Public Utilities Reports

PUR Guide 2012 Fully Updated Version

Available NOW!
PUR Guide

This comprehensive self-study certification course is designed to teach the novice or pro everything they need to understand and succeed in every phase of the public utilities business.

Order Now

California Experiment: Dynamic Pricing for the Mass Market

Will the state launch a full-scale rollout of dynamic tariffs?
Fortnightly Magazine - July 1 2003

gold standard of experimental designs includes before/after measurements on treatment/control groups. In practice, trade-offs must be made in all experimental designs because of budget, time, and political constraints. The SPP will include statistically comparable control and treatment groups and will feature random assignment of customers to these groups. However, because of the time constraints under which it is being conducted, the SPP will not have "before" measurements on the treatment groups. Nor will it obtain separate measurements on the effects of enabling technologies and information treatments. It will not test hourly prices or extreme day prices, nor will it test the effect of varying the length or timing of the peak period.

Decisions, Decisions

The SPP sample was developed through a Bayesian approach, based on decision analysis.

Given the wide range in potential net benefits of dynamic pricing noted earlier, California could make the wrong decision and implement a particular dynamic pricing option that is not cost-effective. The Bayesian approach uses estimates of the potential net benefits of the SPP treatments based on a priori information about price elasticities and the costs of sampling to determine sample size. For example, if Treatment A is likely to generate large net-benefits but there is great uncertainty in that result, it is appropriate to draw a large sample for A. Similarly, if Treatment B is always cost effective and Treatment C is never cost effective, there is no economic reason to sample either B or C.

SPP will use regression analysis to estimate the impact of time-varying tariffs on customer usage. This will involve the estimation of various demand equations that express usage in a time period as a function of prices in the various pricing periods and other causal variables such as climate, appliance ownership, and household demographics.

A typical demand equation for on-peak usage would look like this:
Ln (On-Peak kWh) = A + B1*Ln (On-Peak Price)
+ B2*Ln (Off-Peak Price)
+ B3*(A/C Ownership)*(CDH)*
Ln(On-Peak Price)
+ C1*(A/C ownership)*(CDH)
+ C2*(Persons Per Household)
+ C3*(Dwelling Type)
+ C4*(Pool/Jacuzzi)
+ C5*(Electric clothers dryer)
+ C5*(Income)

Various mathematical functions will be fitted to daily and monthly data. The best functional form will be chosen by reviewing a diagnostic battery of statistical tests.

Once the models have been estimated, they will be used to test a variety of hypotheses about how customers respond to time-varying rates (see companion article by King and Chatterjee, p. 27).

The SPP will go live in July and run through December 2004. The initial demand models will be estimated late this year. Their results will feed into a preliminary cost-benefit analysis of dynamic pricing, which is expected to be conducted in December.

  1. Decision 03-03-036, Interim Opinion in Phase 1 adopting pilot program for residential and small commercial customers.
  2. An additional budget of $2 million has been approved for testing (a) the impact of providing customers a choice of control technologies and (b) gateway systems that are always in two-way contact with the utility and can simultaneously control a variety of appliances in customer homes.


How the SPP Came To Be

The