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Consortium for Electric Reliability Technology Solutionshttp://certs.lbl.gov
CERTS-CAISO-CECDemand Response Demonstration
2nd Technical Advisory Committee Meeting
April 4, 2005
CERTS-CAISO-CEC Demand Response Demonstration Project –
Meeting Objectives
Update TAC on status of CERTS Demand Response Demonstration Project
Present and discuss draft Test Plan, including preparatory statistical analysis
Present and discuss proposed monitoring screens for real-time observation of the tests
Receive guidance from CAISO and TAC on technical issues for completing the test plan, including monitoring/verification procedures
CERTS-CAISO-CEC Demand Response Demonstration Project - Agenda
9:00 Welcome/Introductions – Dave Hawkins
9:05 Purpose/Organization of this Meeting – Joe Eto
9:10 Presentation of Test Plan – John Kueck
9:30 Statistical Analysis – Roger Wright
10:00 SCE Load Management Technologies – Mark Martinez
10:20 Data Collection, Communication, and Presentation – Arup Barat
10:40 Review of Technical Feedback and Direction from CAISO and TAC – Joe Eto
11:00 Adjourn
CERTS-CAISO-CEC Demand Response Demonstration Project - Organization
Project Sponsor: CEC Public Interest Energy ResearchEnergy Systems IntegrationRon Hofmann (510) 547-0375
Project Coordinators: Joe Eto, LBNL/CERTS (510) 486-7284Dave Hawkins, CAISO (916) 351-4465
Technical Leads: John Kueck, ORNL (865) 574-5178 Brendan Kirby, ORNL (865) 576-1768Bob Yinger, SCE (626) 302-8952
Mark Martinez, SCE (626) 302-8643Carlos Torres, SCE (626) 302-8364Roger Wright, RLW Analytics
(707) 939-8823 Thomas Yeh/Arup Barat, Connected Energy (585) 697-3800Dave Watson, LBNL (510) 486-5562
CERTS Demand Response Test Plan
John Kueck and Brendan KirbyOak Ridge National Laboratory
865-574-5178 [email protected] [email protected]
Objectives
Demonstrate that demand response can provide the ancillary service of spinning reserve for system contingencies in a manner that will be adopted by system operators:
Build operator confidence regarding the value of demand response as an alternative to traditional approaches for providing spinning reserve.
Set the technical basis for modifying reliability rules to allow utilization of demand response for spinning reserve.
Demonstrate and benchmark statistically the reliability of large numbers of small responsive loads; compare this to the current responsiveness of generation.
Objectives, Contd.
Demonstrate ability to target demand response to geographic sub-regions.
Demonstrate that demand response can provide spinning reserve economically and reliably through modest enhancements to existing centralized systems.
Demonstrate to IOU distribution system planners the ability of demand response to influence the timing of distribution system upgrades.
A Test of Demand Response
Curtailment of air conditioning loads on a SCE Distribution circuit.
This is a “typical” circuit with residential and commercial loads.
The test is to be a benchmark in establishing the reliability of demand response.
We need your input in the test planning.
This test has two objectives: Demonstrate that when load is curtailed by a
dispatch signal, the available MW demand response of a specific circuit can be precisely predicted with a 90% statistical confidence level using three variables: time of day, day of week, and temperature.
Demonstrate that the load can be curtailed reliably and quickly on the issuance of a dispatch signal. The load shed is expected to start within 10 seconds of the signal and be fully implemented within two minutes.
Test Overview The distribution circuit power level will be sampled via
SCADA every 16 seconds. A group of local data loggers will also be installed
during the test to increase the statistical precision of the data analysis.
The rigorous, statistical approach taken for the design of this test will be discussed by Dr. Roger Wright.
The Zin circuit has roughly 2350 residential accounts and 169 commercial accounts.
Based on seasonal load profiles, there is approximately 850 kW of air conditioning load on this circuit.
Residential and Commercial Customers
SCE plans to implement a special contract for the test with 400 to 500 residential customers and 50 to 100 commercial customers.
Residential customers will be curtailed with a local switch.
Commercial customers will be curtailed with a thermostat that will be programmed to actually curtail, not to just set back the temperature.
