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Price Risk Management: Electric Power vs. Natural Gas

Fortnightly Magazine - February 1 1996

the previous day's price. Day-to-day variation in gas commodity prices are limited (em generally up to 5 percent per day.

s Monthly and Seasonal. Monthly variation in gas price is likely to be larger than for power. Seasonal variation can be significant in power markets with large amounts of hydro generation.

s Yearly. Gas prices are likely to vary more from year to year than power prices.

One reason for these discrepancies in volatility lies with the storage advantage enjoyed by gas. Working gas storage capacity runs about 20 percent of average demand; storage delivery capacity is about 40 percent of peak day demand. By contrast, pumped storage capacity in the power sector totals only about 3 percent of peak demand.

Withdrawals from storage in the gas industry can also exert a large impact on gas prices. Since the physical act carries a low cost, storage withdrawals are determined in large part by expectations of future commodity prices. And because price expectations are volatile, they add significantly to gas market volatility. In the power sector, by contrast, the competitive price is set in every hour by the variable cost of the marginal power generator. Storage plays a minor factor.

Short-run variable costs also differ significantly.

Once a well is in place, the variable costs of producing natural gas are very low relative to the full long-run costs (e.g., $0.20/MMBtu versus $2.00/MMBtu), because production is capital intensive. Thus, no variable cost floor can limit short-run competitive prices. Other factors, such as interfuel competition, keep short-run gas prices above $0.20/MMBtu. However, lack of a cost floor contributes to price volatility.

But on the other side, the variable costs of producing electrical energy are high, because once the plant is built, the variable costs are the fuel (e.g., gas or coal). Competitive electric energy prices are close to being equal to the fuel costs of the marginal unit. Thus, a significant cost floor limits the volatility of short-run prices.

Historical Data: Unequal

One common approach to assessing price uncertainty is to examine historical data. For example, stock price analysts often calculate standard deviations and correlations with other historical factors. Statistical analysis can also be applied to options, which are closely related to futures prices. An option awards the right to buy in the future at a specified price; a futures price is the commitment to buy at a fixed price in the future. Not surprisingly, the analysis for estimating option value is closely related to the process for estimating forward price uncertainty. In fact, the Black-Scholes formula for pricing options includes a standard deviation as a key input, and analysts often use statistically derived estimates.

Nevertheless, statistical estimates may prove disadvantageous for the power industry. First, underlying conditions may change as the industry deregulates, making the past an unreliable predictor of the future. For example, our recent study of historical price information for Public Service Co. of New Mexico (PSNM) indicated aver-age annual prices of $22 per megawatt-hour (Mwh) with a standard deviation of $2/Mwh. However, this year's prices were several standard deviations below $22/Mwh;