Although today microgrids serve a tiny fraction of the market, that share will grow as costs fall. Utilities can benefit if they plan ahead.
Dynamic Pricing and Low-Income Customers
Correcting misconceptions about load-management programs.
Track A yielded results on two categories of low-income customers. First, snapshots of low-income and high-income customers were compared, with low-income customers having an average income of $40,000, and high-income having an average income of $100,000. Second, the price responsiveness of customers on the state’s CARE program, who receive a discount on their electricity bill, was compared with the responsiveness of non-CARE customers.
Overall, high-income households were somewhat more price-responsive than low-income households; however, the difference wasn’t substantial.
Track A compared the peak demand reductions of low-income ($40,000) and high-income ($100,000) customers, as well as CARE vs. non-CARE customers. Customers with average incomes of $100,000 exhibited average peak reductions of 16 percent on the CPP-F rate, while customers with average incomes of $40,000 exhibited average peak reductions of 11 percent (see Figure 6) . Similarly, non-CARE customers exhibited average peak reductions of 16 percent while CARE customers exhibited peak reductions averaging 3 percent.
Within Track B, designed to be representative of the low-income community, customers that received only information reduced peak demand by 1.15 percent, while those that were also placed on the CPP-F rate reduced peak demand by 2.6 percent.
Dynamic Pricing Evaluation
In several pilots and programs that utilities have performed, low-income customers did respond to dynamic rates, and many such customers benefitted even without shifting load. Consequently, when evaluating dynamic pricing, it’s important to recognize that such rates may be beneficial to a large percentage of low-income customers.
While there’s mixed evidence on the magnitude of the responsiveness of low-income customers relative to other customers, there’s 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 exceeding 10 percent.
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. Finally, restricting access to dynamic rates may, in fact, be harmful to a large percentage of low-income customers.
1. See the white paper, “The Impact of Dynamic Pricing on Low Income Customers,” posted on the Web site of the Institute of Electric Efficiency: http://www.edisonfoundation.net/iee/reports/index.htm.
2. In this sample, smaller customers tended to have flatter load shapes, and therefore also tended to experience immediate bill decreases. So, for the sample as a whole, the revenue change was close to zero, even though there were more winners than losers.
3. Another Smart Energy Pricing pilot was carried out in summer 2009, but there are no separate low-income results from the 2009 analysis.
4. In this pilot, the baseline was calculated by identifying ten non-event non-holiday weekdays preceding an event day, choosing the three highest kWh days while omitting any days not within 10 percent of the THI for the event day, and using these remaining days to calculate an average 24-hour load profile for each PTR customer.
5. In this case of hardship, comparisons were made based on treatment customers only, since there were no control customers with hardship status.
6. Although preliminary results from the Wolak