The advent of the smart grid is sparking interest in intelligent rate design. But while state and federal goals encourage more efficient rate structures, regulatory and political considerations...
The Barriers to Real-Time Pricing: Separating Fact From Fiction
is .15, compared with .09 for customers without on-site generation. The elasticity for the least electrically intensive customers is .07, compared with .04 for customers without on-site generation. For firms in the pulp and paper industry, the presence of on-site generation doubles the elasticity from .15 to .30.
It would be useful to know what types of customers are likely to shift more load. It also would be useful to know how much load relief can be expected from RTP, and whether the amount of load relief varies with a one-part design versus a two-part design. What segments are likely to shift more load? How applicable is the load shifting information contained in EPRI's StatsBank to utilities that are not included in that database? Is load-shifting information stable and reliable over time?
A related issue deals with market segmentation and targeting. Prior work suggests that customers with on-site generation, discrete production processes, and previous experience with interruptible tariffs are more likely to benefit from RTP. What is the best recruitment strategy for signing up these customers? How much education is needed to get customers acclimatized to the incentives provided by RTP?
A final issue relates to implementation strategy. Some utilities have only a handful of customers on RTP. Can better results be obtained? Some have argued that customers face real transactions costs when switching to a new rate structure, and those costs become a barrier to their joining RTP programs. Are these costs real or perceived? It would be useful to conduct a pilot program before proceeding with full-scale implementation.
Utility Barriers: Rates & Revenue Loss
Utilities have several issues regarding RTP. First, they are concerned about the revenue loss that can arise from RTP. This problem is acute with one-part designs, where revenue loss can arise if the rates are offered on a voluntary basis and customers who have inverse load shapes self-select themselves onto the real-time rate. The customers would lower their bills without shifting any load from peak to off-peak hours. They would benefit, but the utility and non-participating customers would lose. There would be a loss of revenue to the utility, without any reduction in its costs-resulting in a loss of earnings. The lost earnings then would have to be made up by charging other customers a higher price.
This concern can be addressed in three ways: by offering a two-part design; by making the rates mandatory; or by offering a true-up mechanism, ensuring that forecast revenues are recovered. It would be useful to conduct research on how serious is the potential revenue loss associated with RTP. Also, to what extent can it be offset by following a two-part design?
A second issue relates to the potential for gaming associated with two-part designs that require the establishment of a customer baseline usage (CBL). There is some anecdotal evidence that customers may have gamed the selection of their base load when signing up for market-based load curtailment programs. A similar concern may also apply to two-part RTP designs. This could be addressed through researching whether there is any empirical