Intelligent MicroGrid Communication Networks

INTELLIGENT MICROGRID COMMUNICATION NETWORKSTHEME 3, PROJECT 3.2THO LE-NGOC (MCGILL UNIVERSITY)QUANG-DUNG HO (RESEARCH ASSOCIATE)GOWDEMY RAJALINGHAM (MENG STUDENT)CHON-WANG CHAO (MENG STUDENT)YUE GAO (MENG STUDENT)

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SUMMARY

NSMG-Net Project 3.2: Gowdemy Rajalingham

• Performed an evaluation of promising wired and wireless technologies • Conducted a comparative study of potential radio access technology interconnections• Proposed the integration of Wi-Fi Mesh and LTE for the network architecture

Technology Integration and Network Architecture Design

• Determined promising routing protocols (GPSR, RPL) for the NAN• Evaluated performance of GPSR in wireless mesh NAN

Feasibility of wireless mesh for the NAN

• Estimated data rate requirements with IEC 61850 based messaging in ADA scenarios• Examined impact of channel contention at MAC layer on achievable PLC throughput

Applicability of PLC for Advanced Distribution Automation

• Proposing a new aggregator based EV charging control scheme with priority indices• Proposing joint simulation platform to study the effects of communication on FR

Frequency Regulation Using EV Charging Control over Wireless Communications

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Collector

Router

PROPOSED SYSTEM ARCHITECTURE & EVALUATION


Fig. 1 – Neighbor Area Network

Fig. 2 – Simulation Scenario, sweep of cluster size

TABLE 1 – SIMULATION PARAMETERS

Channel Model Simple pathloss – pathloss exponent Lognormal shadowing – variance

MAC layer IEEE 802.11

Routing Protocol Greedy Perimeter Stateless Routing (GPSR)

Performance Metrics Packet Transmission Delay, Packet Delivery Ratio

Traffic Per-node data rate -

Topology Clusters of size -

System Parameters Sweeps of per-node data rate & clusters of size for [dB]

Objective• Determine capabilities and limitations of NAN with GPSR• Investigate NAN clusters performance with various system

parameters

UTILITY

Endpoints1026

LTE

Wired Backhaul xxxxxx xxx

xxx

Command Center

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FEASIBILITY OF CANDIDATE ROUTING FOR NANPerformance Versus Cluster Size and Data RateCurrent Estimates• Base Rate: NIST Data Rate of 0.00195 pps (based on simple meter readings)• Typical AMI deployment NAN size: A few 1000s of smart meters

Results• Can maintain latency < 100 ms for up to 6000 nodes for data rates up to 10x base data rate• Can maintain PDR > 95% for up to 6000 nodes for data rates up to 10x base data rate• At 100x base data rate, to maintain latency < 100 ms and PDR > 95%, cannot exceed a cluster size of roughly 1500


Fig. 4 –, Packet Delivery ratio vs. Cluster sizeCluster size n [node]

10 100 1000

Del

ay [m

s]

0.1

1

10

100

1000

10000

pps = 0.001pps = 0.01pps = 0.1

6000

Fig. 3 –, 95% Percentile of delay vs. Cluster size

Cluster size n [node]10 100 1000

Pac

ket d

eliv

ery

ratio

PD

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

pps = 0.001pps = 0.01pps = 0.1

6000

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PUBLICATIONS[1] Quang-Dung Ho, Gao Yue and Tho Le-Ngoc, “Challenges and Research Opportunities in Wireless Communication Networks for Smart Grid”, IEEE Wireless Communications Magazine, June 2013.

[2] Chon-Wang Chao, Quang-Dung Ho and Tho Le-Ngoc, ”Challenges of Power Line Communications for Advanced Distribution Automation in Smart Grid”, 2013 IEEE Power and Energy Society General Meeting, Vancouver-Canada, July 21-25 2013.

[3] Gowdemy Rajalingham, Quang-Dung Ho and Tho Le-Ngoc, “Attainable Throughput, Delay and Scalability for Geographic Routing on Smart Grid Neighbor Area Networks”, 2013 IEEE Wireless Communications and Networking Conference (WCNC 2013), Shanghai-China, 7-10 April 2013.

[4] Gowdemy Rajalingham and Quang-Dung Ho, “LTE HetNets: Challenges and Opportunities for Integration of Smart Grid Networks”, Technical Report, McGill, April 2013.

[5] Chon-Wang Chao and Quang-Dung Ho, “Communication Standard and Network Infrastructure Considerations for Smart Grid”, Technical Report, McGill, 2012.

[6]Yue Gao and Quang-Dung Ho, “OMNET Implementation of RPL for Smart Grid Neighbor Area Networks”, Technical Report, McGill, December 2012.


