What to Do with All That 2-Way End-Device...

What to Do with All That 2-Way End-Device Data

Joseph E. Childs – Sr. Program Manager

Ryan Brager – DR Product Manager

Yigang Wang, Ph.D. - Corporate Research & Technology (CRT)

51st Annual MINNESOTA POWER SYSTEMS CONFERENCE

What to Do with All That 2-Way End-Device Data

• This session reviews the value assessment of

utilizing two-way data, and addresses some of the

challenges of managing the abundance of data

available. Discussion will occur about optimizing

the data received through AMI, load management,

and volt-var control applications.

First Minneapolis Presentation: Wood, M.C., J. E. Childs, W. E. Marlatt and D. Renne.

1979 Assessing the Air Pollution "Carrying Capacity" of Northern

Puget Sound: An Applications of TAPAS. Conference Proceedings, 14th Conference on Agriculture and Forest Meteorology &

Fourth Conference of Biometeorology, Minneapolis, Minnesota.

Agenda

• The Problems

• Renewable Integration

• Energy Markets

• Value Assessment

• Data Analysis Refresher

• Case Studies

Engineering …. Is the art of doing that

well with one dollar, which any bungler

can to with two after a fashion.

PV Market and Policy Drivers

• Decreasing by 30%/year • Driven by off-shore manufacturing (China)

• Initially driven by mandates and subsidies

Note: Data for solar only http://www.seco.cpa.state.tx.us/publications/renewenergy/

2020: 15%

2025: 25%

2020: 33%

2025: 25% 2020:

2030: 40%

2015: 29%

ME - 2020: 30%

VT - 2025: 24.8%

NH - 2017: 20%

MA - 2020: 22.1%

RI - 2020: 16%

CT - 2020: 27%

NJ - 2021: 20.4%

DE - 2026: 25%

MD - 2022: 20%

2015: 15%

2015: 10%

2020: 20% 2025:

2025: 15% 2020:

2015: 15%

2021: 15%

2021: 18%

105 MW

2015: 5880 MW

10% - 20%

Legislated Renewable Standards Policies and Goals IREC North Carolina Energy Center

“inflection point” • Economic / social benefits prevail (< $0.10/kWh) • Renewable penetration exceeds 5% (point of

stability problems)

PV Price & Penetration Texas Renewable Energy Resource Assessment

• Penetration of PV at inflection point

• Other intermittent sources and loads continue to drive complexity in distribution grid

Distribution System with PV – Power System Models

IEEE 34-Bus Test Feeder with PV integration

Voltage rise

introduced by

reverse power

flow from PV

integration Vo

Intermittency PV Power Output Real vs. Idealized

Intermittency is one major challenge of renewables

Data source: NREL

PV Power Output “Ideal” Insulation

Energy Business Landscape driving Data Analysis

• Regulations

• FERC Orders face challenges (745)

• EPA 111d

• States still have authority on many issues

• Electrical Markets (5, 15, 60 minute) • ISO Markets

• Different market flavors

• Different retail and wholesale settlement

• IOUs and G&Ts required to participate (Moslty)

• Coops and Munis exempt from Competition, but affected

• RTO Markets

• Balancing Authorities

• Inability to Monetize Value

• Electrical Operations • Mismatch between Markets and Operations

• Do More with Less

Too many cooks spoil the stew or Design by committee or …

Markets – ISO/RTO DR Program Comparison

IRC Database

Programs (52)

Attributes (55)

• ISO/RTO Product

• Product / Service Features

• Deployment

• Market Participant Roles

• Communications

• Event Timing

• Telemetry

• After The Fact Metering

• Performance Evaluation Filters:

