SAP ACADEMICRESEARCH CONFERENCEAugust 21, 2009

Next Generation Software Systemsfor Smart Grid and Smart City

CCRD @ UtilitiesPrototype Simulator

MIT - John R. Williams, Abel Sanchez, Bill Mitchell,Sergio HererroSAP - Paul Jurkiewicz, Paul Hofmann , Anne Hardy,Andreas Vogel, Yucel Karabulut, Harry WeppnerHasso-Plattner Institute – Alexander Zeier, JuergenMueller, Patrick-Matthieu Shapranow, Jan SchaffnerUniversity of Colorado, Boulder – Stein Sture, Carl Koval

Overview

Context – Smart Grid Processing Large DataDynamic balancing of electricity supply and demandSimulation of Future Networks (MIT SimulatorGridSim@mit)

Next Generation Utility SystemsProcessing large data streams and distributed data storesLeverage Multi-Core – MIT Simulator (CCRD@utilities )

Integration with latest SAP technology TREX, VOWS andSESCO (Secure Enterprise Services Consumption)

Vision – Smart City IT Infrastructure

Balancing Electricity Supply and Demand –Demand Response Solutions

Alternative energy sourcesintroduce uncertain supplyThe result is a highly dynamiccontrol system

Daily DemandCurves

Advance Metering Infrastructure (AMI)

Meter DataUnification System

DataConcentrators

SAP UtilitySystems

Smart MeterRead event

every 15 mins

AMIHead endSystem

AMIHead endSystem

Meter Read Events Billing

InterpolationExtrapolation

00:00 06:00 12:00 18:00 24:00 06:00 12:00 18:00 24:00 06:00

Kw

Off Shoulder Peak On Shoulder Off Peak

Time of Use (TOU)Buckets

$

DynamicFormula

Table

DynamicFormula

Table

Common Approach – Meter-Data Integration.

SAP PI

SAP PI-Content:

A Metering CodeB Point of DeliveryC …D …E …F …G

Partner PI-Content:

A External CodeB Point of DeliveryC …D …

ProgramCall Service …

[…]

End Call

Data Base

Partner API:

A Service PointB External CodeC …D …E …

ProgramCall API …

[…]

End Call

Data Base

MDUS

Provided by Partner

May bechanged toenable theIntegration

Provided by SAP

SAP for Utilities

Project WorkAMI

Head endSystem

C

C

C

C

e-SO

A W

eb s

ervi

ces

AMIHead endSystem

Millions of events/meter reads

Need to Test Performance 1) Partner Software and 2) New Approaches, such ascolumn data stores and MapReduce eg TREX, HadoopDB, Dryad, etc

Emulation System Driving Events to TestLoad MDUS, CCRD and SAP IS-U

Emulation Mode Produces1 Billion Events per Day

CCRD@utilities simulator

Some Numbers for a Typical Utility

~10 million meters (largest utility is ~30 million)

~100 interval reads per day for each meter = 1 Billion Reads/day

Data read event ~ 100 Bytes = 100 GB /day

End of month billing = Query of monthly data (must run in under 8hours of machine time)

We need in-memory solution for at least 30 days of data = 3 TBDistributed datastore + distributed processing

TREX, MapReduce,GFS, Hadoop,Column Stores,OLAP, Cloud,Multicore

GridSim@MIT – Leverages Multi-Core andMulti-Machine to Simulate Future Networks

Multi-Core Agent Model – Test performance of newtechnologies eg Google’s MapReduce/Hadoop, TREX, DSSand DryadDevelop Prototype Systems

Cross MachineHTTP, TCP/IP

GridSim@MIT Tests Show we Need toUnderstand Multi-Core Architecture

Relative AccessSpeeds

L1Cache 32KB

2-4 cycles

L2Cache512KB

~7 cycles

L3Cache12 MB

14 cycles

RAM64 GB

70 cycles

Disk 10ms10K cycles

CrossMachine

100 ms1 M cycles RAM 64 GB

L2L1 L1

L2L1 L1

L2L1 L1

L3 C

ache

BUS and Snoop Filter – Shares L3 Cache

Core1

Core2

Core3

Core4

Core5

Core6

Car

d 2

Car

d 3

We canoutperform MPI(1 Process perCore)

CCRD Simulator

ManagementLayer

Storage Layer

Consumption LayerGeneration Layer

Meter ReadGenerators

Concentrators

SimulationManager

QueryClient

QueryClient

QueryClient

DataRegister

DRYADHADOOPDBSAP TREX

HPI - TREXSAP – VOWS,SESCO

Smart City will need a new IT infrastructure“More data usually beats better algorithms” – Anand Rajaraman

Facebook has 1 billion photos = 1 PetaBytesThe Internet Archive stores 20 TeraBytes per monthOne FDA Drug Application = 1 PetaByteOne electric utility stores 3 TeraBytes per monthDigital World is roughly 1 ZettaByte =

10,000,000,000,000,000,000,000 BytesA Smart City in 1 year will produce > 10 PetaBytes

Smart Grid ~ 1 PetaByteTransportation, Video Cameras etcSupply ChainDigital Health RecordsCity GovernmentLocation Tracking (cell phone)…

Smart CitySmart City

Next Generation Utility Systems - Collaborationbetween SAP Research, MIT and Hasso Plattner

InstituteIntegrating Electric Vehicles into Smart Grid –

Collaboration between MIT, CU Boulder, NREL

Smart GridSmart Grid

Electric Vehicles and Smart Buildings will bepart of the Smart Grid – Prof. Bill Mitchell,MITMIT, University of Colorado Boulder, National Renewable Energy Lab

“Google CityCar, anyone?”

“Unfold and Ride”2007 Automobile Invention of the Year

“The Anti-Hummer.”

Smart Buildings

Mobility-on-demand systems matchvehicle supply to demand

The Evolution of Smart CityPRE-INDUSTRIAL AGE

only structure and skin

INDUSTRIAL AGE

+ circulation (e.g. electrical, plumbing)

INFORMATION AGE

+ intelligence (e.g. energy control)

circulatorycirculatoryskeletalskeletalSMART CITY AS ASMART CITY AS A ‘‘HUMAN BODYHUMAN BODY’’

nervousnervous

structural aspectsof Smart City e.g. buildings, vehicles

flows of people, energy,, cars,water, etc. in Smart City

control and operationsmanagement after sensing

Smart City Electronic Nervous System

GPS SIMCard

SAP ACADEMICRESEARCH CONFERENCEAugust 21, 2009