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Abstract--This paper conducts a topical review of the

requirements for end-to-end communication systems as the backbone for command and control within Smart Microgrids. The initial lab and field test results from the evaluation of WiMAX and ZigBee as BCIT Microgrid communication network are presented.

Index Terms—Smart Grid, Smart Microgrid, IEC 61850 Standard, Substation Automation, WiMax, Zigbee

I. INTRODUCTION TO SMART GRID HE gradual evolution of the electricity grid towards a more reliable, efficient and secure grid, capable of

exploiting and integrating all sources of energy, including alternative sources is underway. Utilities have begun to introduce distributed intelligence into their existing infrastructures to provide for pervasive control and monitoring using Smart Components. This will allow optimal use of expensive assets, and thus deferment of further investments through demand response, fair energy pricing, peak shaving and reduction of GHG emissions and carbon footprint.

Despite pressing needs to roll out the Smart Grid, certain impediments have slowed down the implementation process. Most notable among these are the absence of standards, regulatory challenges, funding constraints, etc. While such issues are for the utility providers to resolve, help is needed to address other obstacles which are highly technical in nature.

The problem that most utility providers face is not the absence of technology. On the contrary, a multitude of disparate technologies have been developed by the industry (e.g. communication protocols, computing engines, sensors, algorithms, models and etc.), that can potentially address utility applications and resolve issues within the Smart Grid.

However, such technologies have not yet been proven in the context of utility providers’ desired specifications, configurations and architecture. Given the huge responsibility which utilities have in operating and maintaining such critical

Financial support for this work was provided by the Government of BC’s

ICE Fund, the Government of Canada’s WD Fund, BC Hydro and NRCan Canmet Energy.

G. Stanciulescu is with BC Hydro, Vancouver, BC, Canada (e-mail: Giuseppe.Stanciulescu@bchydro.com)

H. Farhangi is with BCIT, Burnaby, BC, Canada (e-mail: Hassan_Farhangi@bcit.ca)

A. Palizban is with BCIT, Burnaby, BC, Canada (e-mail: Ali_Palizban@bcit.ca)

N. K. Stanchev is with BCIT, Burnaby, BC, Canada (e-mail: Nikola_Stanchev@bcit.ca)

infrastructure, they cannot be expected to venture into new territories, new technologies and new solutions without adequate qualification and validation of such competing technologies.

This critical issue which is slowing down the pace at which technological innovations can find their way from labs to the field is addressed by creating a near-real environment, with real loads, distribution gear, and diverse consumption profile to develop, test and validate the required Smart Grid solutions. Such environment is known as a Smart Microgrid.

Furthermore, Smart Microgrids are designed by default to be more efficient (reduced electrical losses) with less peak demand on the utility grid by integrating local generation as well as storage (reduced energy footprint).

Therefore, in addition to a near-real testbed, utilities regard the Smart Microgrids as an opportunity to gradually shift pockets of their system load onto these Microgrids, and as such reduce the aggregate load on their infrastructure.

In general, many utilities believe that the evolutionary trajectory of Smart Grid will pass through the emergence of a collection of Smart Microgrids, capable of transparent integration into a Smart Distributed Command and Control system of Smart Grid [1].

II. OVERVIEW OF SMART MICROGRID British Columbia Institute of Technology (BCIT) in

partnership with BC Hydro, Government of BC (ICE Fund), Government of Canada (WED), NRCan Canmet Energy and private industry have established Canada’s first Campus Based Smart Microgrid on the Burnaby Campus of BCIT in Vancouver.

BCIT’s Smart Microgrid provides for a comprehensive test bed where distributed generation, smart loads, communication networks and distributed intelligence are integrated to create an environment for research, development and systemic integration of constituent components of Smart Grid [2].

BCIT’s Smart Microgrid is an integration of four entities; co-gen plants, campus loads, meshed network and core intelligence. Figure 1 depicts BCIT’s Smart Microgrid topology [3].

Communication Technologies for BCIT Smart Microgrid

Giuseppe Stanciulescu Member, IEEE, Hassan Farhangi Senior Member, IEEE, Ali Palizban, Nikola Stanchev, Member, IEEE

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978-1-4577-2159-5/12/$31.00 ©2011 IEEE

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Fig. 1. BCIT Microgrid System Topology

The above depicted topology is highly simplified. The

campus has its own distribution system, fed by several feeders from BC Hydro. The loads and co-gen plants are spread across a large geographical area and interconnected through a network of BCIT substations.