• Resource Type

• Aggregation Allowed http://www.isorto.org

Data from 2013 updated in 2014

GIWH Markets

Market Capacity Energy Reserve Regulation

AESO ● ● ●

CAISO ● ●

ERCOT ● ● ●

IESO ● ●

ISO-NE ● ● ●

MISO ● ● ●

NYISO ● ● ●

PJM ● ● ● ●

SPP ● ● ●

ISO Count 5 9 8 4

Programs 12 18 11 4

Settlement - ERCOT C&I Baseline

• Middle 8 of 10 Preceding Like Days

• Filter out Non Similar Days

• Take Last 10 Days

• Remove Highest and Lowest

• Average Remaining

• Matching Day Pair

• Today Pair (Yesterday & Today)

• Historic Pairs for 1 Year

• Sum Square Difference Interval Data

• Select 10 Lowest SSD

• Average Intervals

• Statistical Regression Model

• kW(d,h,int) = F(Weather(d), Calenday(d),

Daylight(d))

• Coefficients Calculated Offline

• Uses “Lookup” Factors

Final Baseline

Event Day Adjustment

– Event Day Interval Data

– Baseline Interval Data

– Factor = Baseline Sum /

Event Day Sum

– Final Baseline(t) = Factor*

Unadjusted Baselline(t)

Agenda

• The Problems

• AMI

• DR

• CVR

• Statistical Analysis Refresh

• Data Analysis

“The Graduate” 1967

Mr. McGuire: I just want to

say one word to you. Just

one word.

Benjamin: Yes, sir.

Mr. McGuire: Are you

listening?

Benjamin: Yes, I am.

Mr. McGuire: Analytics

AMI Business Case Value Assessment

ROI Analysis (NPV) Baseline Analysis

10 15 20

Total Cost of Ownership (1,468,000) (1,752,000) (1,985,000)

Total Benefits 1,992,000 2,762,000 3,394,000

Present Value Annualized ROI 3.6% 3.8% 3.5%

Total Benefits/ Cost of Ownership Ratio 1.36 1.58 1.71

NPV of Investment 524,000 1,010,000 1,409,000

Cash Flow Positive (Years) 2

Project Breakeven (Years) 6

65% of AMI Value comes using Interval Data (Customer Service, Operations etc.)

Demand Response Value Assessment

Supply Value Analysis

14,918,634$

45,218,539$

30,299,905$

3Project Breakeven (Years)

Analysis Period

Total System Costs (NPV)

Total Value from System (NPV)

DR Program Benefit (NPV)

Annual Return on Investment

Value/ Cost Ratio

DR Benefit

Equivalent

Supply Side DR System

4,905,297$ 19,823,931$ 14,918,634$

162$ 829$ 668$

ROI (Annual)

DR / Supply Side Ratio

Peaking Plant Comparison

Present Value Costs

Cost per kw

Data Value: Fast DR – Market Value 2 to 5 times capacity and energy markets

Data Cost Reductions: DR System Maintenance & Marketing & Enrollment

CVR: Peak Power Price Model

Savings Voltage Reduction during Peak Hours

Percent/Voltage Reduction (Avg.) per Feeder 3.0% Actual Volts Reduced per Feeder 3.75 CVR Factor 0.7 Actual kW Yield (%) 2.10% kW Reduction from Peak 8,400 Expected Daily Hours of CVR On-Peak 6 Expected Number of Days of Operation per week 5 Annual Hours of CVR 1560 Estimated Yield (reduction) Per Year (kWH) 13,104,000 Peak Generation Cost per KWH $0.08 Peak Demand Charges $126,000.00 Annual CVR Savings Potential $1,174,320

Revenue Loss Peak Distribution Charge (per KWH) to Consumer $0.17 Annual Revenue Loss $2,227,680

Eaton Volt/Var Management Value Assessment Calculator

Data: 5 to 15 second per phase voltage along feeder

Agenda

• The Problems

• Data Analysis Refresh

• References and Methods

• Fundamental Concepts

• Data Samples

• Data Analysis

References and Key Concepts

• Reference Material

• The Signal and the Noise, by Nate Silver. 2012. “Why

So Many Predictions Fail – but Some Don’t”

• Analysis & Forecasting

• Regression vs.

