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 ,

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 5

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 6

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. 7

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 8

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 9

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 10

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 11

Architecture: USN Access Network Level(2)  WIMAX IEEE is a standard technology for wireless wideband access. Ease of installation Support point-to-multipoint or mesh topologies  IEEE s A draft from IEEE for mesh networks Define how wireless devices can be connected to create ad hoc networks Implement over physical layer in IEEE a/b/g/n  IEEE Use existing gaps in the TV frequency spectrum between 54 and 862 MHz Based on the cognitive radio techniques 12

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 Define MAC and PHY layers in low-rate personal area networks (LR-PANs).  IEEE WPAN mesh standard Define a mesh architecture in PAN networks based on IEEE  Upper layers protocols Zigbee: Based on IEEE , specifying protocols used in low consumption digital radio 6LoWPAN: allow to use IPv6 protocol over the base on IEEE

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 and Zigbee (most currently used and mature)  The combination of PLC and Zigbee/IEEE g provides a new concept of home and substation automation with outside interaction. 14

Architecture: USN Access Network Level(5) Figure 3. Communication Network Proposed 15

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 16

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 17

Architecture: Middleware Level(1) Figure 5. Middleware Interaction 18

Architecture: Middleware Level(2) Figure 6. Message Exchange Process 19

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 20

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 21

Optimal Traffic Allocation (2) Figure 7. System model  N q input queues, N 0 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 22

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 23

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 24

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 25

Optimal Traffic Allocation (6) Performance Metrics  Delivery delay over the output networks  Average Financial Cost 26

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 27

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 λ i in – input queues λ j o - 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 28

Illustration  Simulation Results Figure. 8 throughput, delay and financial cost as a function of the exogenous arrival rate λ 1 o in network 1 29

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 30

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 31