Load Research in theSmart Grid Era

Analytics for Harvesting Customer Insights

1

Agenda

• A little history

• Load research 101

• What about the future?

• Conclusions

2

What is Load Research?By Definition:

An activity embracing the measurement and study of the characteristics of electric loads to provide a thorough & reliable knowledge of trends, and general behaviour of the load characteristics of the customers serviced by the electrical industry.

Simply put:

Load Research allows utilities to study the ways their customers use electricity, either in total or by individual end uses.

Mission:

Builds the foundation allowing the corporation to leverage knowledge of electric customer energy use patterns to enhance or protect shareholder value

3

History of Load Research• Load research began in the 1930s…after WWII, there was

significant electric load growth in the U.S….1970s oil embargo slowed the expansion

• Utilities had over-forecasted need for capacity, and started to use load research for a better way to forecast components of their system load

• In 1978, Public Utilities Regulatory Policies Act (PURPA) required the utility industry to develop load research programs as a basis for cost-of-service filings

• In the 1980s, load research progressed from its primary role in class cost allocation and rate design to other purposes such asend use insights supporting EE/DR

• Today, Load Research continues to evolve, supporting multiple business functions while helping utilities embrace the challenges of “Big” data…

Customer

Segment

End-Use

Research

System

Planning

Demand

ResponseEE

Evaluation

EE

Program

Design

Distribution

Planning

Forecasting

Pricing

Cost of

Service

Load

Research

Customer

Segment

End-Use

Research

System

Planning

Demand

ResponseEE

Evaluation

EE

Program

Design

Distribution

Planning

Forecasting

Pricing

Cost of

Service

Load

Research

Slide 4

Load Research Supports Many Business Functions

LRCustomer Side Services & Analysis

Demand Response Performance

Contribution/Impacts On Peak

Demographic Studies

End-Use Load Studies

Market Segmentation & Targeting

Major Account Analysis

Individual Customer Analysis

Distribution Planning

Substation Load Analysis

Transformer Sizing

Circuit Load Studies

Load Management

Loss Studies

Electric Choice

Load Profiling

Forecasting

Settlement

Evaluation

Generation Planning

Net System Output

analysis

Model Development

Capacity Planning

Load Duration Curves

Rates/Pricing1

8760 Class Demand Studies

Billing Determinants

Allocation Schedules

Sample Design & Management

Class & System Peak Analysis

Major Account Demand Analysis

5

1Most Public Services Commissions require that rate case Cost-of-Service studies are based on Load Research demand

allocations - $165B of investment allocated using Load Research

Load Research Overview6

6

The MBSS ratio model is a heteroscedastic, zero-intercept

regression model relating y to a single variable x.

The MBSS Ratio Model

iii

iii

xsd

xy

0

ii x

N

i

i

N

i

i

N

i

i

N

i

i

N

Ner

1

1

1

1

1

1

Primary Equation

Secondary Equation

Here

The three parameters of the model are beta, the error ratio,

and gamma, denoted ,, er

Definition: error ratio

N

i

i

N

i

i

x

er

1

10

Understanding Your Customers

Residential Load

200000

300000

400000

500000

600000

700000

800000

900000

1000000

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct

kW

Local Time

2009

Peak Week

200000

300000

400000

500000

600000

700000

800000

900000

1000000

Sun 14 Mon 15 Tue 16 Wed 17 Thu 18 Fri 19 Sat 20 Sun 21

kW

Local Time

December 2008The Load Research Life Cycle

Need for

Information

Expand

Model

Sample

Design

Sample

Data

Population

Data Prelim.

Plan

Results

beta 0.8

er 0.6

gamma 0.8

n0 97

N 116

n 53

-30,000

-20,000

-10,000

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 20,000 40,000 60,000

Error

Ratio Description of association Example

> 1 Extremely weak but possible DSM end use metering with very poor

tracking data

1 Very weak, conservative

assumption

Market research with poor supporting data

0.8 Rather weak Residential load study

0.6 Weak General service load study

0.5 Typical DSM logger study with good tracking data

0.4 Strong Large C&I load study

0.2 Very strong End use metering vs. DOE-2 simulations

0.1 Extremely strong 1st year persistence study for motors

0 Least possible value, perfect

association

Not expected

Sample Design

& Analysis

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct

kW

Local Time

2009

Residential Juris 1

Breaking Down the System Load

Residential Juris 2

Large Power

Small Power

Very Large Power

System

Pumping

Load Research Processing

7

Initialize System

Import

Population

Data

Import

Sample DataImport

System Data

Plan

Sample

Sample

Design/

Draw

Sample

Post-

Stratify

Hourly

Analysis

Special

Analysis

Report Results

Preliminary

Plan

Sample

DesignAnalysis

Data

Collection

Load Research

Life Cycle

Historically:

An 18-24 Month Analysis Cycle

(Sampling is King!)

