Dynamic Pricing and Low Income Customers -- Can they Co-Exist?

Friday, 09 July 2010 16:05

Written by Ahmad Faruqui and Lisa Wood

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In regulatory proceedings involving business cases for advanced metering infrastructure, the benefits of dynamic pricing often play a prominent role.  Several studies have shown that dynamic pricing can provide substantial benefits to utilities and customers.  It does so by lowering peak demand during hours when the power system is experiencing critical conditions such as extremely high prices or threats to reliability. Hence, dynamic pricing results in avoiding expensive capacity and energy costs in the long term and lowering wholesale market prices in the short term.

Few would dispute the positive impact of dynamic pricing on customers in the aggregate.  However, there continues to be much disagreement about the impact of dynamic pricing on certain segments such as low income customers and senior citizens.  A recent manifestation of this concern was the decision of the Maryland Commission to deny the application of Baltimore Gas & Electric (BGE) Company to deploy a smart grid that would have, among other things, provided dynamic pricing signals to customers.

When it comes to assessing the impact of dynamic pricing on low income customers, there are two viewpoints.  The first one argues that low income customers use relatively less energy during the peak hours which makes their load profiles less peaky (or flatter) than the  average residential customer. This would make them immediate beneficiaries of dynamic pricing which charges more during peak hours and less during off-peak hours.  The second one argues that low income customers use less energy to begin with and have very little ability to shift load from peak to off-peak periods and/or to curtail peak period usage.  Hence, they would be harmed by dynamic pricing.

The controversy has acquired metaphysical dimensions.  It cannot be resolved through a priori reasoning.  Initially, we use data from a load research sample from a large urban utility to assess the impact of dynamic pricing on low income customers in the absence of demand response. Next, we review empirical evidence from five recent utility projects.  These include four innovative pricing pilot programs in California, Connecticut, the District of Columbia, and Maryland and early results from a full scale dynamic pricing program that is being rolled out in California.

We find that a majority of low income customers can benefit even without responding to dynamic pricing programs because they use relatively less energy during the peak hours compared to the average customer.  The percentage of low income customers who would benefit from dynamic pricing ranges from 65 percent to 79 percent depending on which specific rate design is being evaluated.

We also find low income customers do shift their load in response to price signals.  Two studies, carried out by Connecticut Light & Power Company (CL&P) and BGE, find that low income customers were equally price responsive to the average customers, while the California Statewide Pricing Pilot (SPP) carried out jointly by the state’s three investor-owned utilities and the SmartRate program offered by Pacific Gas & Electric Company (PG&E) found that they were less responsive.  The Pepco DC results, on the other hand, showed that low income customers were much more responsive than other customers.   

Summary of Low Income Customer Responsiveness to Dynamic Prices Relative to Average Customer Response

Note: For the PepcoDC pilot, the average residential response excludes low income customers that qualify for the RAD program

While there is mixed evidence on the magnitude of the responsiveness of low income customers relative to other customers, there is strong evidence across these five programs that low income customers do respond to dynamic rates and, in many cases, that response is a load reduction above 10%.  Furthermore, even without responding to dynamic rates, a large percentage of low income customers will be immediate beneficiaries of dynamic rates due to their flatter than average load profiles.  These results suggest that when evaluating dynamic pricing, it is important to recognize that such rates are not harmful, and, in fact, may be beneficial to a large percentage of low income customers.

More detailed results are contained in a whitepaper that can be downloaded from the Institute for Electric Efficiency website.