2014 Integral Analytics, Inc.smartenergycc.org/wp-content/uploads/2014/06/SGCC-Peer-Connect... ·...


• Smart Grid Solutions

• T&D Planning

• Storage & Renewable Evaluation

• DSM Planning & Evaluation

• Marketing Effectiveness

Offices in Ohio, Vermont, Massachusetts and Colorado

Company Overview

Putting Advanced Analytics into Software

to Manage Utility Costs & Risks


• Avista Pullman project overview

• DSMore – Benefit Cost Analysis

• LoadSEER – Spatial Load Forecasting

• SmartSpotter –Targeted Marketing

• IDROP – Intelligent Load Management

Agenda


Distributed Marginal Prices (DMP)

4 – 5 PM


Power Flow Substation

Local DMP Prices (4pm)Transactive Price Signal from IDROP

(Circuit 11XX, Western US Utility)

Copyright 2014 Integral Analytics

$/MWH

Power

Flow Substation

1 Mile


Avista Smart Grid Project


The Leading

Cost-Effectiveness Tool

for Smart Grid Programs

12

2007 AESP

Innovative Product

Award Winner


The DSMore Overview

Accurate Hourly Valuations

– Using weather effects, and covariance of prices and loads, hourly by weather station.

– Using hourly end use load savings, without costly meters.

– Valuing “low probability, high consequence events”.

Compare Across Supply and Demand Resources

– Calculating cost-based and market-based valuations.

– Customize avoided costs to specific customer loads and weather

– Option value accurately values DSM the same way that asset planners value supply

Flexible and Adaptive

– Supports gas & electric programs, numerous rates and program types. Values portfolio of programs and measures.

– Used to calculative measure and program incentives.

– Allows “what if” scenario analysis.

13


Covariance Matters

• Covariance is a key concept in

supply-side asset planning.

Ignored in DSM valuation, but

consequential in determining

risk and value.

• Hourly Covariance example

– Scenario 1 represents valuation

using a unrelated avoided cost and

load profile.

– Scenario 2 the load profile and

avoided cost are co-varied.

– Difference ($500 v. $620) is due to

the co-varying of prices with loads,

or covariance.

Both scenarios average 2 MW and $50 per MWH,

but total costs differ when viewed hourly.

14

Hour MW $/MWh Total $ MW $/MWh Total $

1 2 $50 $100 1 $20 $20

2 2 $50 $100 1 $20 $20

3 2 $50 $100 2 $50 $100

4 2 $50 $100 3 $80 $240

5 2 $50 $100 3 $80 $240

Avg. 2 $50 2 $50

Total $500 $620

Average

Loads & Prices

Hourly

Loads & Prices

• Covariance value (or risk) is what suppliers pay when supply is

short, or weather extreme.

• Accurate valuation of energy requires detailed covariance analysis.


Hourly Covariance Example

15

• 300 kWh Savings for each measure

• Comparison of energy cost savings


Increasing

Market Prices

More Extreme

Weather

Avoided

Generation

Costs ($$)

Loads and

prices are

both driven

by weather

covariance.

If we use

averages we

lose the high

end values.

Hot

Cool

$70/MWH

$20/MWH Weather

Market Price

16

700 Scenarios


Test Distributions and Risk

Min Value Lowest market prices, mildest weather

Max Value Highest market prices, extreme weather

Today’s Value Today’s market prices

Alternative Value Alternative choice for Today’s prices

Option Value Long Run Value over many market prices, all weather

Max

Value

Min

Value

Today’s

Value

Average

Value

Prob (Value)

