When ratepayers become generators, the utility industry is turned upside-down. A warning to legislators, regulators – and even governors – on what to expect.
Better Data, New Conclusions
The authors respond to Roycroft’s reality check.
(This is a response to Low-Income Reality Check by Trevor R. Roycroft.)
Experience with time-of-use pricing programs shows that a large majority of low-income customers will benefit from dynamic prices. In fact, not making such prices available to these customers might be harmful. In the most efficient system, all customers will face the same prices—and policy makers can provide direct relief to ease the burden for low-income customers.
The impact of dynamic pricing on low-income consumers of electricity is of great import in regulatory policy. In our article, we used empirical information from several jurisdictions to arrive at some basic conclusions on A) whether or not low-income customers were likely to respond to dynamic pricing and B) even if they didn’t respond, would low-income consumers be made worse off by dynamic pricing.
We found that low-income consumers did respond to dynamic pricing, but in most cases their rate of price responsiveness was lower than that for non-low-income consumers. We also found that at least for one utility service area, a majority of low-income consumers would be better off if they were moved from flat rate pricing to dynamic pricing. In that particular service area, we had information from a load research sample that included low-income customers. The low-income customers were defined as those whose incomes were a certain multiple of the federal poverty guidelines.
In his article , Dr. Roycroft seemingly reaches conclusions that contradict our findings. But he doesn’t base his conclusions on new data, just on reinterpreting our analysis.
The Roycroft article alleges that we changed our findings across different studies. But one must change one’s position as new data become available. In preparing the report for the Institute for Electric Efficiency (IEE) on which our Fortnightly article is based, we did some additional work on the primary data behind two of the dynamic pricing experiments.
Specifically, the BGE and CL&P pilots didn’t have designated low-income test cells in their design, because the primary purpose of those pilots was to determine the demand response impacts for the average customer, and not low-income customers specifically. For that reason, we had to estimate new regressions models to estimate the low-income consumer impacts separately from the average customer impacts reported earlier in the BGE and CL&P pilot reports. However, since information on income status wasn’t available on every customer, we re-estimated the regression models on the limited sample of customers who had reported their income status. That limitation is acknowledged in our Fortnightly article and in the IEE whitepaper.
But a bigger issue that Roycroft identifies is that we don’t use a standardized definition of low income in our various studies. Although that’s true, it’s because the studies themselves don’t use a standardized definition of low income. It wouldn’t be possible to apply a