1. A collaboration of:
How a Real-Time Data Platform Supports the
Modern Utility
Stefan Wolf
Solution Management, Utilities Business Solutions

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Disclaimer
This presentation outlines our general product direction and should not be relied
on in making a purchase decision. This presentation is not subject to your
license agreement or any other agreement with SAP. SAP has no obligation to
pursue any course of business outlined in this presentation or to develop or
release any functionality mentioned in this presentation. This presentation and
SAP's strategy and possible future developments are subject to change and
may be changed by SAP at any time for any reason without notice. This
document is provided without a warranty of any kind, either express or implied,
including but not limited to, the implied warranties of merchantability, fitness for a
particular purpose, or non-infringement. SAP assumes no responsibility for
errors or omissions in this document, except if such damages were caused by
SAP intentionally or grossly negligent.

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Data from various sources has to be gathered, combined,
and leveraged to support smart grid processes
Consumption and load analytics
Consumption data, customer data, geographical information
Leakage management
Consumption data, customer data, social data
Grid infrastructure analytics, predictive maintenance
Asset data, consumption data, geographical information
Demand response management, virtual power plants
Asset data, consumption data, geographical information,
generation data, weather data
…and many more

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Agenda
Examples from the real world
What we have and are working on
Our vision for the next step

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Real-time load forecasting with HANA for Alliander
Real-time load
forecasting
Load sensor
data into OSI
Continuous display
of historical data and forecast
Real-time alerting

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Real-time load forecasting with HANA for Alliander
Alliander needed
• Reduced effort
• Access millions of sensor measurements in seconds
• Improve compliance by generating auditable load analyses
They got
• SAP software for statistics foundation and SAP PIO* services
• SAP HANA for the sensor data
• Overall reduction of effort to analyze peak load by 65%-75%**
• Real-time display of actual load, historic forecast and future forecast
• 6h load forecast refreshed every 5 minutes
• 4.5 minutes to process 200 million raw measurements and compute yearly
peak load of 1,000 transformers
• Additional insights like comparison of different stations and trend analysis
**Compared to a legacy analysis system, which is basically a combination of an Oracle DB with an MS Access DB and VBA scripts*PIO: Performance Insight and Optimization

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Real-time load forecasting with HANA
Reference architecture
Grid and asset
master data
Grid and
sensor data
Tables
Procedures
for write
back
TablesR algorithm
procedures,
e.g. forecasting
Analytical
reporting
Forecasting
algorithm
Event-driven
analytics
External data:
- weather
Forecasting
results + asset data
Filtered and cleansed
OSI data
Provide forecasts to PI
for internal analysis
*ESP: Event Stream Processor

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Processing of Streaming Data with
Event Stream Processor for Surgutneftegas

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Processing of Streaming Data with
Event Stream Processor for Surgutneftegas
Surgutneftegas needed
• Ability to process ~2.5 billions of SCADA events per day. Filter them, and
store for 5 years.
• Ability to build some simple analytics over event flow.
• Ability to get transactional information from SAP and non-SAP systems.
• Ability to Real-Time analysis of information in different contexts
They got
• SAP Event Stream Processor – for event processing
• SAP Replication Server – for simplify replications
• SAP HANA – for Real-Time analytical calculation over millions of KPI
• Over 6 month stored 140 million events
• All queries run constantly within 1.5 second regardless of selected time
period

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Processing of Streaming Data with
Event Stream Processor for Surgutneftegas

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Reducing TCO with Near Line Storage
SCE decided to move to BW on HANA
• Reduce batch loading time
• Improve reporting performance
One challenge the massive amount of data in BW: 22TB (uncompressed)
BW on HANA provided already significant reduction: 693 GB
• Removal of PSA, Change Logs, DB overhead, misc. files (3.7TB remaining)
• HANA compression (4.8 : 1 to 770GB)
• Removal of some cubes and master data

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Reducing TCO with Near Line Storage
Benefits from use of Near Line Storage (NLS)
• Saving from reduced size for SAP HANA (170GB, 25% of projected size)
• Reduced annual growth of BW from 34% to 12%
• Reduced maintenance fee
• Significantly improved TCO
While maintaining benefits for the user
• Seamless queries, transparent to end user
• Good performance for queries on NLS
NLS Solution from SAP Partner PBS Software provided by Dolphin

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Improving Settlement with HANA for ESB Networks
ESB in Ireland wanted to prepare settlement for smart meter data
• Using standard SAP Energy Data Management (EDM)
• Running 4 aggregations per day at 56 minutes per run
• Smart Metering means eventually having to aggregate interval data for 2.2
million residential customers (today only 8000 customers with interval
meters), estimated to take 8h per run with todays process
Using SAP HANA to accelerate standard process
• Maintain standard process in existing SAP system
• Outsource key steps of the process to SAP HANA for processing
• Using SAP SLT* Replication Server to load data
*SLT: System Landscape Transformation

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Improving Settlement with HANA for ESB Networks
Energy Settlement process being managed in SAP for Utilities
Time-
consuming
steps are
processed in
SAP HANA
Start settlement
Select PODs for
settlement
Aggregate
consumption data
for selected PODs
Exception handling,
documentation,
other steps
Market
communications
Settlement
workbench
Request
Accelerate daily, weekly, and
monthly settlement processes
Enable ad hoc settlement
Integrate perfectly into standard
processes of SAP for Utilities
solutions to support market
communications and audits
Result
SAP Landscape
Transformation*:
tables, profile
data
Time-
consuming
steps are
processed in
SAP HANA
SAP HANA
Settlement data schema
Settlement functions
Joins, aggregations, …
*SAP Landscape Transformation replication server