BACKUP SLIDES

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INTRODUCTIONObjectiveProject 3.2 aims to study and to develop relevant transmission, information processing, and networking techniques for an efficient and reliable IMG Communication Network (IMGCN)IssuesThe successful implementation of the Intelligent MicroGrids (IMGs) requires an efficient communications infrastructure that is cost-effective, scalable, fault-tolerant, secure & satisfies the QoS requirements (data rate, delay, reliability)


SmartMeter

Microgrid

Electric Vehicle

Solar EnegyWind Enegy

Non-Renewable Energy

Substation Substation

Microgrid

Home Area Network (HAN)

Control Center

Wired Backhaul Network

Power System Layer

Communications LayerNeighbor Area Network (NAN)Wide Area Network (WAN)

3G/4G Cellular, Ethernet, Leased Line, Fiber Optics,

Satellite...802.15.4, Zigbee, 802.11,

...

SmartHomeDevice802.15.4, 802.11,

Wireless Mesh, «

10-100 Mbps Coverage of up to several 1000 km2

10-100 Kbps Coverage of up to several km2 10-100 Kbps

Coverage of up to 100 m2

Customer

Fig. x – Full Abstract System Architecture Model

APPLICABILITY OF PLC

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APPLICABILITY OF POWER-LINE COMMUNICATIONSKey Contributions• Calculated the expected data rate requirements with IEC 61850 message architecture and power network parameters • Examined the impacts of channel competition with Carrier Sense Multi-Access/Collision Avoidance (CSMA/CA) algorithm

on saturation throughput (T) and bandwidth requirement• Further details in poster “Throughput Analysis of Narrowband PLC in Advanced Distribution Automation”


Fig. x – Communication PLC Network

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APPLICABILITY OF POWER-LINE COMMUNICATIONSSummary of Results• Expected data rate with only PLC supporting advanced distribution automation is 310.69 kbps• Throughput and bandwidth requirement variation

• T decreases as the number of nodes increases (higher probability of collision)

• The optional Request to Send/Clear to Send mechanism can increase the T with same number of nodes and reduce to growth rate of bandwidth requirement

• Existing field tested PLC technology may not be able to provide enough data rate• Further details in poster “Throughput Analysis of Narrowband PLC in Advanced Distribution Automation”


Fig. x – Communication PLC Network

FREQUENCY REGULATION USING EV CHARGING CONTROL

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FREQUENCY REGULATION USING EV CHARGING CONTROL

Key Contributions• Proposed a new aggregator based electric vehicle charging control scheme with priority indices• Proposed to use the joint simulation platform to study the effects of communication delays and

packet loss• Further details in poster “Cost-Effective Frequency Regulation by Aggregator-based EV Charging

Control via Wireless Communications”


Fig. x – Proposed Control Structure and Neighborhood Mapping

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FREQUENCY REGULATION USING EV CHARGING CONTROLControl System Model• Further details in poster “Cost-Effective Frequency Regulation by Aggregator-based EV Charging Control via Wireless

Communications”


Fig. x – Proposed Control Block Diagram and Joint Simulation Setup

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FREQUENCY REGULATION USING EV CHARGING CONTROLCommunications Model• Further details in poster “Cost-Effective Frequency Regulation by Aggregator-based EV Charging Control via Wireless

Communications”


Fig. x – EV Selection Algorithm

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FREQUENCY REGULATION USING EV CHARGING CONTROLIllustrative Example• Further details in poster “Cost-Effective Frequency Regulation by Aggregator-based EV Charging Control via Wireless

Communications”


Fig. x – Illustrative Example

Number of EV = 120

Ithreshold = 98

PROPOSED ARCHITECTURE

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SURVEY OF TECHNOLOGIESWired Technologies• Economically feasible when network cables and related facilities are pre-existing and readily available at

acceptable low costs• More suitable for back-haul links for large volume of traffic

• Example: Digital subscriber line (DSL), leased line, power line communications (PLC), fiber optics …


Fig. x – Potential Technologies

Wireless Technologies• Home Area Network

• 10-100 kbps• Coverage area of up to 100 m2

• Example: ZigBee, WirelessHART, 6LowPan, Bluetooth, …

• Neighbor Area Network• 10-100 kbps• Coverage area of up to several km2

• Example: Wi-Fi, Wi-Fi Mesh, …• Wide Area Network

• 10-100 Mbps• Coverage area of up to several 100 km2

• Example: WiMax, LTE, …

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CANDIDATE WIRELESS ARCHITECTURES


TABLE X – LTE PERFORMANCE CHARACTERISTICS [1]

Latency • Best case latency• 6 ms for short packets (<40 bytes)• 11 ms for longer packets (>40 bytes)

Max Users

• Max users is less than the number of LTE Resource Blocks available• LTE Control Channels are bottlenecks• Data aggregation is necessary

eNB

UE(a) Direct Transmission

LTE

M2M Gateway Smart Meter

Wi-Fi

(b) Multi-hop Transmission with Wi-Fi clusters

(c) Multi-hop Transmission with LTE Small cellsLTE Small Cell

Fig. X – Potential NAN/WAN Interconnections

LEGEND: Excellent, Adequate, Deficient

TABLE X INTERFERENCE LATENCY THROUGHPUT SCHEDULING SELF-ORGANIZING NETWORKS

DIRECT TRANSMISSION

Interference LTE uplink latency

•No data aggregation•Wasted capacity

High complexity No need for SONs

MULTI-HOP WITH WI-FI CLUSTERS

•Out of band w.r.t. LTE•Interference

Extra tier-ing delay

•Data aggregation at GW & in mesh network•Potential for network coding

Lower complexity (aggregation)

