Load Impact Estimation for Demand Response Resources

Nicole Hopper, Evaluation Manager

July 14, 2009National Town Meeting on Demand Response and Smart Grid

Washington, DC

2

Overview

• Context and uses for DR load impact estimation– Operations vs. settlement vs. resource planning

– Ex post vs. ex ante load impacts

• Why establish load impact protocols for DR?– California was first

– Ontario is well underway

• Estimation methods, key concepts and issues • Conclusions

3

Different Analytical Needs for Different Purposes

• Need to know the resources available under varying operating conditions (i.e., location, time of day, notification lead time, market prices, weather)

• Requires ex-ante estimation

• Estimates needed at the aggregate level (possibly by location)

• Prediction accuracy is critical

SETTLEMENTOPERATIONSLONG-TERM RESOURCE PLANNING

• Need customer buy-in into method

• Participants want payment quickly

• Requires ex-post estimation

• Estimates needed at the individual participant level

• Prediction accuracy affects costs (overpayments /penalties)

• Ability to compare DR with other resources is critical—need to forecast load impacts under the conditions for which the system is designed

• Requires ex-ante estimation that reflects changes in participant mix

• Aggregate estimates required

• Prediction accuracy affects resource mix and reliability

E.g., NAESB standards

4

Load Impacts Can Be Used Throughout the Life Cycle of a DR Resource

Load Impact Protocols

ex ante

evaluation(months)

ex anteload impacts

ex postload impacts

validation

settlement(days)

program planning(1-3 years)

ex ante program impacts

ex ante program cost effectiveness

operations(hours à days)

resource planning(up to 20 years)

ex ante resource impacts

ex ante resource cost effectiveness

new/revised programs

MW targets

dynamc operational

model

What LI can be expected from various DR options under a range of system conditions?

What LI occurred when the program

was called?

ex post program impacts

statistical analysis of

historical data

How much DR can be expected tomorrow?...in the next hour?

individual customer impacts

For what LI should customers be compensated?

ex post

5

Estimating DR Resource Impacts Requires a Different Approach than Energy Efficiency

• Standardized methods for energy efficiency (EE) load impact estimation are not appropriate for DR, for a variety of reasons

Energy Efficiency Demand Response

Tied to installation of more efficient devices that provide same service level

Tied to behavioral actions & lower service levels

Benefits accrue over lifetime of device Benefits tied to continuation of programs & customer participation

“Always on”—benefits are in effect whenever the device is in use

Benefits are event driven and there may be constraints on their frequency and timing

The magnitude of resources is more-or-less constant

Resources may vary significantly over time and conditions (e.g., weather) and may be largest when they are needed most

Benefits mostly derive from avoided energy (MWh)

Benefits mostly tied to avoided capacity (MW)

Free-ridership rates must be quantified Not so relevant—customers typically don’t “do” DR in absence of a program or price incentive

DR provides option value—insurance—even when it is not used

6

The Role of Load Impact Protocols

• Set minimum requirements for estimating and reporting load impacts– Focus on what to provide, not how to do the job

• For ex post load impacts, streamline outputs to suit the program– Define program types based on features that affect analysis

• e.g., frequency of events, variation in event timing/duration/number of participants curtailed

– For programs with few events, report impacts for each event day– For programs with frequent events, report results for representative event conditions

• e.g., average event day or each of top 5 system load days

• For ex ante load impacts, define common day types and event conditions– 1-in-2 (normal) and 1-in-10 (extreme) weather year conditions

• Weather-sensitive DR, such as direct load control, has higher impacts under the extreme conditions for which the electric system is designed

– E.g., report impacts for all 24 hours of monthly system peak day, and average of top ten peak days

• Future enrolment scenarios left to system planners in Ontario, but included in California protocols

7

Load Impacts Involve Estimating what Customers Would Have Used in the Absence of DR Incentives

