Roadmap to integration

8/10/2019 Roadmap to integration

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2 IEEEpower & energy magazine may/june 20141540-7977/14/$31.002014IEEE

Digita l Object I denti fier 10.1109/ MPE.2 014.2301515

Date of p ublica tion: 17 Apr il 2014 IMAGELICENSED

BYINGRAMP

UBLISHING

SSMART GRID-RELATED BLOGS, NEWSLETTERS,

and conferences have endured numerous debates anddiscussions around the issue of whether or not the smart

grid is being integrated correctly. While most debates

focus on approach, methodology, and the sequence of

what needs to be done, there is insufficient discussion

about what is actually meant by smart grid integra-

tion. This article attempts to present a holistic view of

smart grid integration and argues for the importance

of developing system integration maps based on a

utilitys strategic smart grid road map.

Faced with diverse technological, organizational, and

business issues that adversely affect the bottom line, util-

ity companies are contemplating immediate changes and/

or upgrades of their technologies, business processes, and

organization. At the same time, however, the realities of

insufficient resources, regulatory impediments, and tech-

nological hurdles have prevented the development of con-

crete plans and concerted actions in this regard.

A closer look at mainstream discussions within the

utility industry reveals that, despite consensus about

the need for change, there is no agreement across the

board in any given utility about a smart grid road map

and integration map. The absence of industrywide

standards and blueprints for smart grid integration has

further compounded the issue. The silo mentality of

the constituent parts of the utility organization drives

the generation folks to push for expanding generation

capacity through the integration of renewables, the

transmission people to urge expansion of transmis-

sion capacity through automation, and the distribu-

tion community to argue for integration of new assets,

technologies, and intelligence on the downstream side

of the network.

A Road Mapto Integration

By Hassan Farhangi


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may/june 2014 IEEEpower & energy magazine 53

Furthermoreand given the fact that each group has traditionally been exposed to cer-

tain vendors and technology providers for its respective siloeach constituency tends toregard the technologies and solutions offered by those vendors as the answer to much larger,

systemwide problems. And in the utility environment, these problems by default transcend

the confines of a single silo.

The situation is further complicated by the diversity of views, interests, and approaches

advocated by vendors and technology providers in the field. Influenced (and constrained) by

its core competencies and technologies, each vendor defines the problems, and therefore the

solutions, in the way that best suits its own technologies and products. One should therefore

not be surprised to hear different suppliers put different spins on basic concepts such as dis-

tribution automation, demand response, and so on. The irony is that they are mostly sincere

in what they are advocating. The issue is whether any of their prescriptions is the Holy Grail

needed to solve the utilitys smart grid integration puzzle. This seems to be a reenactment

of Rumis story of the blind men and the elephant. Each person has his own understanding

of what the creature is based on what part he has managed to touch. The absence of sight

(or light) has convinced each and every one of the righteousness of his version of the truth,

ignoring the fact that the smart grids systemwide issues require all its constituent parts

to work together and implement a collective strategy for doing what needs to be done. In

Rumis words, If each of us held a candle there, and if we went in together, we could see it.

Smart Grid DevelopmentUtilities in North America have had their fair share of challenges in taking the first step on

their path to full implementation of the smart grid, namely, large rollouts of smart metering

across their distribution circuits. The reaction of the public to the push by utility companies

to implement smart metering took many in the industry by surprise. In addition to open

calls by consumer associations to do away with the idea, many jurisdictions saw the intro-

duction of symbolic resolutions, passed by county and municipal councils, banning smart

meter installation. In response to this backlash from customers, many North American

utilities have had to either slow down smart metering rollouts or devise opt-out programs

while investing in information campaigns to reach and influence their customers.

Despite the specific form that the consumer backlash took (e.g., concern about the health

effects of RF radiation, the privacy and security of customer data, or an imminent rise

in the cost of energy), one could see that such concerns were primarily attributed to an

absence of buy-in for this new technology on the part of utility customers.

