32,863
33,046
0
25
6,192
1,921
274
1,149
1,710
321
1,101
545
219
373
EMarketLPcon
$ 53.3
$ 54.7
$
2.3
$
5.8
23,849
16,977
3,862
14,707
1,972
4,599
3,560
12,237
4,743
3,253
925
1,033
737
17,064
4,605
5,016
10,468
20,129
2,024
88,594
32,620
30,547
7,009
1,140
270
1,198
390
321
1,220
507
215
401
EMarketSpectra
$ 64.3
$ 69.7
$
$
24,024
16,466
3,866
12,013
1,864
4,601
3,678
11,851
4,743
3,314
925
1,033
737
17,067
4,605
5,047
11,951
21,751
2,450
85,250
36,151
30,585
13
2
6,478
772
143
1,610
1,511
2,963
1,176
511
219
259
Spectra
$ 61.4
$ 62.3
$
$
23,430
16,698
3,869
14,258
1,998
4,629
3,488
12,840
5,339
3,571
1,111
799
790
17,628
4,899
6,537
9,158
20,370
2,027
88,951
31,555
32,932
6
80
7,100
1,397
508
934
320
701
1,071
550
125
468
Market
$
61.3
$
65.4
$
1.4
$
3.9
22,552
16,575
3,863
14,230
1,935
4,762
3,581
12,441
5,276
3,712
1,140
480
537
17,628
4,899
6,537
8,656
21,619
2,324
87,220
32,599
32,927
-
-
444
5,731
2,275
385
1,050
320
226
1,213
564
170
357
10
Conclusions
•
Market outcomes have broadly matched
benchmark modelled results:
−
•
−
•
Comfort should be taken from the fact that:
–
All models considered have matched high level
market outcomes.
Market outcomes through both an extreme dry
period (2012) and a prolonged extreme wet period
(2009-2010) have been largely driven by the
unavoidable costs of generating and managing the
system:
−
−
•
–
–
•
Market results are still dominated by hydrology.
Physical reservoir management is all about
managing what inflows turn up, when, and with very
limited storage capacity.
•
Market price outcomes are the result of balancing
escalating thermal costs against too much reservoir
spill in a fashion that ensures security of supply is not
compromised.
However care should be taken in drawing strong
conclusions about the goodness of outcomes
without an appropriate context and sensitivities:
–
−
This is to be expected.
However the gap between market outcomes and
benchmark outcomes is not large.
Imperfections should be considered in the light of the
significant successes – particularly in market
investment and the allocation of risk.
Price outcomes in particular show a large
sensitivity to changes in input assumptions.
Three years on from the implementation of the
EIA what conclusions can be drawn about the
‘new’ market arrangements:
–
The market has not been a perfect reflection of a
centrally controlled benchmark:
−
−
Reservoir operation (both physical and pricing) is
being managed in a rational manner.
Market outcomes are close to what could be
achieved under central control.
A range of different reservoir management tools
demonstrate a good ability to shadow the market.
Not a lot has changed – indeed similar behaviours
are seen in aggregate to those observed in
previous hindcast exercises:
•
•
–
–
Thermal stations generate when they ‘should’.
Reservoirs are being managed ‘appropriately’.
However market prices do appear more volatile
with short lived events pushing prices up quickly.
There is some small evidence of the market
holding Waitaki reservoirs higher than the
benchmark during 2012.
Additional Material
EPOC Jul 2014
Version 1.0
Grant Telfar, Meridian Energy
July 2014
12
Hindcasting at Meridian
•
Hindcasting is an analytical approach that
can be applied to examine how decision
support tools, analysis, and key
assumptions performed after the fact:
−
−
•
We seek to answer the question of market
performance by first addressing the
question of what is an appropriate metric to
use in measuring the actions of the market:
−
•
A priori forecast vs ex post outcomes.
Hindcasting is seen by Meridian as part of
good internal management.
Starting with a metric that judges behaviour
from the perspective of what is best for NZ.
Traditionally in the NZ context stochastic
reservoir and power system models are
applied:
−
−
−
−
−
Spectra
SDDP
DOASA
Emarket
...
•
From a NZ inc perspective these models
seek to balance the costs of excess
thermal fuel burn against the costs of
excess system shortage:
–
–
•
The decision support models used here:
−
−
−
•
In the face of significant hydro uncertainty.
To minimise the NZ fuel (offer) cost.
Spectra: legacy ECNZ stochastic DDP
LPcon: Meridian internal stochastic DDP
EMarket: Energy Link market simulation
tool – 2 node & 22 node configuration
LPcon is an in-house Meridian hydrothermal power system model:
−
−
−
−
−
−
−
2 stage optimisation/simulation.
Weekly resolution and a 15 block LDC.
Stochastic DDP creating water-values.
Simple thermal offers/cost.
DC load flow – 22 regions used.
Dynamic risk and reserves.
Diurnal wind characteristics.