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0.5

1.0

1.5

2.0

2.5

0:00

3:00

6:00

9:00

12:0

0

15:0

0

18:0

0

21:0

0

0:00

Hour Ending

Reference Load (kW) Observed Load (kW)A

vera

ge C

usto

mer

(K

W)

Load Impact

DR Event Window

Load Impact = Reference Load – Observed Load

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The Best Estimation Method Depends on the Context and Ultimate Use of Load Impacts

• For program settlement, baseline methods are typically used– Load on prior days, sometimes with same-day adjustments, used to predict load

on event days in the absence of DR– May be subject to gaming and typically can’t incorporate key drivers of demand

response into ex ante estimates

• For resource planning and operations, impact estimation is typically based on regressions developed from historical data over a longer period (i.e., a whole program year)– Statistically robust models allow quantification of key drivers of DR

• Variation in consumer activity across hours, days, months and seasons• Variation in temperature• Variation in incentives (e.g., prices, capacity payments, program availability)• Changes in customer characteristics over time

– If event data is limited, regression-based reference load estimation can be combined with assumptions about load drops or cycling strategies to produce valid impact estimates

• Engineering analysis, common in EE impact estimation, is typically not suitable for DR load impact estimation

Model Bias Should be Quantified

• No model will perfectly predict load, but you can assess the direction and degree of bias

• In this example, average AC loads are well predicted across a range of temperatures– Above 97 degrees, there is slightly more bias

9

10

Model Bias can be Reduced by Estimating Loads with and without DR from the same Model

0.0

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1.0

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2.0

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3.5

0 3 6 9 12 15 18 21 24

Hour Ending

Ave

rag

e kW

Actual kW Predicted with DR Predicted without DR

Impact Based on Difference Between Regression-Based

Predicted Load With and Without DR

Impact Based on Difference Between Regression-Based

Reference Load and Actual Load

DR Event Window

• Subtracting actual observed load during events from a modeled reference load introduces bias

• This can be “netted out” by predicting both the baseline and the “actual” usage from the same model

11

Conclusions

• Ex ante impact estimation that captures the option value of DR is the key to “operationalizing” DR resources– Long-term planning—giving full credit to DR’s ability to offset

supply when it is needed most– Operations—providing predictive models to include DR in

market operations• We are making progress, but have a ways to go…

– Convincing planners and operators will require building a track record of load impacts, based on empirical data, using sound methodologies that produce unbiased results

• An industry goal should be to develop a shared library of DR impact estimates, estimated under common conditions so that results can be compared and translated– DR load impact protocols are a key policy tool for achieving

this goal

12

For more information, please contact:

Nicole HopperEvaluation Manager

Ontario Power Authority120 Adelaide St. W., Suite 1600

Toronto, Ontario M5H 1T1416-969-6274

nicole.hopper@powerauthority.on.ca

13

California Utilities Are Developing an Extensive Library of Empirical Impact Estimates for a Variety of DR Options

• Each spring, California’s IOUs must produce hourly load impact estimates for all DR resources for selected day types and event conditions

• Ex post load impacts must be provided for each event day in the prior year

• Ex ante load impacts must be developed for a common set of weather, day type and event conditions for the average customer and for the program as a whole

– Typical event days

– Monthly system peak days

– 1-in-2 and 1-in-10 year weather conditions

• Load impacts must be provided for each of roughly 12 local capacity areas

• Many estimates are provided for different business categories and other customer segments

• A few examples are provided on the following slides (tens of thousands of similar tables have been filed with the CPUC in recent months)

14

PG&E’s SmartAC Program, Typical Event DayAverage Customer, 1-in-2 Year Weather Conditions

Enrolled Accounts (Forecast) 210,241 100.0%

SmartAC Only Accounts 183,967 87.5%

Dually Enrolled Accounts 26,274 12.5%

TABLE 1: Menu options

Result Type Average Uncertainty Adjusted Impact - Percentiles

Program SmartAC 10th 30th 50th 70th 90th

Program/Portfolio Program Specific 1:00 0.16 0.16 0.00 67.9 0.00 0.00 0.00 0.00 0.00Weather Year 1-in-2 (2004) 2:00 0.11 0.11 0.00 66.4 0.00 0.00 0.00 0.00 0.00Forecast Year 2012 3:00 0.08 0.08 0.00 65.2 0.00 0.00 0.00 0.00 0.00

