SISTEMA PARA CONTROL DE POZO Y PREVENCIÓN DE REVENTONES

This study uses NEMMCO’s reported pre-dispatch total demand

region, expressed in Megawatts (MW) by half-hour trading interval for the sample period. Total demand is defined by NEMMCO as the total forecast regional demand

18 _{Available for download from NEMMCO’s website at} http://www.nemmco.com.au/data/market_data.htm

against which a dispatch solution is performed. For any particular interval and region this is determined by NEMMCO as19:

∑

∑

∑

= G L NI AIL F D ADE

DT i i i i i( )

Where:

D tal Demand ing I

∑ Generator W ( , the itial alues f

scheduled generation units within n, measured at thei rator te and reported by SCADA – NEMMCO’s Superv ntrol ata Ac s

∑ Initia CADA sche -lo ion le the

interval;

N onn itial M Regio net of rconn ows

into and out of the region;

∑ “Total Allocated Interc Loss pres ∑(MW losses X

egional Loss Allocation) . “MW losses” represents actual power losses due to

T is To per Trad nterval;

Giis “∑ Initial M SCADA)” sum of in MW v or all

the regio r gene rminals isory Co And D quisition ystem;

Li is “∑Load l MW(S )”, the duled base ad generat vel for

Ii is“Net Interc ector In W into n”, the all inte ector fl

AILi is onnector es” is re ented by

R

physical leakage from the transmission system. Regional Loss Allocation is a NEMMCO pre-determined static loss factor for each interconnector;

Fi(D) is Demand Forecast, a per-interval demand adjustment that relates the demand

at the beginning of the interval to the target at the end of the interval;

ADE is “Aggregate Dispatch Error”, an adjustment value used by the NEM to account for disparities between scheduled and actual dispatch for all scheduled generation units in the region;

19 _{Source: NEMMCO Document “Regional Demand Definition” , Version 1.0, 17 June, 2004,} The demand determination model is here presented as it is in the NEMMCO demand definition document.

Summary statistics by region for each demand series are presented in Table 4.1.

7/12/1998 to 1/4/2005

NSW1 QLD1 SA1 SNOWY1 VIC1

Table 4.1: Descriptive Statistics for Total Demand (MW) by region

Mean 8042.87 5118.64 1451.41 29.58 5355.72 Median 8106.43 5157.00 1454.71 18.76 5335.44 Maximum 12838.14 8231.95 2833.22 736.89 8524.07 St. Dev 1287.40 837.72 263.574 27.79 761.55 Skewness 0.01 0.07 0.75 Minimum 3294.38 2023.65 731.48 0.00 2239.46 1.40 -0.04 Kurtosis 2.65 2.62 4.44 10.69 2.94 Jarque-Bera 568.30 771.56 20003.84 309405.6 54.44 # Obs 110719 110719 110719 110719 110719 (p-value) (0) (0) (0) (0) (0)

ADF Test Stat (1% crit.value) -20.55 (-3.43) -16.50 (-3.43) -25.61 (-3.43) -16.73 (-3.43) -20.46 (-3.43)

NSW1 has the highest mean, median and maximum demand observations of the five regions for the period. New South Wales is Australia’s most populous state so we would expect that demand for electric power to be highest in the NSW1 region. The other regions follow generally in order of state population, with VIC1 next highest, followed by QLD1, SA1 and SNOWY1. It should be noted that SNOWY1 represents a cluster of hydroelectric generation assets in the snowy mountains regions of New South Wales, rather than a geographical region or state like the other 4 NEM regions. As such almost all of the electric power produced by generation plant in SNOWY1 services demand arising primarily in New South Wales. To a lesser extent Victoria

and Queensland are serviced with power from SNOWY1 via the interconnectors joining those states with New South Wales.

The distributions of demand observations are slightly negatively skewed for VIC1, and positively skewed for NSW1, QLD1, SA1 and SNOWY1; and that the Demand series’ for NSW1, QLD1, SA1, SNOWY1 and VIC1, demonstrate positive kurtosis. Jarque-Bera (JB) statistics reject the hypothesis of normal distribution at the 1% level of significance for all 5 regions. Augmented Dickey-Fuller statistics reject the hypothesis of a unit root at the 1% level of significance, indicating stationarity consistent with the findings of Worthington et al. (2003)20.

An interesting characteristic of t ccurrence of zero demand from time to time, as illustrated by Figure one. Over the sample period, a zero level of demand is observed 1799 times. Figure 4.1 indicates that at times of zero demand we also observe a market-clearing price. This condition may be attributable to the business activities Snowy Hydro Pty Ltd, the operator of the Snowy Hydroelectric Scheme. A significant component of Snowy Hydro’s earnings derive from its

distributors and retailers, as protection against price spikes.

he SNOWY1 is the o

activities in the sale of hedge contracts to demand-side market participants, primarily

20 _{Interestingly, De Vany and Walls (1999) find that electricity prices in 10 of 11 regional markets in} the USA demonstrate significant non-stationarity. This difference may be a result of market operation and design - that the prices considered by their study were prices for supply determined by over-the- counter bilateral supply agreements.

0 20 40 60 80 100 0 10 20 30 40 50 DEMAND PRICE D and ( W Sp i A/ h )

5/9/1999, Illustrating the Occurrence of Zero Demand Associated With a Market Clearing Spot Price.

/operator sells cap-style option contracts on spot electricity with a ecific exercise price over a specified quantity of electricity. In the first instance it

.

t holder the excess of the spot price over e exercise price for the specified quantity of electricity. Hydroelectric generation is

e m M ) o t Pr c e ( $ M W 5/9 6/9 7/9 8/9 9/9 10/9 11/9

Figure 4.1: SNOWY1 Half-Hourly Demand and Spot Price for the Week Commencing

The generator sp

receives premium income for the sold option If the exercise price of the cap is exceeded, cap is in the money and the holder will exercise their right under the cap. On exercise the generator pays the contrac

th

described as a “fast-start” generation technology – hydroelectric generation plant can typically be called into production within one to two minutes of activation21. This capacity to generate and to commence production quickly provides a natural hedge

from the dam and falls through large pipes to a generation plant at the foot of the dam wall. The kinetic can usually be started o

coal, oil or gas-fired pl

21 _{In hydroelectric power generation, water is stored in a reservoir behind a dam wall. Water is released} energy of falling water drives generator turbine blades to produce electrical energy. The flow of water r stopped within one to two minutes. Other generation technologies, such as ant, require that fuel be burned to heat water, producing steam that turns generator turbine blades to produce electricity. Gas turbine and some oil-fired plant can be called into production in the order of 20 to 30 minutes, with coal-fired plant requiring 8 to 48 hours for orderly start-up and shutdown. (source: “Flipping the Switch”, Salomon Smith Barney, 1998).

against the exercise risk of the sold contract. The generator can bid its production capacity into the pool at the exercise price and quantity of its sold contracts to cover its exercise risk. If the spot price exceeds the exercise price of the sold cap, the operator will be called into production at its bid price and level of production, receiving the spot price over the production quantity specified by its sold contracts. As a result a quantity of electricity is produced by the SNOWY1 generation assets and sold at a prevailing spot market price, but that quantity sold is not necessarily associated with a level of physical demand in the SNOWY1 region. On this basis, Snowy Hydro effectively operates as a peak producer with its generation assets dormant for much of the time and only producing when prices are high but with a supplementary income stream of premiums earned from the sale of option contracts.