Pricing | VaasaEtt Global Energy Think Tank

The Long-Run Effects of a Time-of-Use Demand Charge

Modifications in demand due to time-of-use (TOU) pricing have potential to improve the
efficiency of electric power supply. With long lead times for plant construction, utility planners
need long-run estimates of response to TOU rates. Existing evidence is primarily drawn
from short-run TOU experiments. We provide estimates of long-run response to a nonexperimental
residential TOU rate offered by Duke Power. The rate contains a demand charge
applied to the maximum rate of energy consumption during the peak period. We find that
customer response increases over time in a manner that enhances the ability of TOU rates
to reduce system peak.

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The Value of Dynamic Pricing in Mass Markets

The simpler forms of dynamic pricing, in which prices vary only during extreme supply conditions, may capture many of the economic benefits of real-time pricing, and may be suitable for wide-scale deployment to mass-market consumers, for whom dynamic pricing options have large been ignored.

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Residential Customer Response to Real-Time Pricing: The Anaheim Critical-Peak Pricing Experiment

This paper analyzes the results of a critical peak pricing (CPP) experiment involving 123
residential customers of the City of Anaheim Public Utilities (APU) over the period June 1, 2005
to October 14, 2005. Using a nonparametric condition mean estimation framework that allows for
customer-specific fixed effects and day-of-sample fixed effects, I find that customers in the
treatment group consumed an average of 12 percent less electricity during the peak hours of the day
on CPP days than customers in the control group. There is also evidence that this reduction in
consumption for customers in the treatment group relative to customers in the control group is larger
on higher temperature CPP days. The impact of CPP events is confined to the peak periods of CPP
days. Mean electricity consumption by customers in the treatment group is not significantly
different from that of customers in the control group during the peak or off-peak periods of the day
before or day after a CPP event. Much of the estimated consumption reduction of treatment
consumers relative to control group consumers during peak periods of CPP days is due to reductions
from a higher level of consumption by treatment group customers in non-CPP days. The
consumption reductions paid rebates during CPP days are almost 7 times the reduction in
consumption due to CPP events predicted by the treatment effects estimate, which provides strong
evidence of an overly generous method for setting the reference level for peak period consumption
relative to which customers are issued refunds during CPP days. The paper closes with a discussion
of the challenges associated with implementing a CPP rate with a rebate mechanism as the default
rate for residential customers.

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Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets

Electricity restructuring, as it has been implemented in numerous U.S. states and around
the world, has been advocated as a means of producing and consuming electricity more
efficiently. In many cases, the results so far have fallen well short of the goals, with the
California electricity crisis of 2000-01 being just the most publicized disappointment.
While there has been heated debate about the reasons for these failings, there is
remarkable agreement over at least the broad outline of one response: the demand side of
the industry should play a more active role, receiving economic incentives to help
balance supply and demand. The way in which this notion should be implemented,
however, is still the subject of a great deal of debate.
In this monograph, we present an overview and analysis of the possible approaches to
bringing an active demand side into electricity markets. We begin in section I by
describing the ways in which economic incentives can be introduced on the demand side.
We discuss the fundamental economics of establishing these incentives and the economic
loss from systems that lack demand-side participation. We analyze the effect of these
incentives on the efficiency and competitiveness of the market, discuss how time-varying
electricity pricing need not conflict with the goals of customer bill stability and meeting
retailer revenue requirements, and we evaluate who the winners and losers are from
implementing price-responsive demand. In section II, we move from the more general to
specific issues of implementing time-varying prices. We discuss approaches to setting
the retail prices, what metering and communications equipment are necessary for various
forms of price-responsive demand, and how billing processes would have to change to
adapt to the more complex pricing system. We also examine the role that demand
response could play in matching supply and demand. In section III, we examine the
ways in which customer response to time-varying prices. We discuss both the potential
responses that are envisioned by those who study optimization of power use and the
actual responses that have taken place in pilot and long-term programs. We conclude in
section IV with some policy recommendations.