Day Type Typical Event Day - Top 15 4:00 0.05 0.05 0.00 64.1 0.00 0.00 0.00 0.00 0.00LCA ALL 0.5 5:00 0.03 0.03 0.00 63.0 0.00 0.00 0.00 0.00 0.00

6:00 0.03 0.03 0.00 62.1 0.00 0.00 0.00 0.00 0.00

TABLE 2: Output 7:00 0.03 0.03 0.00 61.5 0.00 0.00 0.00 0.00 0.00

Average Impact (2-6 pm) 0.25 8:00 0.04 0.04 0.00 62.9 0.00 0.00 0.00 0.00 0.00Average Percent Load Reduction (2-6 pm) 24.0% 9:00 0.05 0.05 0.00 66.4 0.00 0.00 0.00 0.00 0.00

10:00 0.10 0.10 0.00 70.5 0.00 0.00 0.00 0.00 0.00

11:00 0.15 0.15 0.00 74.7 0.00 0.00 0.00 0.00 0.00

12:00 0.26 0.26 0.00 78.6 0.00 0.00 0.00 0.00 0.00

13:00 0.41 0.41 0.00 82.2 0.00 0.00 0.00 0.00 0.00

14:00 0.61 0.61 0.00 85.3 0.00 0.00 0.00 0.00 0.00

15:00 0.81 0.63 0.18 87.5 0.12 0.16 0.18 0.21 0.24

16:00 1.01 0.77 0.24 89.0 0.18 0.22 0.24 0.27 0.31

17:00 1.14 0.86 0.28 89.3 0.21 0.25 0.28 0.31 0.35

18:00 1.16 0.88 0.28 88.4 0.21 0.25 0.28 0.31 0.35

19:00 1.05 1.13 -0.09 85.9 -0.12 -0.10 -0.09 -0.08 -0.06

20:00 0.85 0.94 -0.09 81.9 -0.12 -0.11 -0.09 -0.08 -0.06

21:00 0.63 0.70 -0.07 77.6 -0.09 -0.08 -0.07 -0.06 -0.05

22:00 0.48 0.54 -0.06 74.0 -0.08 -0.07 -0.06 -0.05 -0.04

23:00 0.35 0.39 -0.04 71.3 -0.06 -0.05 -0.04 -0.04 -0.03

0:00 0.25 0.25 0.00 69.4 0.00 0.00 0.00 0.00 0.00

Uncertainty Adjusted Impact - Percentiles

10th 30th 50th 70th 90th

Daily 9.82 9.19 0.63 95.8 0.49 0.57 0.63 0.69 0.78

Reference Load (kW)

Observed Load (kW)

Weighted Temp (F)

Hour Ending

Load Impact (kW)

Due to size of the time interval, the figure does not reflect the semi-instatenous drop in load attributable to the direct load control technology employed

Cooling Degree Hours

(Base 75)

Reference Energy Use

(kWh)

Observed Energy Use

(kWh)

Change in Energy Use

(kWh)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1:00

3:00

5:00

7:00

9:00

11:0

0

13:0

0

15:0

0

17:0

0

19:0

0

21:0

0

23:0

0

Reference Load (kW) Observed Load (kW)

15

PG&E’s SmartRate Tariff, Typical Event DayAverage Customer, 1-in-2 Year Weather Conditions

Enrolled Accounts (Forecast) 97,599 100.0%

Peak Day Pricing Only Accounts 82,068 84.1%

Dually Enrolled Accounts 15,530 15.9%

TABLE 1: Menu options

Result Type Average Uncertainty Adjusted Impact - Percentiles

Program Peak Day Pricing 10th 30th 50th 70th 90th

Program/Portfolio Program Specific 1:00 0.72 0.75 -0.03 72.4 -0.03 -0.03 -0.03 -0.03 -0.03 $0.10 $0.13

