How will the technology and policy changes now sweeping through the industry affect the architecture of the utility grid? Will America build an increasingly robust transmission infrastructure, or...
Inclining Toward Efficiency
Is electricity price-elastic enough for rate designs to matter?
provide for different seasonal and geographical baseline allowances. The differentials between baseline and non-baseline rates were not established using marginal cost studies. Instead, rate affordability was the driving concern. It led to a 15-percent differential between baseline and non-baseline rates and additionally led to a 20 percent discount called CARE for low-income customers.
During the 2001 energy crisis, when average rates increased by nearly 4 cents/kWh, rate affordability again was paramount among policymakers’ concerns. The legislature passed special legislation (Assembly Bill 1X) that froze rates for the existing first block (which was the baseline usage) and a second new block that was equal in size to 30 percent of the first block with rates set at the original non-baseline rate level. Three new blocks were created to pass through the incremental costs of managing the crisis, yielding a five-block rate design. 11 The energy crisis revenue allocation also shifted two-thirds of the residential customers’ shares of increased costs to commercial and industrial customers. The gradual reversal of this inter-class rate subsidy, coupled with capped Tier 1 and Tier 2 rates and increasing costs, have led to upper-tier residential rates that now are between 2 and 3 times the lowest baseline rate levels. The combined effect of higher rates, milder temperatures, and a state-wide energy-crisis advertising campaign helped reduce annual usage by 10 percent in 2001.
A much earlier study used data from a controlled experiment involving inclining block rates that took place in the mid-1980s in Wisconsin. It found much smaller price elasticities that ranged between -0.02 to -0.04. 12 These estimates were not statistically significant for the summer-peaking season. However, statistically significant price elasticities of -0.04 were found in California’s recent experiment with dynamic pricing that ran during 2003-2005. 13 Notably, the California price elasticities were derived when dynamic prices were superimposed on the state’s exising inclining block rate design.
A RAND Corp. study for the National Renewable Energy Laboratory in 2005 reported price elasticities by state. 14 It used nearly 30 years of consumption and pricing data to estimate both short-run and long-run electricity price elasticities for residential and commercial customers. It estimated residential price elasticity at -0.24 in the short run and at -0.32 in the long run.
A finding common to most studies is that higher-use customers have bigger price elasticities. That may be because they have more discretionary use, higher incomes and higher education levels.
Based on a synthesis of the best available information, the Brattle Group assembled triangular probability distributions for residential price elasticities by block for both the short run and the long run (See Table 1) . Short-run responses are driven by behavioral changes and long-run changes by equipment and building shell changes. Long-run responses reflect customers’ acquisitions of energy-efficient appliances and homes.
Generally (but not always) Block 1 price elasticities might be expected to be lower than Block 2 price elasticities. Also, price elasticities in the two blocks likely would correlate, and long-run price elasticities would be substantially higher than short-run price elasticities. Using these assumptions, analysts can estimate the magnitude of energy