How a real time platform supports the modern utility

A collaboration of:

How a Real-Time Data Platform Supports the

Modern Utility

Stefan Wolf

Solution Management, Utilities Business Solutions

© 2013 SAP AG or an SAP affiliate company. All rights reserved. 3 Public

Disclaimer

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pursue any course of business outlined in this presentation or to develop or

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SAP's strategy and possible future developments are subject to change and

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errors or omissions in this document, except if such damages were caused by

SAP intentionally or grossly negligent.


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


Agenda

Examples from the real world

What we have and are working on

Our vision for the next step


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


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


Real-time load forecasting with HANA

Reference architecture

Grid and asset

master data

Grid and

sensor data

Tables Procedures

for write

back

Tables R 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


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





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


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



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



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



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


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


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


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


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


Agenda




© 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 EDW High Performance

OLTP

Information and Real-time Data Movement

Inte

gra

ted

Mo

del

ing

an

d

Met

adat

a

Integ

rated S

ystems

Man

agem

ent an

d L

and

scape

Common Programming APIs

IQ ASE

ESP SQL Anywhere

Replication Server, Data Services

Po

wer

Des

ign

er C

on

trol C

enter

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


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


Agenda





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

MDUS Data 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


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


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

© 2013 SAP AG or an SAP affiliate company. All rights reserved.

Thank you

Contact information:

Stefan Wolf

Solution Management

408-627-5581

[email protected]


A collaboration of:

Stefan Wolf

SAP

[email protected]

Technology

How a real time platform supports the modern utility