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Real-Time Pricing: Ready for the Meter? An Empirical Study of Customer Response

Fortnightly Magazine - November 1 1998

which to measure changes in consumption for the RTP billing. The incremental component is based on marginal costs during an hour. Hourly energy charges are set equal to the sum of forecasted marginal operating and outage costs plus a small risk recovery adder.

While two-part pricing is more difficult to implement, it is economically more efficient because energy prices are much closer to the marginal cost. The two-part tariff also offers less price risk to the customer since real-time prices apply only to peak usage. Theoretically, a two-part tariff should lead to a greater degree of peak-load reduction than a one-part tariff.

1 Information on the quantity of electricity self-generated by the customers was not available. A translog cost function could not be estimated, nor is there any calculation of cross-price elasticities between self-generation and Pareto's generation. Since these customers take electricity under Pareto's day-ahead RTP program, the dependent variable in the data set was corrected for first-order auto-correlation to account for any correlations between present and past consumptions of electric power.

2 A log-linear model is estimated to measure the customer's own-price elasticity of electricity consumption. The advantage of using a log-linear function is that the coefficients of the variables are the estimated elasticities and that it is easy to compute. The disadvantage of estimating a log-linear function is a loss of information, as the relationship between price (P) and quantity of electricity (Y) for these customers (as seen in a data plot) does not appear to be log-linear. However, it has been shown that alternative, complex, non-linear functional forms provide similar results.

3 This section is based on extensive discussions with Mike O'Sheasy, manager for rate design at Georgia Power Corp., and Raymond Vice, manager for operations and engineering at Southern Company Services.

4 Fuel costs for electricity and alternative fuels such as natural gas and oil, together with Operations and Maintenance costs, the cost of new generating units, the capitalization structure and an allowed return on equity are used to develop a forecast of retail electricity prices by class. Customer forecasts, completed using regression models, feed into the various energy models. The short-term (one to three years) energy models use an advanced time series regression method (ARIMA, or "Auto Regressive Integrated Moving Averages", an econometric method used for time series analysis), which relies on the most recent observations to produce the forecast. Patterns are deciphered from the data, and the deviations from these patterns influence the forecast. Variables that drive energy sales for each customer class are then selected for their significance to that sector to develop the long-term (four to 20 years) energy models, which use an end-use methodology. These models represent the elements of energy use in fine detail. Each major energy-using activity - refrigeration, space heating, and the like - is identified and the corresponding energy consumption is specified.

Since the short-term and long-term energy forecasts are produced using different methods that focus on various drivers, these results must be reconciled. This reconciliation includes adjusting for the short-term models that utilize weather for the last seven