Figure 2 shows a typical Single Line Diagram for a selected subset of campus loads, substations and co-gen plants. The diagram stipulates the presence of two command and control nodes. One is a Microgrid Controller which is responsible for the overall supervisory function of the microgrid. It keeps track of demand curves and the generating capacity, and dispatches the available energy based on pre-determined preferences and attributes.

Microgrid Network Control Centre, on the other hand, supervises various data/control transactions between Microgrid components and hosts the required enterprise applications, such as Demand Response, Asset Management, Load Shedding, etc.

Fig. 2. BCIT Smart Microgrid Typical Single Line Diagram

To ensure the integrity of data and command transactions

between various components of the microgrid, three distinctly separate networks have been implemented.

Fig. 3. BCIT Smart Microgrid Network Topology

Shown in Figure 3, the three main components of BCIT

Microgrid Network are a Home Area Network (HAN), a Local Area Network (LAN) and a Wide Area Network (WAN). The figure lists the communication protocols and the applications used within each of these networks.

III. SMART MICROGRID DISTRIBUTION SYSTEM The low voltage distribution system on the campus

comprise of a network of more than 20 substations. Most of these substations are old and in need of further structural and functional upgrade. As part of campus’s long term renewal, plans are underway to redesign and upgrade the campus electricity distribution system. Discussions are underway to explore the possibility of upgrading some or all of these substations to partial or full IEC 61850 compliance.

The IEC61850 standard Error! Reference source not found. which was developed as the standard for “Communication Networks and Systems in Substations” is emerging as the prevalent standard covering all components and devices in a substation and beyond [5].

Having its roots in the Utility Communication Architecture, IEC 61850 defines objects and functions required for protection, control, measurement and monitoring functions within a substation. The standard aims to define a standardized set of functions, objects and protocols for various substation components, made by different vendors, to be integrated into an open and seamless network topology.

Given its relative success in establishing itself as the industry standard for substation automation, calls are made by various industry proponents to expand the reach of the standard beyond the substation, and possibly into the upstream and downstream components of the distribution system. Currently studies are underway to extend the application of IEC 61850 into renewable energy resources, electric vehicle charging station and energy storage systems.

Fig. 4 shows IEC 61850 building blocks. One of the major differences in this system with the

traditional substation automation system is in the Process Bus. Field signals such as voltage and current measurements, breaker and disconnect switches status are converted into network signals in the merging units for seamless integration into the rest of the system.

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Fig. 4. IEC 61850 Building Blocks

Similarly command signals are transmitted to the field switches via the network to the Smart Breaker Merging units where they are decomposed and applied to the breaker. The signals aggregated in the merging unit can be accessed by any device on the network such as IEDs without the need for extra hard wiring. IEDs are intelligent Electronic devices similar to the traditional relays but with greater software functionality and flexibility. These IEDs contain protection, control and monitoring functions and can be configured to be in hot standby or online modes to make the system more reliable and fault tolerant.

Due to the networking structure of the system any network topology and redundancy scheme can be readily configured depending on the need and cost targets. Since communication between different components of the system is done through a LAN network the cabling and installation costs are much lower than the traditional substation automation systems [6].

A. Distributed Control System using IEC61950 Smart Microgrids are emerging as the basic building blocks

of the future Smart Grid, where a variety of loads with different profiles may be supplied through a highly controlled distribution system integrated with various power generation sources. As such, the Smart Microgrid will rely on strategic implants of distributed control and monitoring systems within and alongside its existing electrical assets.

Consequently, the establishment of cost effective and efficient communication infrastructure between various components of Smart Microgrid is the key to the range and diversity of functions and capabilities it could support.

Furthermore, modern Microgrids need a distributed control and information systems that can deliver information to a wide range of users in near real time and automate certain tasks and functions that streamline operational control and performance.

As shown in Figure 5, an IEC 61850 communication network interconnects all of the IEDs that are used for protection, control, and monitoring of the whole system. The Microgrid controller works as a supervising controller and based on the economic and technical constrains calculates set-points for the controllable components of the system.

For example the Microgrid controller determines the amount of power that CHP (Combined Heat and Power) can

generate or the amount of power that storage can store or supply to the grid at any point in time.

These sent points are sent to the corresponding IEDs of CHP and the energy storage system and then the IEDs control the CHP and the storage accordingly to keep the target set point.

IEDs are intelligent agents that can work stand alone or as a slave controller following a master control system.