What is interesting is that very few, if any, utilities have attempted to answer the more

fundamental question of why their customers should embrace this new technology with

open arms. What will make customers want to be willing participants in this process?Would smart meters reduce utility bills? Would it provide customers with more reliable

Perspectiveson Smart

Grid Development


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4 IEEEpower & energy magazine may/june 2014

service? Would smart metering protect customers vital

information and personal data? What would the short- and

long-term impacts of smart metering be on customer engage-

ment? What is next after smart metering? What future func-

tionalities and capabilities would be enabled through smart

meters that would be beneficial to customers?

In fact, not only have very few attempts been made to

answer these questions adequately and convincingly, but

some utilities have added fuel to the fire by suggesting that

smart meters will help with customer behavior change orload control through time-of-use (ToU) mechanisms and

dynamic pricing. Without a well-formulated plan to prove to

customers that such behavior change will not and should not

happen at the cost of their convenience or at their expense,

such suggestions have only reinforced public perceptions

that smart metering is nothing but a quick money-grabbing

exercise on the part of cash-strapped utilities trying to fill the

holes in their budgets on the backs of rate payers.

The questions that arise here are these: Why wouldnt

utilities confront such misconceptions head on and commu-

nicate to their customers the benefits of smart metering? Why

wouldnt they portray smart metering as the first step toward

smart grid integration and all the unprecedented capabilities

that a smart grid will offer their customers? Why wouldnt

they attempt to convince their customers that a smart grid

will effectively empower them to be active stakeholders and

players in energy and service transactions?

Although there could be many reasons for such a discon-

nect between utilities and their customers, some have specu-

lated that either utilities have not yet managed to develop a

strategic road map for the smart grid or if they have, that there

was very little consensus across their organizations on the

integration plan and on a realistic schedule for implement-

ing it. Regardless of the root cause, pundits have seen this

as a failure on the part of the utilities to formulate the right

communication plans to help their systems, organizations,

staff, infrastructure, assets, and, ultimately, their customers

navigate collectively through this uncertain yet exciting tran-

sition to a new set of service transactions, energy paradigms,

and fundamentally different roles and responsibilities.

It goes without saying that no utility has ever discounted

the need for a strategic smart grid road mapand subse-

quently a smart grid integration mapprior to making such

large investments in their assets and infrastructure. The

question is therefore not the existence of such blueprintsbut simply their role in driving (and informing) the major

technology investment commitments utilities are making

today. The litmus test for this process is to ask a series of

questions so as to ascertain how conducive each investment

is to a seamless transition from a less intelligent grid to an

intelligently integrated smart grid.

Strategic Smart Grid Road MapsAs discussed earlier, the need for the development of stra-

tegic road maps for smart grids was recognized early on by

many practitioners and planners in the utility industry. Suchwork began by identifying utilities business and corporate

objectives and goals, recognizing the most critical issues and

impediments to reaching those goals, and devising plans for

how to address them. Figure 1 depicts an early attempt by a

group of experts from the British Columbia Institute of Tech-

nology (BCIT) and BC Hydro who worked collaboratively

over a period of several months to formulate an R&D as well

as demonstration road map for their joint smart microgrid

initiative at BCITs Burnaby campus. This collaborative

effort took into consideration what each party was hoping to

achieve from the joint project, the modalities of their respec-

tive development efforts, the realities on the ground, and the

resources and technologies needed to achieve those goals

over a five-year period.

As Figure 1 demonstrates, the road map highlighted the

need for several constituent streams, each informing as well

as enabling other streams along the way. For example, the

energy management system (EMS) stream included several

layers of sophistication and features, based on the avail-

ability of certain assets and capabilities provided by other

parallel streams, such as advanced metering infrastructure

(AMI), communication infrastructure, and load and asset

management. The interplay among these functionalities,

made possible by the integration of their respective technolo-

gies, was conceived as enabling stepwise jumps in the range

of capabilities the initiative was expected to provide.