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Automated Critical Peak Pricing Field Tests: 2006 Pilot Program Description and Results

During 2006 Lawrence Berkeley National Laboratory (LBNL) and the Demand
Response Research Center (DRRC) performed a technology evaluation for the Pacific
Gas and Electric Company (PG&E) Emerging Technologies Programs. This report
summarizes the design, deployment, and results from the 2006 Automated Critical
Peak Pricing Program (Auto-CPP). The program was designed to evaluate the
feasibility of deploying automation systems that allow customers to participate in
critical peak pricing (CPP) with a fully-automated response. The 2006 program was in
operation during the entire six-month CPP period from May through October.

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Appendix F: Impact of Dynamic Pricing On Low-income Customers: Quantifying the Benefits of Dynamic Pricing In the Mass Market

Dynamic pricing offers electric customers lower prices during most hours of the summer while raising prices
significantly for a small percentage of hours when system conditions are critical (typically 2 to 3 percent of
all summer hours). The primary attraction of dynamic rates such as critical peak pricing (CPP) or real-time
pricing (RTP) is that these rates provide direct incentives to reduce electricity usage when the electrical
system is most stressed because they reflect daily peak marginal costs.1,2
Some have expressed concern that dynamic pricing may adversely impact low-income customers. In
jurisdictions where dynamic prices are under consideration, many utilities are currently pilot testing some
type of CPP rate.3 In this appendix, we summarize the impact of CPP on low-income customers based on
empirical results from the California Statewide Pricing Pilot (SPP) of 2003-04.4 The results show that there
is no statistically significant difference in bill-savings across income groups. This means that high-income
customers on a dynamic rate do not benefit more than low-income customers, on average. However, taking
usage into account, low-income customers in very high usage groups may find it difficult to “save” under a
CPP rate. From a policy perspective, alternative dynamic pricing options should be considered for this group
of high-usage, low-income customers. Depending on the definition of high usage, this represents about 2.2
percent to 5.7 percent of all households in the U.S. or 4.2 percent to 11 percent of all low-income
households. (See Tables F-1 and F-2).5 One obvious solution is to offer a peak-time rebate (PTR) rather than
CPP to this specific group of high-usage, low-income customers. In addition, low-income customers in the
low-usage group could be offered a choice between PTR and CPP. In the District of Columbia, as part of its
dynamic pricing pilot program, Pepco is currently offering a PTR (also called a critical peak rebate or CPR)
to customers that are currently on the Residential Aid Discount (RAD) program. The California SPP consisted of three tracks: Track A, which included a statistically representative sample of
customers; Track B, which focused on low-income customers in areas of San Francisco (located in close
proximity to a power plant); and Track C, which focused on customers in San Diego that had smart
thermostats.7 Track A comprised four climate zones while Tracks B and C focused on single climate zones.
In this appendix, we examine the impact of dynamic pricing on low-income customers based on the results
of the SPP. First, we summarize the results of a recent study that focused on the final three-month period of
the SPP: July 1 to September 30, 2004.8 These results are indicative of an established program. Second, we
provide results for Track B customers only, which represent low-income customers over the entire SPP (15 months from July 2003- September 2004). Finally, we provide results for Track A customers, which
represent the general population of California over the 15-month period. Using Track A, we compare lowincome
customers to other customers in the same track. As shown below, each of these comparisons shows
that low-income customers do respond to dynamic prices. However, as pointed out earlier, there may be very
specific groups of customers that should be targeted for PTR rather than CPP.

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AmerenUE Critical Peak Pricing Pilot

AmerenUE, in conjunction with a Missouri Collaborative formed as the result of a rate case settlement, launched a Residential Time-Of-Use (“RTOU”) Pilot study in the Spring of 2004.