Curtailment Durations The curtailment durations will be set to
twenty minutes. Twenty minutes is ample time to verify
the exact amount of load that has been shed.
Installed metering will also show how quickly it was shed.
There will be 60 tests over July, August and September at random times between the hours of 2 pm and 6 pm. 60 tests are needed for statistical rigor.
Curtailment Duration In the actual provision of reliability services, the
curtailment durations would be longer than 20 minutes.
A longer curtailment test duration would make it difficult to contract for this large number of tests because customers would be concerned about multiple air conditioning outages.
Longer curtailment intervals could be provided by simply dispatching a longer curtailment since the load management methods being tested provide direct control of the load.
In this test, we are not addressing the issue of appropriate payment to enlist participation in longer duration outages, but only the issues of predicting response accurately, and delivering it reliably once the dispatch controls have been installed.
Test Plan Steps Contract with residential and commercial customers and
modify SCE Load Management and Demand Response systems to control air conditioning loads within the circuit.
Install sufficient numbers of two types of load control devices on this feeder to ensure interruptions can be observed statistically at the feeder. At time of device installation, record the size of the air conditioning compressor.
Install spot meters on a designated sample of the controlled units.
Assemble the data acquisition and communications system.
Conduct a series of 20 minute duration demand response tests during July, August and September, 60 tests total.
Test Plan Steps Retrieve the spot meters and their data. Corroborate load changes observed at the feeder with spot
metering placed on a sample of controlled loads within the feeder; express findings statistically.
Measure load drop and response times, including latency in observability; express findings statistically.
Characterize load drops as function of relevant influences (temperature, time of day, day of week) with an eye toward extrapolation of findings to other feeders that having differing saturations of controllable end-use loads; express findings statistically to determine response confidence level.
Present and discuss results with stakeholders (IOU distribution system planners, IOU dispatchers, CAISO operators, relevant WECC/NERC committees) to confirm adequacy of present analysis and priorities for future research in this area.
Questions
How many accounts with substantial AC load are served by the circuit?
What does the circuit’s load look like?
Does the circuit have substantial AC?
How accurately can we predict the circuit load in the absence of curtailment?
How many units do we need to curtail?
How many units do we need to monitor?
Accounts with AC LoadResidential Commercial Total
1,958 151 2,109
250 857 60 917
300 707 58 765
350 580 53 633
400 476 50 526
450 371 49 420
500 294 47 341
Current Accounts
Min
AC
per
Mon
th
For example, the circuit serves 1,958 residential accounts. 857 residential accounts have at least 250 kWh of estimated AC use per month.
Load on the Circuit
The load varies from a low of 2 MW to a high of 9 MW. There are a few gaps and bad measurements but the data are remarkably clean.
Weekly PeaksWeek Of Peak Peak At6/6/2004 4.87 Sun Jun 6, 2004 6:12PM6/13/2004 5.44 Mon Jun 14, 2004 4:38PM6/20/2004 6.09 Fri Jun 25, 2004 5:26PM6/27/2004 4.86 Mon Jun 28, 2004 2:58PM7/4/2004 5.95 Tue Jul 6, 2004 3:10PM7/11/2004 8.24 Tue Jul 13, 2004 4:40PM7/18/2004 8.79 Tue Jul 20, 2004 4:20PM7/25/2004 8.48 Mon Jul 26, 2004 4:22PM8/1/2004 6.81 Fri Aug 6, 2004 5:10PM8/8/2004 9.42 Tue Aug 10, 2004 4:00PM8/15/2004 7.49 Tue Aug 17, 2004 5:12PM8/22/2004 5.88 Fri Aug 27, 2004 4:16PM8/29/2004 8.99 Wed Sep 1, 2004 4:12PM9/5/2004 9.16 Wed Sep 8, 2004 3:50PM9/12/2004 7.27 Sun Sep 12, 2004 3:38PM9/19/2004 7.71 Wed Sep 22, 2004 10:40AM9/26/2004 6.70 Mon Sep 27, 2004 4:46PM
Load during the Peak Week
Load during the Peak Day
These are 2-minute spot measurements. The random variation is about 1% of load.
