Heterogeneous Networks for Smart Grid Communication Architecture and Optimal Traffic Allocation

Heterogeneous Networks for Smart Grid

Communication Architecture and Optimal Traffic Allocation

Presented by: Ran ZhangSupervisor: Prof. Sherman(Xuemin) Shen,

Prof. Liang-liang Xie

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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

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OUTLINE Background


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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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) NetworkDistinct characteristics in terms of bit rate, delay, packet loss rate and cost.

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OUTLINE Background


Heterogeneous Communication Architecture• Ubiquitous Sensor Network (USN) Access Network Level• USN Next-generation Network (NGN) Level• USN Middleware Level


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 controlEconomic: high cost

• NB-PLCUsed for electric company communications, meter reading and home automationWorking frequency: 150KHz in Europe and 450KHz in United StatesDelivery rate: 2 to 128kb/s

• BPLUsed in in-home LANs and access NetworksBandwidth: 10 to 100Mb/s

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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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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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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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Figure 3. Communication Network Proposed

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OUTLINE Background


Heterogeneous Communication Architecture• Ubiquitous Sensor Network (USN) Access Network Level• USN Next-generation Network (NGN) Level• USN Middleware Level


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




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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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 l i

q units Uij(t)<=min{Cij(t), Qi(t)} Fractions of packets cannot be transferred from a buffer to another, and thus Uij(t)=n l i

q

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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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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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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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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 λi

in – 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 λ1

o in network 1

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OUTLINE Background




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

Heterogeneous Networks for Smart Grid Communication Architecture and Optimal Traffic Allocation

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