Heterogeneous Networks for
Smart Grid
Communication Architecture and Optimal Traffic Allocation
Presented by: Ran Zhang
Supervisor: Prof. Sherman(Xuemin) Shen,
Prof. Liang-liang Xie
Main Reference
[1] Levorato, M., Mitra, U., “Optimal allocation of heterogeneous
smart grid traffic to heterogeneous networks,” Smart Grid
Communications (SmartGridComm), IEEE International
Conference on, pp. 132–137, 2011
[2] Zaballos, A., Vallejo, A. and Selga, J.M., “Heterogeneous
Communication Architecture for the Smart Grid,” Network, IEEE,
vol. 25 , no. 5, pp. 30-37, 2011
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OUTLINE
 Background[1]
• Traditional Energy Grid vs. Smart Grid
• Heterogeneity of Smart Grid Communication
 Heterogeneous Communication Architecture[2]
• User Sensor Network (USN) Access Network Level
• USN Next-generation Network (NGN) Level
• USN Middleware Level
 Optimal Traffic Allocation to Heterogeneous
Networks[1]
• System Model
• Illustration of Optimal Allocation Strategy
 Conclusions
3
OUTLINE
 Background
• Traditional Energy Grid vs. Smart Grid
• Heterogeneity of Smart Grid Communication
 Heterogeneous Communication Architecture
• User Sensor Network (USN) Access Network Level
• USN Next-generation Network (NGN) Level
• USN Middleware Level
 Optimal Traffic Allocation to Heterogeneous
Networks
• System Model
• Illustration of Optimal Allocation Strategy
 Conclusions
4
Background – Traditional vs. Smart(1)
 Traditional Energy Grid
•
•
Tree like hierarchically-controlled structure
Production -> Delivery -> Distribution to dispersed users
 Smart Grid
•
•
•
Distributed Production Models
Deployment of Energy Market – trade energy
Implementation of Demand Response – individuals to receive periodic energy
pricing information
Fig 1. Smart Grid Overview
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Background – Traditional vs. Smart(2)
 Demand
•
•
The increasing complexity of the production and consumption model
 distributed control, control entities fully coordinate
Energy Trading + periodic energy pricing information obtain
 timely and reliable exchange of critical information among the control entities.
 Solution
•
Information Communication Network for Smart Grid
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Background – Heterogeneity
 Traffic heterogeneity in terms of QoS requirements
•
•
Control Packets – small size and stringent delay
Large Best Effort Packets – large size and relaxed delay
 Information network heterogeneity
• Internet
• Wireless Access Networks
• Power Line Communication (PLC) Network
Distinct characteristics in terms of bit rate, delay, packet loss rate and cost.
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OUTLINE
 Background
• Traditional Energy Grid vs. Smart Grid
• Heterogeneity of Smart Grid Communication
 Heterogeneous Communication Architecture
• Ubiquitous Sensor Network (USN) Access Network Level
• USN Next-generation Network (NGN) Level
• USN Middleware Level
 Optimal Traffic Allocation to Heterogeneous
Networks
• System Model
• Illustration of Optimal Allocation Strategy
 Conclusions
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Architecture
 End-to-end integration of
heterogeneous technologies
based on IP
 Ubiquitous Sensor Network
Architecture (USN)
 Interoperability with the next
generation network (NGN) as
the smart grid backbone
 Decentralized middleware to
coordinate all the smart grid
functions
Figure 2 Layers of a USN architecture
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Architecture
 Sensor networks: transmit and
collect information
 Access Networks: collect info
from sensors and facilitate
communication with a control
center or external entities (NGN)
 USN Middleware: collect and
process data (send requests)
 Application platform
Figure 2 Layers of a USN architecture
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Architecture: USN Access Network Level(1)
Access Baseline Technology
 Power Line Communication (PLC)
• Dedicated, especially suitable for situations underground or in enclosed
places
• Drawbacks
Technique: low rate, lack of control
Economic: high cost
• NB-PLC
Used for electric company communications, meter reading and home automation
Working frequency: 150KHz in Europe and 450KHz in United States
Delivery rate: 2 to 128kb/s
• BPL
Used in in-home LANs and access Networks
Bandwidth: 10 to 100Mb/s
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Architecture: USN Access Network Level(2)
 WIMAX
•
•
•
IEEE 802.16 is a standard technology for wireless wideband access.
Ease of installation
Support point-to-multipoint or mesh topologies
 IEEE 802.11s
•
•
•
A draft from IEEE 802.11 for mesh networks
Define how wireless devices can be connected to create ad hoc networks
Implement over physical layer in IEEE 802.11a/b/g/n
 IEEE 802.22
•
•
Use existing gaps in the TV frequency spectrum between 54 and 862 MHz
Based on the cognitive radio techniques
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Architecture: USN Access Network Level(3)
Sensor Communication Technology
 A mesh network is suitable for smart grid sensor network
•
•
Self-configuration and self-organization: easy to add new nodes
Robust and reliability
 IEEE 802.15.4
•
Define MAC and PHY layers in low-rate personal area networks (LR-PANs).