Smoothing the Load
The smoothed value is the numerical average of the five original values, i.e., the centered, 10-minute moving average.
date hour minute Original Smoothed8/10/2004 15 52 9.37337 9.367638/10/2004 15 54 9.40278 9.370698/10/2004 15 56 9.34125 9.374448/10/2004 15 58 9.33326 9.382998/10/2004 16 0 9.42155 9.374598/10/2004 16 2 9.41613 9.354828/10/2004 16 4 9.36074 9.336118/10/2004 16 6 9.24241 9.290648/10/2004 16 8 9.23970 9.246258/10/2004 16 10 9.19420 9.240258/10/2004 16 12 9.19420 9.257398/10/2004 16 14 9.33076 9.259018/10/2004 16 16 9.32811 9.27669
Original vs. Smoothed Load
The graph shows one hour, centered at the 3:58 PM peak. There is still substantial variation in the smoothed load.
9.0
9.1
9.1
9.2
9.2
9.3
9.3
9.4
9.4
9.5
Original
Smoothed
Energy Print of the Load
This graph shows the load in color. The x-axis is the day and the y-axis is the hour of the day.
Load versus Temperature
The upper graph shows the outside temperature. The bottom graph shows the load. Clearly the load is driven largely by temperature.
Peak Temperatures vs. Loads
There is a high correlation between the peak temperature and peak load
Week OfPeak Temp Peak At
Peak Load Peak At
6/6/2004 85.97 Sun Jun 6, 2004 1:46PM 4.79 Sun Jun 6, 2004 5:48PM6/13/2004 85.90 Sun Jun 13, 2004 2:32PM 5.39 Mon Jun 14, 2004 4:38PM6/20/2004 92.09 Fri Jun 25, 2004 2:46PM 5.96 Fri Jun 25, 2004 5:26PM6/27/2004 84.20 Sun Jun 27, 2004 1:32PM 4.81 Mon Jun 28, 2004 3:52PM7/4/2004 93.27 Sat Jul 10, 2004 2:18PM 5.88 Tue Jul 6, 2004 3:08PM7/11/2004 98.30 Mon Jul 12, 2004 1:02PM 8.19 Tue Jul 13, 2004 5:14PM7/18/2004 96.20 Tue Jul 20, 2004 2:32PM 8.67 Tue Jul 20, 2004 4:24PM7/25/2004 98.39 Sun Jul 25, 2004 2:16PM 8.42 Mon Jul 26, 2004 4:26PM8/1/2004 95.09 Sat Aug 7, 2004 1:46PM 6.76 Fri Aug 6, 2004 5:12PM8/8/2004 101.89 Tue Aug 10, 2004 1:46PM 9.38 Tue Aug 10, 2004 3:58PM8/15/2004 91.27 Tue Aug 17, 2004 2:16PM 7.44 Tue Aug 17, 2004 5:10PM8/22/2004 92.38 Sat Aug 28, 2004 1:18PM 5.82 Fri Aug 27, 2004 4:18PM8/29/2004 101.60 Wed Sep 1, 2004 2:32PM 8.89 Wed Sep 1, 2004 4:52PM9/5/2004 101.40 Tue Sep 7, 2004 2:02PM 9.11 Wed Sep 8, 2004 3:48PM9/12/2004 92.40 Sun Sep 12, 2004 1:32PM 7.16 Sun Sep 12, 2004 3:42PM9/19/2004 96.20 Sat Sep 25, 2004 1:32PM 7.59 Wed Sep 22, 2004 10:44AM9/26/2004 95.20 Sun Sep 26, 2004 1:32PM 6.64 Mon Sep 27, 2004 4:24PM
Statistical Plan
This is an example of a Smart Thermostat curtailment. The blue curve is the baseline load in the absence of curtailment. The red curve is the actual load of the units.
Assumptions
The average nonresidential account has 2 ACs Curtailments are for 20 minutes between 2 pm
and 6 pm on weekdays Each AC unit is completely curtailed A typical residential unit is 3 tons with 3 kW
operating load and a duty cycle of 50%. A typical commercial unit is 4 tons with 4 kW
operating load and a duty cycle of 70%.