Test Values Based On Varied

Market Price / Weather Scenarios

Option Value

Test results are driven (significantly) by market prices and weather

RISK Assessment

Probability That

Test Result Is

Less Than X

Tests

Minimum Today's Alternate Option Maximum

Value Value Value Value Value

Utility Test 1.17 2.53 2.74 3.25 8.36

TRC Test 1.49 3.23 3.51 4.15 10.68

RIM Test 0.52 1.36 1.47 1.74 4.52

RIM (Net Fuel) 0.63 1.73 1.87 2.21 5.91

Societal Test 1.77 3.51 3.78 4.43 23.51

Participant Test 2.15 2.24 2.24 2.24 3.03

17


The DSMore Interface

Easy to Use Excel Interface

– Familiar interface

– Allows custom calculations to feed model inputs

– One file per measure

Fast Processing

– Vary fast processing of hourly analysis

– Batch processing and aggregation of measures

Summary Reports

– Accurate weather normal participant savings and program

economics.

– Annual cash flows

– Present value of cash flows using discount rates

18


Spatial Load Forecasting

to Target EE/DR/RE

Resources


Where resources get deployed MATTERS

• DR, EE, solar tend to cluster spatially

• Natural load growth (and sprawl) is

spatially correlated

• EV adoption is clustered, causing risk to

specific circuits

Need to account for Spatial Covariance to

maintain grid integrity

Spatial Covariance


Locational

Avoided Costs

Depend on

Forecasts of

New Load,

Distributed

Generation and

EV growth

Locational Avoided Costs


• Locational Targeting and Valuation

• GIS-based spatial forecasting modeling

• 20 years of satellite imagery, usage, growth and

land use changes.

• Multiple layers of Smart Grid value can be co -

optimized

– Commodity (DR, EE, supply risk, IRP)

– Marketing (EE earnings, billing options)

– Locational Assets (T&D, voltage, switching, phase balancing)

• Enables TRUE Integrated Resource Planning for

EE, SmartGrid


Hourly Peak kVA per acre

0 50

Future Land Use

Transportation

Plug-in Electric Vehicle Penetration

Optimal Solar Power Sites

Demand Side Management / Load Control

Customer Locations / Per Capita Growth INPUTS

OUTPUTS

LoadSEER Projections


Satellite Analytics


Circuit Level Analysis


Copyright 2013 Integral

Analytics

26


South Fresno DPA – 2013

50 %

75 %

100 %

125 %

Relative Loading


Analytics

27



50 %

75 %

100 %

125 %

Relative Loading


Analytics

28



50 %

75 %

100 %

125 %

Relative Loading


Analytics

29


LoadSEER – Load at Risk

Capacity Load at Risk, MVA per acre

Height ~ MVA Density // Ratio of Top:Base ~ Load At Risk


1. Greater participation

2. Lower customer

acquisition costs

3. Higher Net Savings

4. Greater Yield

(kW/kWh)

5. Higher avoided cost

of service

Analytics help find the sweet

spot for portfolio programs


Manufacturer

With Co-Gen

Newspaper

University

Peak

Charged

Electric

Vehicle

Office Tower

Water Park

Street

Lights

Flat Loads (lower price) Peaky Loads (higher price)

Smart Charged

Electric Vehicle

Even with standard tariff

pricing, we can target

higher cost customers

with more unique energy

efficiency programs to

lower costs for all

Average: $42/ MWH.

Range $30 to $120/MWH.

RED LINE shows a

distribution of

COST OF SERVICE

And smart meter data now

allows us to calculate the

actual cost to serve for each

customer, individually.

Avoided Costs By Customer

Average Costs (6 cents) vs Marginal Costs (3 to 10 cents)


Micro-

targeting

Broad segments

Market Research/Offer promotion ($300,000) ($250,000)

# Customer Getting Offers 25,000 25,000

# Participants (Response Rate) 1,000 750

Avoided Costs: Energy .65/kWh .45/kWh

2,000 kWh (micro) vs.