Although, Microgrids often incorporate local co-gen facilities, they are not required by default to be entirely energy self-sufficient. In other words, local generation may not always be possible or desirable, thus keeping Microgrids dependent on the Utility Grid for the balance of their peak demand. That means that in certain off-peak hours, Microgrids may work entirely off the grid and operate as an independent island. It may also be possible to carve off parts of the Microgrid and run it independently of the Utility Grid using local co-gen facilities.

Figure 5 shows a typical microgrid similar to what is under construction at BCIT campus. Usually all of the components of the microgrid such as generation resources and loads are connected at lower voltage distribution levels in close proximity and as such there is no need for a high voltage distribution system.

Since Microgrid contains all of the elements of a large mesh, its characteristics resemble a large interconnected network. Consequently, an integrated, yet distributed command and control system is required to ensure the realization of Microgrid’s target functions. Some of the main functions of a Microgrid command and control systems are:

• Optimization of generation and energy storage resources to adequately respond to variations in the loads on the grid and to maintain target levels of power quality, performance and economic return

• Control of frequency and voltage to a desired stable level when microgrid is disconnected from the main gird in islanded mode

• Management of the microgrid assets to maximize expected performance and extend service life

• Maintain protection against faults which system may experience in grid tied or islanding operation

• Seamless operation and bump-less switching from islanding to grid tied connection and vice versa

• Maintain visibility of the status of operation and health of the system

In order to realize the above functions, a fast and reliable communication system is required. The command and control functions between the Microgrid control center and each component of the system needs a seamless communication system that guarantees secure and optimum performance.

Further research into the requirements and characteristics of an end-to-end communication system for Smart Microgrid demonstrated the need for a hybrid system. The design team realized early in the process that due to differing needs and requirements of individual termination points in the Microgrid, different communication systems with various

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capabilities and characteristics need to be integrated into the three distinct networks depicted in Figure 3.

Furthermore, each of HAN, LAN and WAN networks may include a diverse set of physical and MAC layer technologies for command and data transactions. However, there is a need for a common set of objects to be recognized and handled by all communication nodes regardless of which network they may part of. We believe IEC 61850 objects and protocol may form the basis of the much needed end-to-end vocabulary needed to facilitate the command and data interchange between all components of Smart Microgrid.

IV. SMART MICROGRID COMMUNICATION SYSTEMS Given the need for a hybrid approach to the design of the

communication system for BCIT’s Smart Microgrid, suitable communication technologies were incorporated into HAN, LAN and WAN parts of the system. For example, while the HAN network was the exclusive domain of ZigBee (IEEE 802.15.4-2003 standard), we experimented with ISM (Industrial Scientific Medical) Band RF, ZigBee and PLC technologies for the LAN network. In the remainder of this

Fig. 5. Microgrid with its communication network

paper, we will discuss some of the results obtained with specific technologies implemented for WAN (Wide Area Network) and LAN (Local Area Network) networks. In future papers, we will discuss results obtained with other technologies implemented at BCIT Smart Microgrid.

A. WiMAX as BCIT Microgrid’s WAN backbone As discussed earlier, WiMAX 16D at 5.8 GHz was studied

as the backbone for BCIT Microgrid WAN network. Currently, two WiMAX sub projects are being handled: performance test and campus deployment.

The current WiMAX 16D 5.8 GHz network is designed as the Wide Area Network (WAN) communication infrastructure of the electrical substations in the campus. At some substations WiMAX provides the required redundancy for the Facilities’ fiber optic network. The goal of the benchmarking tests is to evaluate the performance metrics of the WiMAX equipment in the light of substation automation. We attempted to understand how WiMAX will perform for small

data packets as low as 64 bytes and for time critical application which require very small latency and zero packet loss.

Let’s explain why we believe zero packet loss is very important for time critical application. If any error occurs on the WiMAX link then higher layer protocol will try to retransmit the packet in order to get the correct data. Each retransmission will increase the time for delivery of the message. In other words, the total delay of the network in the case of error packets will be equal or greater than the latency multiplied by the number of retransmissions.

The typical test setup is based on the coax cable connection between the Base Station (BS) and the Customer Premise Equipment (CPE) in order to avoid radio signal degradation as a result of interferences and multipath propagation. Variable RF attenuator is used to set the path attenuation between the BS and the CPE.

