sap academic research conference · 2019. 11. 12. · dynamic balancing of electricity supply and...
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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