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What Changes Energy Consumption? Prices and Public Pressures

Policymakers often seek to limit energy prices following market shocks, and instead issue public
appeals to reduce demand. This article presents new evidence on how price changes and
conservation appeals affect energy consumption, using household-level data from California’s
energy crisis during 2000 and 2001. The evidence indicates that when policymakers cap energy
prices following market shocks, they preclude substantial—and quite rapid—reductions in energy
use. The data also reveal that conservation appeals and informational programs can produce
sustained reductions in energy demand.

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Smart Meter, Customer Choice and Profitable Time-of-Use Rate Option

We describe the results of a time-of-use (TOU) rate option experiment which demonstrates that offering
a TOU option can be profitable to a utility. The option’s profitability is attributable to: (1) smart meters;
(2) large price differentials between TOU periods that minimize the adverse selection problem of freeriders;
and (3) the success of marketing efforts that enhance customer acceptance. This finding refutes the
common belief that rate options are necessarily unprofitable to a utility and unwanted by small users. We
explore the significance and relevance of our findings in the emerging world or retail access. TOU rate
options will continue to be useful to unregulated energy suppliers, regulated wires companies and electricity
consumers.

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Hydro One Networks Inc. Time-of-Use Pricing Pilot Project Results

In March 2007, Hydro One Networks Inc. (“Hydro One”) received approval from the Ontario Energy Board (“OEB”) to undertake a pilot project using funding from the 3rd tranche CDM budget involving 500 residential, farm and small general service (under 50 kW) Distribution customers for 5 months (May to September 2007) to assess their response to time-of-use (“TOU”) pricing. Instead of paying the Regulated Price Plan (“RPP”) commodity prices, pilot participants were asked to pay the OEB-approved RPP TOU rates during the pilot period.
This study was required because results from other TOU pilot projects undertaken by the OEB or other LDCs in the Province may not be directly applicable to Hydro One’s customers since most of our customers are rural-based and have higher electricity usage due to great reliance on electric equipment such as electric space and water heating. The main objectives of the pilot were:
• To assess the customer responses to the RPP TOU rates versus the two-tiered threshold RPP;
• To assess the effectiveness of the real-time in-home display monitors (“RTM”) in conjunction with RPP TOU rates;
• To assess the communication and settlement support required for implementing RPP TOU rates.
Major findings
• Pilot participants were responsive to the RPP TOU rates and were able to shift and conserve part of their load. For a typical customer on RPP TOU rates, the load-shifting impact averaged 3.7% in the summer months and the conservation impact averaged 3.3%.
• Providing RTMs to customers on RPP TOU rates helped them respond even more. On a normal summer day, the load-shifting impact averaged 5.5%, while the conservation impact averaged 7.6%. On a hot summer day (over 30°C), the load-shifting impact was even more pronounced at 8.5%.
• Extrapolating the load-shifting impact (8.5%) on a hot summer day to all Hydro One residential customers would yield a summer peak load-shifting impact of about 150 MW. Extrapolating the load-shifting impact to all residential customers in the Province would result in a much higher impact.
• 76% of pilot participants under the RPP TOU rates paid a lower electricity bill as a result of load-shifting, compared to the regular RPP rates. Savings attributable to conservation would be incremental. Customers who were better off gained on average about $23 during the pilot (about $6 per month), while customers who were worse off on average lost about $7 (less than $2 per month).
• 72% of participants indicated that they would like to remain on the RPP TOU rates, and 87% claimed they changed their behaviour during the pilot. Only 4% found the changes in their daily activities in response to the RPP TOU rates to be inconvenient.

• 63% of participants with an RTM found it useful to help them conserve electricity. On average, customers thought they would save 9% on electricity consumption by using the RTM.
• Of the 200 small general service (under 50 kW) customers contacted, only 2 agreed to participate in the pilot. Analysis of the hourly load profiles for the small general service customers who declined participation in the pilot revealed that these customers on average could be worse off by about $10 per month in their electricity bill in the summer if they did not shift load and/or conserve. Further analysis using generic load profiles shows that small general service customers could be better or worse off under RPP TOU rates depending on the industry in which they operate, their specific hourly electricity consumption patterns and their ability to shift load and/or undertake conservation initiatives.

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