The WiMAX equipment under test is compliant to IEEE802.16-2004(d) which is sometimes referred as WiMAX 16D or Fixed WiMAX. The equipment is designed for license exempt band at 5.8GHz with 10MHz channel bandwidth.

1) Problem Description – Packet Losses at 64 Bytes Data Packets:

In order to measure the maximum bandwidth of the WiMAX radio link the working modulation and coding scheme (MCS) should be 64QAM3/4 (the maximum available for IEEE802.16d) [7]. On the other hand, the carrier-to-interference and noise ratio (CINR) is maintained greater than 28dB to ensure a high quality of the signal over the RF interface. All performance measurements are performed for the standard Ethernet packet sizes according to RFC2544 – 64, 128, 256, 512, 1024, 1280 and 1518 bytes [2].

The initial tests of the WiMAX equipment demonstrated that even at perfect RF conditions the packet losses were significant as shown in Table 1. Furthermore, the packet losses increased up to 4.6% for small packet sizes down to 64 bytes. The test data rate was 9Mbps which is the expected throughput for DL/UL split ratio of 50%. The network status of BS radio showed no packet losses on the RF interface. All packet errors occurred in the network processor according to the BS network status report.

TABLE 1: PACKET LOSSES VS. PACKET SIZE (9MBPS & 64QAM3/4 IN DL

Frame size, Bytes

Average packet loss, % Before optimization

After optimization

64 4.671 0.241 128 0.074 0.044 256 0.044 0.070 512 0.034 0.043 1024 0.039 0.017 1280 0.031 0.006 1518 0.034 0.005

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2) Solution - Packet Losses at 64 Bytes Data Packets: The above discussed problem was communicated with the

WiMAX equipment vendor who acknowledged that the problem was related to the Ethernet stack of the radio controller. It was optimized for Internet applications with typical packet size between 1024 and 1518. Note that the use case of WiMAX radio for substation automation requires small packets support.

The WiMAX vendor performed an optimization of the Ethernet queue and buffer settings. Figure 6 shows the packet losses as function of the data rate for 64 bytes packets before and after the firmware hot fix.

The last column on Table 1 shows significant improvement of the performance after optimization for 64 bytes packets - 20 times decrease of the packet losses at the maximum data rate 9Mbps. Further tuning of the Ethernet settings will reach the limitation of the processor resources, radio loading and clock speed which are typical factors. On the other hand, increases in small-packet performance may come at the cost of decreased full-size packet performance [9].

B. ZigBee as BCIT Microgrid’s LAN backbone ZigBee (based on IEEE802.15.4) mesh network at 2.4GHz

license exempt band is used as communication infrastructure for temperature sensors, load control devices and smart meters in the BCIT campus area. These are low bandwidth and non-critical applications.

Figure 6: Packet error rate as function of the data rate for 64 bytes packets,

before and after the optimization of the Ethernet stack The mesh network topology provides alternative routes

between the center data collector point and the end nodes. Especially, it is very useful for deployment in neighborhood area network, characterized as an environment with a lot of obstructions for the radio waves: trees (foliage), concrete buildings, people, vans and etc. So far, BCIT has deployed 2 ZigBee networks: Northern campus and BCIT Residential Area.

1) Problem Description – Interference between ZigBee and Wi-Fi Network:

During the initial deployment the team used the ZigBee coordinator feature for automatic selection of the frequency channel of the network [10]. The network in Northern campus ran smoothly. In contrast, in the BCIT Residential area we observed a lot of packet losses. Furthermore, the smart meters allow flexibility of selection of the parameters for remote reading and in this way matching to the available bandwidth. The increase of the read parameters and number of bytes, respectively, resulted in higher probability of occurrence of error packets.

2) Solution – Interference between ZigBee and Wi-Fi Network:

Further investigation of the problem revealed that the cause of the lost packets was an interference between the ZigBee Network and the campus Wi-Fi (based on IEEE802.11b, g) network at 2.4GHz. Both networks share the same spectrum. Furthermore, the ZigBee coordinator selected channel 18 which is in the middle of channel 6 of the Wi-Fi network as shown on Figure 7.

Moreover, Wi-Fi and ZigBee use the same protocols for accessing the medium – Carrier Sense Multiple Access (CSMA). The essence of the protocol is that the radio first listens to the channel and starts transmission only if the channel is determined not to be busy. This protocol proved to cause packet losses in our deployment scenario.

