Copyright © 2014 The Brattle Group, Inc.

Dynamic Pricing: Transitioning fromExperiments to Full Scale Deployments

Michigan Retreat on Peak Shaving to Reduce Tasted Energy

Sanem Sergici, Ph.D.

August 06, 2014

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Why does price responsive demand (PRD) matter?

  Avoided or deferred resource costs

  Reduced wholesale market prices

  Fairness in retail pricing

  Customer bill reductions

  Facilitating deployment of distributed generation resources

  Environmental benefits

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More than 200 time-varying rate tests have been conducted, with a broad range of outcomes

Results of Recent Time‐Varying Pricing Pilots

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When the impacts are grouped by rate type, a pattern begins to emerge

Note: Chart shows only price treatments and excludes treatments with enabling technology

Results of Recent Time‐Varying Pricing Pilots

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Enabling technologies significantly boost customer price response

Notes:Chart shows only treatments testing price and technology side‐by‐sideTechnology included automation (e.g. smart thermostats) and/or information (e.g., in‐home displays)

Price Response with and without “Enabling Technology”

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The peak-to-off-peak price ratio further explains some of the variation in impacts

0%

10%

20%

30%

40%

50%

60%

 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21

Peak Red

uctio

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Peak to Off‐Peak Price Ratio

Price Only Price+Tech

Note: 2 Price only outliers were removed from the regression

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Customers respond to prices, but there is much more to the equation

▀ Impacts persist across several years and consecutive events▀ Enabling technologies boost price responsiveness ▀ Responsiveness dependent on temperature and humidity

Decisions made during implementation planning can dramatically impact results

▀ Simplicity of program design▀ Activities raising awareness in customers, i.e., pre‐and‐post event messaging 

▀ Whether a program is opt‐out or opt‐in

We’ve learned a lot from these pilots

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  Scientifically valid sample design and M&V method

  Continued for four consecutive summers (2008‐2011)▀ The Brattle Group carried out the impact evaluation for each of the summers and analyzed whether price‐responsiveness persists over time

More than 11 different treatments tested over the course of  four years

Nearly 950 treatment customers at its height

Yielded invaluable information for the design of BGE’s full‐scale pricing program

Case Study: Baltimore Gas & Electric’s Smart Energy Pricing (SEP) Pilot

2008 2009 2010 2011

Peak Time Rebate (Price Only) X X X X

Peak Time Rebate + Energy Orb  X X

Peak Time Rebate + Energy Orb + AC Switch X

Peak Time Rebate + Energy Orb + Smart Thermostat X

Peak Time Rebate + Smart Thermostat X

Dynamic Peak Price (DPP)* X

Peak Time Rebate + Change in Notification Period X X

Peak Time Rebate + Change in Event Window X

Peak Time Rebate + In Home Display/Portal X X

Peak Time Rebate + Legacy DLC Program X

Legacy DLC Program X

Control Group X X X X* DPP = CPP + TOU. This was also combined with an Energy Orb and AC Switch

SEP pilot tested 11 treatments over the course of four years

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Findings informed BGE’s full scale deployment of dynamic pricing  Customers were responsive to the price signals

▀ Customers responded similarly to the CPP and PTR rates▀ Elasticity of substitution ranged from 0.100 to 0.149 (depending on weather conditions)

▀ The peak impacts ranged from 23 to 34 percent at a 10:1 price ratio (depending on the weather)

▀ Daily price elasticity was estimated at ‐0.05

Customers who were on the price‐only treatment for four years showed persistence in their price responsiveness

BGE rolled out PTR rates to 315,000 customers in the Summer of 2013

▀ Default tariff for residential customers▀ 82% of the customer engaged

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Several utilities are achieving significant participation through aggressive opt‐in programs

▀ Time‐of‐use (TOU) rates at APS and SRP▀ Variable peak pricing (VPP) at OG&E

 Others are rolling out default programs for the mass market▀ Pepco▀ BGE▀ Sacramento Municipal Utility District (SMUD)▀ The Province of Ontario, Canada

Residential dynamic pricing is transitioning to a new phase: full scale deployment

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Residential TOU Enrollment Rates

Source: The Brattle Group

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Residential Dynamic Pricing Enrollment Rates

Source: The Brattle Group

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Case Study: Ontario’s Residential TOU Program

Besides Italy, Ontario is the only region in the world to deploy Time‐of‐Use (TOU) rates for generation charges to all customers who stay with  regulated supply 

TOU rates were deployed in Ontario to incentivize customers to curtailelectricity usage during the peak period and possibly to reduce overallelectricity usage

