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Harnessing Data Distribution Service in Next Generation Smart Energy Systems


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Traditionally, the coordination and control of energy power systems has been delivered through centralized management systems. However with the advent of more intelligent field devices generating massive amounts of data, along with a dynamic landscape of distributed power generation such as renewables (solar, wind), microgrids and storage, combined with new customer driven technologies (electric vehicles and home automation systems), a new architecture employing both centralized and distributed information management is necessary to enable effective management of the energy power system.

The new architecture needs to deliver benefits that are not sufficiently met by existing utility infrastructure including scalable data and information management, near real-time response times, enhanced situational awareness, interchangeability, distributed control, greater energy efficiency and reduced total cost of ownership.

This presentation will detail how DDS is a required element within the new architecture as a message bus protocol to address the performance and security needs of critical operational control systems such as those used by microgrids and for substation automation.

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Harnessing Data Distribution Service in Next Generation Smart Energy Systems

  1. 1. Harnessing DDS in Next Generation Smart Energy Systems Webcast November 5, 2014
  2. 2. Webcast Presenters Stuart Laval – Manager, Technology Development, Duke Energy Stuart Laval is a member of Duke Energy’s Emerging Technology office, where his primary responsibility is on Smart Grid telecom-related activities. He also brings over 10 years experience in product development at manufacturers of utility equipment, cellular radio modules, and power semiconductor devices. Stuart has contributed to the successful launch of over 20 product innovations in mid-voltage smart grid sensors, 2G/3G wireless communication, consumer lighting, and audio amplifiers. Stuart holds Bachelors and Masters degrees in Electrical Engineering and Computer Science from MIT and a MBA from Rollins College. Angelo Corsaro – CTO, PrismTech Angelo Corsaro, Ph.D. is Chief Technology Officer (CTO) at PrismTech where he directs the technology strategy, planning, evolution, and evangelism. Angelo leads the strategic standardization at the Object Management Group (OMG), where he co-chairs the Data Distribution Service (DDS) Special Interest Group and serves on the Architecture Board. Angelo is a widely known and cited expert in the field of real-time and distributed systems, middleware, and software patterns, has authored several international standards and enjoys over 10+ years of experience in technology management and design of high performance mission- and business-critical distributed systems. Angelo received a Ph.D. and a M.S. in Computer Science from the Washington University in St. Louis, and a Laurea Magna cum Laude in Computer Engineering from the University of Catania, Italy.
  3. 3. DDS Overview Angelo Corsaro, PhD Chief Technology Officer
  4. 4. Copyright PrismTech, 2014 The DDS Standard DDS is an Object Management Group (OMG) Standard for efficient, secure and interoperable, platform- and programming-language independent data sharing DDS standardises: - Programming API - Interoperable wire-protocol - Extensible Type System - Data Modeling - Remote Procedure Call
  5. 5. Copyright PrismTech, 2014 Data Distribution Service (DDS) DDS provides a Global Data Space abstraction that allows applications to autonomously, anonymously, securely and efficiently share data DDS’ Global Data Space is fully distributed (decentralised), highly efficient and scalable QoS QoS ... QoS QoS DDS Global Data Space Data Writer Data Writer Data Writer Data Reader Data Reader Data Reader Data Reader Data Writer TopicA TopicB TopicC TopicD
  6. 6. Copyright PrismTech, 2014 Data Distribution Service (DDS) DataWriters and DataReaders are automatically and dynamically matched by the DDS Discovery A rich set of QoS allows to control existential, temporal, and spatial properties of data QoS QoS ... QoS QoS DDS Global Data Space Data Writer Data Writer Data Writer Data Reader Data Reader Data Reader Data Reader Data Writer TopicA TopicB TopicC TopicD
