Ralph R. Mabey, trustee in the Chapter 11 bankruptcy proceedings of Cajun Electric Power Co-op., has entered into an amended asset-purchase agreement with Louisiana Generating LLC for the purchase...
Retail Risk-Based Pricing
customer profile types, with a minimum of 14 percent for "BusHiLF" and a maximum of 53 percent for "Residential High" (ResHi) (see Table 1).
Since the daily energy uncertainty is caused by variability in weather, and prices are correlated to weather through system energy demand, we have observed that increased volumetric uncertainty can lead to increased cost and risk of service. We can use the differences in volumetric load uncertainty of different customers to develop risk-based prices that account for the observed uncertainty.
Cash Flow at Risk
For most utilities, the principal element of uncertainty in cash flows is related either to the cost of supply or the quantity demanded. The most serious issues that affect the future financial viability of a utility are unexpected costs and inadequate revenues. The impact of customer demand and price on a utility follows from the cash flow equation of gross margin. 6 The only variable in the gross margin equation that does not have a random component is the customer price P ni. Thus, our problem becomes focused on the optimal P ni to reflect the market and volumetric risk associated with serving customer load Q ni. Since customer load and cost of service are stochastic variables, we need to jointly simulate the components of the gross margin function to assess the individual elements of risk posed by a customer. For example, a fuel oil pumping station may have highly volatile demand that is uncorrelated with electricity prices, whereas a commercial office building has demand closely correlated with prices. Through simulation, we are able to capture the nature and magnitude of uncertainty surrounding each stochastic variables and their joint relationship. The result is a distribution of gross margin for each customer.
Risk can be systematically measured through examining the difference between the mean and the fifth percentile of the gross margin distribution. 7 We refer to this difference as the gross margin at risk (GMaR), as illustrated in Figure 2 on p. 47.
The mean gross margin (green dashed line) is $2.9M, and the 5th percentile (red dotted line) is -$2.4M, so the GMaR (solid black line) is $5.3M.
RBP seeks to reduce the difference between the mean and the lower tail of the gross margin distribution for the entire utility by selectively setting prices higher for customers with greater risk and reducing costs for customers that pose less risk. RBP prices can be scaled to achieve the same expected revenues for the utility, but they afford some additional downside protection against extreme events that affect the cost of supply.
Most economists would insist that assessment of the marginal contribution of risk should be determined relative to marginal costs or opportunity costs of the spot market. 8 Under the opportunity cost criteria, the gross margin equation would simplify with the elimination of the fuel and variable cost components, leaving the cost-of-service analysis exclusive with respect to the market. However, a utility's own cost of supply combined with the costs or earnings through daily balancing exchanges may be a more fitting first step for