Predicting the Baseline Load Predictors included:
Average circuit load on prior weekday Actual load just prior to the curtailment Current and prior temperature and enthalpy Load on two adjoining feeders
All feeder loads were smoothed using a 10-minute moving average
We were generally able to predict the load with a standard error of 1% ten minutes into the curtailment.
Expected Statistical Precision
This plan assumes 80% commercial participation and 50% residential. N is the potential number of units. n is the number of controlled units. The expected impact is 906 kW with an error bound of 159 kW, for 18% relative precision at 90% confidence.
Source N In Prog nImpact
AnalysisStd
ErrorErr Bnd
Rel Prec
Baseline 9,000 90 148 2%E$T 120 0.8 96 269 18 30 11%Switches 850 0.5 425 638 31 51 8%Impact 906 97 159 18%
Expected Statistical Precision
This plan assumes 50% commercial participation and 25% residential. The expected impact is 487 kW with an error bound of 154 kW, for 32% relative precision at 90% confidence – not good enough. Clearly we need to very aggressive marketing.
Source N In Prog nImpact
AnalysisStd
ErrorErr Bnd
Rel Prec
Baseline 9,000 90 148 2%E$T 120 0.50 60 168 14 23 14%Switches 850 0.25 213 319 22 36 11%Impact 487 94 154 32%
End Use Monitoring If each unit is 100% curtailed, the potential controllable load is
equal to the load of the controlled units. The controllable load will vary by temperature, time of day and
possibly day of week. We need to supplement the information from the curtailments
(e.g., 60 calls) with added data. Extended End Use Monitoring
– 5-minute battery-powered loggers installed on a sample of the controlled units can measure the actual load throughout the summer.
– How many do we need? Real Time End Use Monitoring
– Time synchronized ‘Smart spot meters’ gathering real time data from a sample of controlled units can characterize the time based behavior and responsiveness of the controlled units
– 10 meters on residential units and 10 on commercial units
Expected Statistical Precision from End Use Monitoring
A sample of 50 units gives 18% relative precision at 90% confidence. 75 gives 15%. 100 gives 13%. 150 gives 10%. Added precision will come from statistically modeling the repeated measurements throughout the summer.
SourcePartici- pants
Unit Impact sd
Sample
Total Load Std Err Err Bnd
Rel Prec
E$T 96 2.8 1.8 11 269 50 82 31%Switches 425 1.5 1.5 39 638 97 160 25%Impact 521 50 906 100 165 18%
Assumptions, Case A Analysis
Conclusions We need to control about 525 units to see the impact reliably in
the SCADA data. We need virtually 100% signal reception and no take back from
other units serving the site. We should make a 20-minute call each weekday. The calls should be pre-scheduled but staggered to avoid
contaminating the baseline. We need to monitor 50 to 100 units on a 5-minute basis to help
model the size of the curtailable load. We need to monitor 10 residential units and 10 commercial
units in real time to understand the time responsiveness of the curtailment devices.
Some variation in the magnitude of the impact is unavoidable – but we should be able to demonstrate that the aggregate resource is responsive and reliable.
In larger-scale, the impact should be increasingly predictable.