  7. 7. Copyright PrismTech, 2014 Key Highlights Elegant and High Level Data Sharing Abstraction Polyglot and platform independent • Java, Scala, C, C++, C#, JavaScript, CoffeeScript etc. • Android, Windows, Linux, VxWorks, etc. Peer-to-Peer by nature, Brokered when useful
  8. 8. Copyright PrismTech, 2014 Key Highlights Content and Temporal Filtering (both sender and receiver filtering supported) Queries 20+ QoS to control existential, temporal, and spatial properties of data
  9. 9. Copyright PrismTech, 2014 Domain DDS data lives within a domain A domain is identified with a non negative integer, such as 1, 3, 31 The number 0 identifies the default domain A domain represent an impassable communication plane DDS Domain
  10. 10. Copyright PrismTech, 2014 Partitions Partitions are the mechanism provided by DDS to organise information within a domain Access to partitions is controlled through QoS Policies Partitions are defined as strings: - “system:telemetry” - “system:log” - “data:row-2:col-3” Partitions addressed by name or regular expressions: - ”system:telemetry” - “data:row-2:col-*” Partitions
  11. 11. Copyright PrismTech, 2014 Topic A Topic defines a domain-wide information’s class A Topic is defined by means of a (name, type, qos) tuple, where • name: identifies the topic within the domain • type: is the programming language type associated with the topic. Types are extensible and evolvable • qos: is a collection of policies that express the non-functional properties of this topic, e.g. reliability, persistence, etc. QoS QoS QoS QoS Name QoS Topic Type ... TopicA TopicB TopicC TopicD
  12. 12. Copyright PrismTech, 2014 Topic and Instances As explained in the previous slide a topic defines a class/type of information Topics can be defined as Singleton or can have multiple Instances Topic Instances are identified by means of the topic key A Topic Key is identified by a tuple of attributes -- like in databases Remarks: - A Singleton topic has a single domain-wide instance - A “regular” Topic can have as many instances as the number of different key values, e.g., if the key is an 8-bit character then the topic can have 256 different instances
  13. 13. Duke Energy Emerging Technology Office Harnessing DDS in Distributed Intelligence Platform Stuart Laval 11/3/2014 Copyright © 2014 Duke Energy All rights reserved. page 1
  14. 14. Duke Energy Test Areas: Integrated Grid Ecosystems Substation • Solar PV • Energy Storage • Dist. Mgmt System • PMU (6) • Weather stations (7) Sherrill’s Ford, Rankin, McAlpine Substations Customer Premise ~60 homes served by McAlpine circuits • Solar PV • Home Energy Manager • PEV • Charging Stations • Smart Appliances • Demand Response • In-home load monitoring Distribution Circuit 6 McAlpine circuits • Line Sensors (200+) • Solar PV • CES, HES Energy Storage • Comm. Nodes (3,000) • Intelligent Switches • DERMS/DMS • AMI metering (14,000) 11/3/2014 Copyright © 2014 Duke Energy All rights reserved. page 2
  15. 15. DIP: “Internet of Things” Platform for the Utility Open Standards Node 4G LTE, Wi-Fi, GPS Ethernet, Serial PLC, RF ISM, Bluetooth I/O, Metrology, Fiber • Processor(s) + Memory • Linux-based OS • Open API Messaging • 3rd Party Apps • Security / Network Mgr IP Network Smart Assets Smart Meter Transformer Other Nodes Line Sensor Distributed Energy Resources Capacitor Bank Intelligent Switch Street Light X ELECTRIC GRID DEMAND Smart Generation Continuous Emission Monitoring Weather Sensor SUPPLY Legend Required Optional UTILITY DATA CENTER Head End A Head End B Head End N Data Center Message Bus Network Router IP Router Capabilities Optional Connectivity Distributed Computing 11/3/2014 page 3 Copyright © 2014 Duke Energy All rights reserved.
  16. 16. Flexible Hardware & Software Platform 4 Retrofit Inside Cabinet Pole Mounted Enclosure Padmount Enclosure Substation Rackmount Server(s) Integrated in End Device (as Software) Copyright © 2014 Duke Energy All rights reserved.
  17. 17. Enabling Distributed Energy Resources with Intelligence at the Edge Current State – Centralized Decision-Making Future State – Distributed Decision-Making Meter Sensor Transformer Transformer Battery Storage Cellular Network Utility Office Rapid Swing in Production Meter Line Sensor Node Battery Storage Cellular Network Utility Office Rapid Swing in Production Line Sensor Head End Update Model Response Decision + Update Model Response Decision Line Sensor Head End >1 Min < 0.25 sec 5 Solar PV Solar PV “Pass-Thru” “Field Message Bus” Copyright © 2014 Duke Energy All rights reserved.
  18. 18. Field Test: Community Energy Storage Shifting & Smoothing Node w/ Field Msg Bus In-rush Smoothing Copyright © 2014 Duke Energy All rights reserved.