SONs are Wi-Fi mesh networks

MULTI-HOP WITH LTE SMALL CELLS

•Interference•Coverage gaps •Power control needed

Extra tier-ing delay

Data aggregation at small cell BS

Lower complexity (aggregation)

Need for SONs for LTE femto-cells

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PROPOSED SYSTEM ARCHITECTURE


TABLE X – LTE PERFORMANCE CHARACTERISTICS [1]

Latency • Best case latency• 6 ms for short packets (<40 bytes)• 11 ms for longer packets (>40 bytes)

Max Users

• Max users is less than the number of LTE Resource Blocks available• LTE Control Channels are bottlenecks• Data aggregation is necessary

eNB

UE(a) Direct Transmission

LTE

M2M Gateway Smart Meter

Wi-Fi

(b) Multi-hop Transmission with Wi-Fi clusters

(c) Multi-hop Transmission with LTE Small cells

LTE Small Cell

Fig. X – Potential NAN/WAN Interconnections

Backhaul for Cellular Network

Base Station

Neighbor Area Network (NAN)Wide Area Network (WAN) 802.11 Wireless Mesh, «

3G/4G Cellular

SmartMeter

Data Aggregation Point (DAP)

Fig. X – Proposed NAN/WAN Interconnections

Collector

UTILITY

xxx

xxx xxx

xxx

Command Center

Router

Endpoints1026

Fig. x – Neighbor Area Network

NEIGHBOR AREA NETWORK

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Collector

UTILITY

xxx

xxx xxx

xxx

Command Center

Router

Endpoints1026

SmartMeter

Microgrid

Electric Vehicle

Solar EnegyWind Enegy

Non-Renewable Energy

Substation Substation

Microgrid

Home Area Network (HAN)

Control Center

Wired Backhaul Network

Power System Layer

Communications LayerNeighbor Area Network (NAN)Wide Area Network (WAN)

3G/4G Cellular, Ethernet, Leased Line, Fiber Optics,

Satellite...802.15.4, Zigbee, 802.11,

...

SmartHomeDevice802.15.4, 802.11,

Wireless Mesh, …

10-100 Mbps Coverage of up to several 1000 km2

10-100 Kbps Coverage of up to several km2 10-100 Kbps

Coverage of up to 100 m2

Customer

Fig. x – Potential TechnologiesFig. x – Full Abstract System Architecture Model


Network Characteristics• Network of smart meters,

repeaters, collectors• Static, line powered,

heterogeneous multi-tiered network

• Communications protocols must be robust, scalable, self-configurable and self-healing

Traffic Characteristics• Multiple-Point-to-Point• Point-to-Multiple-Point• Point-to-Point• Large volume of devices• Short bursty packets• Quality of Service (QoS)

differentiation• Mix of real-time ( < 10

ms) and non-real-time traffic (seconds - min)

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Collector

UTILITY

xxx

xxx xxx

xxx

Command Center

Router

Endpoints1026


Network Characteristics• Network of smart meters, repeaters, collectors• Static, line powered, heterogeneous multi-tiered network• Communications protocols must be robust, scalable, self-configurable

and self-healing

Traffic Characteristics• Multi-point-to-point, point-to-multi-point, point-to-point• Large volume of devices with short bursty packets• Quality of Service (QoS) differentiation• Real-time (<10ms) & non-real-time traffic (sec/min)

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FEASIBILITY OF CANDIDATE ROUTING FOR NANObjective• Determine the capabilities and limitations of NAN

• With respect to ability to host MicroGrid applications

• With GPSR routing protocol• Thus, performance of NAN clusters with various

system parameters is investigated

Expected Results• As channel conditions worsen, performance

degrades due to more likely packet corruption and retransmissions

• As data rate increases, higher chance for channel contention, back-offs and packet retransmissions lead to increased delay and reduced reliability

• As cluster size increases,• Increase in network load and average hop

count • Significant increase in network delay with

decreasing PDR


TABLE X – SIMULATION PARAMETERS

Channel Model Simple pathloss – pathloss exponent Lognormal shadowing – variance

Radio Access Technology IEEE 802.11

Network Routing Protocol Greedy Perimeter Stateless Routing (GPSR)

Performance Metrics Packet Transmission DelayPacket Delivery Ratio

Traffic Per-node data rate -

Topology Clusters of size -

System Parameters Investigated

Sweeps of variance , per-node data rate and clusters of size Default values: , , [dB]

Fig. x – Simulation Scenario, sweep of cluster size

Documents

Intelligent MicroGrid Communication Networks