New Technologies For The Sustainable Enterprise; keynote @Wharton

New Technologies For The Sustainable Enterprise

Paul Hofmann, SAP Labs North America

Wharton, May 9th 2011

© 2011 SAP AG. All rights reserved. 2

What Does SAP Do?

Sales Order

Management

Production Planning Talent Management

Financial/Mgmt

Accounting

Business Intelligence

© SAP AG 2010. All rights reserved. / Page 2


EVERY 2ND DOLLAR OF WORLD TRADE RUNS ON SAP

Suppliers andCustomers

Manufacturing

CRO

Receiving

Export Compliance

Fulfillment

COO

SourcingCFO

Customs Operations

DISTRIBUTIONCENTER

CUSTOMER

SUPPLIER

CUSTOMS/REGULATORY AGENCY

CUSTOMS/REGULATORY AGENCY

100,000 Companies Run SAP

http://www.outofthecloset.tv/phoenix/images/logos/PEPSI_LOGO.jpg

http://www.michaelpage.fr/products/profiles/images/logo_pg.gif

http://www.brandsoftheworld.com/download/?id=1407

http://www.bakerhughesdirect.com/cgi/bhi/myHomePage/myHomePage.jsp?BV_SessionID=@@@@1462991070.1193686864@@@@&BV_EngineID=cccfaddmgjeggdmcefecfefdfmldhfl.0

http://www.slb.com/images/about/brand/bluelogo.jpg

http://www.bechtel.com/default.htm

http://www.7-eleven.com/default.asp

http://www.celanese.com/index/celanese_home.htm

http://www.deere.com/

http://www.topnews.in/files/cisco1.gif


Summary of SAP Today

SAP AG in 2010 revenues: $ 16.5 billion

~53,000 SAP employees

100,000 companies run SAP software

121,000 installations

Provides 26 industry solutions

1 Suite

12 million users in 140+ countries

Unique partner ecosystem

More than 1.3 million community members (SDN and BPX)

More than 3,850 industry partners


Engineering &

Construction

5,000 concurrent active

users

Supply Chain

Management

4.5M characteristic

combinations (SKUs) &

512 GB - 1TB memory

in live cache

Consumer Products

1.4 million sales order

line items per day

Utilities

25 million business

partners – 85 million

service and sales orders

per year

Business Intelligence

> Analytics over > ~50 TB

data in memory with BIA

Human Capital Management

Payroll calculations for 500,000

employees in 3 hours

Order Management

10 million on-line item orders invoiced per day

Portal

1 million users at one

customer and 500,000 at

many customers

Our Resource Consumption Requirements MMassive and Diverse

Banking

40 million customers – up

to 8 million transactions

an hour

SAP Business Suite

SRM SCM ERPPLMCRM

~ 400 Million lines of code

© SAP AG 2010. All rights reserved. / Page 6


What Can ICT Industry Do?

“The ICT (Information and Communications Technology)

industry is responsible for 2% of global CO2 emissions.

ICT solutions have the potential to be an Enabler

to reduce 30-50% of the 98% CO2 emitted by non-ICT industries.”


© SAP

Path to Sustainability

Challenges Leading to Innovation Opportunities

Greening IT IT for Greening Greening SAP

Challenge

Reducing IT related

CO2 emissions by

optimizing energy

footprint of SAP-

related software and

hardware

Challenge

Providing integrated

solutions for:

measuring,

aggregating,

reporting,

analyzing and

optimizing

environmental data

Challenge

Identifying, structuring

and coordinating

programs for a targeted

reduction of SAP’s

environmental impact

combined with

communicating the

success


© SAP

SAP’s Role In The Clean Tech Movement

ENVIRONMENTAL ACCOUNTING

For carbon impact

Carbon just another currency

CARBON CAP AND TRADE

Across the Supply Chain

Final Product

CO2

CO2

CO2CO2

Three Technology Mega Trends

Mobile - Pervasive Connectivity

5.3 B cell phones worldwide (77%) - 1.4 B sold in 2010 vs.15 B shoes

Data Growth Follows Moore’s Law

1.2 million Petabyte in data have been created 2010 up from 160 Exabyte in 2007

By 2020 it will be 35 Zetabyte (IDC, UC Berkeley and UC San Diego)