  19. 19. Field Message Bus: The Distributed “Internet of Things” Enabler 7 • Interoperability between OT, IT, & Telecom • Modular & Scalable Hardware and Software • End-to-End Situational Awareness CIM DDS Distributed Intelligence Platform Copyright © 2014 Duke Energy All rights reserved.
  20. 20. Distributed Architecture: Telecom Networking Vision Multi-level Hierarchy: Seamless, Modular, Scalable AMI Smart Meters Protection & Control Distributed Energy Resources Router Wide Area Network (WAN) Middleware Firewall (Utility Datacenter) MDM Upper Tier Central Office Corporate Private Network Head end SCADA Legend Core Processor Application Processor Middle Tier Nodes (e.g. substation) Lower Tier Nodes (e.g. grid) End Points Devices Router Middleware Router Middleware Field Area Network (FAN) Local Area Network (LAN) DMS Local Area Network (LAN) Physical Transport Virtual Telemetry Tier 5 DIP Node Virtual Firewall Copyright © 2014 Duke Energy All rights reserved. 11/3/2014 page 8
  21. 21. Convergence of OT and IT Use-Case App(s) OPEN API MESSAGE BUS Analytics Messaging Security Translation OT System or Device IT DDS, MQTT, AMQP, CoAP Publish Subscribe Publish DNP Modbus Smart Meter Cap Bank Intelligent Switch FCI line Sensor Subscribe OT Compression Publish Subscribe Publish Subscribe Other Transformer Telco Router Battery/PV Inverters Publish Subscribe Head-End DMS Sandbox Pi Copyright © 2014 Duke Energy All rights reserved.
  22. 22. Why Employ a Field Message Bus Architecture with DDS? • Pub-Sub Advantages vs. Polling • Standard Interfaces & Dictionary • Flexibility & Resiliency • Unlocks Modularity • Scalable Infrastructure • Organizational Efficiencies Copyright © 2014 Duke Energy All rights reserved. page 10
  23. 23. Why is the DIP (w/DDS) Important for Duke Energy? page 11 • Provides accurate control and alleviates intermittency of distributed energy resources • Provides the ability to scale independently, as needed, without needing a system wide rollout • Takes cost out of the business by reducing integration time and effort • Allows Duke to be at the forefront of developing new regulations and policies Copyright © 2014 Duke Energy All rights reserved.
  24. 24. VORTEX in Smart Energy and Utilities Angelo Corsaro, PhD Chief Technology Officer
  25. 25. Copyright PrismTech, 2014 The Vortex Platform Vortex enables seamless, ubiquitous, efficient and timely data sharing across mobile, embedded, desktop, cloud and web applications Vortex is based on the OMG DDS standard Vortex Device Tools Integration MaaS Vortex Cloud
  26. 26. Smart Metering with VORTEX
  27. 27. Copyright PrismTech, 2014 Large Scale Smart Metering Tens of millions of Smart Meters for various kinds of utilities, e.g., electricity, water, etc. More and more utilities companies want react in real-time consumption and usage patterns Edge analytics are key for scalability, yet aggregated information is also needed Data should be “scoped” but not sealed, in order terms, when needed an application should be able to get down to a smart counter data stream, regardless of its deployment
  28. 28. Copyright PrismTech, 2014 Adding Hierarchy Nations are usually organised in Regioni
  29. 29. Copyright PrismTech, 2014 Adding Hierarchy Each Regione, say Tuscany is further organised in Province Notice that in this picture, Firenze does not denote the Town but the Provincia headed by the town of Florence
  30. 30. Copyright PrismTech, 2014 Adding Hierarchy Each Provincia is further organised in Comuni Did you notice Vinci? That’s where “Leonardo Da Vinci” comes from. “Da Vinci” is the Italian for “From Vinci”
  31. 31. Copyright PrismTech, 2014 Adding Hierarchy Each Comune is further organised in Quartieri (well… things are a bit more complex in reality) We will assume that no further hierarchy will be added beyond that of a Quartiere and that the level just below is that of individual users and thus smart counters
  32. 32. Copyright PrismTech, 2014 The Full Picture smart-counter smart-counter smart-counter smart-counter smart-counter smart-counter
  33. 33. Copyright PrismTech, 2014 Challenges Data Rates - The volume of data involved at a national scale — several tens of millions of updates per second — push toward an architecture in which data is aggregated throughput the hierarchy - Nonetheless, we want to make it possible — when necessary — to tap into any un-aggregated data stream Analytics - Where are the analytics executed? Does that matter? We’d like the flexibility to deploy analytics wherever make sense and let the data flow to them
  34. 34. Copyright PrismTech, 2014 Challenges Locality - Communication should exploit locality so to improve efficiency and reduce latency. Unicast and Multicast - Depending on the deployment we may be able to take advantage of some form of IP multicast, will the platform we able to exploit it?
