How a real time platform supports the modern utility

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How a real time platform supports the modern utility

  1. 1. A collaboration of: How a Real-Time Data Platform Supports the Modern Utility Stefan Wolf Solution Management, Utilities Business Solutions
  2. 2. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 3Public 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.
  3. 3. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 4Public 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
  4. 4. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 5Public Agenda Examples from the real world What we have and are working on Our vision for the next step
  5. 5. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 6Public 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
  6. 6. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 7Public 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
  7. 7. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 8Public 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
  8. 8. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 9Public Processing of Streaming Data with Event Stream Processor for Surgutneftegas
  9. 9. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 10Public 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
  10. 10. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 11Public Processing of Streaming Data with Event Stream Processor for Surgutneftegas
  11. 11. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 12Public 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
  12. 12. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 13Public 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
  13. 13. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 14Public
  14. 14. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 15Public 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
  15. 15. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 16Public 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
  16. 16. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 17Public 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
  17. 17. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 18Public 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
  18. 18. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 19Public 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
  19. 19. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 20Public 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
  20. 20. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 21Public 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
  21. 21. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 22Public Agenda Examples from the real world What we have and are working on Our vision for the next step
  22. 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
  23. 23. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 26Public 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
  24. 24. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 27Public Agenda Examples from the real world What we have and are working on Our vision for the next step
  25. 25. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 28Public 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
  26. 26. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 30Public 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
  27. 27. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 32Public 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. 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
  29. 29. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 34Public A collaboration of: Stefan Wolf SAP stefan.wolf@sap.com

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