13
Comparison: Pukaki and Tekapo Storage
PKI+TEK Storage Market
PKI+TEK Storage Spectra
PKI+TEK Storage EMarket-SP
Sep12
Jun12
Mar12
2,750
2,500
2,250
2,000
1,750
1,500
1,250
1,000
750
500
250
0
Dec11
Sep11
Jun11
Mar11
Dec10
Sep10
Jun10
Dec09
Sep09
Mar10
Hyd Seq Beginning: 2009-2010
2,750
2,500
2,250
2,000
1,750
1,500
1,250
1,000
750
500
250
Jun09
storage [GWh]
Pukaki & Tekapo Hydro Storage:
LPcon, Spectra & EMarket
PKI+TEK Storage LPcon
PKI+TEK Storage EMarket-LP
PKI+TEK Historical Average
14
Comparison: NZ Storage (No Manapouri)
3,750
3,500
3,250
3,000
2,750
2,500
2,250
2,000
1,750
1,500
1,250
1,000
750
500
250
0
Sep12
Jun12
Mar12
Dec11
Sep11
Jun11
Mar11
Dec10
Sep10
Jun10
Mar10
Dec09
Hyd Seq Beginning: 2009-2010
Sep09
3,750
3,500
3,250
3,000
2,750
2,500
2,250
2,000
1,750
1,500
1,250
1,000
750
500
250
-
Jun09
storage [GWh]
NZ Hydro Storage:
LPcon, Spectra & EMarket
ALL Storage Market
ALL Storage LPcon
ALL Storage Spectra
ALL Storage EMarket-LP
ALL Storage EMarket-SP
NZ Historical Average
15
Comparison: Meridian Generation
Meridian Hydro Generation:
LPcon, Spectra & EMarket
Hyd Seq Beginning: 2009-2010
400
350
250
200
150
100
50
Man+Wind
Market
LPcon
Spectra
EMarket-LPcon
Dec12
Sep12
Jun12
Apr12
Jan12
Oct11
Jul11
May11
Feb11
Nov10
Aug10
May10
Mar10
Dec09
Sep09
-
Jun09
generation [GWh]
300
EMarket-Spectra
16
Comparison: Thermal Generation
Thermal Generation:
LPcon, Spectra & EMarket
Hyd Seq Beginning: 2009-2010
400
300
250
200
150
100
Market
LPcon
Spectra
EMarket-LPcon
Sep12
Jun12
Mar12
Dec11
Sep11
Jun11
Mar11
Dec10
Sep10
Jun10
Mar10
Dec09
-
Sep09
50
Jun09
generation [GWh]
350
EMarket-Spectra
17
Comparison: NI Market Prices
NI Wholesale Market Prices:
LPcon, Spectra & EMarket
$225
$225
$200
$200
$175
$175
$150
$150
$125
$125
$100
$100
HLY LPcon
HLY EMarket-LP
HLY2201 Market
HLY EMarket-SP
Sep12
Jun12
Mar12
Dec11
Sep11
Jun11
$-
Mar11
$-
Dec10
$25
Sep10
$25
Jun10
$50
Mar10
$50
Dec09
$75
Sep09
$75
Jun09
baseload price [$/MWh]
Hyd Seq Beginning: 2009-2010
NI Spectra
18
Comparison: SI Market Prices
SI Wholesale Market Prices:
LPcon, Spectra & EMarket
$225
$225
$200
$200
$175
$175
$150
$150
$125
$125
$100
$100
BEN LPcon
BEN EMarket-LP
BEN2201 Market
BEN EMarket-SP
Sep12
Jun12
Mar12
Dec11
Sep11
Jun11
$-
Mar11
$-
Dec10
$25
Sep10
$25
Jun10
$50
Mar10
$50
Dec09
$75
Sep09
$75
Jun09
baseload price [$/MWh]
Hyd Seq Beginning: 2009-2010
SI Spectra
19
Comparison: HVDC Transfers
HVDC Transfers:
LPcon, Spectra & EMarket
Hyd Seq Beginning: 2009-2010
75
Sep12
Jun12
Mar12
Dec11
Sep11
Jun11
Mar11
Dec10
Sep10
Jun10
Mar10
Dec09
-25
Sep09
25
Jun09
energy transfer [GWh]
125
-75
Market
LPcon
Spectra
EMarket-LPcon
EMarket-Spectra
20
Conclusions
•
•
•
Market outcomes have broadly matched
benchmark modelled results.
Market outcomes through both an extreme
dry period (2012) and a prolonged extreme
wet period (2009-2010) have been largely
•
driven by the unavoidable costs of
generating and managing the system.
The market has not been a perfect reflection
of a centrally controlled benchmark:
−
−
•
However the gap between market outcomes
and benchmark outcomes is not large.
Imperfections should be considered in the
light of significant successes – particularly in
market investment and the allocation of risk.
Comfort should be taken from the fact that:
−
Reservoir operation (both physical and
pricing) is being managed in a rational
manner.
•
–
–
Market outcomes are close to what could
be achieved under central control.
A range of different reservoir management
tools demonstrate a good ability to shadow
the market.
However care should be taken in drawing
strong conclusions about the goodness of
outcomes without an appropriate context
and sensitivities:
–
Price outcomes in particular show a large
sensitivity to changes in input assumptions.
Three years on from EIA implementation
not a lot seems to have changed – similar
behaviours are seen to those observed in
previous hindcast exercises:
•
•
•
Thermal stations generate when they
‘should’.
Reservoirs are being managed
‘appropriately’.
Market prices appear reasonable.