Demand Response and the California Information Display Pilot

Demand Response and the California Information Display Pilot

2005 AEIC Load Research Conference

Myrtle Beach, South Carolina

July 11, 2005

Mark S. Martinez, Manager, Program Development, SCE

Craig Williamson, Research Director, EPRI Solutions

http://www.sce.com/sc3

2 Copyright © 2005 EPRI Solutions, Inc. All rights reserved.

Background

In 2003, California embarked on Statewide Pricing Pilot (SPP), a study to determine customer responsiveness to dynamic pricing rates

Time of Use (two part)

Critical Peak Pricing (Fixed)

Critical Peal Pricing (Variable)

$12M study designed to develop inputs into Advanced Metering Infrastructure business cases from three IOUs in 2005


Smart Thermostat – Enabling Technology

Due to the short-term notification of the CPP-V Super Peak, customers were offered “smart thermostats” as basic enabling technology

These thermostats automatically adjusted customer’s HVAC temperature during Super Peak period

Residential customers were also offered water heater and pool pump controls

Some commercial customers were offered small energy management systems


SCE and SDG&E Smart Thermostat

3) BroadcastCurtailment

MessageWireless

Fan Coil Unit

Carrier EMiThermostat

Silicon Energy REM

SCEOperator

2) Curtailment Message

Pager

Emi UIOB Standard WebBrowser

1) CurtailmentRequest

4) Unique AcknowledgmentWireless

[A user override will generatea real-time message from

the EMi back to the server]

5) Acknowledgments from all EMi’s

6) Verification ofLoad Reductionand Override

Notices


Potential Enhanced CPP Treatments

SPP specific web site (what to do when you want to “Smart Shift and Save”)

More timely inbound usage feedback

• Post event notice (email and letter)

• Monthly comparison bill or statement

SPP “Report Card” or activity report

Additional event or price notification

In-home usage or energy display

Additional enabling technologies


Research Purpose – Information Display Pilot (IDP)

Does more timely feedback on household energy use increase average level of peak energy savings?

Dynamic rates may be difficult to understand, and existing “price signals” may not generate response to consequences in a timely manner – what would?

Awareness, understanding, and behavior modification for SPP are based on materials provided, and bill feedback – could we do better?

What would be incremental increase in energy savings and load reduction from enhanced deployment of information and technology?


Direct Feedback Methodology

“Push” approach that is scheduled (mailers) or polled on demand (observe) – Options:

• Provide more frequent information on consequences of customer behavior with energy usage – “Who left all the lights on?”

• Make price signal and events more obvious to consumer – “in their face”

• Provide direct usage information on a near real-time basis


Newsletter – focus group feedback

Groups generally favor IDP newsletter, but this alone might not change behavior during peak periods.

Some skeptical of information in IDP treatments: not representative of home/lifestyle.

Some had mixed responses about whether they want information on/with bill, in hard copy, or by e-mail.

Most with internet access want info via internet, but were mixed regarding how wished to be alerted about information.


IDP Impact Assessment Goals

•Assess the load impact of providing enhanced information treatments to customers, over and above the impact of enabling technology and the rate/price

•Control for other factors, including– Customer size (high-low consumption)– Day-specific conditions– Treatment installation date

•Groups include residential and commercial customers on the CPP-V rate (2003 Track A SPP sample)

•Challenge – only 62 total customers available


Impact Assessment Process

•Include both a treatment and a control group in the analysis, both on CPP-V

•Collect interval load data during pre-treatment period for both groups, begin treatments, then collect post-treatment data

•Use Energy Orb installation date as start of treatment

• Newsletters started at about the same time

•Energy Orbs installed from 7/28/04 through 8/31/04

•Pre-treatment and post-treatment periods are different for different customers


Impact Assessment Methodology

Difference of Differences approach• Corrects for changes over time (pre and post) and recognizes differences

between treatment and control groups

First calculate average for each customer for pre and post period (customer-specific)

Then average all customers in each sample cell for control and treatment group

First difference: Treatment – control for each hour in both the pre-treatment and post-treatment periods

Second difference: (difference in the post-treatment period) – (difference in the pre-treatment period)

Calculate weighted average of cell differences, using weights based on the number of treatment customers in each cell


Pre and Post Differences (after averaging)

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2004 IDP Sample Design

IDP Treatment Group (enhanced)

• 33 residential customers on CPP-V

• 29 commercial customers on CPP-V

IDP Control Group (standard)

• 100 residential customers on CPP-V

• 138 commercial customers on CPP-V

Residential customers located in SDG&E areas, and C/I customers located in SCE territory

IDP Control Group was the 2004 Track A


2004 IDP Results - Residential

Difference of differences approach, with CPP-V as common rate in treatment and control groups, and adjusting for pre and post

treatments periods for both groups

Residential (SDG&E) Load ImpactAll days, actual time

-1.5

-1

-0.5

0

0.5

1

0 2 4 6 8 10 12 14 16 18 20 22 24

Time

Ave

rag

e kW


IDP Residential – average for 2-hour event days

Difference of differences approach, using “relative time” to treat different days consistently

