Dinner keynote at Wharton May 9th 2011 @ 11th Annual Strategy and the Business Environment Conference (SBE) jointly with the 3rd Annual Research Conference Alliance for Research on Corporate Sustainability (ARCS)

1. New Technologies For The Sustainable Enterprise Paul Hofmann, SAP Labs North America Wharton, May 9th 2011

2. What Does SAP Do? Financial/Mgmt Accounting Sales Order Management Talent Management Production Planning Business Intelligence © SAP AG 2010. All rights reserved. / Page 2

3. SUPPLIER CUSTOMER DISTRIBUTIONCENTER CFO CRO Sourcing CUSTOMS/ REGULATORY AGENCY CUSTOMS/ REGULATORY AGENCY Customs Operations COO Fulfillment Receiving Manufacturing EVERY 2ND DOLLAR OF WORLD TRADE RUNS ON SAP Suppliers and Customers Exports Imports Export Compliance GLOBAL ENTERPRISE

4. 100,000 Companies Run SAP

6. 100,000 companies run SAP software

7. 121,000 installations

8. Provides 26 industry solutions

11. 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.”

13. aggregating,

14. reporting,

15. analyzing and

16. optimizing environmental dataChallenge Identifying, structuring and coordinating programs for a targeted reduction of SAP’s environmental impact combined with communicating the success © SAP 2008 / Page 8

17. SAP’s Role In The Clean Tech Movement Final Product ENVIRONMENTAL ACCOUNTINGFor carbon impactCarbon just another currency CARBON CAP AND TRADEAcross the Supply Chain CO2 CO2 CO2 CO2 © SAP 2008 / Page 9

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

24. 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

25. Warren Powell et al. Princeton University - Operations Research and Financial Engineering Optimal Learning & In-Memory Handle Uncertainty

26. Solve Very Compute Intensive ProblemsLike 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 Load Electricity prices

27. Modeling uncertainty in power scheduling The effect of modeling uncertainty in wind 2% wind 40% wind Uncertain forecast Perfect forecast Constant wind

28. 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.

29. John Williams et al. MIT Auto ID Lab Multithreading Real Time Event Platform

30. Rapid Growth of Events and Messaging Platforms 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 households 8 hours  seconds A Comparative Study of Data Storage and Processing Architectures

31. Smart Meter Reading Problem Data Generation Data Persistence Data Processing

32. Electronic Nervous System GPS SIM Card

33. Multithreading Real Time Events & Messaging Platform 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 A Comparative Study of Data Storage and Processing Architectures

34. Pacific Northwest National Labs (PNNL) GridLAB-D For Comprehensive Grid Simulations

35. California Statewide Cumulative Investment Through 2020 To Achieve Renewable Portfolio Standard Goals Governor Schwarzenegger signed Executive Order S-21-09 to adopt regulations increasing California's Renewable Portfolio Standard (RPS) to 33% by 2020.  Need to forecast financial and operational impacts before investing

36. CalPower – A Hypothetical California Utility with 15% Renewable Generation Today CalPower generation portfolio today CalPower RPS goal in ten years 2010 2020 Total Renewables: 33% Total Renewables: 15% Geo Thermal: ?% Geo Thermal: 4% Biomass: 3% Biomass: ?% Solar: 3% Solar: ?% Natural Gas: ?% Wind: 5% Wind: ?% Nuclear: 18% Natural Gas: 48% Nuclear: ?% Coal: ?% Coal: 19% Traditional Technologies: 85% Traditional Technologies: 67%

39. Step 1: Use GridLAB-D To Model Objective & Constraints Today’s Power Sale Portfolio Goal – Year 2020 Weather Model Renewable Portfolio Standard 33% Natural Gas 48% 2.4 GW Coal 19% CalPower’s Load Models Constraints Total Peak Capacity Other 7% Maximum Wind Maximum Coal Wind 5% Nuclear 18% Solar 3% (GW)

40. Step 2: Compare Different Plans

41. Step 3: Drill Down Analysis Of Exception Days And Risks

42. Step 3: Drill Down Analysis Of Exception Days And Risks

43. Exception Day Risk Mitigation Strategies Use stored power to close the gap Decrease demand in response to supply drop 1. Adopt demand response OPEX Exception Day Risk 2. Invest in power storage technologies CAPEX Exception Day Risk

44. RPS Study Takeaway: GridLAB-D Solution Provides Larry The Answers He Needs Plan C 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). SAP User Experience Team helps business customers access results, and increase precision of their KPI forecasts. Plan B Plan A 2020 Portfolio C Questions: Peak Total Capacity: 5GW CAPEX:$1405/MWh OPEX: $167/MWh Total Cost:$15,566M Total CO2 emission:5MT Avg. CAIDI:1.63 Hours 2020 Portfolio B Questions: Larry’s questions answered Peak Total Capacity: 5GW CAPEX:$15,306.77 M OPEX: $368/MWh Total Cost:$15,566M Total CO2 emission:5MT Avg. CAIDI:1.63 Hours “Which plan offers the best expected total cost?” Questions: 2020 Portfolio A “Which plan minimizes financial and service quality risks?” Peak Total Capacity: 5GW 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 “How do we mitigate these risks?”

45. © SAP 2008 / Page 34

46. Thank You! Contact information: Paul Hofmann SAP Labs, Palo Alto paul.hofmann@sap.com www.paulhofmann.net