NETWORK CONTROL SOLUTIONS BASED ON AN EDGE COMPUTING ARCHITECTURE Open Field Message Bus (FMB) Brussels (Belgium) December 2015

Transcription

1 NETWORK CONTROL SOLUTIONS BASED ON AN EDGE COMPUTING ARCHITECTURE Open Field Message Bus (FMB) Brussels (Belgium) December 2015

2 INDEX 01 Utilities Trends: Towards a Smart Grid 02 Edge Computing Architectures 03 Real Time Integration Platform / Open FMB 04 Network Control Solutions / Closed-Loop Mission Control Ecosystem 05 Project Examples 2

3 INDEX 01 Utilities Trends: Towards a Smart Grid 02 Edge Computing Architectures 03 Real Time Integration Platform / Open FMB 04 Network Control Solutions / Closed-Loop Mission Control Ecosystem 05 Project Examples 3

4 UTILITIES TRENDS: TOWARDS A SMART GRID CHALLENGES FOR UTILITIES Future networks will depend on a smart grid to ensure resilient and sustainable delivery of energy to support many functions. A smart grid ensures the following objectives: Remote monitoring of network facilities, allowing the automation of diagnosis and operations as well as self healing mechanisms. Allows the secure and reliable integration of distributed generation. Safe management of the EV recharging infrastructure and e-storages. Interacts through HEM systems, allowing citizens to monitor their utilities consumption (water, gas, power, etc.) and make decisions. 4

5 UTILITIES TRENDS: TOWARDS A SMART GRID MAKING THE NETWORK VISIBLE HV/MV MV Traditionally, only the MV circuit head is monitored P,Q,S,I V L1 L2 L3 D1 D2 SS MV/LV Sec. Substation measurement D3 i P i P losses P SS D1 D2 D3 D3 Residential meters In a traditional system, only MV feeder breakers are monitored (through the Scada System). Consequently, nodal demand can only be estimated based on the installed capacity. In a Smart Grid, consumptions come from Smart Meters as well as other information captured from multi-purpose sensors. Monitoring technical losses, detecting potential non-technical losses. Collecting grid data to be used in the ADMS for analytics, protection coordination, reactive power compensation, service restoration, etc. Interacting bi-directionally with consumers and HEM systems, allowing the implementation of demand management schemes. 5

6 INDEX 01 Utilities Trends: Towards a Smart Grid 02 Edge Computing Architectures 03 Real Time Integration Platform / Open FMB 04 Network Control Solutions / Closed-Loop Mission Control Ecosystem 05 Project Examples 6

7 EDGE COMPUTING ARCHITECTURES EDGE COMPUTING BASED ON INTELLIGENT DISTRIBUTED NODES A novel concept pioneered by Indra, among others, conforming the basis for the IoT. It is an intermediate layer between the Cloud and the devices of the IoT. Layer of intelligence at the edge of a network to process some analytics and make some decisions there, instead of processing everything in the cloud. 7

8 Data EDGE COMPUTING BASED ON INTELLIGENT DISTRIBUTED NODES Volumen Processing Capacity EDGE COMPUTING ARCHITECTURES Decision making taking place at different levels by independent but coordinated nodes conforming a loosely-coupled and distributed architecture (edge computing) Control Centers Primary Substations Secondary Substations Field Devices 8

9 EDGE COMPUTING ARCHITECTURES EDGE COMPUTING BASED ON INTELLIGENT DISTRIBUTED NODES Intelligent Nodes: The fundamental building blocks for edge computing architectures. For example, ispeed (Indra s real time platform / field message bus) has been developed based on the basic and multipurpose distributed intelligent node PGDIN (Power Grid Distributed Intelligent Node) 9

10 INDEX 01 Utilities Trends: Towards a Smart Grid 02 Edge Computing Architectures 03 Real Time Integration Platform / Open FMB 04 Network Control Solutions / Closed-Loop Mission Control Ecosystem 05 Project Examples 10

11 ISPEED: REAL TIME INTEGRATION PLATFORM / OPEN FMB ispeed: REAL TIME INTEGRATION PLATFORM / OPEN FMB BASED ON AN EDGE COMPUTING ARCHITECTURE Components Integrated information model, as CIM, IEC Real Time Middleware oriented to the publish/subscribe approach Standard Data Distribution Service (DDS) Different domain data spaces Different Quality of Service (QoS) optimization Large Data Storing technologies CEP (Complex Event Processing) Specification and development of rules and algorithms Identification of use cases Basis: Integration Bus Different data sources are integrated in a common data model Available for multiple architectures and programming environments Allows interfacing with multiple protocols Enables systems interoperability SMART- GRID oriented XTPP (Extreme Transaction and Processing Platform) to manage large amounts of information in real time Oriented to the distribution of logic between nodes located at different network levels Analytical capabilities over huge amounts of information through new technologies (Storm/BigData) 11

12 ISPEED: REAL TIME INTEGRATION PLATFORM / OPEN FMB GLOBAL ARCHITECTURE: CONCEPTUAL OVERVIEW 12

13 ISPEED: REAL TIME INTEGRATION PLATFORM / OPEN FMB GLOBAL ARCHITECTURE: APPROACH TO INTEROPERABILITY ispeed Global Data Space 13