  35. 35. Copyright PrismTech, 2014 Conceptual Solution Conceptually, it would be nice to have a solution in which the infrastructure, could allow us to concentrate simply on which we want to produce/consume Yet, the infrastructure was smart enough to ensure that data sharing was efficient and scalable
  36. 36. Copyright PrismTech, 2014 System Architecture Italia Toscana Firenze Empoli Firenxe Centro smart-meter smart-meter Campo Marte ... Vinci ... Rifredi smart-meter ... smart-meter Lazio ... Sicily ... Siena Catania ... Siracusa Aci Castello Acireale ... Catania Avola Noto ... Siracusa Centro ... Ognina smart-meter ... smart-meter
  37. 37. Copyright PrismTech, 2014 Taking a Slice At the bottom level we have the live data coming from smart meters Higher up in the hierarchy we are interested in aggregated analytics For instance, the major of the city may be interested in average consumption at a Quartiere-level while the President of the region may be interested in analytics aggregated by the Provincia Yet at any point in time, anybody should be able to get down to any kind of data Italia Toscana Firenze Firenxe Centro Nation-level Analytics Region-level Analytics Provincia-level Analytics City-level Analytics Quartiere-level Analytics Real-Time Data smart-meter ... smart-meter
  38. 38. Copyright PrismTech, 2014 Designing the Information Model enum Smart-Meter Analytics UtilityKind { ELECTRICITY, GAS, WATER }; struct Meter { string sn; UtilityKind utility; float reading; float error; }; #pragma keylist Meter sn struct Index { string key; float value; }; typedef sequence<Index> IndexSequence; struct UtilityAnalytics { string scope; UtilityKind utility; IndexSequence indexes; }; #pragma keylist UtilityAnalytics scope
  39. 39. Copyright PrismTech, 2014 Information Organisation DDS Partitions will be used to scope data and provide a flexible way of aggregating it Meter data will be published in a partition composed by nation:region:province:city:quarter:device-­‐sn - italia:toscana:firenze:vinci:centro:a1b27fdz35 - italia:sicilia:catania:acireale:cappuccini:4cafebabe1 Analytics are produced using data at scope n are injected at scope n-1. As an example, the average consumption for the quartiere cappuccini is produced using meter data from italia:sicilia:catania:acireale:cappuccini:* and published into italia:sicilia:catania:acireale:cappuccini and so on Notice that the use of partitions makes very easy to decide which over which sets of data the analytics have to compute
  40. 40. Copyright PrismTech, 2014 Analytics Analytics The different deployment of VORTEX Cloud are federated to behave as a single instance. This logical instance optimally matches and routes information over a very large scale exploiting locality Region Provincia City Quartiere Appliance low-power radio protocol smart meters VORTEX Device VORTEX Cloud VORTEX Cloud VORTEX Device VORTEX Cloud VORTEX Device VORTEX Device VORTEX Cloud VORTEX Cloud VORTEX Cloud VORTEX Cloud Analytics Analytics
  41. 41. smart-u
  42. 42. Copyright PrismTech, 2014 smart-u smart-u is a VORTEX demo that illustrates how to implement a smart metering solution For illustrative purposes, some analytics are computed using ESPER other are hand-coded As you’ll see with this demo, while scoping information for scalability you can easily access it at any level In addition analytics can be deployed where it makes the most sense Through the VORTEX platform data can be injected and consumed across any platform, Web, Mobile, Embedded, Enterprise and Cloud!
  43. 43. Copyright PrismTech, 2014 Summary In this presentation we have performed how VORTEX can address the challenges of building large-scale real-time smart metering architectures The combination of Vortex Device and Vortex Cloud made it very easy to create and deploy our Internet-Scale, multi-device applications
  44. 44. Further information ▶ For further information please contact us directly ▶ angelo ▶ (on-demand live demo available) ▶ Or via social media