Each BCIT residential building hosts 5 hotspots. The ZigBee devices are mounted indoor and outdoor. It is very likely that the nodes of the indoor Wi-Fi network cannot detect transmission of outdoor ZigBee node. That’s why both transmitters emit simultaneously and collision occurs resulting in lost packets, especially if the ZigBee receiver is located indoor in the vicinity of the Wi-Fi transmitter. This problem is commonly referred to as “hidden node” for Wi-Fi networks [11]. Keep in mind that the Wi-Fi receiver is a wideband with 22MHz bandwidth which result in an increase of the noise floor. Consequently, the received weak signal from outdoor ZigBee device is likely to be under the noise floor and not detected.

During the initial automatic channel selections the ZigBee coordinator scans the channels in order to find one with the least interference. In our case in the Residential area the coordinator was installed in building SW11 in which Wi-Fi channel 6 is not in use. That’s why the coordinator selected the channel in the middle of channel 6. On the other hand, in the other residential building there is at least one hot spot transmitting on channel 6. Sharing of the channel and the “hidden node” problem resulted in significant interference between Wi-Fi and ZigBee networks.

It should be clear that the interference between ZigBee and Wi-Fi network will not be observed as long as there is no “hidden node”. It is very likely that such problems could be avoided if both ZigBee and Wi-Fi networks are collocated in the same indoor environment.

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The best solution which we found is to select channel 20 for the ZigBee network. It is one of four frequency channels (15, 20, 25 and 26) which lies in between the Wi-Fi channels as shown on Figure 7 and will be free of Wi-Fi interference. Note that the BCIT residential Wi-Fi network is managed by the BCIT IT services which professionally selected 3 non-overlapping channels (1, 6 and 11). It is possible that in a typical residential environment users may select one of the 11 overlapping channels allocated for IEEE802.11b, g in North America for home Wi-Fi network. In that case only ZigBee channels 25 and 26 will be free of interference.

It is not straightforward to make a direct channel assignment for the ZigBee network as it would always rely on the automatic channel selection approach. On the other hand, the coordinator scan predefined list of channels. In our case we limited the list to a single channel - channel 20.

We found that some ZigBee Pro compliant radios don’t support the last channel 26. In that case only 3 channels free of Wi-Fi interference would be available.

Figure 7: 16 frequency channels for ZigBee and 3 non-overlapping Wi-Fi channels at 2.4GHz band. NE25 the coordinator for the Northern campus, SW11 coordinator for the BCIT residential area

V. CONCLUSIONS The requirements for communication systems within

BCIT’s Smart Microgrid are constantly evolving as a function of the nature and scope of desired functionality and Use Cases. We have already observed certain issues and shortcomings of the contemporary communication systems for data and command transactions within Smart Microgrid systems. Such communication systems have been optimized as asymmetric infrastructure for internet transactions where upstream nodes (internet servers) require a fat pipe to send their data to downstream termination points, while downstream nodes (clients) require a slim pipe to send their commands upstream to servers. In contrast, a Smart Microgrid, requires an asymmetric infrastructure where the fat pipes are needed to send vast amounts of data from the termination points to utility servers, while expecting a slim pipe from upstream servers to termination points to enable the transport of commands. As an example, WiMAX network is regarded as a good candidate for WAN in the Smart Grid. However, the system integrators should be aware of the fact

that the WiMAX radio is usually not optimized for small packets (as low as 64 bytes) which are typical for SCADA applications. The equipment vendor may want to perform an optimization of the Ethernet stack of the radio for small packets to some extent limited by the processor resources, radio loading and clock speed. In certain networks of the Smart Grid the ZigBee devices offer secure mesh network topology in license exempt band 2.4GHz.

Moreover, there is an assumption that ZigBee and Wi-Fi networks can coexist. BCIT team found that this is only possible under certain constraints. That’s why we recommend as a good practice the system integrator to select for the ZigBee network one of four channels free of Wi-Fi interference. Especially, this is needed when the ZigBee network is deployed in relatively large scale as LAN (NAN) in which the ZigBee nodes are very likely to hide from the indoor Wi-Fi nodes resulting in significant data packet losses. The research team at BCIT will continue working with its partners to discover feasible solutions for such structural discrepancies.

VI. REFERENCES [1] H. Farhangi, “The Path of Smart Grid,” IEEE Power and Energy, Vol. 8,

No 1, January 2010. [2] H. Farhangi, "Intelligent Microgrid Research at BCIT," presented at

IEEE EPEC’08, Korea, October 2008. [3] H. Farhangi, “Campus Based Smart Microgrid at British Columbia

Institute of Technology in Vancouver, Canada,” presented at Cigre International Symposium, Bologna, Italy, September 2011.

