EDGE WP5 COMMUNICATION INFRASTRUCTURE - STATUS
F E B R U ARY 2 5 - 2 0 1 5
Jacob T. Madsen (AAU)
Mislav Findrik (FTW)
EDGE
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WP5 Timeline
Now
M1-M3
M4-M6
M7-M9
T5.1
T5.2
Milestones
M5.1.1
M5.2.1
M5.1.2
M5.2.2
M5.1.3
M5.2.3
M5.1.4
M5.2.4
M5.1.5
M5.1.5
Internal Report (IR5.1)
Internal Report (IR5.1)
Deliverable 5.1
Deliverable 5.1
Simulation model
Simulation model
Internal report (IR5.3)
Internal report (IR5.3)
Deliverable 5.2
Deliverable 5.2
M10-M12 M13-M15 M16-M18 M19-M21 M22-M24 M25-M27 M28-M30 M31-M33 M34-M36 M37-M39 M40-M42 M43-M45 M46-M48
M5.1.1
M5.1.2
M5.1.3
M5.1.4
M5.1.5
x
M5.2.1
M5.2.2
M5.2.3
M5.2.4
M5.2.5
x
Requirements and interfaces for QoS mechanisms
Requirements and interfaces for information management
Network support architecture and component descriptions
Network simulation models for QoS in Smart Grid scenarios
Network simulation models for information management in Smart Grid scenarios
Final specifications of network support architecture
Evaluation of network support evaluation in Smart Grid
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2 case studies
Layered Network Model
Generalization
Network Model Specification
Network Aware Information Management
•
NAIM components are mapped to the case
studies, and described in D5.1
•
In the case studies we are focused on:
• Information Quality metric mmPr for
different types of controllers
(Information Quality Assessment)
• Adaptation of data collection process
based on mmPr
(Observation Points & Data Collection)
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EDGE Communication Architecture Status
• What do we have now?
• Detailed specification of data flows, network
architectures/topologies and technologies for the case studies
• Jacob (AAU) – Wind Park Control
• Simulation of the fatigue estimator over the communication
networks
• Preliminary analysis of data quality metric
• Mislav (FTW) – Control of Flexible Intelligent Customers
• Analytical mmPr models for periodic controllers
• Network topology definition for simulation studies
• Inital analysis of Network-Control adaptations
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WP5
PROGRESS REPORTS ON CASE STUDIES
FTW
Mislav Findrik
EDGE
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Case Study: Control of Flexible Intelligent Customers
Main goal summary in T5.2:
I - Enable meta-data for quality characterization of consumption/
generation/actuation data.
II - Provide and evaluate algorithms for access to and forwarding of sensor and actuator
information across variable, heterogeneous communication networks.
Network QoS (WP5.1)
Sensors/
Controllers
Actuators
Data Access
Mechanism
Dynamic Requirements
Input
Information
Quality
Characatization
Input
Info.+
Meta Data
Actuation Commands
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Controller
Case Study – Motivation
•
Top level controller running in a periodic time steps
Quality of data
„Simplified Approach“
Top level controller
Time step
1 controller, 1 sensor
Controller
Aggregator
Time step
Pulling data
Pulling, Pushing
Data, Event based
Sensor
Flexible loads
Pushing data
Three basic ways to access the data:
- Pulling (request-respond)
- Pushing (periodic updates)
- Event-based
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Wind farm scenario
•
•
Control scenario defined by Jesús
Impact of network on fatigue estimator:
• 2 fatigue estimators
• 2 different sensor readings
• Comparing gives us a mismatch probability
• Communication network is:
• IP-network: Fiber optics
• Sensor network: Bus (exponential delay)
• UDP as communication protocol
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WP5
DIRECTIONS AND FUTURE WORK
AAU
Jacob T. Madsen
EDGE
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Directions and future work
•
•
•
Current scenarios mainly consider Information
Quality assesment and Data Collections
Future work will have to consider Network QoS
monitoring and Network QoS estimation &
prediction
Considerations for entire communication
network not yet analysed
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Directions and future work
•
•
For the EDGE project:
• Communication architecture for the network betwen modules
• Timing requirements for modules communication between each other
• QoS requirements for modules
• Prioritization of information for module information
• Analysis of QoS measurement requirements
For WP6:
• Given the architecture and timing requirements we find the most interesting part of the communication network
to simulate
• Add simulation of network to WP6 framework
• QoS measurements in WP6 framework
• Analysis of mismatch probability in communication network
• Possibility of adding responsive control based on measured QoS
EDGE
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