Operational Analytics for Utilities - Stanford...

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Operational Analytics for Utilities

Creighton Oyler ([email protected])

Patrick Steigler ([email protected])

April 15, 2014


Goals for today:

1. Arm you with information to intelligently discuss

some key forces shaping the electric utilities

industry

2. Deep dive into one example of technology at

work in a real life setting


Why Bother Talking About Utilities

Utility industry behaviors will have a

significant impact on the commercial success

of any new energy-related product or service


TRUE or FALSE?

Global regulation encouraging renewables adoption have been a success.

BY WHAT MEASURE?

Reliable, Safe, Clean, and Affordable…electricity


Installed Capacity


Agenda

Industry Backdrop

Euphemism City: “Revenue Protection”

Q&A


Key Concepts

1. Peak

2. Rate Setting

3. Intermittency


M

1. Peak Load Drives Infrastructure Sizing

Utility must have enough generation and T&D capacity to meet the

highest demand moment of the year


M

1. So What’s the Big Deal?

1/3 of generation capacity…..

…utilized only 7% of the time


2. Basic View of Utility Rate Setting Mechanism

Capital Base ($)

Annual Electric Consumption (kWh)

Unit Price ($/kWh) =


3. Renewables Intermittency

WIND


3. Renewables Intermittency

BACKUP


Recap

1. Electric infrastructure is sized for peak periods

2. Electric rates set to allow for cost recovery (plus profit)

3. Most renewable generation (less Hydro) is intermittent

4. Batteries are not a cost-effective option (yet) to solve

intermittency

5. Utilities legally required to be backup energy provider


The dilemma

Increased Costs

= Higher Cost Per kWh

Decreased kWh Consumed


Germany


German Installed Solar Capacity Is Huge….


…But Still Just a Portion of Realized Generation

a Geothermal/Wind/Solar/Other Source: International Energy Agency, Fitch

23.5 15.7

64.6 66.9

4.4 4.7

7.5 12.7

Germany (2008)

Germany (2012)

Renewables

Hydro

Combustible Fuels

Nuclear

Germany Realized Generation Mix (%)


Customer Bills Rise While Utilities Earn Less

Electricity Prices in Germany Normalised Consumer CPI vs. Wholesale Baseload

Source: Bloomberg, Fitch

50

60

70

80

90

100

110

120

130

140

150

Jan 08 Mar 09 May 10 Jul 11 Sep 12 Oct 13

Consumer Wholesale (January 2008 = 100)

• The rising bills include higher subsidies and network charges

• Utilities earn less in generation segment while supply margins remain modest


Credit Ratings of European Utilities

Source: Fitch

• Portfolio composition: 100+ entity ratings (split between networks and integrated and generators)

• Over 50% of ratings in BBB range, around 25% in A range and 15% in BB range

• 46% are either BBB+ or A-

• Existing ratings drifting down, new ratings on average lower than before

Historical Distribution of Ratings

EMEA utilities dynamic sample

0%

20%

40%

60%

80%

100%

Jun 07 Feb 08 Oct 08 Jun 09 Feb 10 Oct 10 Jun 11 Feb 12 Oct 12 Jun 13

AA A BBB BB B CCC


There Must be Good News Here Somewhere… So, what’s the impact on emissions?

• Largely a function of increased industrial activity, carbon dioxide emissions increased compared to the crisis year of 2009

• But increased coal burning and nuclear decommissioning in Germany add to the trend

Source: European Commission

0

50

100

150

200

250

300

350

400

450

500

2009 2010 2011 2012

Germany UK Spain Italy France Poland (mt CO2)

ETS Verified Emissions


What Options Exist?

Cost per kWh sold ($/kWh)

Goal

Decrease Costs

Increase No. of kWh Consumed

Value Levers

Streamline operations, reduce and restructure controllable costs

Consider M&A or divestitures to achieve synergies or reconsider business structures

Change the regulatory paradigm (e.g. changing renewable portfolio standards, etc.)

Capture, Retain Customers

Evaluate opportunities for new products and services

Change the Business Model

Advanced Analytics

Advanced Analytics

Advanced Analytics

Source: ‘The Math Does Not Lie: Factoring the Future of the US Electric Power Industry’ – Deloitte Energy & Resources Dbriefs, Jan 2013


Agenda

Industry Backdrop


Q&A


M

Global Prevalence of Energy Theft

Source: Awesense (graphic); Oracle Analysis (figures)

China

United States

India

0.7

0.7

6.3

1.0

0.5

0.4

4.7

1.2

0.6

0.4

0.3

0.2

Russia

1.3

Canada

Germany

Italy

Japan

United Kingdom

Brazil

France

South Africa

Mexico

ANNUAL VALUE OF 1% THEFT $B

Assumption: $0.12/kWh retail


M

Local Impact of Energy Theft

Pacific Gas and Electric Example

Assumption: $0.12/kWh retail

2013 MWh sales: 84,045,146

2013 Profit: $828m

Value of 1% theft: $101m

1% = 12% of profit

Source: Awesense (graphic); Oracle Analysis (figures)


Jumper cables diverting electricity around the meter

Meter

Cables


Tap at weatherhead diverting wires around

meter

Meter

Diversion


Data Curation

Intelligent Data Systems

require curation

Google Maps –

– Many people working

actively to correct errors

– Appears to work

automatically but the

algorithms only get better

via manual input

Platform to curate meter data

Deal with Data Issues and

Discover Relationships:

Voltage

Missing Data

Bad Data

Voltage

Consumption


Model

Technical Loss =

2*Resistivity*Length*Current

Power (kWh) = Current * Voltage

…Based on data received…

Expected Voltage = 240 -

2*Resistivity*Length*(Power/Voltage)

Most likely non-technical loss when

Actual Metered Voltage < Expected

Voltage


PURPOSE BUILT SOLUTION

ADAPTABLE PLATFORM

Solution Platform


Agenda

Industry Backdrop


Q&A


We’re Hiring

irecruitment.oracle.com

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