The smart microgrid initiative implemented by BCIT and

BC Hydro reinforced the notion that smart grid integration

consists of several concurrent streams, designed to introduce

intelligence (and thereby command and control) into strate-

gic areas of the system, providing capabilities and functional-

ities that transcend the legacy silo architecture of the system.

The nature of these capabilities and their intended reach will

determine which assets and/or subsystems must be integrated

in order to realize the target functionality. As Figure 2 dem-onstrates, smart grid integration does not always have to be

This article attempts to present a holistic view of smart gridintegration and argues for the importance of developing systemintegration maps.


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BCIT/BCHydro

Smart MicrogridRD&D

Road Map

Legend:

Status: Proprietary and Confidential

C2: HAN Network

C3: WAN Network

C4: DistributionAutomation

Demand-SideManagement

D1: Intelligent

Transportation Network

EV Charge Pilot

E1: Rural DCMicrogrid

A6: Microgrid

Islanding

A4: Smart Grid

Control Center

A5: Expansion of MicrogridCo-Gen Capacity

C5: MicrogridAsset Management

B2: DynamicTariffs

A3: Advanced

EMS

Residence Competition

C1: AMI Infrastructure B1: LAN Network A1: Basic EMS

A2: MobileEMS

Author: Dr. Hassan Farhangi Date: 5 Feb. 2011 Version 0.7

EMS

Stream

Revenue

StreamAutomation

Stream

EVStream

DCStream

Pilot IP Tool Paper

AMIDatabase

Architecture

MobileEMS

Ver1

LoadShedding

Integrationof

ThermalTurbine

NetMetering

Protectionand

Switching

Synchronization

Energy

Transactions

RevenueModels

EMSVer4

Integrationof

Storage

Integrationof

WindTurbine

Integrationof

SolarModules

DCDistribution

Network

DCProtection

andSwitching

DC Microgrid Pilot BCIT Campus IPP Pilot

Microgrid

Controller

Integration

SGCCTopology

EMSVer3

EMSVer5

End-Customer

Experience

End-Customer

Experience

V2GandG2V

SocialScience

FactorsinEnergy

Management

LoadPrediction

andProfiling

EVCharge

Manager

ScientificPaper

OnDSM

ScientificPaper

OnDCMicrogrid

Scheduling

EMSVer2

PricingSignal

Broadcast

Maximum

DemandTariff

TimeofUse

Tariff

As

set

Management

Dash

board

Microgrid

Asset

Man

ager

Integrated

MicrogridSen

sor

Network

Microgrid

Substations

Automation

ScientificPaper

OnIEC61850

MobileEMS

Dashboard

EndCustomer

Experience

SocialScience

FactorsinEnergy

Management

ScientificPaper

OnEMS

WiMax(1.8GHz

and3.6GHz)

DedicatedFibre

Portal

Technology

EMSVer1

ZigBeeNetwork

withSmartEnergy

Profile

MODBUS

Integration

Techniques

ANSI-C12.19

DataAggregation

E2EPLCandRF

SMINetwork

MDMS

ANSIC12.22

SmartMetering

LoadControlfor

HWTandBBH

ZigBeeSensor,

Thermostatand

IHD

Building

Automation

2009

2010

2011

2012

2013

figure 1.A typical strategic road map.


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end to end or for all capabilities. Different smart grid func-

tions require the integration of different assets, with different

capabilities and different requirements. As such, a smart grid

integration map must adhere closely to the utilitys strategic

road map and to the intended smart grid functionalities that

need to be enabled in each stage of development.

In practice, smart grid integration has taken many

twists and turns. It is unlikely that North American utili-

ties have followed similar modalities and approaches to

smart grid integration. It is more logical to assume that

each utility has taken a unique path toward implementing

its smart grid plans.