The Promise of Today:

An Analysis Cycle in Weeks/Days/Hours

(Sample may be unlimited!)

Meter

Installations

Surveys

& Audits

Population

Billing Data

Gaining Customer Insights Data Visualization (Visualize-IT)

8

A picture is worth a thousand words…one of the main goals of data visualization

is to make it possible to absorb large amounts of data quickly

Big Box Retail School Thermal Storage Athletic Field GroceryCommercial Cat Fish Pond

Gaining Customer Insights – Data Fusion

9

• Today’s challenge is no longer the availability of primary data but the integration of that primary data with secondary sources…

• Utilities need effective strategies for extracting value and insights from interval load information…data fusion can help

Smart Energy Program AnalysisSmart Energy Offers

Time-based pricing

Direct load control

Energy info displays

Smart thermostats

Prepaid electricity

Applications

Smart appliances

EV plans

Self generation

Others

10

Source: KEMA, adapted from Reliant Energy

Awareness

Control Action

Consumer

Choice

Portal

EID

Alerts

Peer comparison

Advice & Behavioral

Switches/Plugs

Direct load control

Thermostat

Mobile/

Internet

HAN Gateway/

Tablet

Buy/Use

HVACBuy/Use

Water Heating

Buy/Use Lighting

Buy Time-

Based Rate Plan

Buy/Use Appliance

Buy Prepay Rate Plan

¢

Gaining Customer InsightsGetting Under the Load Profile

• Getting underneath the total facility load curve continues to be an industry goal

• Work continues on innovative, cost-

effective measurement techniques for

collecting load data as an alternative to

more expensive sub-metering– Non-Intrusive Load Monitoring (NILM)

– Statistical Methods like Conditional Demand Analysis (CDA)

– Subtraction Algorithms

– Low-Cost Sensors

• EPRI supported NILM research along with recent innovations and new market entrants have revitalized this topical area

11

Getting Under the CurveConditional Demand Analysis (CDA)

• CDA produces an estimate of the allocation of total consumption for a customer among the end uses within the customer site

– An old statistical idea that’s new again

– Low cost, analytical heavy option

– Performs well with large signature appliances with differentiation

– Typically fit to annual or monthly whole premise consumption, but, can also be fit on daily or hourly load data

– Linear and/or Non-linear models can be used

– Multi-collinearity can be an issue

12

Getting Under the Curve – Subtraction Algorithms

• Some load shapes are so discernible that simple subtraction algorithms work quite effectively for first order approximations

• Used extensively in pricing and demand response program evaluations

• Set up test/control (comparison) environment for study

13

Electric Vehicles

Demand

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

Hour

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Average Hourly Weekday Comparison: Phase 2 vs Phase 3Appliance: Total Appliance Bundle Analysis Type: Demand

Time of Use Phase 2 Phase 3

Getting Under the Curve – Low Cost Sensors

14

• Relatively modest investment since ELCAP

• Increasing utility interest in end-use information & performance

• Recent regional Initiatives (NEEP, NWPPC)

• Cost a concern…

• Some limited deployments

Where to Start…Assessing the Four Stages of Analytic Readiness

15

Data Analytics Check-Up• A comprehensive

roadmap for predictive analytics starts with segmenting each opportunity into functional components (14 listed) that a utility can evaluate on their own merit, taking into account the impacts to current business processes and IT systems

16

Road Map Strategy• Group the opportunities into

feasible sets based on ROI, ease of implementation, impact on business processes, and investments in IT systems

•

• Assemble these sets and associated investments into a comprehensive roadmap

• Lay out the opportunities along a short-, mid- and long-term horizons for funding and implementation

17

Conclusions

• Load research and load research techniques make as much sense today as they did in the past

• More data doesn’t necessary mean more information

• Planning for the future paramount to enterprise analytics

18

SAFER, SMARTER, GREENER

www.dnvgl.com

Thank You!

19

DNV GL

Claude.godin@dnvgl.com

1 919-539 -231

Curt.puckett@dnvgl.com

1 517-529-6277