Inventario de especies, individuos y condiciones de encierro a Condiciones de encierro

In order to be used for forecasting releases, the DR release model presented in Section 6.7 requires only forecasts of the estimated MWV, the spot price, and a series of inflows. As mentioned earlier in this chapter, inflow modelling is well-established, and there are many series of synthetic and historic inflows available. When combined with a synthetic or historic inflow sequence, the release model can be used in conjunction with the NZEM spot price model presented in the previous chapters of this thesis to form a simulation model of storage levels and spot prices over any period of time. The only inputs required in the simulation are a starting storage level and a series of inflows. The steps in the simulation are as follows.

Given a starting storage level S0 and an inflow series I, for each day t, starting from t=1:

1. Calculate the MWVt from St-1 using the relative storage level methodology.

2. Calculate the spot price, Pt, as a function of the MWVt using the NZEM price

model.

3. Calculate the release, Rt, as a function of the MWVt, Pt and It-2 to It+3 using the

DR release model.

4. Calculate St = St-1 + It – Rt.

Using these steps we can backcast over the sample period from 1 April 1999 – 30 June 2003 to assess the combined simulation model’s performance in forecasting storage levels over the period from which the parameters were estimated. The forecasted storage trajectories can then be used for forecasting prices, using the models presented in previous chapters.

Figure 6.12 below shows the actual storage levels from April 1999 to June 2003, as well as the median simulated storage levels and 95% simulation limits of the storage levels (i.e. 95% of simulated storage levels are within these levels). As can be seen, the simulated storage trajectory (red line) tracks the actual trajectory (blue line) very well over the majority of the sample period, with the exception of four continuous months in late 2000. During these months, actual releases were much less than simulated releases, which led the model to underestimate storage levels for a number of days. However,

releases were vastly underestimated in January 200120, which led to the two trajectories

converging again. 92.6% of the actual storage trajectory lies within the 95% simulation limits (dashed green lines), and, excluding those four months, only one day’s storage level is outside the limits.

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This is discussed further in the following chapter, which analyses release behaviour in this period in more detail

0 500 1000 1500 2000 2500 3000 3500 4000 4500 April 199 9 July 199 9 Oct ober 199 9 Janu ary 2000 April 200 0 July 200 0 Oct ober 200 0 Janu ary 2001 April 200 1 July 200 1 Oct ober 200 1 Janu ary 2002 April 200 2 July 200 2 Oct ober 200 2 Janu ary 2003 April 200 3 S to ra g e L e v e l (G W h )

Observed Storage Level Median Simulated Storage Level 97.5th Percentile

2.5th Percentile

Figure 6.12: Actual aggregate NZEM storage trajectory (blue line), median simulated storage trajectory using simulation model (bold red line) and 95% simulation limits (dashed green lines), April 1999 – June 2003

Simulating storage trajectories as in Figure 6.12 allows simulated relative storage levels also to be calculated, and it is from these that water values and prices can be estimated.

As a further form of model and data validation, Figure 6.13 shows the average releases for each day of the week over the sample period, both real and simulated. The lower releases on Saturdays and Sundays are clearly evident.

0 10 20 30 40 50 60 70 80

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Day of the Week

R e le a s e ( G W h ) NZ Real Release NZ Simulated Release

Figure 6.13: Average real releases per day and average simulated releases per day, April 1999 - June 2003

6.10

_Conclusions

This study of release behaviour in a market context has resulted in some important insights into the way the NZEM operates. Most important of these is the fact that high spot prices do not necessarily encourage generating companies to release a large amount more water than they would have normally. On the contrary, the lower the RSL appears to be (and the higher the MWV), the less will be released, as generating companies act conservatively to avoid running out of water. Therefore, releases are driven to a much greater extent by the relative storage level and the inflows than by the spot price.

The effect of the spot price on release is so small that it could even be concluded that the dynamics of the spot market have very little influence on how the generating companies manage their reservoirs. However, this is not entirely true. One component missing from this model is the contract level of the hydro generating companies. In New Zealand, each of the major generating companies is vertically integrated to a degree, presenting each

with a level of implicit retail contracts. As shown by Batstone (2003), risk-averse generating companies will generate to these contract levels, regardless of the spot price. Due to the dynamics of the spot market, a company has a limited incentive to deviate from this contract level. Assuming they have market power, if they generate more power,

the spot price will decrease and each MW they produce will earn less revenue21; if they

generate less, the spot price will increase and they will have to purchase power off the spot market at that higher price. As a result, with each company generating at or very near their contract level, net trading between companies on the spot market is likely to be

very minor. Therefore, while the actual level of the spot price has little influence on

release levels, the operation of the spot market (including the influence of contracts)

helps to determine how much generating companies will offer to generate each day.

With the introduction of a release model that accurately models market hydro reservoir storage trajectories, the combined price and release simulation model is now able to forecast and backcast over a wide range of inflow sequences. The most important consequence of this is that it can model market behaviour in periods where no market actually existed, as long as a sequence of inflows exists. This makes the combined model very powerful, and able to be applied in many different hypothetical and historic situations, several of which are explored in the following chapter.

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Generating an amount greater than their contract level will decrease the spot price, assuming the company has market power, however this may still be profitable as long as the marginal revenue they receive from this generation is greater than their marginal cost of generation. Although the spot price falls, the increase in revenue may still be sufficient to generate.

7

A

PPLICATIONS OF THE HYDRO

SIMULATION MODEL

7.1

_Introduction

As explained at the conclusion of the previous chapter, the price and release models developed in this thesis may be combined into a Monte Carlo simulation model of New Zealand’s aggregate storage levels and daily average spot prices, requiring as input only a starting storage level and an inflow sequence. This leads to several interesting applications of the model, some of which are detailed in this chapter. For example, in Section 7.2 two well-known hydrological events in New Zealand’s electricity history are examined in more detail to identify how the market might have been “expected” to behave, given the hydrological conditions observed. In Section 7.3, comparisons are made between expected market behaviour and the storage behaviour observed under the different regimes since 1980. Finally, in Section 7.4 a “long-run” price duration curve (PDC) is estimated, showing that prices in the 1999-2003 period have been, on average,

higher than would be expected in the longer term. The implications of this finding are discussed at the conclusion of the chapter.

The results presented in this chapter should be viewed with some prudence, however. Any forecasting or backcasting over a specific time period using a model whose parameters were estimated using a set of data from another time period should be examined with the knowledge that the conditions which generated the data in the two periods may be quite different. The release model attempts to estimate firms’ behaviour given specific market and hydrological conditions, however in this chapter other inflow sequences are used as input to estimate how the market may have behaved given a wider range of inflows. It should be considered that demand conditions (i.e. location and profile of demand) and the supply mix are not taken into consideration in the release model, and differences in reservoir management behaviour may be due to factors that are not modelled explicitly. Further discussion of this issue is provided in later sections of this chapter.