Weather Year 1-in-2 (2004) 2:00 0.63 0.66 -0.02 71.1 -0.02 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

Forecast Year 2009 3:00 0.58 0.61 -0.02 70.0 -0.02 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

Day Type Typical Event Day - Top 15 4:00 0.55 0.57 -0.02 69.0 -0.02 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

LCA ALL 0.5 5:00 0.54 0.56 -0.02 67.9 -0.02 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

6:00 0.58 0.60 -0.02 67.1 -0.02 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

TABLE 2: Output 7:00 0.66 0.69 -0.02 66.8 -0.02 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

Average Impact (2-6 pm) 0.21 8:00 0.72 0.75 -0.02 68.7 -0.03 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

Average Percent Load Reduction (2-6 pm) 16.5% 9:00 0.73 0.76 -0.03 72.3 -0.03 -0.03 -0.03 -0.03 -0.03 $0.10 $0.13

10:00 0.76 0.78 -0.02 76.2 -0.02 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

11:00 0.81 0.83 -0.02 79.9 -0.02 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

12:00 0.87 0.89 -0.02 83.3 -0.02 -0.02 -0.02 -0.02 -0.02 $0.10 $0.13

13:00 0.95 0.96 -0.01 86.4 -0.01 -0.01 -0.01 -0.01 -0.01 $0.10 $0.13

14:00 1.02 1.05 -0.03 89.0 -0.03 -0.03 -0.03 -0.03 -0.03 $0.10 $0.13

15:00 1.15 0.95 0.20 90.8 0.20 0.20 0.20 0.20 0.20 $0.70 $0.13

16:00 1.20 1.00 0.20 91.8 0.20 0.20 0.20 0.20 0.20 $0.70 $0.13

17:00 1.31 1.10 0.21 91.8 0.21 0.21 0.21 0.21 0.21 $0.70 $0.13

18:00 1.40 1.18 0.22 90.8 0.22 0.22 0.22 0.22 0.22 $0.70 $0.13

19:00 1.37 1.16 0.21 88.7 0.21 0.21 0.21 0.21 0.21 $0.70 $0.13

20:00 1.38 1.43 -0.05 85.2 -0.05 -0.05 -0.05 -0.05 -0.05 $0.10 $0.13

21:00 1.38 1.49 -0.11 81.3 -0.11 -0.11 -0.11 -0.11 -0.11 $0.10 $0.13

22:00 1.30 1.39 -0.09 78.2 -0.09 -0.09 -0.09 -0.09 -0.09 $0.10 $0.13

23:00 1.10 1.18 -0.08 75.8 -0.08 -0.08 -0.08 -0.08 -0.08 $0.10 $0.13

0:00 0.89 0.91 -0.03 74.0 -0.03 -0.03 -0.03 -0.03 -0.03 $0.10 $0.13

Uncertainty Adjusted Impact - Percentiles

10th 30th 50th 70th 90th

Daily 22.63 22.26 0.37 139.3 0.37 0.37 0.37 0.37 0.37

Reference Load (kW)

Observed Load (kW)

Weighted Temp (F)

Hour Ending

Load Impact (kW)

Dynamic Price OAT Price

Due to size of the time interval, the figure does not reflect the semi-instatenous drop in load attributable to the direct load control technology employed

Cooling Degree Hours

(Base 75)

Reference Energy Use

(kWh)

Observed Energy Use

(kWh)

Change in Energy Use

(kWh)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1:00

3:00

5:00

7:00

9:00

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0

13:0

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19:0

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23:0

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Reference Load (kW) Observed Load (kW)

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SCE’s Summer Discount Plan (A/C Cycling)Average Residential Customer, 1-in-10 Year Weather

