Using AMI Data for DR M&V Webcast - EPRI · 2019-06-05 · Key Lessons Learned • AMI meter data correlate approximately 70% of load events for large, simple loads (water heaters

Chris Holmes

Sr. Project Manager May 15, 2013

Using AMI Data for DR M&V Webcast

2 © 2013 Electric Power Research Institute, Inc. All rights reserved.

Our 40 Year History Our Future

Control Switch

•No override

•No adjustability

= Predictable utility value

Smart Communicating Devices

•Event override

•Customer adjustable

= Variable utility value

Context for the Study: Transition to Smart Residential Devices


Context for the Study: Third Party Entities and M&V Challenges


Using AMI Data to Determine DR Participation ( 161 D and 170 A Joint Collaborative Project)

Objectives • Investigate the ability and accuracy of

measuring demand response impacts using whole-home interval meter data.

Processing of AMI data appears to be a viable approach for assessing DR participation for common managed load types.

Key Lessons Learned • AMI meter data correlate approximately

70% of load events for large, simple loads (water heaters and HVAC)

• When direct measurement is not possible, AMI data may be used to infer receipt and response to DR events, given proper baselines.

• Higher resolution AMI data provides improved results.

How to Apply Results • Incorporate into strategy for future

DR programs involving smart, communicating appliances with customer-defined responses.

Product ID: 1025415


Generic Demand Response Categories

Demand Response Evaluation, Measurement, and Verification: A Synthesis of Evolving Protocols and practices. EPRI, Palo Alto, CA: 2008, 1018194


2012 Case Study – Using AMI Data for M&V

Joint project between 161D (IntelliGrid) and 170A (Energy Efficiency and Demand Response) to investigate under what conditions and end-uses customer variable response may be measured at the premise meter (measurement and verification)

– Utilize the laboratory residential load bank to test detection of customer load response

– Alternative end-uses i.e. water heaters, thermostat settings, etc.

– Variable load response, full/partial/combined – Periods of usage integration i.e. sixty minute, fifteen

minute, five minute, one minute


Residential Load Bank Features Call out

Description

1 Operator Interface

2 Control Circuit Interface – E-Stop Switch (Emergency Power Off), Power On, Control Power On/Off

3 Indication Lamps L1-N, L2-N (Green) - Input power phase loss Control/Hardware Healthy (Green) – System ready to run Test In Progress (Yellow) – Indication of system running in auto mode. Fault (Red) – Hardware or Software error.

4 Indication Lights – Receptacle energized indication lights

5 Meter Socket (Milbank 200A single-phase) 6 Main Input Power Access – Terminal block

located inside bottom right, accessible through knock out.


Features

To ensure that .

Call Out

Description

7 Four Auxiliary Contacts (AC-1, AC-2, AC-3, AC-4) - Dry contacts 15A, 250VAC. Contacts, available to operator to program and switch the contacts in the same manner as power is switched to the receptacles.

8 Sixteen NEMA 5-15R 15A switched receptacles

9 Four 240V receptacles (Two NEMA 14-30R 30A) and (Two NEMA 14-50R 50A) switched receptacles

10 200A Load Center (200A Main Circuit Breaker, 24ckt)


Approach: Laboratory Assessment Laboratory Assessment • Laboratory set up with residential

loads • Configurable AMI meter (1-min., 5-

min., 15-min., 60-min. whole premise reads)

• Four loads of interest air conditioner (HVAC), water heater, pool pump and refrigerator for DR.

