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Retail Risk-Based Pricing

A new approach to rate design.
Fortnightly Magazine - March 2004

easy to muddle volumetric risk with weather sensitivity. However, we have shown that accurately delineating the nature of volumetric risk derived from weather results in different retail prices than under more traditional approaches.

RBP improves a utility's bottom line through reducing the effect of more weather-sensitive customers on cash flows. The result of RBP is reduced cash flow variance from extreme weather events and market events. By more effectively pricing risk of each customer class' cost of supply, RBP reduces the volatility of cash flows. In turn, this reduces the cash reserve requirements. This freed capital can find more product uses with the applied RBP as a mechanism for helping preserve capital adequacy.

Beyond positively affecting a utility's bottom line, RBP heeds to pricing rationalism for both regulated entities and competitive retail offerings. By introducing individual customer characteristics and associated cost-of-service risks into a unified pricing methodology, retail electric prices more closely approximate the marginal cost of production, and the extent of cross subsidies can be better known. In economic terms, inclusion of the cost-of-service components creates additional pricing efficiencies that, on average, benefit consumers. The gains for both customers and utilities make RBP an attractive option to develop electric rates for today's energy markets.

Endnotes

  1. Nationally, energy markets are starting to evolve toward a common structure with regional transmission organizations (RTOs) and locational market prices (LMP).
  2. The true cost of service follows conditions where the marginal cost equals the marginal revenue.
  3. Bauer, John, ; March 29 Supplement, Vol. 19, Issue 1, p. 219.
  4. Commonly, utilities calculate the cost of fuel and purchased power equally among all customer classes on an average-cost basis.
  5. Hartman, Raymond S, Jensen, Kenneth A, Seiden, Kenneth P., . London: Apr 1994. Vol. 26, Iss. 4; p. 363. Gross Margin as defined here is equivalent to EBITDA.
  6. Dorris, Gary; Dunn, Andy, , October 2001, Vol. 79, Issue 10, p. 32; (AN 5423572) "Making the Shift to Earnings at Risk."
  7. Hotelling, Harold. "The General Welfare in Relation to Problems of Taxation and of Railway and Utility Rates." July 1938, pp. 242-69.
  8. Companies developing competitive retail offerings typically do not have an enterprise gross margin revenue requirement, leaving the third term as zero.
  9. The commercial software application used to model energy load and markets and develop RBP is PowerSimm. TM
  10. Structural state-space models reflect uncertainty of the effect of each explanatory variable on price as well as the unexplained component. The explained component contains uncertainty in the parameter estimate of each fundamental variable influencing price. The unexplained component captures the random noise in electricity prices that cannot be explained by fundamental variables or time series terms. The split regression shown in Figure 5 contains a relatively modest amount of noise in electricity price of +/- $5/MWh when load is less than 44 GW. When load is greater than 44 GW, the unexplained noise "switches" to a higher state and captures uncertainty in prices of +/- 40/MWh.
  11. The influence of each structural variable on market prices follows from maximum likelihood regression analysis.
  12. These are interval data read (IDR) customers.
  13. Exclusive of