Stack of DVDs halfway to Mars

High Performance Computing – Real Time Analytics/Decision Making

In-Memory and multi-core for the enterprise at 1/30 of the price of mainframe

Mainframe power at desktop


© SAP

Multicore – No More Free Lunch

Heat

Power Leakage

Physical Limitations

4 GHz

CPU Clock Speeds Over Time


The Big Challenge of Parallelism/Concurrency

Key messages Parallelism/concurrency is a big challenge for the

IT industry

Multicore combined with cheap memory is a big

opportunity for in-memory computing and real

time analytics

From my perspective, parallelism is the biggest

challenge since high-level programming languages.

It’s the biggest thing in 50 years because industry is

betting its future that parallel programming will be

useful.

– David Patterson, UC Berkeley [ACM06]

SAPs In-Memory Technology

Analytics at the speed of thought - HANA (High Performance Analytic Appliance)

“SAP’s in-memory technology has the potential to threaten Oracle by producing

faster transactions”, SAP Uses Hardware To Hit Oracle’s Database Hegemony

in Forbes, March 22nd

Tape is Dead, Disk is Tape, Flash is Disk, RAM Locality is King, J Gray, MS 12/06

A Modern CPU waits a lot

For RAM – 100 to 400 cycles translated in miles to the next state

for flash – 5000 cycles country

for disk – 1,000,000 cycles Mars


Big Iron - Commodity HPCDesign by SAP

Enterprise Supercomputer - 1/30 Price of Mainframe

5 X 4U Nodes (Intel XEON x7560 2.26Ghz)

160 cores (320 Hyper-threads) 5 X 32

5 TB memory total, 30TB solid state disk

160 GB/s InfiniBand interconnect per node

Scalable coherent shared memory (via ScaleMP)

Developers don’t need additional skills for in-memory

Data base becomes data structures

Scalable DB on virtualized HW – Alternative to Cloud


Warren Powell et al.

Princeton University - Operations Research and Financial Engineering

Optimal Learning & In-Memory Handle Uncertainty


Solve Very Compute Intensive Problems

Like Stochastic Optimization @Princeton

Juggle intermittent energy from wind, solar & volatile electricity prices

to meet time-varying loads – Princeton has the algorithms

With BigIron we can reduce compute time from days to minutes!

Wind speed

Electricity prices

Load


The effect of modeling uncertainty in wind

Modeling uncertainty in power scheduling

0

2

4

6

8

10

12

Uncertain forecast Perfect forecast Constant wind

2% wind

40% wind


Modeling Uncertainty In Power Scheduling

Designing energy portfolios….

… is like building a stone wall. You can do a perfect job with a perfect

forecast. The challenge is dealing with uncertainty.


John Williams et al.

MIT Auto ID Lab

Multithreading Real Time Event Platform

© 2011 SAP AG. All rights reserved. 20A Comparative Study of Data Storage and Processing Architectures

Verizon and T-Mobile: 2-3 days to generate phone bill

iTunes: 24 hours to generate bill

Uninterrupted Growth of online billing systems (Hulu, Netflix…)

Dynamic Pricing on SmartGrid requires design of infrastructure capable of

ingesting millions of events in quasi-real time

Goal: Design a

multi-threaded

system that

produces the

electricity

consumption bill of a

city of 1M

households8 hours seconds

Rapid Growth of Events and Messaging Platforms


Users

Energy

Producers

Data

Generation Data

Persistence

Data

Processing

Smart Meter Reading Problem

•

ELECTRONIC NERVOUS SYSTEM

Analytical

Approach

Inductive

Approach

No Prior

Knowledge

Perfect

Knowledge

No Data Complete Data

Data

Knowledge

2

2

1exp

2

i

i

xy

X1 -1 Y1 0.02540487

X2 -0.9 Y2 0.02779527

X3 -0.8 Y3 0.03010825

X4 -0.7 Y4 0.03228947

X5 -0.6 Y5 0.03428442. . . .. . . .. . . .