• Time Series vs.

• Non-Parametric and Distribution-Free Methods

• Reporting

• Net Present Value

• Baseline

Analysis & Modelling Fundamentals

• Representative Sample Data

• Independent measurements of dependent and predictor variables

• Non-biased sample set

• Measurement errors are random (or distribution is known)

• Sample size is adequate

• Model errors (variance is stable)

• Range of predictors is adequate

• Continuous Over Prediction Range

• Model Assumptions

• Weak Exogeneity. Predictor variables are fixed and error free

• Linearity vs. Non-Linear. Predictor relationship can be expressed

as a*X + b*Y where a and b are constants

• Independence. Errors are uncorrelated

• Minimal Multi-Collinearity. Predictors don’t have perfect correlation.

Sample Data – Plot of Runtime vs. Temperature Data Error

Outliers

Variance

Sample not Uniform

Sample:

~146,000 data points

50 Thermostat Data Logs

June 1 – September 30

(with some missing data)

Out of Range – Not Continuous

• What are the sources of bias and errors? • Is the relationship linear?

• What is going on with the outliers?

• Is the variance constant with temperature?

• What are the sources of the Variance?

• If I clean up the data what are the side effects?

Asymptotic Bend

Agenda

• The Problems

• Data Analysis

• PacifiCorp – Forecasting and Program Design

• Residential and Small Commercial

• ~100,000 program participants

• Single & Multi Family Residential

• Small Commercial

• Operational for last 10 years

•Operating Parameters

• June – August

• 14:00 – 20:00 Control Hour Window

• Non-Holiday Weekdays

• Western Electricity Coordinating Council

•Climate

• High Altitude Desert

• Large Daily Temperature Swings

• Low Humidity During Control Hours

Rocky Mountain Power Cool Keeper AC Program

Customer Segmentation

• What are the key customer segments and how

do they differ?

• Are their subpopulations within a segment that

affect my program?

• What is the normal customer usage pattern?

What do we want to understand about our customers?

Segment Comparisons – Average Daily Usage

Peak Time (Hour) Peak Value (% Run) KWh (Control Period)

Start End High Average Max Average

Single Family 17 20 81% 68% 17.97 14.76

Multi-Family 18 21 55% 45% 7.58 6.02

Commercial 16 19 60% 50% 12.51 10.21

Single Family Runtime – August Data

Single Family 17 20 81% 68% 17.97 14.76

Multi-Family Runtime - August Data

Multi-Family 18 21 55% 45% 7.58 6.02

Commercial Runtime - August Data

Commercial 16 19 60% 50% 12.51 10.21

Questions from stakeholders

• Was the event successful?

• How many? How much?

• Were our customers inconvenienced?

• Are we controlling at the right level?

• What is the snapback?

• How did it effect my peak?

• Is there an energy efficiency effect? How Much?

What do your stakeholders want to know about a DR event?

PacifiCorp - 14 August Event Analysis

• Event Statistics

• Started: 14:30 Ended: 17:30

• Run & Shed • Logged in 15:00- 18:00

• 16:00 & 17:00 have full hour shed times

• Analysis Statistics

• Participation – 96,047 • Single Family - 65,764 participated

• Multi-Family – 23,017 participated

• Commercial – 7,266 participated

• Results – 81.4 MWs estimated • Single Family – 67.5

• Multi-Family – 9.6

• Commercial – 4.6

• Recovery

• Segments back to normal run % by 22:00 • Note: Multi-Family Usage increases until morning hours