Residential (SDG&E) Load Impact2 hour event days

-1.5

-1

-0.5

0

0.5

1

-12 -10 -8 -6 -4 -2 0 2 4 6 8

Time relative to start of event

Ave

rag

e kW

Load Impact

Start Time

2-hour end

Start Flashing


IDP Residential – individual 2-hour event days

Difference of differences approach, using “relative time” to treat days with different start times consistently

Residential (SDG&E) Load ImpactAll 2 hour event days

-1.5

-1

-0.5

0

0.5

1

-12 -10 -8 -6 -4 -2 0 2 4 6 8


Ave

rag

e kW

Aug 11

Aug 27

Sep 9

Sep 10

Start Time

2-hour end

Start Flashing


IDP Residential – average for 2-hour event day with 90% confidence intervals


Residential (SDG&E) Load Impact2 hour event days, with 90% confidence interval

-4

-3

-2

-1

0

1

2

3

-12 -10 -8 -6 -4 -2 0 2 4 6 8

Time

Ave

rag

e kW

Load Impact

Upper 90% CI

Lower 90% CI

Start Time

2-hour end

Start Flashing


IDP Residential – average for 5-hour event days


Residential (SDG&E) Load Impact5 hour event days

-1

-0.5

0

0.5

1

-12 -10 -8 -6 -4 -2 0 2 4 6 8


Ave

rag

e kW

Load Impact

Start Time

5-hour end

Start Flashing


IDP Residential – individual 5-hour event days


Residential (SDG&E) Load ImpactAll 5 hour event days

-1.5

-1

-0.5

0

0.5

1

-12 -10 -8 -6 -4 -2 0 2 4 6 8


Ave

rag

e kW

Aug 31

Sep 8

Start Time

5-hour end

Start Flashing


IDP Residential – average for 5-hour event day with 90% confidence intervals


Residential (SDG&E) Load Impact5 hour event days, with 90% confidence interval

-4

-3

-2

-1

0

1

2

3

-12 -10 -8 -6 -4 -2 0 2 4 6 8

Time

Ave

rag

e kW

Load Impact

Upper 90% CI

Lower 90% CI

Start Time

5-hour end

Start Flashing


2004 IDP Results - Commercial

Difference of differences approach, with CPP-V as common rate in treatment and control groups, and adjusting for pre and post

treatments periods for both groups

Commercial (SCE) Load ImpactAll days, actual time

-4-3.5

-3-2.5

-2-1.5

-1-0.5

00.5

1

0 2 4 6 8 10 12 14 16 18 20 22 24

Time

Ave

rag

e kW


IDP Commercial – average for 2-hour event days


Commercial (SCE) Load Impact2 hour event days

-6

-5

-4

-3

-2

-1

0

1

-12 -10 -8 -6 -4 -2 0 2 4 6 8


Ave

rag

e kW

Load Impact

Start Time

2-hour end

Start Flashing


IDP Commercial – individual 2-hour event days


Commercial (SCE) Load ImpactAll 2 hour event days

-7-6-5-4-3-2-101234

-12 -10 -8 -6 -4 -2 0 2 4 6 8


Ave

rag

e kW

Aug 11

Aug 27

Sep 9

Sep 10

Start Time

2-hour end

Start Flashing


IDP Commercial – average for 5-hour event days


Commercial (SCE) Load Impact5 hour event days

-4

-3

-2

-1

0

1

-12 -10 -8 -6 -4 -2 0 2 4 6 8


Ave

rag

e kW

Load Impact

Start Time

5-hour end

Start Flashing


IDP Commercial – individual 5-hour event days


Commercial (SCE) Load ImpactAll 5 hour event days

-6-5-4-3-2-101234

-12 -10 -8 -6 -4 -2 0 2 4 6 8


Ave

rag

e kW

Aug 9

Aug 10

Aug 31

Sep 8

Start Time

5-hour end

Start Flashing


Impact Analysis Conclusions

Residential customers show an impact during Super Peak, and also during the 4 hour warning period

Commercial customers, while they are very positive about the program, do not show a consistent impact. But something appears to be happening

None of the results are statistically significant• Limit of small sample sizes

• Variability of the commercial customer types

Customer feedback indicates that treatments are somewhat responsible for the apparent impacts, either alone or combined

Individual analysis of each customer or subgroups would be beneficial in “teasing out” specific behavioral effects


Recommendations for 2005

Sample sizes are obviously too small – recommend increasing to improve significance

Residential customers may be responding to both the Energy Orb and the newsletter – recommend a bifurcation of treatments or focus on one treatment

Commercial customers are too variable – need to increase sample size and also focus on one treatment

Difference of differences approach can be repeated more effectively if Super Peak events and treatment start times are properly coordinated


IDP for 2005

• Proposed research objectives for 2005 include

- Continue to evaluate incremental effect of enhanced treatments on peak load impacts

- Augment commercial segment with CPP-V customers from SPP (non-Track A) and offer visual and informational treatments

- Augment residential segment with CPP-F from SPP to evaluate incremental load impacts of monthly information treatments for residential CPP

• Continuation of current sample and augmenting residential sample with CPP-F customers receiving newsletter treatments have been approved.

Demand Response and the California Information Display Pilot

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