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Low-Income Reality Check
Evaluating the impact of dynamic pricing.
the data is drawn from a pilot or general billing records; how the “low-income” classification was developed; or the number of residential accounts sampled. 10
The Wood/Faruqui article also assumes that these pricing plans can be implemented at no cost to ratepayers. It seems unlikely that ratepayers will have the ability to participate in CPP rates without paying for smart meters. The costs of smart meters must be recovered, and will reflect in customer bills. Thus, claims regarding the benefits of CPP plans for low-income customers aren’t well supported. A more reasonable interpretation of “winners and losers” arising from CPP plans must include the impact of the cost of enabling the CPP plans on customer bills, including the cost of smart meters.
A key component of the “smart grid transformation” is the response that policy makers can expect from price signals sent to consumers. It’s reasonable to expect that consumers with higher incomes will have load to shed during peak periods; higher-income households are more likely to have central air conditioning, pool or spa pumps, dishwashers, washers and dryers, etc., than are low-income households. The ability to shed load opens the possibility for both customer savings and social benefits from peak demand reduction. However, the level of benefits available for low-income consumers is another matter. A lower potential consumer pricing response might provide fewer benefits to offset the costs of implementing dynamic pricing.
The limited information that is available from programs such as those at CL&P, BGE, Pepco and PG&E shows that low-income customers exhibit a reduced ability to benefit from dynamic pricing, and points to characteristics of low-income households, such as more frequent moves, that should be addressed in dynamic pricing and demand response programs. Policy makers should be sensitive to the position in which low-income consumers are likely to find themselves when confronting the transition to the smart grid—a limited ability to respond to market incentives, which will likely limit the benefits available. Safeguards should be built into dynamic pricing programs to counter low-income customers’ more limited ability to capture benefits from dynamic pricing.
(Lisa Wood and Ahmad Faruqui respond to this article here.)
1. The Wood/Faruqui article presents analysis from a September 2010 white paper published by the Institute for Electric Efficiency titled “The Impact of Dynamic Pricing on Low Income Customers.” This response to the Wood/Faruqui article also applies to the IEE Whitepaper.
2. “Verified” in the sense that pilot participants were associated with a low-income energy assistance program or otherwise associated with poverty status.
3. Conn. DPUC Docket No. 05-10-03RE01 Compliance Order No. 4. “ Appendix C Plan-IT Wise Results—Supplemental. Analysis by the Brattle Group ,” Ahmad Faruqui, PhD, and Sanem Sergici, PhD, February 19, 2010.
4. The IEE Whitepaper (p. 16) also states that the subset of participants for which income data is known exhibits a higher level of demand response than the overall pilot population, indicating sample bias.
5. Ahmad Faruqui, PhD, and Sanem Sergici, PhD, “BGE’s Smart Energy Pricing Pilot, Summer 2008 Impact Evaluation,” Brattle Group. Prepared for Baltimore Gas