1,400kWh (broad) saved

$1,300,000 $472,500

T&D, Capacity Value NPV $1,820,000 $567,000

$800 incentive x participants ($800,000) ($600,000)

VALUE $2,020,000

$189,500

10x More Value in Targeted Approaches


We Bring Key

Demographic

Variables We

Know Matter

We Interact

Demographic

Variables with

Load Shapes to

Score Potential


Least valuable

Most valuable


Cost of Service


Integrating Distributed

Resources into Optimal

Portfolios


Load Control Options


Choreograph End-Uses

• Coordinate the natural duty cycles of appliances across

customers,

• produces substantial savings in total peak demand,

• flattening peak load but keep energy sales constant.

• Optimizations have been applied in practice with no reported complaints by customers.

• Reductions in peak demand reduce volatility, and risk. Result can be multi-million dollar changes in utility capital plans and spinning reserve requirements.

• Creating the Virtual Power Plant – Load follows Supply


• Determines least cost resources to

dispatch – mathematically guaranteed

– Choreographs loads across customers

• To maximize value to utility

– Renewable generation integration

– Peak demand reduction

– Cost to serve reduction

– Load balancing

– Plant following

Optimizes Load Control


Collect Network and Customer Data

Develop Resource Level Forecasts

Collect Constraints Use Data to

Develop Dispatching

Send Dispatching Commands to Load Control Hardware

IDROP Method


• Actively micro-dispatches

resources

– Water Heaters

– Air Conditioners

– Heat Pumps

– Commercial Refrigeration

– Electric Vehicle Chargers

– Onsite Renewables

– Storage Batteries

Dispatchable Resources

• Fast: < 5 seconds dispatching possible


IDROP

Applications


Circuit Load Leveling

Minimize Volatility While Maintaining kWh

MW

Lo

ad

Load Choreography


Minimize Cost of Service

Demand is shifted away from the highest cost periods and customers' total

demand for heating/cooling is still met, without customer involvement.


Total Load kW

Generation Costs Assumes

10% Smart

Charge

Dynamic Dispatching

AC 2 to 5 degrees

WH 1 to 4 hours

Smart Charging EV

“Plant Following”

Plant Following


Thermal Storage

IDROP maximizes value of RE Generation

by controlling thermal storage


• Drive down procurement costs

• Offset power procurement and

bid demand into the market

• Balance the grid, avoid

outages and extend asset life

• Reduce system peaks

• Reduce spinning reserves and

• Offset renewable intermittency

IDROP Benefits


• Covariance used in supply-side asset planning is

ignored in DSM valuation, key to determining DSM risk

and value

• Hourly and Spatial Covariance (grid)

– Time value of energy savings

– Natural load growth cluster spatially

– DR, EE, EV, solar cluster spatially

• Problems arise when Covariance is Ignored

– Cinergy hub 1999, California 2000, Texas (now), Alberta

• Accurate energy valuation requires detailed

covariance analysis

– Energy prices are non-normal to use Black Scholes methods

– Value at risk analytics miss key risks at 99th percentile

Covariance Matters


• DMP are like LMP for distribution grid

• Values Supply-side (kW) avoided cost and grid-side costs (KVAR, voltage, power factor) simultaneously

• Single price signal per house, per customer to spark innovative savings at exactly

– The right PLACE

– The right TIME

– The right AMOUNT


DMP Paper located at: http://www.integralanalytics.com/files/documents/related-documents/Distributed%20Marginal%20Prices%20(DMPs).pdf

http://www.integralanalytics.com/files/documents/related-documents/Distributed Marginal Prices (DMPs).pdf





4 – 5 PM


Power Flow Substation

Local DMP Prices (4pm)Transactive Price Signal from IDROP

(Circuit 11XX, Western US Utility)

Copyright 2014 Integral Analytics

$/MWH

Power

Flow Substation

1 Mile

2014 Integral Analytics, Inc.smartenergycc.org/wp-content/uploads/2014/06/SGCC-Peer-Connect... ·...

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Transcript of 2014 Integral Analytics, Inc.smartenergycc.org/wp-content/uploads/2014/06/SGCC-Peer-Connect... ·...