Retail Risk-Based Pricing
electricity prices. Figure 4 illustrates the relationship of system load on prices. 11
The uncertainty of electricity prices and the variables that drive prices are simulated using a structural state-space model with regime switching. The model captures the structural elements that drive electricity prices and the stochastic elements of uncertainty around these fundamental drivers, plus the unexplained stochastic noise in electricity prices.
Applying RPB to a Retail Portfolio
In this section, we present an example of applying RBP to a small retail portfolio. Our example examines the effect of applying the RBP methodology to two retail portfolios in the ERCOT market: 1) a group of BusHiLF load profile customers; and 2) a group of large commercial office buildings. 12 The portfolio of BusHiLF customers has relatively low weather sensitivity and volumetric risk compared with other load profile customers, as shown in Table 1. The portfolio of commercial office buildings (Commercial) has nearly the same absolute volumetric risk as the BusHiLF portfolio but carries a larger sensitivity to changes in weather as opposed to changes in office occupancy rates. By having two portfolios with comparable volumetric risks but different weather sensitivities, we further illustrate the importance of accurately articulating the weather, load, and price-risk relationship as part of RBP and of reducing the amount of gross margin at risk.
The traditional pricing method for energy 13 has been developed on a expected value based on the average cost of service plus a fixed margin. 14 The BusHi portfolio has an original wholesale rate of $44/MWh compared with $43/MWh of the Commercial portfolio. By applying the RBP formula in equation 2, the average retail prices change so that the BusHi portfolio is $41.7/MWh and the Commercial portfolio is $45.3/MWh. The BusHi portfolio's RBP rate decreases by $2.3/MWh because load has a relatively limited sensitivity to changes in weather (i.e., a small weather sensitivity). The Commercial portfolio's rates increase because its load is highly correlated with weather and weather has a pronounced correlation with price in ERCOT.
The changes in pricing from traditional rate design methodologies to the RBP rates significantly reduce the uncertainty in gross margin at risk. The expected gross margins shown in Figure 5 over a 12-month cycle, corresponding to the upper lines for the combined portfolio for BusHi and Commerical, are $2.5 million under both pricing structures. Although the portfolios have the same expected gross margin over the annual forecast horizon, the RBP line contains significantly smaller fluctuations in month-to-month gross margin revenue. RBP has a pronounced effect on reduced volatility in gross margin at the 5 percent level. The annual gross margin at risk is $3.8 million under the original rate structure versus $2.9 million under RBP rates. Thus, RBP achieves a 30 percent reduction in GMaR over traditional rates while maintaining the same expected revenue.
The benefits of RBP are pronounced, but these benefits are achieved only through application of the appropriate analytical rigor. RBP requires a complex series of integrated simulation models to capture accurately the nature and magnitude of each customer's cost of service risk. It is