Early Integration AttemptsGiven the fact that the electricity distribution network in almost

all the jurisdictions across North America had long been over-

due for a major overhaul, the first step many utilities took in

smart grid integration consisted of limited rollouts of one-way

automated meter reading (AMR); this was followed by major

investments in two-way AMI. As Figure 3 suggests, the over-

whelming justification for investments in AMI was its enabling

role in facilitating the move toward an eventual realization of

smart grid functions. That understanding convinced many utili-

ties in North America to plan for major smart metering invest-

ments. Many projects were announced, and pilots sprang up

across the continent. In the absence of a well-formulated smart

grid integration plan specifying how smart metering would actu-

ally lead to a smart grid, pilot project evaluations focused on the

requirements for smart metering rather than its forward compli-

ance with future smart grid functions. As such, most pilots were

perceived to be successful, resulting in substantial follow-oninvestments in smart-metering projects.

As the full cost of ownership of AMI systems became

clear, however, and given regulatory constraintsand in

the absence of clear revenue modelsutilities found it

increasingly difficult to justify AMI capital expenditures.

In addition to the less-than-convincing cost-benefit models

for AMI, the consumer backlash against smart metering

slowed down AMI rollouts in many jurisdictions across

North America. The absence of clear smart grid road maps

and utilities unconvincing arguments in favor of smart

metering prompted many experts in the field to express

doubts about the entire rationale for AMI; many ques-

tioned whether or not the smart grid was being integrated

backwards. Some, for instance, suggested that return on

investments (ROIs) would be more palatable if distribu-

tion substations were automated first. Others pointed to the

need to start at the top, upgrading the utilitys enterprise

applications in the back office and on the enterprise bus

before attempting to invest at the bottom of the chain.

The fact of the matter is that all those questions were very

valid. And the reason why such doubts were being expressed

had everything to do with the absence of a utility smart grid

integration map that would have demonstrated how AMI

was going to be leveraged to gradually upgrade the func-

tional capabilities of the grid. In The Path of the Smart

Grid [IEEE Power & Energy Magazine, January 2010],an early attempt to demonstrate linkages between tech-

nologies and capabilities along the path from smart meter-

ing to the smart grid was presented. In Figure 3, the author

argued that AMI would have to be perceived as an enabling

platform for two-way communication and distributed com-

mand and control among all previously unmonitored anduncontrolled components of the distribution system. The

figure 2.A smart grid functional integration map.

Real-Time Simulation and Contingency Analysis

Distributed Generation and Alternate Energy Sources

Self-Healing Wide-Area Protection and Islanding

Asset Management and Online Equipment Monitoring

Demand Response and Dynamic Pricing

Participation in Energy Markets


6/15


figure 3.Smart grid technologies and capabilities.

Intelligent Appliances

Customer Portals

Distributed/Co Gen

Emission Control

Load Management

Preventive/Self-Healing

Substation Automation

Distribution Automation

Customer Information System

Asset Management

Outage Detection and Restoration

Demand Response

Automated Billing

Investments

Network

Management

IntelligentApplications

Intelligent

Agents

Two-Way

Communication

Smart

Sensors

DistributedControl

AMR

AMI

Smart

Grid

CapabilitiesTechnologies

ROI

One-WayCom.

articlefurther emphasized that the protocols, topologies, and

architecture of AMI systems had to be designed as forward-

looking sources of sensory, status, and alarm information to

allow future (and still undeveloped) smart grid capabilities

to reach and be integrated with the downstream side of the

utility system. In other words, the specification of the AMI

system had to take into account the communication, data

and command exchange, and access requirements of future

smart grid applications.

What the figure attempted to further demonstrate was the

notion that smart grid integration can be broadly divided into

two categories. One operates in local domains using global

system attributes, such as demand response or outage detec-

tion, that require access to real-time local data with local

analytics and local decision-making processes. The second

operates over multiple domains, requiring wide-area situ-

ational data awareness and an overview of the system con-

straints as a whole and system operational objectives, such

as management of distributed energy resources, self-healing,

outage prevention, and so on. While the first category is

enabled through a well-designed, forward-looking AMI sys-

tem with appropriate latency, throughput, availability, and

resilience requirements, the second relies on a well-designed

and optimally integrated network of distributed systems

with suitable security, scalability, and access protocol speci-

fication that enables efficient distributed command and con-

trol through a multilayer, multitier, and multiagent system.