[4] IEC.IEC.CH, IEC61850 Standard “Communication networks and systems in substations”, 2002-2005 (www.iec.ch)

[5] Kl. P. Brand, V. Lohmann, W. Wimmer “Substation Automation Handbook,” Utility Automation Consulting Lohmann, Swizerland, 2003.

[6] H. A. Palizban, H. Farhangi, “Low Voltage Distribution Substation Integration in Smart Microgrid”, presented at ICPE 2011–ECCE Asia, May 2011.

[7] IEEE Standard for Local and metropolitan area networks. Part 16: Air Interface for Fixed Broadband Wireless Access Systems, IEEE Standard 802.16-2004, Oct. 2004.

[8] S. Bradner, and J. McQuaid,” Benchmarking Methodology for Network Interconnect Devices,” RFC 2544, Mar. 1999.

[9] "Small packet traffic performance optimization for 8255x and 8254x Ethernet controllers," Application Note AP-453 rev. 1.0, Intel, Sept. 2003.

[10] IEEE Standard for Local and metropolitan area networks. Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), IEEE Standard 802.15.4-2003, Oct. 2003.

[11] S. Ray, D. Starobinski, and J.B. Carruthers, "Performance of wireless networks with hidden nodes: a queuing-theoretic analysis", in Proc. Computer Communications 2005, pp.1179-1192.

VII. BIOGRAPHIES Giuseppe Stanciulescu (M’1990) received his MSc from the Polytechnic Institute ‘Traian Vuia’ Timisoara, Electro-technical Faculty, Romania in 1990. He has more than 20 years of experience in R&D, design and manufacturing of Power Electronics for AC and DC Power Systems with his primary focus targeted towards industrial applications, automation, Telecom and Utility system solutions. Currently he is a Sr. Strategic

Technology Professional with the Office of Chief Technology Officer at BC Hydro, Canada and he is the project lead in a series of Smart Grid and Microgrid related projects in collaboration with industry, academia and

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government. He is a member of the Association of Professional Engineers and Geoscientist of British Columbia (APEG) and a member of the Institute of Electrical and Electronics Engineers (IEEE).

Hassan Farhangi (M’1978, PhD 1982) is Director of Research at the Technology Centre of British Columbia Institute of Technology in Burnaby, BC, and Adjunct Professor at the University of British Columbia (UBC) and Simon Fraser University (SFU). Dr. Farhangi is the chief system architect and the Principal Investigator of BCIT’s Smart Microgrid at its Burnaby Campus in Vancouver, British Columbia, and the Scientific Director and Principal Investigator of NSERC Smart MicroGrid

Network (NSMG-Net). He is a member of IEC CSC TC57 WG17 (IEC 61850), Cigre WG C6.21 (Smart Metering) and Cigre WG C6.22 (Microgrids Evolution). Dr. Farhangi is a member of Association of Professional Engineers and Geoscientists of British Columbia (APEG), and a senior member of Institute of Electrical and Electronic Engineers (IEEE).

Ali Palizban (PhD 1997) is the Program Head of Electrical Power and Computer Control of the Department of Electrical and Computer Engineering of British Columbia Institute of Technology (BCIT) in Vancouver, Canada. His field of teaching and research includes Power System Analysis, Substation Automation, Control Systems, and Simulation and Modeling. Dr. Palizban obtained his PhD degree in Electrical Engineering from University of New South Wales, Australia. His work experience over the past 25 years spans a

broad range including power industry, Hybrid and Electric Vehicle industry, Consulting Engineering, Research and Development. He currently leads the BCIT Microgrid and IEC61850 Substation Automation Research Teams. Dr. Palizban is a member of Association of Professional Engineers and Geoscientists of British Columbia (APEGBC).

Nikola Stanchev (M’1999, PhD 2008) is a Research Associate at the Technology Centre of British Columbia Institute of Technology in Burnaby, BC. He graduated from the Technical University of Sofia, Bulgaria. His employment experience included Ray Sat Antenna Systems, Cosmo Bulgaria Mobile, PMC-Sierra, and University of British Columbia. His special fields of interest included ultra wideband communications, wireless channel sounding, and digital signal

processing. He is a member of IEEE Communication Society. Since 2010 he serves as chair of the Continuing Education Committee at IEEE Vancouver Section.