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Similar to USTDA US-ASEAN Hanoi Vietnam 13th November 2012
Similar to USTDA US-ASEAN Hanoi Vietnam 13th November 2012 (20)
USTDA US-ASEAN Hanoi Vietnam 13th November 2012
- 1. Beyond MDM Smart Grid Applications and Analytics Slide 0 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 2. • Company Founded in 1999 • Corporate headquarters in San Mateo, California. • Circa 250 employees • 2 software development centers • Sales and support teams in US, Europe and Asia. Initial Concept – Meter Data Management Platform for mass market deployments – Meter data to support multiple uses across a Utility business – AMI & Meter vendor neutral – Embrace systems integrators and other business partners. Today - Smart Grid Application Platform - EnergyIP® January 2012 - eMeter became part of the Siemens Smart Grid Division. Siemens Smart Grid Division supplies products, solutions and services for intelligent and flexible electricity network infrastructures. www.usa.siemens.com/smartgrid 1 eMeter Corporation © 2010
- 3. Utilising AMI data and services in the Smart Grid Horizontal IT Solutions Utility End Consumer Operational IT Transmission Distribution Mgt. Consumer-specific Smart Meters & Mgt. Systems System (DMS) Communication Devices & Automation EnergyIP Optimizing energy networks, usage and consumer experience. Smart Grid Applications Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 4. Addressing some Critical Problems Balancing Renewables Customer Operations Load Shifting Generation Load RENEWABLES PROSUMERS Reduction of System Outage Prevention / Efficiency Losses Restoration Generation kWh $, €
- 5. Beyond MDM Smart Grid Application Platform • Data Collection & Processing • Data Synchronization • Unification of Data and Services • Validation, Estimation and Editing • Exception Handling • Automated Meter Provisioning • AMI Device Management • Business Process Automation • Event Filtering and Analysis • Automated Reports • Smart Grid Applications • Advanced Analytics 4 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 6. Advanced Analytics Customer Segmentation Rate Modeling Outage Analysis Load Profiling Event and Work Orders Energy Theft Power quality Grid Loss Unbilled Energy Demand Response Program Evaluation Distributed Generation Monitoring Asset Monitoring Data Collection Analysis HV and LV Network Component Loading AMI System, Agents & SLA monitoring ... and more! Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 7. Is this the Grid of the Future? eMeter Corporate Logo To ensure the integrity of the eMeter brand it is necessary to understand how to use the logos. Minimum Clear Area A minimum clear area has been created around each logo. This area should always be kept free of any graphic elements and/or messages. The gray lines in these illustrations show how the minimum clear area is calculated. In all cases, an area equal to the cap height of eMeter “r” must remain clear on all sides of the logo. The gray bars in these illustrations indicate the visual height, width, vertical center and horizontal center of the logo. (see the next slide) Revenue Protection Slide 6 Copyright © 2011 eMeter Corp. All rights reserved.
- 8. Non Technical Losses Identifying Theft Patterns 7 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 9. Is this the Grid of the Future? eMeter Corporate Logo To ensure the integrity of the eMeter brand it is necessary to understand how to use the logos. Minimum Clear Area A minimum clear area has been created around each logo. This area should always be kept free of any graphic elements and/or messages. The gray lines in these illustrations show how the minimum clear area is calculated. In all cases, an area equal to the cap height of eMeter “r” must remain clear on all sides of the logo. The gray bars in these illustrations indicate the visual height, width, vertical center and horizontal center of the logo. (see the next slide) $360 Million Stolen Each Year in US Network Equipment Monitoring Slide 8 Copyright © 2011 eMeter Corp. All rights reserved.
- 10. Service Point Metering Slide 9 Copyright © 2011 eMeter Corp. All rights reserved.
- 11. Virtual Metered Transformer Slide 10 Copyright © 2011 eMeter Corp. All rights reserved.
- 12. Identifying Overloaded Assets System Load Curve 11 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 13. Applications for Grid Operations System and Asset Loading Analysis – Peak contributors System Load Analysis 12 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 14. Network load analysis http://earthdaytolucalake.wordpress.com/
- 15. System Load Slide 14 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 16. Targeted Demand Response Slide 15 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 17. Individual Peak Loads Slide 16 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 18. Applications for Grid Operations System and Asset Loading Analysis System Load Curve 17 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 19. Time of Use Analysis Slide 18 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 20. Outage Outage Outage Management Slide 19 Copyright © 2011 eMeter Corp. All rights reserved.
- 21. Outage Event Correlation Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
- 22. neil.beckwith@siemens.com +44 7921 244 428 Slide 21 Copyright © 2012 eMeter, A Siemens Business. All rights reserved.
Editor's Notes
- Good afternoon, and thank you for the opportunity to speak today.I’m Neil Beckwith and I represent eMeter a business sector of Siemens.My topic today aims to share some of the ‘real world’ use cases eMeter has developed from experience gained with Smart Grid customers in North America.In particular I would like to make a case for some careful consideration around the potential role for MDM relative to any Smart Grid deployment.
- To shine some light on the demand on this last leg of the gridSpecific algorithms to derive kva demand from kwh usage.Once we know the demand at each service point - massive-scale aggregation is possible.
- Created hundreds of thousands of virtual meters at the transformer, circuit section, and substation level. Can we start to measure the likelihood of transformers failing?Virtual metering implemented in this way represents a multi million dollar avoided cost. Performance? It’s a lot of data: - One day’s 15 minute interval data aggregatedby distribution transformer for 1.6m meters.Result: 200million records processed = 20m output records in under 120 seconds.Running on IBM Netezza.
- Load duration curve for a defined period provides engineers with useful metrics to predict the likelihood of transformer failure based on loading.
- DR and TOU pricing are 2 solutions proposed solutions.Question is how do we best use these to drive consumer behavior?First need to understand the data – to identify the problem.
- So we start by taking a look at overall peaks that are too high
- Once we find those areas of the grid that are most overloaded, we can start to target the most fruitful customers as candidates for DR. ‘Bang for buck’.