SAP ACADEMIC RESEARCH CONFERENCE · 2019. 11. 12. · Dynamic balancing of electricity supply and...
Transcript of SAP ACADEMIC RESEARCH CONFERENCE · 2019. 11. 12. · Dynamic balancing of electricity supply and...
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SAP ACADEMICRESEARCH CONFERENCEAugust 21, 2009
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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
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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
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Balancing Electricity Supply and Demand –Demand Response Solutions
Alternative energy sourcesintroduce uncertain supplyThe result is a highly dynamiccontrol system
Daily DemandCurves
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Advance Metering Infrastructure (AMI)
Meter DataUnification System
DataConcentrators
SAP UtilitySystems
Smart MeterRead event
every 15 mins
AMIHead endSystem
AMIHead endSystem
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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
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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
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Emulation System Driving Events to TestLoad MDUS, CCRD and SAP IS-U
Emulation Mode Produces1 Billion Events per Day
CCRD@utilities simulator
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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
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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
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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)
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CCRD Simulator
ManagementLayer
Storage Layer
Consumption LayerGeneration Layer
Meter ReadGenerators
Concentrators
SimulationManager
QueryClient
QueryClient
QueryClient
DataRegister
DRYADHADOOPDBSAP TREX
HPI - TREXSAP – VOWS,SESCO
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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)…
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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
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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.”
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Smart Buildings
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Mobility-on-demand systems matchvehicle supply to demand
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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
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Smart City Electronic Nervous System
GPS SIMCard
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SAP ACADEMICRESEARCH CONFERENCEAugust 21, 2009