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Measures of generator unit performance are uncertain.
An important issue is the granularity of the starting price models. In this case we start with the hourly power prices that have been directly linked to the daily gas prices. This allows us to disaggregate volatility and correlations down to the daily level (the minimum gas-price period) and ensure these critical profiles are not lost by an averaging process. More important, we are able to project the changing relationship between gas and power prices through time. There are thus two “random” factors that affect electricity and fuel prices.
Long-run (LR) factors such as technology, population changes, and gross domestic product (GDP) differences will result in a long-run random effect on prices. Long-term volatility tends to be small compared to the short-term shocks. These random effects will have a limited effect on individual years particularly in the near term, but an increasingly important effect over the long term. The effect will be to show an increasing variance over time. We assume that LR volatility does not mean revert and follows a standard Brownian motion process.
Random factors such as weather, outages, and short-run liquidity effects will be captured in the short-run volatility parameter. These short-run “shocks” are assumed to be temporary deviations from the equilibrium. This process tends to be more significant in driving what is commonly perceived as price volatility and will capture the now infamous price spikes within the electricity price process.
Global Energy and others throughout the industry have concluded that the short-run shocks are mean reverting. In other words, they will revert to the equilibrium price after some time has passed.
The mean-reverting process can be likened to applying a piece of elastic between the observed price and the equilibrium price. A random factor continues to be applied to the price as it moves through time, but as it moves further away from its equilibrium price a proportionately increasing force is applied to it to pull it back. The speed of mean reversion, a key input variable in this process, determines how quickly prices revert to equilibrium.
Once we have identified the short-run and long-run parameters, it is necessary to calculate the related correlations. In this analysis we are correlating all the fuel and electricity prices within a region for both the short- and long-run conditions.
Using our Planning & Risk stochastic simulation tool, Global Energy developed a range of prices that could occur in the future. Figure 6 shows the expected average monthly electricity price in Entergy compared to the 10th and 90th percentiles of the price forecast. The stochastic results do not represent a true confidence band that spans multiple months. Rather, these results reflect forecast “error bands” indicating that the resultant price distribution for each month is independent of the stochastic behavior depicted in previous months. 2
Generating Unit Operational and Economic Uncertainty
As part of its ongoing analysis of regional power markets, Global Energy examined the performance of three existing generation technologies under our current Global Energy Reference Case Price Forecast:
- Gas steam;
- Simple-cycle combustion turbine; and
- Combined-cycle combustion turbine.