GPS SIM Card

http://senseable.mit.edu/realtimerome/images/IMG_5597.jpg

http://images.google.com/imgres?imgurl=http://updatecenter.britannica.com/eb/image?binaryId=99545&rendTypeId=4&imgrefurl=http://thoughtsonglobalwarming.blogspot.com/2008/03/mit-professor-improves-solar-cell-27.html&usg=__-BgmiMyAW3S7HOHf4XHUQWY1O6M=&h=362&w=550&sz=25&hl=en&start=13&um=1&tbnid=HZUgywSkHn4mhM:&tbnh=88&tbnw=133&prev=/images?q=solar+cells&hl=en&rlz=1T4ADBF_enUS314US316&sa=N&um=1

http://images.google.com/imgres?imgurl=http://www.absolutecomfort.us/pics/Air_Conditioner.jpg&imgrefurl=http://www.absolutecomfort.us/services.html&usg=__HT18OIix2CBx0Dymndo3YtUJV2g=&h=400&w=336&sz=35&hl=en&start=4&um=1&tbnid=Q2cFYn0fM6oJsM:&tbnh=124&tbnw=104&prev=/images?q=air+conditioner&hl=en&rlz=1T4ADBF_enUS314US316&sa=N&um=1

http://images.google.com/imgres?imgurl=http://www.esato.com/gfx/news/img/img_1853_sim-card_450x360_1183034283.jpg&imgrefurl=http://www.esato.com/news/article.php/id=1708&h=300&w=450&sz=91&hl=en&start=4&usg=__mDgNPb3bAYWyWCucmhI3pHzqgns=&tbnid=Vrkpo4V2TBi3dM:&tbnh=85&tbnw=127&prev=/images?q=Sim+Card&gbv=2&hl=en&rls=ig

© 2011 SAP AG. All rights reserved. 23A Comparative Study of Data Storage and Processing Architectures

Platform that handles billions of events/day AND large numbers of

threads on one machine (> 1 million), e.g. Siemens 500k events/s

RDBMS (used by today’s MDUS vendors) provides good query

performance but does not scale to millions of households (8 h)

Prototype for SmartGrid allowing to ingest smart meter data in real

time, do dynamic pricing (4 buckets), store in DFS & do real time

analytics

Bill for 1 M households in seconds

Multithreading Real Time Events & Messaging Platform


Pacific Northwest National Labs (PNNL)

GridLAB-D For Comprehensive Grid Simulations


California Statewide Cumulative Investment Through 2020

To Achieve Renewable Portfolio Standard Goals

Need to forecast financial and operational impacts before investing

Category 20% RPS 33% RPS in 2020

New renewable generation $32.8 Billion $95.3 Billion

New transmission $ 4.0 B $12.3 B

New conventional generation $ 15.0 B $ 6.9 B

Total CAPEX required $ 51.8 B $ 114.5 B

Governor Schwarzenegger signed Executive Order S-21-09 to adopt

regulations increasing California's Renewable Portfolio Standard (RPS) to

33% by 2020.


CalPower – A Hypothetical California Utility with 15%

Renewable Generation Today

2010 2020Total Renewables: 15%

Traditional Technologies: 85%

Total Renewables: 33%

CalPower generation

portfolio todayCalPower RPS

goal in ten years

Geo Thermal: 4%Biomass: 3%

Solar: 3%

Wind: 5%

Nuclear: 18%

Coal: 19%

Natural

Gas: 48%

Traditional Technologies: 67%

Geo Thermal: ?%Biomass: ?%

Solar: ?%

Wind: ?%

Nuclear: ?% Coal: ?%

Natural

Gas: ?%


Plan C

Total: 33%

Wind : 14%

Solar PV 12%

Biomass: 3%

Geo Thermal: 2%

Other renewable: 2%

2020 Portfolio C

Peak Total Capacity: 5GW

CAPEX: $1405/MWh

OPEX: $167/MWh

Total Cost: $15,566M

Total CO2 emission: 5MT

Avg. CAIDI: 1.63 Hours

Questions:

Plan BTotal: 33%

Wind : 8%

Solar PV 15%

Biomass: 3%

Geo Thermal: 2%

Other renewable: 2%


CAPEX: ? $ M

OPEX: $368/MWh




Questions:

Study Future Options For CalPower’s Generation Portfolio

Larry Nolan

Operations,

Sr. Analyst

CalPower LLC

Balance financial performance, quality

of service, and operational risks

Goals

Investigate future options of

generation capacity plans for the

next 10 years

Analyze potential KPI changes and

risks

Tasks

Lack of supporting evidence to

evaluate future performance –

need data of how the RPS change

might affect the company in the

long run

Pain Points

Plan A

Total: 33%

Wind : 16%

Solar PV 8%

Biomass: 4%

Geo Thermal: 5%

Other renewable: 0%

2020 Portfolio A


CAPEX: ? $M through 2020

OPEX: ? $M

Total Cost: ? $M

Total CO2 emission: ? Tons

CAIDI: ? hours/year

Questions:

“Which plan offers the best expected total cost?”

“How do we mitigate these risks?”

“Which plan minimizes financial & service quality risks?”

2020 Portfolio B

CAIDI: Customer Average Interruption Duration Index


Step 1: Use GridLAB-D To Model Objective & Constraints

Today’s Power Sale

Portfolio

Constraints

Goal – Year 2020

Renewable Portfolio Standard

33%

Wind5%

Natural Gas 48%

2.4 GWCoal19%

Nuclear18%

Other7%

Solar3%

CalPower’s

Load Models

Weather Model

(GW)

Total Peak Capacity

Maximum Wind

Maximum Coal


Step 2: Compare Different Plans


Step 3: Drill Down Analysis Of Exception Days And Risks


Step 3: Drill Down Analysis Of Exception Days And Risks


Exception Day Risk Mitigation Strategies

1. Adopt demand response

2. Invest in power storage

technologies

OPEXException

Day Risk

CAPEXException

Day Risk

Decrease demand in response to supply drop

Use stored power to close the gap


RPS Study Takeaway: GridLAB-D Solution Provides Larry

The Answers He Needs

“Which plan offers the best expected total cost?”

“How do we mitigate these risks?”

“Which plan minimizes financial and

service quality risks?”

1. Comprehensive model of utility operations, including the distribution level. Can model distributed generation, and can model loads at high resolution to make more precise forecasts of operations KPIs (e.g. CAIDI, CO2) and financial KPIs (OPEX, CAPEX).

2. SAP User Experience Team helps business customers access results, and increase precision of their KPI forecasts. Plan C

Total: 33%

Wind : 14%

Solar PV 12%

Biomass: 3%

Geo Thermal: 2%

Other renewable: 2%

2020 Portfolio C


CAPEX: $1405/MWh

OPEX: $167/MWh




Questions:

Plan BTotal: 33%

Wind : 8%

Solar PV 15%

Biomass: 3%

Geo Thermal: 2%

Other renewable: 2%


CAPEX: $15,306.77 M

OPEX: $368/MWh




Questions:

Plan A

Total: 33%

Wind : 16%

Solar PV 8%

Biomass: 4%

Geo Thermal: 5%

Other renewable: 0%

2020 Portfolio A


CAPEX: $1,414.04 M

OPEX: $13,726.04 M

Total Cost: $15,140.08 M

Total CO2 emission:

145,765,543.95 T

CAIDI: 1.63 hours/year

Questions:

2020 Portfolio B

Larry’s questions answered


© SAP

Thank You!

Contact information:

Paul Hofmann

SAP Labs, Palo Alto

[email protected]

www.paulhofmann.net

mailto:[email protected]