Devices Control

Control % Control

Available

Total 103,383 96,047 2,755 97% 4,581

Single Family 70,783 65,764 1,780 97% 3,239

Multi-Family 24,670 23,017 827 97% 826

Commercial 7,930 7,266 148 98% 516

Segment Comparisons – Event Day

Runtime Analysis 14-Aug-15

Single

Family

Multi-

Family Commercial

13 56% 27% 43%

14 51% 32% 46%

15 39% 30% 41%

16 42% 25% 31%

17 56% 28% 33%

18 78% 38% 41%

19 71% 52% 53%

20 61% 48% 47%

21 53% 47% 41%

22 45% 44% 39%

23 44% 42% 36%

Event Start 14:30

Event End 17:30

Single Family – Event Analysis Baseline Day Load

24 Aug, 27

Jun, 1,2,3

27 Jun,

1,2,3 Jul

27 Jun, 1,3

12 (672) 6,972 6,549

13 511 8,322 7,901

14 (2,350) 4,184 4,394

15 23,426 28,728 29,068

16 61,531 65,302 66,358

17 64,702 67,459 69,041

18 37,808 40,404 42,590

19 (14,408) (11,428) (9,917)

20 (7,237) (4,227) (2,176)

21 (2,588) 1,674 3,985

22 (3,431) 718 3,074

23 (4,171) 1,085 3,319

Per Unit Yield

24 Aug, 27

Jun, 1,2,3

27 Jun,

1,2,3 Jul

27 Jun, 1,3

12 (0.01) 0.11 0.10

13 0.01 0.13 0.12

14 (0.04) 0.06 0.07

15 0.36 0.44 0.44

16 0.94 0.99 1.01

17 0.98 1.03 1.05

18 0.57 0.61 0.65

19 (0.22) (0.17) (0.15)

20 (0.11) (0.06) (0.03)

21 (0.04) 0.03 0.06

22 (0.05) 0.01 0.05

23 (0.06) 0.02 0.05

% Error Hour

-16% 12

-8% 13

-4% 14

-3% 15

13% 22

19% 23

Single Family – Snapback Scaled to Event Day

Event Snapback Summary 14-Aug-15

Duration

(Hours)

Event Load Reduction 132.8 4

Event Snap Back (125.6) 7

Difference 7.3

Snapback % 95%

kWh per Home 0.11

Event Snapback - Scaled to Event Day

Event Day

Scaled

Baseline

14-Aug-15 2-Jul-15

of Day

Hour MWh MWh Yield State

12 1 95.1 94.4 (0.7) ~

13 2 110.2 109.4 (0.8) ~

14 3 129.1 121.1 (8.0)

15 4 117.2 132.4 15.1 -

16 5 89.3 138.3 49.0 -

17 6 96.1 145.0 48.9 -

18 7 128.0 147.8 19.8 -

19 8 179.5 149.4 (30.2) +

20 9 163.1 139.4 (23.6) +

21 10 139.8 122.9 (16.9) +

22 11 120.9 104.6 (16.3) +

23 12 102.7 88.7 (14.0) +

0 13 83.3 70.8 (12.5) +

1 14 66.2 54.2 (12.0) +

2 15 49.7 41.0 (8.7)

3 16 39.5 31.5 (8.0)

4 17 31.2 25.4 (5.8)

5 18 24.3 20.8 (3.5)

Event Load Reduction (-)

Event Load Reduciton (+)

Evant Scale Period (~)

Agenda

• Data Analysis

• Final Statements

DR Forecasting Signal or Noise?

Cool Keeper Program Segment Comparison

30-Jun Average AC Runtime

Hour Church

Single

Family

Multi-

Family

12 4.7% 50.9% 32.8% 47.2%

13 4.2% 56.7% 35.3% 51.0%

14 3.7% 62.3% 38.3% 53.2%

15 4.5% 67.2% 40.7% 56.8%

16 2.9% 72.1% 42.6% 59.4%

17 3.2% 75.2% 47.4% 59.7%

18 3.8% 79.2% 49.8% 58.0%

19 5.9% 78.2% 52.6% 55.5%

20 14.0% 74.4% 52.7% 51.4%

21 10.3% 65.9% 50.6% 47.6%

22 8.6% 60.0% 49.4% 43.1%

23 6.0% 52.0% 48.3% 40.1%

Cool Keeper Program Hours

What to Do with All That 2-Way End-Device...

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