In other words, the figure emphasized the fact that smart

grid integration should be built on forward-looking infor-mation and command and control architectures capable of

meeting the functional and operational requirements of a

gradually evolving smart grid system, with incremental

needs for higher levels of performance, scalability, and

resilience and without the need for costly departures from

its original design and implementation. It goes without say-

ing that once investments are in place, utilities find it almost

impossible to undo commitments to AMI, substation auto-

mation, and so on to upgrade their assets so as to enable

new smart grid functionalities. And that means the cost and

the pain associated with the transition from the legacy grid

to the smart grid will to a large extent depend on the suit-

ability of the utilitys smart grid integration map supporting

that transition.

Building the Smart GridThe irony is that there is an element of truth in every

approach to building a smarter grid. This diversity of views

can only be attributed to the fact that without a doubt there

is more than one way to integrate a smarter grid. Depend-

ing on a variety of potentially conflicting and yet interacting

drivers (priorities, regulations, legacy assets, organizational

and process issues, and so on), different utilities may choose

different points of departure for their long journeys toward

the smart grid. And consequently, the trajectory each utility

takes in integrating its system with different smart grid func-

tionalities, even if similar starting points are adopted, may

prove to be quite unique and dissimilar from others.

Regardless of where that starting point is, however, it is

crucial for utilities to spec out their journeys (as much asthey possibly can, given all the unknowns) in such a way


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that subsequent moves toward other areas of the system can

be realized seamlessly and without the need to substantially

change or upgrade the assets that have already been rolled

out. As an example, if after AMI rollout the plan is to take

substation automation as the next area for investment, the

utility should ensure that subsequent downstream moves to

implement demand curtailment or load shedding as well as

upstream moves to implement asset management or self-

healing can be realized without having to change or upgrade

the investments made for substation automation and AMI.

Figure 4 shows the extent to which the specifications of

various parts of the system need to be taken into account

to ensure seamless integration of components, assets, and

functions in both the spatial and temporal domains. It

emphasizes the notion that the sphere of influence of smart

grid capabilities varies considerably across different func-tions. Some span upstream layers of the system, involving

enterprise functions, utility operations, and revenue man-

agement (e.g., contingency management, asset management,

energy market participation, and so on), while others tra-

verse local downstream layers of the system, involving field

and prosumer-facing assets (e.g., demand response, load

management, and so forth). It is the latter group that places

a heavy burden on the AMI system, as it requires close inte-

gration and tight operational linkages to AMI system com-

ponents, protocols, and technologies. A poorly designed and

implemented AMI system would prove to be inhibitive for

the efficient implementation and/or correct operation of such

downstream smart grid functions.

The Smart Grid Integration MapAs discussed above, the point of departure on the path to the

smart grid and the particular set of smart grid capabilitieseach utility may want to achieve will not be the same across

figure 4.A layered smart grid system integration map.

Corporate HR Finance Billing and

AccountingDOC

Management ERP

Trading Scheduling Settlements Forecasting

SystemPlanning

CapacityPlanning

NetworkPlanning

DataWarehousing

DataWarehousing

Maintenance

Scheduling

Parts/

Supply

Work

Management

Asset

Management

EMS DMS OMS DSM

CIS

Fiber

Network

Station Bus(Revenue Data)

Process Bus(Breakers, Switchgear,

Reclosers, Transformers)