14 INDEX 01 Utilities Trends: Towards a Smart Grid 02 Edge Computing Architectures 03 Real Time Integration Platform / Open FMB 04 Network Control Solutions / Closed-Loop Mission Control Ecosystem Conceptual Scope Architectures 05 Project Examples 14

15 NETWORK CONTROL SOLUTIONS / CLOSED-LOOP MISSION CONTROL ECOSYSTEM ECOSYSTEM GLOBAL VIEW Smart Grids Optimal Architecture Distribution Management System (idms) Operator Training Simulator (iots) Analytics Server (ipoweranalytics) ispeed Head end SCADA Head end Gen. & FACTS Head end MV/LV subs Simulated domain Real Domain ispeed CEP (Complex Event Processing) Field (IEC-104, 101, 102, ftp, etc) 15

16 INDEX 01 Utilities Trends: Towards a Smart Grid 02 Edge Computing Architectures 03 Real Time Integration Platform / Open FMB 04 Network Control Solutions / Closed-Loop Mission Control Ecosystem Conceptual Scope Power Flow Analytics Server 16

17 NETWORK CONTROL SOLUTIONS / CLOSED-LOOP MISSION CONTROL ECOSYSTEM ipoweranalytics (ANALYTICS SERVER) Architecture idms Static Network data synchronization Analytic modules control DDS Web services Power Analytics Module (IU client) Topology data management Real Time Share Memory Network Topology Islands identification Optimal actions calculation Proposal/Automatic application Results analysis Power Analytics (server) Reading Writing Tasks execution control Algorithms Manager HPC MPI/OpenMP control State Stimation Fault Location & Isolation Service restoration OPF VoltVar Control Power Flow Server 0 Server n State Estimation measures Optimal Actions/Manouvers proposals Measurements / State Changeso Subscription DDS DDS Publishing DDS ispeed (DDS) 17

18 NETWORK CONTROL SOLUTIONS / CLOSED-LOOP MISSION CONTROL ECOSYSTEM ipoweranalytics (ANALYTICS SERVER) Analytic functions Version Algorithmic Function Description (completed) EED - Distribution Networks State Estimation OPF OPF losses Joule Losses optimization through network reconfiguration. OPF_constraints Constraints resolution through network reconfiguration. MESH Meshes identification Real Time monitoring of meshed network areas FLISR ARS Optimal Service restoration after isolated fault (Dec 2015) VoltVAR Voltage constraint control Real Time voltage control to ensure constraints compliance, thought reactive control (generation/condensers/statcom) and tap changers Voltage control- OPF Losses optimization and feeders balancing throught reactive power control (generation/condensers/statcom) and tap changers Voltage reduction within operation limits to minimize energy consumption through control of generation/condensers/statcom and tap changers (Jun 2016) FLIRS Fault Location and Isolation Identification of fault location and calculation of required network reconfiguration to isolate Fault with minimum loss of supplied power. GAD Active Demand Control Direct/Indirect control of demand PDCP Short Term demand forecast Short Term network condition forecast to allow preventive operation. 18

19 NETWORK CONTROL SOLUTIONS / CLOSED-LOOP MISSION CONTROL ECOSYSTEM ipoweranalytics (ANALYTICS SERVER) User interface: Primary console Network Real Time KPI Manouvers proposals Network static characteristics Manual analytics execution 19

20 INDEX 01 Utilities Trends: Towards a Smart Grid 02 Edge Computing Architectures 03 Real Time Integration Platform / Open FMB 04 Network Control Solutions / Closed-Loop Mission Control Ecosystem Conceptual Scope Power Flow Analytics Server 05 Project Examples 20

21 PROJECT EXAMPLES R&D PROJECTS ENERGOS Research on methods and technologies for automated and intelligent management of energy distribution networks of the future (with GNF) NEMO&CODED IMPONET Development of an advanced metering infrastructure that meets the requirements and expectations of future metering needs (with Gas Natural Fenosa) INTEGRIS INTelligent Electrical Grid Sensor communications (with ENDESA and Italian A2A) REDES 2025 Development of technological solutions for the 2025 Spanish Network (national project with REE, Gas Natural Fenosa, ENDESA and IBERDROLA) 3e - HOUSES Energy Efficient e-houses (with Gas Natural Fenosa) PRICE Smart Grid Demonstration Project (200 k customers) (with IBERDROLA and Gas Natural Fenosa) 21

22 PROJECT EXAMPLES TESTBEDS AND PILOT PROJECTS DUKE ENERGY Coalition of the Willing consortium for the development of a Reference Architecture and Framework for distributed intelligent nodes interacting with each other SGIP Smart Grid Interoperability Panel Open FMB Initiative: development of a Field Message Bus to support field based applications that enable distributed logic based on grid edge nodes NRECA Smart America challenge: demonstrate the viability of the Agile Fractal Grid concept for the benefit of the National Rural Electric Cooperative Association (NRECA). 22

23 Avda. de Bruselas Alcobendas, Madrid Spain T F Thank you