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New Technologies For The Sustainable Enterprise; keynote @Wharton

New Technologies For The Sustainable Enterprise; keynote @Wharton



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 ...

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)



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  • Mobile banking in SA has almost doubled to 44% up from 27% in one year. 12% are now sending money from phone to phone. In Africa mobile minutes are currency.M-PESA (mobile pesa=money) is a branchless banking service, very successful in Kenya used by over 10 M people or 50% of the adult population; joint venture between Vodafone and Safaricom. Example of Prahalad’s developing countries leap frog the West.A story from a friend’s servant in SA. His pregnant wife having complications late in the night and they needed to get to the doctor. This was very late in the night and their residence was far from town. Neither the gentleman nor the lady had airtime to call the doctor. However the lady was a registered customer for Mobile Banking service. With no other option left to get airtime, a solution lay in her hands under her thumb. She got onto her phone and was able to buy airtime and called the doctor who diagnosed the issue on phone and the lady was relieved of the pain.Moore’s law for data creation - amount of data is doubling every 18 month
  • Larry will be using gridlabd to forecast the performance of different capacity plans in the next ten years, the first step he needs to do is to build case and as the input to the simulation tool. This is a summary of the goals and constraints Larry want gridlabd to know. You‘ll see here the first thing is calpower‘s current capacity portfolio, and larry will also import calpower‘s current load models build by the power engineers. The input also includes thirdparty data such weather forecast, and maybe data from the utilities existing databases such as ERP system or GIS systems. And also Larry sets some constraints to the case such as the maximum wind capacity cannot be larger than 3 GW. After building the case and, goals are cearly set, what larry is going to is to run the simulation in GridlabD, and he‘ll get the simulation results in the output dashboard.
  • Executive summary of some of the best case portfolios and the KPI value associated with each plan. Here Larry is able to compare the plans and have a clear overview of the advantage and disadvantages of each plan.
  • Look at the exception days, find a big gap on this day.
  • To summarize: In these case studies, we illustrate GridLAB-D‘s capability to forecast financial and operations KPIs for electric power utilities.

New Technologies For The Sustainable Enterprise; keynote @Wharton New Technologies For The Sustainable Enterprise; keynote @Wharton Presentation Transcript

  • New Technologies For The Sustainable Enterprise
    Paul Hofmann, SAP Labs North America
    Wharton, May 9th 2011
  • What Does SAP Do?
    Financial/Mgmt Accounting
    Sales Order Management
    Talent Management
    Production Planning
    Business Intelligence
    © SAP AG 2010. All rights reserved. / Page 2
    Customs Operations
    Suppliers and
    Export Compliance
  • 100,000 Companies Run SAP
  • 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
  • Consumer Products
    1.4 million sales order line items per day
    Business Intelligence> Analytics over > ~50 TB data in memory with BIA
    Human Capital Management
    Payroll calculations for 500,000 employees in 3 hours
    1 million users at one customer and 500,000 at many customers
    Our Resource Consumption Requirements MMassive and Diverse
    SAP Business Suite
    Supply Chain Management4.5M characteristic combinations (SKUs) & 512 GB - 1TB memory in live cache
    40 million customers – up to 8 million transactions an hour
    Engineering & Construction
    5,000 concurrent active users
    ~ 400 Million lines of code
    25 million business partners – 85 million service and sales orders per year
    Order Management
    10 million on-line item orders invoiced per day
    © 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.”
  • Path to Sustainability Challenges Leading to Innovation Opportunities
    Greening IT
    IT for Greening
    Greening SAP
    Reducing IT related CO2 emissions by optimizing energy footprint of SAP-related software and hardware
    Providing integrated solutions for:
    • measuring,
    • aggregating,
    • reporting,
    • analyzing and
    • optimizing environmental data
    Identifying, structuring and coordinating programs for a targeted reduction of SAP’s environmental impact combined with communicating the success
    © SAP 2008 / Page 8
  • SAP’s Role In The Clean Tech Movement
    Final Product
    ENVIRONMENTAL ACCOUNTINGFor carbon impactCarbon just another currency
    CARBON CAP AND TRADEAcross the Supply Chain
    © SAP 2008 / Page 9
  • 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
  • Multicore – No More Free Lunch
    CPU Clock Speeds Over Time
    4 GHz
    Power Leakage
    Physical Limitations
    © SAP 2008 / Page 11
  • The Big Challenge of Parallelism/Concurrency
    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]
    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
  • 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 Hegemonyin 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 statefor flash – 5000 cycles countryfor 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 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
    Electricity prices
  • Modeling uncertainty in power scheduling
    The effect of modeling uncertainty in wind
    2% wind
    40% wind
    Uncertain forecast
    Perfect forecast
    Constant 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
  • 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
  • Smart Meter Reading Problem
    Data Generation
    Data Persistence
    Data Processing
  • Electronic Nervous System
    GPS SIM Card
  • 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
  • Pacific Northwest National Labs (PNNL)
    GridLAB-D For Comprehensive Grid Simulations
  • 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
  • CalPower – A Hypothetical California Utility with 15% Renewable Generation Today
    CalPower generation portfolio today
    CalPower RPS goal in ten years
    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%
  • Study Future Options For CalPower’s Generation Portfolio
    Plan C
    2020 Portfolio C
    Plan B
    Larry Nolan
    Peak Total Capacity: 5GW
    OPEX: $167/MWh
    Total Cost:$15,566M
    Total CO2 emission:5MT
    Avg. CAIDI:1.63 Hours
    2020 Portfolio B
    Sr. Analyst
    CalPower LLC
    Peak Total Capacity: 5GW
    CAPEX:?$ M
    OPEX: $368/MWh
    Total Cost:$15,566M
    Total CO2 emission:5MT
    Avg. CAIDI:1.63 Hours
    Plan A
    2020 Portfolio A
    • Balance financial performance, quality of service, and operational risks
    Peak Total Capacity: 5GW
    CAPEX: ? $M through 2020
    OPEX: ?$M
    Total Cost:? $M
    Total CO2 emission:? Tons
    CAIDI:? hours/year
    • Investigate future options of generation capacity plans for the next 10 years
    • Analyze potential KPI changes and risks
    Pain Points
    “Which plan offers the best expected total cost?”
    • Lack of supporting evidence to evaluate future performance – need data of how the RPS change might affect the company in the long run
    “Which plan minimizes financial & service quality risks?”
    “How do we mitigate these risks?”
    CAIDI: Customer Average Interruption Duration Index
  • Step 1: Use GridLAB-D To Model Objective & Constraints
    Today’s Power Sale Portfolio
    Goal – Year 2020
    Weather Model
    Renewable Portfolio Standard
    Natural Gas
    2.4 GW
    CalPower’s Load Models
    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
    Use stored power to close the gap
    Decrease demand in response to supply drop
    1. Adopt demand response
    Exception Day Risk
    2. Invest in power storage technologies
    Exception Day Risk
  • 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
    Peak Total Capacity: 5GW
    OPEX: $167/MWh
    Total Cost:$15,566M
    Total CO2 emission:5MT
    Avg. CAIDI:1.63 Hours
    2020 Portfolio B
    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?”
    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?”
  • © SAP 2008 / Page 34
  • Thank You!
    Contact information:
    Paul Hofmann
    SAP Labs, Palo Alto