The Brattle Group was retained by Ontario Power Authority to undertake the impact evolution of the TOU program

▀ Three year assignment; the 1st Year Impact Evaluation results are presented here, the 2nd year study is underway

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Impact Evaluation Challenges

 Non‐experimental design

  Sheer size of data (hourly data on four million customers over three years)

  Presence of more than 70 LDCs in the province

 Mild peak and off‐peak price ratio (1.5:1) 

 Multiple pricing periods

  Customers could opt‐out of regulated TOU tariff and switch to retail providers

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Impacts Measured in the Study

For each LDC analyzed in the Study, we quantified the following impacts and elasticities by customer class and by season:

▀ Load shifting impacts▀ Peak demand impacts▀ Conservation impacts▀ Elasticity of substitution▀ Overall conservation elasticity

These results and the details of the study are discussed in, “Impact Evaluation of Ontario’s TOU Rates: First Year Analysis”, report prepared for Ontario Power Authority, November 2013http://www.brattle.com/system/publications/pdfs/000/004/967/original/Impact_Evaluation_of_Ontario's_Time‐of‐Use_Rates‐First_Year_Analysis_Faruqui_et_al_Nov_26_2013.pdf?1386626350

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An ideal impact evaluation study has two important design elements

  1‐ A control group to serve as a proxy for the behavior of the treatment group customers in the absence of a treatment  2‐ Pre‐treatment period data on both control and treatment customer groups to net‐out the pre‐existing differences in between the two groups 

  In this construct, the true impact would be measured by: (T2‐T1) ‐ (C2‐C1)

Control Group Treatment Group

Post‐ Treatment

Pre‐Treatment

T2C2

T1C1

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Full-scale deployment of TOU rates poses two challenges for impact evaluation

Problem Solution

1‐ The TOU rollout was not designed asa randomized controlled experiment,there was no control group

In the 1st year study, we tookadvantage of the phased nature of theTOU roll‐out within the LDCs toconstruct a proxy control group• Eligible customers were allocated into

two groups using the median TOU startdate

• Treatment and control groups wererandomly selected the from these twobuckets

2‐ For some LDCs, the TOU rates weredeployed very shortly after the AMIdeployment. This implies that there isa very short window with pre‐TOU dataavailable

In the sample design process, wedefined eligible customers to beincluded in the study as those whohad:• at least 6 months of pre‐TOU data• at least 12 months of post‐TOU data

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Overview of the Methodology

We employ a two‐pronged approach to measure TOU rate impacts

▀ Estimate an advanced model of consumer behavior called the “addilog demand system” to discern load shifting effects that are triggered by the TOU rates and to estimate inter‐period elasticitiesof substitution

▀ Estimate a simple monthly consumption model to understand the overall conservation behavior of the customers and estimate an overall price elasticity of demand

By using the parameter estimates from these two models and solving them together, we calculate the impact that TOU rates on energy consumption by pricing period and for the month as a whole 

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Our models were estimated over several pricing periods

  Summer Time Periods  (May‐October)

 Winter Time Periods  (January‐ April, November and 

December

Period Hours TOU Window

1 ‐ Weekends & Holidays

2 9 pm ‐ 7 am Off‐peak

3 7 am ‐ 11 am Mid‐peak

4 11 am ‐ 5 pm Peak

5 5 pm ‐ 7 pm Mid‐peak

6 (*) 7 pm ‐ 9 pm Off‐peak

Period Hours TOU Window

1 ‐ Weekends & Holidays

2 9 pm ‐ 7 am Off‐peak

3 7 am ‐ 11 am Peak

4 11 am ‐ 5 pm Mid‐peak

5 5 pm ‐ 7 pm Peak

6 (*) 7 pm ‐ 9 pm Off‐peak

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Overview of Residential Class Results

There is significant evidence of load shifting across all LDCs▀ Reduction in usage in the peak and mid‐peak periods (generally highest in the peak periods), increase in usage in the off‐peak periods

Load shifting is higher in the summer rate period than the winter▀ Summer peak period impacts range from ‐2.6% to ‐5.7%▀ Winter peak period impacts range from ‐1.6% to ‐3.2%

  Peak period substitution elasticities range from ‐0.12 to ‐0.27

  Evidence on conservation is limited

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Ontario residential TOU impacts compared to TOU pilots from around the globe

LDC#1LDC#2

LDC#3

LDC#4

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Peak Red

uctio

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Peak to Off‐Peak Price Ratio

TOU Only Arc with OPA Summer Impacts ‐ (N = 42)

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General Service Class Conclusions

There is some evidence of load shifting across all LDCs▀ Reduction in usage in the peak and mid‐peak periods, generallyhighest in the peak periods. Small increase in usage in the off‐peak periods

