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 Zhang Supervisor: Prof. Sherman(Xuemin) Shen, Prof. Liang-liangXie

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

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

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

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

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

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.

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

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

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

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

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

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

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.

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

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

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

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

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

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

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)=nliq

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

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

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

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

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

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

Illustration • Simulation Results Figure. 8 throughput, delay and financial cost as a function of the exogenous arrival rate λ1o in network 1

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