17

SCE’s Base Interruptible Tariff, Typical Event DayAverage Customer, 1-in-2 Year Weather Conditions

TABLE 1: Menu options Uncertainty Adjusted Impact - Percentiles

Type of Results Average Enrolled Account 10th 30th 50th 70th 90th

Weather Year 1-in-2 (2002) 1:00 1338.0 1338.0 0.0 70.1 -7.7 -3.2 0.0 3.2 7.7

Forecast Year 2013 2:00 1331.2 1331.2 0.0 69.2 -7.7 -3.2 0.0 3.2 7.7

Day Type TYPICAL EVENT DAY - Top 15 3:00 1327.8 1327.8 0.0 68.3 -7.7 -3.2 0.0 3.2 7.7

Customer Characteristic All Customers 4:00 1332.8 1332.8 0.0 67.5 -7.7 -3.2 0.0 3.2 7.7

TABLE 2: Output 5:00 1365.1 1365.1 0.0 66.9 -7.7 -3.2 0.0 3.2 7.7

Number of Accounts 677 6:00 1415.8 1415.8 0.0 66.6 -7.7 -3.2 0.0 3.2 7.7

Average FSL (kW) 201 7:00 1447.9 1447.9 0.0 66.5 -7.7 -3.2 0.0 3.2 7.7

Proxy Date N/A 8:00 1474.1 1474.1 0.0 68.6 -7.7 -3.2 0.0 3.2 7.7

9:00 1483.5 1483.5 0.0 72.6 -7.7 -3.2 0.0 3.2 7.7

10:00 1487.7 1487.7 0.0 77.2 -7.7 -3.2 0.0 3.2 7.7

11:00 1491.9 1491.9 0.0 81.7 -7.7 -3.2 0.0 3.2 7.7

12:00 1467.9 1482.9 -15.0 84.6 -22.7 -18.1 -15.0 -11.8 -7.3

13:00 1417.7 1443.4 -25.7 86.6 -33.5 -28.9 -25.7 -22.6 -18.0

14:00 1407.9 1118.1 289.8 87.8 282.1 286.7 289.8 293.0 297.6

15:00 1390.4 204.7 1185.6 88.0 1177.9 1182.4 1185.6 1188.8 1193.3

16:00 1371.5 195.2 1176.3 87.5 1168.5 1173.1 1176.3 1179.4 1184.0

17:00 1355.0 185.7 1169.2 86.1 1161.5 1166.1 1169.2 1172.4 1177.0

18:00 1338.4 176.2 1162.2 84.1 1154.5 1159.1 1162.2 1165.4 1170.0

19:00 1361.9 532.9 829.0 81.1 821.3 825.8 829.0 832.2 836.7

20:00 1380.3 978.8 401.4 77.7 393.7 398.2 401.4 404.6 409.1

21:00 1402.6 1149.6 253.1 74.8 245.4 249.9 253.1 256.3 260.8

22:00 1403.9 1276.1 127.8 73.1 120.1 124.6 127.8 131.0 135.5

23:00 1389.6 1370.1 19.5 71.7 11.8 16.4 19.5 22.7 27.3

0:00 1379.7 1424.8 -45.2 70.5 -52.9 -48.3 -45.2 -42.0 -37.4

Uncertainty Adjusted Impact - Percentiles

10th 30th 50th 70th 90th

Daily 33,562.5 27,034.4 6,528.1 165.4 6490.3 6512.6 6528.1 6543.6 6566.0

Cooling Degree

Hours (Base 70)

Reference Energy Use

(kWh)

Observed Energy Use

(kWh)

Change in Energy Use

(kWh)Due to size of the time interval, the figure does not reflect the semi-instatenous drop in load attributable to the direct load control technology employed

Reference Load (kW)

Observed Load (kW)

Weighted Temp (F)

Hour Ending

Load Impact (kW)

0.0

200.0

400.0

600.0

800.0

1,000.0

1,200.0

1,400.0

1,600.0

1:00

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Reference Load (kW) Observed Load (kW)