• Correlations assume “perfect” baseline

• Linear multivariate restricted maximum likelihood (REML) estimation model in SAS

• Implied verifiability for the four loads estimated using 1-min., 5- min., 15-min., 60-min. whole premise reads


Laboratory Assessment Results

60-min. 15-min. 5-min. 1-min. HVAC 0.6751 0.7292 0.778 0.7908 Water Heater 0.5878 0.6267 0.6901 0.6312 Pool Pump 0.1428 0.1023 0.131 0.0828 Refrigerator 0.1836 0.029 0.0288 0.0286

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0.20

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0.60

0.80

1.00

% L

oad

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d Correlation Estimates for the All Periods


Laboratory Assessment Results (2)

Week day periods from 2 PM to 8 PM which represents typical summer on-peak periods when DR events are likely to occur

60-min. 15-min. 5-min. 1-min. HVAC 0.6815 0.7144 0.7281 0.7634 Water Heater 0.5529 0.549 0.6123 0.638 Pool Pump 0.1434 0.1789 0.1939 0.2592 Refrigerator 0.1424 0.1731 0.1821 0.1984

0.00

0.20

0.40

0.60

0.80

1.00

% L

oad

Red

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orre

late

d

Correlation Estimates for the Summer On-peak Period


Minnesota Valley Electric Co-op HVAC Cycling Events in Summer 2012


Minnesota Valley Electric Co-op. Interruptible Load Correlations for Alternative Baselines (Interruption Date, June 27, 2012)


Results

The following results were derived from the study • Laboratory Assessment

– Higher measurement resolutions improve the ability to implicitly verify end-use loads

– 67% of the HVAC load, 58% of the water heater load, 24% of pool pump load and18% of refrigerator load reduction can be verified using 60-minute AMI measurements

– Larger loads show higher correlation while multi-state loads show poor correlation

• Field Assessment using Minnesota Valley Electric Coop. (MVEC) data – HVAC DR event data showed 72% correlation – Other sources of error exist – baseline errors, behavioral effects, cross-

correlational effects – May still require the socialization of the impact

Further research required to evaluate NILM for DR Measurement

&Verification (M&V).


M&V using NILM (Non-Intrusive Load Monitoring) Technology What is NILM ? • Analytical methods or technology that

measures an aggregate quantity such as whole premise energy consumption (kWh), voltage, current at the premise to determine consumption (kWh) of individual loads within the premise.

• Originally developed by Dr. G.W.Hart at MIT in the 1980s funded by EPRI.

• Multiple hardware and software products available today.

• Potential benefits : Real-time whole premise and end use energy and cost feedback, load research, energy management and equipment diagnostics.

Key benefits • Non-intrusive : Permits measuring

individual end use loads without entering customer premises

• Cost of Implementation: Low compared to submetering.

Concept


NILM Evaluation Objectives

Measurement Accuracy

• Interval Usage (1 min, 5 min, 15 min, 60 min)

• Daily Usage

• Weekly Usage

Disaggregation Performance

• Loads isolated (#)

• Energy Coverage (%)

• Repeatability of Isolation

Cost

• Initial

• Installation

• O &M


Laboratory Evaluation (Metered Reference)

Load Control & Measurement

Residential Loads

Metered Whole Premise Load

Metered End Use Loads

NILM Product

A

NILM Product

B

NILM Product

C

NILM Product

D


Results & Conclusions

• NILM can be used for DR M&V, though with additional cost and inaccuracy burdens.

• Non-intrusive load monitoring is an evolving technology. • Best-available NILM disaggregation accuracies in the 85-90% range

observed in laboratory evaluations. • Advanced algorithms, technologies are being commercialized,

accuracies expected to improve. • Gaps: Utility-targeted applications, performance metrics, standards

development, industry-accepted protocols for evaluating NILM. Next Steps • Round II testing • Field evaluation of NILM technologies in Summer – Fall 2013

comparing with direct measurements. • NREL workshop scheduled for summer 2013 to address product

specifications, standards and metrics development.


Questions?


For More Information Contact:

Chris Holmes 865-218-8116 [email protected]

Brian Seal 865-218-8181 [email protected]

Krish Gomatom 865-218-8070 [email protected]

mailto:[email protected]�




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Using AMI Data for DR M&V Webcast - EPRI · 2019-06-05 · Key Lessons Learned • AMI meter data correlate approximately 70% of load events for large, simple loads (water heaters