 IEEE 802.15.5
•
•
WPAN mesh standard
Define a mesh architecture in PAN networks based on IEEE 802.15.4
 Upper layers protocols
•
•
Zigbee: Based on IEEE 802.15.4, specifying protocols used in low consumption
digital radio
6LoWPAN: allow to use IPv6 protocol over the base on IEEE 802.15.4
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Architecture: USN Access Network Level(4)
Conclusions
 Metropolitan/wide area networks
•
•
WIMAX will work from the core to the high/medium voltage substations
PLC from these substations up to the homes
 Home area Networks
•
Mesh networks: 6LoWPAN, IEEE 802.15.5 and Zigbee (most currently used and
mature)
 The combination of PLC and Zigbee/IEEE 802.15.4g provides a new
concept of home and substation automation with outside interaction.
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Architecture: USN Access Network Level(5)
Figure 3. Communication Network Proposed
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OUTLINE
 Background
• Traditional Energy Grid vs. Smart Grid
• Heterogeneity of Smart Grid Communication
 Heterogeneous Communication Architecture
• Ubiquitous Sensor Network (USN) Access Network Level
• USN Next-generation Network (NGN) Level
• USN Middleware Level
 Optimal Traffic Allocation to Heterogeneous
Networks
• System Model
• Illustration of Optimal Allocation Strategy
 Conclusions
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Architecture: NGN Level
 An NGN is a packet-based network in which service–related functions are
independent of the underlying transport-related technologies
 Support generalized mobility – consistent and ubiquitous service provision
 Open Service Environment (OSE) capabilities of ITU’s NGN model
 QoS parameters and security constraints should be well mapped among
heterogeneous technologies to obtain suitable end-to-end technologies
Figure 4 OSE functionalities
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Architecture: Middleware Level(1)
Figure 5. Middleware Interaction
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Architecture: Middleware Level(2)
Figure 6. Message Exchange Process
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OUTLINE
 Background
• Traditional Energy Grid vs. Smart Grid
• Heterogeneity of Smart Grid Communication
 Heterogeneous Communication Architecture
• User Sensor Network (USN) Access Network Level
• USN Next-generation Network (NGN) Level
• USN Middleware Level
 Optimal Traffic Allocation to Heterogeneous
Networks
• System Model
• Illustration of Optimal Allocation Strategy
 Conclusions
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Optimal Traffic Allocation (1)
 Problem : Try to dynamically allocate traffic with different QoS
requirements in terms of throughput, delay and failure probability to
information networks with different performance characteristics
 System Model
• The system is divided into input queues, comprised of buffers
associated with a different QoS requirement and output networks,
representing the various options for the delivery of the packets.
• Input queues and output queues are connected by links associated with
a potentially time varying channel in order to model variations in fading
and capacity
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Optimal Traffic Allocation (2)





Figure 7. System model
Nq input queues, N0 output queues, slotted time operations.
The packet size is expressed in units
Packets entering the input queue i have fixed size equal to liq units
Uij(t)<=min{Cij(t), Qi(t)}
Fractions of packets cannot be transferred from a buffer to another, and thus Uij(t)=n liq
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Optimal Traffic Allocation (3)



Figure 7. System model
Packets in queue j are served at rate uj units/time slot.
Retransmission at most Fij times with failure probability ρij
Delivery Delay Dj
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Optimal Traffic Allocation (4)
System Dynamics
 Assumptions: Ai(t) and Ej(t) are i.i.d random variables
 Update rule for input queue i is
 Update rule for output queue j is
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Optimal Traffic Allocation (5)
Performance Metrics
 Long-time Average throughput
 Average waiting time
waiting time in input queue I
waiting time spent by a packet transferred from the input queue i to output network j
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Optimal Traffic Allocation (6)
Performance Metrics
 Delivery delay over the output networks
 Average Financial Cost
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Optimal Traffic Allocation (7)
Optimization Problem
 The performance metrics defined above are all functions of the allocation policy Uij(t)
 Minimize/maximize one of the performance metrics given the constraints of the other
average performance metrics, with guarantees on the mean rate stability of the
system queues
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Illustration
 Input queues
queue1: Large packets with relaxed delay constraints
queue2: Small packets with stringent delay constraints
 Output queues
queue 1: shared wired Internet network (large delivery rate, small delay,
large amount of exogenous traffic, small financial cost)
queue 2: shared wireless networks (relatively large output rate and small
delay, large amount of exogenous traffic, high financial cost)
queue 3: PLC (small output rate, large delivery delay, no exogenous traffic,
on financial cost)
 Packets Arrival
λiin – input queues
λjo - exogenous packets
 Objective
Minimize the overall financial cost while keeping the queues stable and meet
constraints on the throughput and output buffer plus delivery delay
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Illustration
 Simulation Results
Figure. 8 throughput, delay and financial cost as a function
of the exogenous arrival rate λ1o in network 1
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OUTLINE
 Background
• Traditional Energy Grid vs. Smart Grid
• Heterogeneity of Smart Grid Communication
 Heterogeneous Communication Architecture
• User Sensor Network (USN) Access Network Level
• USN Next-generation Network (NGN) Level
• USN Middleware Level
 Optimal Traffic Allocation to Heterogeneous
Networks
• System Model
• Illustration of Optimal Allocation Strategy
 Conclusions
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Conclusions
 Distributed energy production, consumption and dispersed users in
smart grid system pose a great necessity for ICT infrastructure
 The heterogeneity of smart grid control and application messages
and the available delivery networks requires an integrated system
that can achieve interoperability among the heterogeneous
technologies seamlessly
 Traffic assignment (admission control) problem is far more
complicated and need efforts for future exploration
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