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Improving Settlement with HANA for ESB Networks
SP_NORTH
800,000 interval meters
104,000 classical meters
Classic
DB
SAP
HANA
Improvement
Factor
Assignment of metering
points
640 sec 83 sec
Aggregation of interval data 12,700 sec 42 sec
Total* 13,340 sec
(222 min.)
125 sec
(2 min.)
~107
*The energy settlement for the other (smaller) settlement units provides the same results.
First Performance Results

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Improving Collections with HANA for Consumers Energy
Consumers Energy wanted to improve their collections reporting
• Current process complex and time consuming
• 2 analysts working for weeks to produce report
• Manual work was error prone
Using SAP HANA in a PoC* to create automated and improved process
• Create a dedicated Datamart in SAP HANA for collections reporting
• Provide tailored User Interface for agents
• Using SAP SLT Replication Server to load data
*PoC: Proof of Concept

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Improving Collections with HANA for Consumers Energy
Benefits from HANA Data Mart
• Data automatically replicated
• Push-Button access to reports
• High confidence in correctness
User Interface
Conceptual Star Schema

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What is a spatially enabled database?
Key capabilities delivered in SAP HANA
• Store, process, manipulate, share, and
retrieve spatial data directly in the database
• Process spatial vector data with spatial
analytic functions:
• Measurements –
distance, surface, area, perimeter, volume
• Relationships –
intersects, contains, within, adjacent, touches
• Operators –
buffer, transform
• Attributes –
types, number of points
• Store and transform various 2D coordinate
systems
• Process vector data
• Implements the ISO/IEC 13249-3 standard
and Open Geospatial Consortium (1999
SQL/MM standard)
Internal
point
line
polygon Multi-polygon

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Key Capabilities
Energy infrastructure company needed to perform pipeline
integrity management analysis to identify high-risk
transportation & distribution pipes that are close to
structures. This required pre-processing and analyzing huge
amounts of spatial data.
Previously, it took more than 3.5 hours for this analysis on
legacy architecture. SAP HANA PoC implementation
brought the compute time to less than 2.5 seconds allowing
the company to perform ad-hoc asset management and
reduce potential outages and avoid catastrophic failures.
Additionally, geospatial visualization was used to estimate
maintenance cost per year for electricity stations.
84,000x
3.5hours to less than
2.5seconds in PoC
New capabilities
by combining geospatial
with transactional data
Utilities Case Study
European company providing energy infrastructure related services

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Agenda
Examples from the real world
What we have and are working on
Our vision for the next step

22. © 2012 SAP AG. All rights reserved. 23
The SAP Real-time Data Platform (RTDP)
Business
Warehouse
Business
Intelligence
Mobile &
Embedded
ERP
In-Memory / Realtime
SAP HANA
SAP Real Time Data Platform
Stream Analytics Mobile & Embedded
Open EDWHigh Performance
OLTP
Information and Real-time Data Movement
IntegratedModelingand
Metadata
IntegratedSystems
ManagementandLandscape
Common Programming APIs
IQASE
ESP SQL Anywhere
Replication Server, Data Services
PowerDesigner
ControlCenter
Real life benefits we saw:
 SAP HANA to reduce
processing times
 NLS to reduce TCO while
maintaining superior speed
 ESP to manage high-velocity
streaming data
 Native support for spatial data
Elements of the RTDP are
available now
SAP providing clear path to
reduced complexity and cost

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PSE&G
Field Crews
Comprehensive IT/OT Solution
Field Devices
Mutual Aid
Dashboards
Government
Public
Realtime Operations
SCADA, EMS,
DMS, OMS, DSM
PI Enterprise Data
Infrastructure
ERP, ESB
CRMB,
EAM, IVR,
Scheduling &
Dispatch
Weather
GIS
Realtime
Enterprise
Materials
Warehouse

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Agenda
Examples from the real world
What we have and are working on
Our vision for the next step

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We can leverage the generic RTDP
for a Utilities focused platform
MDUS: Meter Data Unification and Synchronization
NIS: Network Information Service
SCADA: Supervisory Control and Data Acquisition
SMA: Smart Meter Analytics
SAP Real Time Data Platform for Utilities
Further
SAP
Solutions
e.g. SMA,
CEM,
Predictive
Maintenance
Partner
Solutions
e.g.
Space Time
Insight,
Choice
Revenue
Intelligence
SAP BI
MDUSData Historian
GIS ERP
External
Provider
SAP Business Suite
incl. Utilities Solution
Customer
Solutions
SCADA, NIS
etc.
AMI Headend
SAP Multichannel Platform for
Utilities
Data and
computing
layer
Data sources
Visualization
and
application
layer
AMI: Advanced Metering Infrastructure
BI: Business Information
CEM: Customer Energy Management
ERP: Enterprise Resource Planning
GIS: Geo Information System

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Meeting the New Business Needs Requires Two
Platforms in One
BUSINESS
PROCESS
PLATFORM
INFORMATION
PLATFORM
Powered by SAP HANA® software
Trading & Portfolio
Services
Innovative Tariffs
Mobility Services
Virtual Power Plants
Predictive
Maintenance
Forecasting
Demand Response
Management
Outage
Management
Smart Home
Energy
Management
Data Quality
Data Analysis
User Access
Data Capturing
Data
Management

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Summary
The components of the SAP Real Time Data Platform
are here and can be used now to significantly improve
utilities specific process
The real-time data platform for utilities is on its way
To enable and benefit from the smart grid utilities
should take the first steps toward this platform now
You don’t need to do a “Big Bang”, there are many
options. The best path depends on your priorities,
your environment and your current situation

28. © 2013 SAP AG or an SAP affiliate company. All rights reserved.
Thank you
Contact information:
Stefan Wolf
Solution Management
408-627-5581
stefan.wolf@sap.com

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A collaboration of:
Stefan Wolf
SAP
stefan.wolf@sap.com