▀ A few odd results, most likely an artifact of the heterogeneity inthe General Service class

▀ Impacts far smaller for General Service than Residential class

No clear pattern of winter versus summer load shifting impacts▀ Summer peak period impacts range from 0% to ‐0.6% ▀ Winter peak period impacts range from ‐0.2% to ‐1%

Evidence on conservation is negligible

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Concluding remarks

  Residential customers responded to the TOU rates by shifting their usage from peak to off‐peak and mid‐peak periods 

  The load shifting impacts for general service customers were far smaller than those estimated for the residential customer class and results are not as distinct, with some odd substitution patterns

  Evidence on energy conservation was negligible and generally insignificant in both the residential and general service class

 We found no evidence of self‐selection bias associated with customers’ opting out of the TOU rates into retail rates

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BibliographyAhmad Faruqui and Sanem Sergici, “Dynamic pricing of electricity in themid‐Atlantic region: econometric results from the Baltimore gas andelectric company experiment,”, Journal of Regulatory Economics,Volume 40: Number 1, August 2011.

Ahmad Faruqui and Sanem Sergici, “Household response to dynamicpricing of electricity–a survey of 15 experiments,” Journal of RegulatoryEconomics (2010), 38:193‐225

Ahmad Faruqui, Sanem Sergici and Ryan Hledik, “Piloting the SmartGrid,” with Ryan Hledik and Sanem Sergici, The Electricity Journal,Volume 22, Issue 7, August/September 2009, pp. 55‐69.

Peter Fox‐Penner, Smart Power‐ Climate Change, the Smart Grid, andthe Future of Electric Utilities, Island Press, 2010

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APPENDIX

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Determining Sample Sizes We expected the peak and conservation impacts flowing from the TOU rates to be small  due to the mild ratio of peak to off‐peak prices

▀ This implied that we would need larger sample sizes to be able to detect a statistically significant impact than other studies which had used higher price ratios 

 We conducted “statistical power calculations” to determine the minimum treatment and control group sizes to achieve a pre‐determined statistical precision level

  Roughly 106,000 residential customers and 150,000 general service customers were sampled

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The Econometric Estimation of the Addilog system

We estimate the Addilog system using the “seemingly unrelatedregression (SUR)” estimation routine

▀ SUR employs random effects estimator in the context ofunbalanced panels (time‐invariant fixed effects are accounted forfirst differences)

Parameter estimates from the Addilog system readily yieldelasticity of substitution for all five periods relative to the 1stperiod

Other elasticities (such as own price and cross price elasticities),can also be derived from the estimated addilog system but thatis not a trivial task

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The Monthly Conservation Model

Where:X may refer to non‐TOU variables such as weather, socio‐demographic  variables, etc.e refers to 

We estimate the monthly conservation model using fixed effects estimation corrected for the 1st order autocorrelation

Parameter estimates from this equation yield the overall price elasticity of demand

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Generalized Addilog System

Where:‐ X may refer to non‐TOU variables such as weather, census characteristics, etc.‐ v refers to random disturbance

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Presenter Information

SANEM SERGICISenior Associate│ CambridgeSanem.Sergici@brattle.com +1.617.864.7900

Dr. Sanem Sergici is a Senior Associate in The Brattle Group’s Cambridge, MA office withexpertise in electricity markets, applied econometrics, and industrial organization. AtBrattle, the focus of Dr. Sergici’s work has been on assisting electric utilities, regulators,and wholesale market operators in their strategic questions related to energy efficiency,demand response, and customer behavior in the context of Smart Grid. Dr. Sergici hassignificant expertise in the design and evaluation of a variety of demand response andbehavior‐based energy efficiency programs; development of load forecasting models;ratemaking for electric utilities; and energy litigation. She has recently completedseveral long‐term resource planning projects that involve the development of scenariosand strategies for an electric system to meet long‐range electric demand whileconsidering the growth of renewable energy, energy efficiency, other demand‐sideresources. She has spoken at several industry conferences and published in severalindustry journals.

The views expressed in this presentation are strictly those of the presenter(s) and do not necessarily state or reflect the views of The Brattle Group, Inc.

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About Brattle  The Brattle Group provides consulting and expert testimony in economics, finance, and regulation to corporations, law firms, and governments around the world. We aim for the highest level of client service and quality in our industry.  We are distinguished by our credibility and the clarity of our insights, which arise from the stature of our experts, affiliations with leading international academics and industry specialists, and thoughtful, timely, and transparent work. Our clients value our commitment to providing clear, independent results that withstand critical review. 

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