SCE Load Management
Mark Martinez
Southern California Edison
(626) 302-8643
Summer Discount Plan
Also known as AC cycling program 154MHz private network that continuously
communicates one way to SCE devices Device is installed as part of D-APS tariff
that allows SCE to curtail air conditioner via thermostat wiring for billing credit
Various program participation levels from 50% to 100%, unlimited events
Triggered on Stage 2 or local emergency
Current SDP Resource
Residential Base = 163.8 MW 86,042 SA Enhanced = 74.3MW 40,847 SA
Commercial Base = 35.8 MW 2,000 SA Enhanced = 6.7MW 452 SA
Total load = 280.6MW
Dispatch Procedures (ACCP) Interrupt notice is sent from the Dispatcher to the
Broadcast Master Controller (BMC) Devices to be interrupted are sent via Daily and
Weekly files, which are fed through the Gateway and saved in the ELMA database
BMC builds the appropriate device download commands and queues this to be sent to the Port Expander
Port Expander sends the commands to the modem on the Remote Site Controller, which then encodes, transmits, and verfies the appropriate message has been broadcasted (A/C or API switch)
Cycling strategy for 50%
Shed LoadShed Load
Shed Load
Shed Load
Sta
rt-T
ime
15 Minutes
30
Min
ute
s
45 Minutes
TimingSequence
60
Min
ute
s
VHF antenna towers & locations
Customer Installation SDP
Smart Thermostat Program (E$T)
Small Commercial pilot program to test demand responsiveness with t-stat
Two-way communicating thermostat on public paging network
Thermostat is given to customer and can be controlled by SCE
Customer receives cash bonus for participation, deductions for override
Currently 8,250 thermostats installed
SCE Energy$mart ThermostatSM
Package AC Units
E$T Participation
0
500
1000
1500
2000
2500
Coastal H Desert LowDesert
OrangeCounty
Inland SanGabriel
Devices
Accounts
SCE 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
Itron web based application
Dispatch Procedures (E$T)
Device IDs are stored in Itron application REM and tied to group and account info
Curtailment command is initiated by logging into web-based application and scheduling event for initiation
Page is sent to devices (900MHz public network) and event is set up in receiver
At scheduled time of event, device activates curtailment strategy
Load Impact (kW/ton)
Event Date: 8/9/04 (4oF, 3:00-5:00pm)
Air Conditioner Hourly Average Duty Cycles
05
101520253035404550
01:00
02:00
03:00
04:00
05:00
06:00
07:00
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
End of Hourly Bin
Coastal Orange County San Gabriel Valley Riverside/SB High Desert Low Desert Unassigned
Event Date: 8/9/04 (4oF, 3:00-5:00pm)
Indoor Room Hourly Average Temperatures
74
76
78
80
82
84
86
01:00
02:00
03:00
04:00
05:00
06:00
07:00
08:00
09:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
End of Hourly Bin
Coastal Orange County San Gabriel Valley Riverside/SB High Desert Low Desert Unassigned
Comparable load relief via different Strategies
50% AC Cycling versus4 deg. F Thermostat Control
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
HE of the Peak Day
kW
per
Cu
sto
mer 50% cycling
AC Peak Day Usage
Thermostat 4 0F
Four Hour Load Shed from HE1400 to HE1800
Data Collection, Communication, and Presentation
Arup Barat
Connected Energy
(585) 697-3800
Communication Topology
Data Monitoring Strategies1. Real Time Feeder Monitoring
• Used for load profile projection and monitoring the result of a dispatch command at the aggregated circuit load level.
• Data collected by SCADA system at 4-8 sec intervals and aggregated at Connected Energy’s systems.
• Will be displayed in real time.
2. End Use Monitoring (on a representative sample)
1. Extended monitoring: Measure the actual end use load throughout the summer in order to analyze the load drop during curtailment periods and model the size of the load. Needs 5-minute single channel load data using data loggers (50-100).
2. Real Time monitoring. To characterize the time based behavior and responsiveness of the load shedding devices during a curtailment period. Smart spot meters and real time data (10 residential, 10 commercial)
Common data collection and correction strategies
Time Synchronization All times clocks on disparate systems will be
synchronized to the US Naval Atomic clock server All real time data will be time stamped accordingly
Data correction for systematic errors Systematic errors in the extended end use monitoring
data will be corrected at the end of the test period at the aggregated system.
Baseline Prediction model Calculated baseline prediction model data will be
continuously updated as new real time data is gathered. This will be available for real time display
Data Presentation
On a Connected Energy hosted, dedicated website throughout the duration of the test
Real Time data Aggregated Views – circuit and end use sample and
calculated data Live trends – real time updates.
Summarized data Predefined Reports Exported Data – available in multiple formats for
further analysis
Aggregated Circuit View
Available Power for Shedding (kW)
Baseline Load Profile
Actual Load Profile (kW)Predicted Load Profile (kW)
Dispatch Profile
Dispatch Profile
Reports
Summarized reports Dispatch event detailed report
After the completion of each dispatch event to summarize the load behavior.
Aggregated dispatch events reportAt the end of test period to summarize
behavior and statistical findings Aggregated end use load responsiveness report
Aggregated time response behavior of AC units