System stability through cloud-enabled energy automation An essential building block for the digitalization of distribution networks

Transcription

1 European Utility Week Vienna, November 3-5, 2015 System stability through cloud-enabled energy automation An essential building block for the digitalization of distribution networks Prof. Dr. Michael Weinhold, CTO, Siemens Energy Management siemens.com/euw

2 Agenda Challenges in the transmission and distribution grid Digitalization to secure grid stability Cloud computing introduction and application examples Page 2

3 The world of our customers is changing From centralized power and unidirectional grid Distributed Energy Systems to Decentral and Distributed Energy Systems and bidirectional balancing 1 Transmission Changing generation mix 2 Generation capacity additions Distance from source to load Distribution and Consumption Decentralization (public/private) Refurbishment/ upgrades Page 3

4 Customer trends drive dramatic change in energy systems Affordability Availability Environment Renewables and conventional Forecasting Distributed energy Generation control HVDC/FACTS Electrification Agility in energy Automation Digitalization Grid stability Security Power to X MDM e-mobility Customer engagement HVDC/FACTS = High Voltage Direct Current/Flexible AC Transmission Systems MDM = Meter Data Management Page 4

5 How to manage the explosion and the value of data? Phasor Measurement Units Renewables and conventional Distributed energy HVDC/FACTS Power to X e-mobility Data Smart Homes Distribution Automation Outage Management System upgrade Graphical Information Systems (GIS) Remote Terminal Unit upgrades Substation Automation Systems Workforce Management Automated Metering Infrastructure Page 5

6 End-to-end digitalization offerings for our customers Digitalization Enterprise IT IVR GIS Network planning Asset mgmt WMS/ mobile Weather Forecasting Web portals CIS/ CRM Billing Enterprise Service Bus Spectrum Power Platform Grid control applications CIM EnergyIP Platform Market driven applications Automation Electrification Transmission Smart Smart Distribution Smart Consumption transmission distribution consumption and microgrids Page 6

7 combining grid control and market driven applications Digitalization Cloud enabled applications Public cloud Enterprise IT IVR GIS Network planning Asset managem ent WMS/mo bile Weather Forecasti ng Web portals CIS/CRM Billing Enterprise Service Bus Spectrum Power platform Grid control applications CIM EnergyIP platform Market driven applications Global Interoperability: DNP3, IEC & 60870, OpenADR, DLMS, ANSI Cyber security Smart transmission Smart distribution Smart consumption and microgrids Automation Electrification CIM Common Information Model (IEC 61970) Base OT function Domain-specific platform OT/IT function External OT communication Internal IT platform function Enterprise IT function Customer-specific/3 rd party function Cloud-implemented IT web service function Page 7

8 Cloud computing - types Hybrid Cloud Private / Exclusive Public Cloud On Premises / Owned Off Premises / Third Party Page 8

9 Cloud computing - characteristics and benefits On-demand self service Resource pooling Broad network access Rapid elasticity Measured service Self-service provisioning und as-needed availability Scalability increasing usage of common infrastructure Reliability and fault-tolerance guarantees high quality in infrastructure Optimization and Consolidation improving efficiency in economic and ecological standards Quality of Service helping single users to supervise and control without additional effort. Page 9 Source: NIST: National Institute of Standards and Technology

10 Cloud computing services Content Monitoring Identify Object Storage Compute Application Collaboration Platform Runtime Infrastructure Block Storage Communication Finance Database Network Page 10 Source:

11 Cloud benefit: simple and user-friendly fault localization DSOs are looking for simple grid management solutions New cloud-based applications offer a clever and cost efficient alternative for fault localization in their networks Page 11

12 Today s fault and outage management advanced fault detection and localization (overhead lines) NOP FPI WITH GATEWAY COMMUNICATION FPI WITH INTEGRATED COMMUNICATION Page 12 FPI: Fault Passage Indicator

13 FLiC fault localization in the cloud NOP FPI WITH GATEWAY COMMUNICATION FPI WITH INTEGRATED COMMUNICATION Page 13 FPI: Fault Passage Indicator

14 Easy to localize and to process Page 14

15 Cloud benefit: cost-efficient data analytics for everyone Distribution networks are aging and investments in upgrades are difficult Distributed generation is placing new demands on the low voltage networks New loads EVs in particular are a specific challenge to distribution infrastructure DSOs are looking for ways to target investments in automation and controls to manage these challenges Cloud-based data analytics focused on low voltage network operations can target problems and help optimize investments in network assets and in low voltage automation Page 15

16 Low voltage network transformer load analysis Aggregating meter load data to create a virtual telemetry point Using virtual load data to assess to asset ratings and alert to overload conditions Project asset life degradation based on actual peak loading, service history and weather data Predictive maintenance to avoid failures Asset optimization and right sizing Page 16

17 Low voltage network energy balance RTU XFMR XFMR XFMR CT DC SPV Meter SPV Meter AUX Node ASPV Meter ASPV Meter ASPV Meter ASPV Meter ASPV Meter ASPV Meter Low Voltage Substation Low Voltage Grid Balance Area Customer Metering Points Meter Meter Meter Meter Meter Meter SDP SDP SDP FB SDP SDP SDP FB Low Voltage Line - LVL Low Voltage Line - LVL Track patterns of loss to assess type/cause Technical & nontechnical losses Create and manage low voltage grid balance areas Identify high loss circuits, areas for investigation Accommodate net power flows related to distributed generation Page 17

18 Low voltage network operational voltage analytics Assess voltage drops across low voltage feeders Identify regulator malfunctions Identify over/under voltage service points potentially unsafe conditions Consider optimal deployment of Voltage Controls, Volt/VAR optimizations Page 18

19 Contact Prof. Dr. Michael Weinhold CTO Siemens Energy Management Postfach Erlangen Phone: +49 (9131) Mobile: +49 (174) siemens.com/euw Page 19