WiMax

Network

Public

Network Proprietary RF

Narrowband/Broadband

PLC

Microwave

Network

Broadband

PLC

MDM IVR

GIS

Biz Ops

System Planning

Engineering

Sys Ops

Customer Service

Backhaul Coms

Field Coms

Last Mile Coms

Revenue Data

Two-Way Coms

IPP

WiMax

ResidentialMetering

CommercialMetering

IndustrialMetering

Net Meters Gateways

PV Wind Biofuel Small

Hydro

Color Code Data COM Asset

HR: Human Resources Department

ERP: Enterprise Resource Planning

DOC: Document Management

EMS: Energy Management System

DMS: Distribution Management System

OMS: Outage Management System

DSM: Demand-Side Management

GIS: Geographic Information System

CIS: Customer Information System

MDM: Metering Data Management

IVR: Interactive Voice Response

PLC: Power Line Communication

WiMax: IEEE 802.16 Wireless-Networks Standard

RF: Radio Frequency

PV: Photovoltaic

Biz Ops: Business Operations

Sys Ops: System Operations

Coms: Communication Systems

IPP: Independent Power Producers

VVO: Volt/Var Optimization

CVR: Conservation Voltage Reduction


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all jurisdictions. Having said that, and regardless of a util-

itys current baselines, operational priorities, and organiza-

tional abilities to devise smart grid system integration maps,

the ideal way to approach smart grid system integration is

to analyze each smart grid capability in terms of its core

decision-making and data-customer/command-supplier

interface requirements. That analysis will identify to whichdomain such functions will belong; to which layer they will

have to reside or be attached; and what their data processing,

command and control, interface protocol, and communica-

tion requirements will be.

Figure 4 is an attempt to take the smart grid functions

of Figure 3 and map them across different layers of a fully

integrated smart grid system. In such an approach, smart

grid functions are seen as cutting across multiple layers of

utility structures, including but not limited to corporate,

engineering, field operations, and distribution systems. This

approach turns the utilitys traditional silo structures on its

head, as it traverses organizational boundaries for efficientand cost-effective realization of target smart grid functions.

What is critical in this approach is not how a particular func-

tion needs to be realized, but where it belongs as an entity

providing other entities in its vicinity with the services for

which it is designed. Association with a given layer will then

determine the performance metrics of the assets needed to

support the efficient operation of that capability.

Moreover, the layered approach attempts to identify the

nature of each layer in terms of the dominance of data pro-

cessing and communication technologies versus the utilitys

traditional assets. This does not necessarily mean that a layer

that is dominated by data processing does not depend on

communication technologies or other existing utility assets.

By its nature, each smart grid capability will have to rely on

all three constituent components of the smart grid: power

systems, telecommunication, and information technology.

Furthermore, the layered approach embeds within it the

notion of the temporal and spatial requirements of each layer.

More stringent requirements for access to real-time data will

place a layer closer to layers that produce such data and vice

versa. In other words, the proximity of layers to each other

is directly proportional to their interface and data and com-

mand exchange requirements. As an example, the EMS and

the volt/var optimization (VVO) and conservation voltage

reduction (CVR) layers have to be in close proximity to each

other and to the field assets with which they have a direct,

real-time, and unimpeded data exchange relationship. The

same is not true for the billing layer, which can be placed

further away from and without a need for real-time connec-

tions to field assets.

It goes without saying that not all functions within each

layer need to be integrated at the same time. Each utility

could pick and choose one or more functions from each

layer and decide when and how they need to be realized.

Regardless of the integration plan for each function, how-ever, what is critical is to understand which layer it will

belong toand as such, what its data processing, com-

mand and control, interface protocol, and communication

requirements will be. This understanding will ensure that

the architecture of the system, the communication topology,

the adopted technologies, and the associated protocols are

chosen in such a way that they will lend themselves to the

future integration of new functionalities and capabilities.That is the only way to ensure that the gradual transition to

the smart grid is managed without excessive reengineering

and expensive overhauls.

As discussed, each utilitys enterprise function places a

particular set of requirements on different layers of the sys-

tem in terms of its vital specifications, such as data struc-

tures, protocols, security regime, latency, throughput, and,

last but not least, interactions with the actual assets. In real-

ity, of course, applications can and should reside where their

function is required: some will exist within a substation,

some in the utility back office, and others on the enterprise

bus. Neverthelessand regardless of the environment towhich they are attachedeach application must have the

ability to communicate seamlessly and efficiently with rel-

evant system nodes as and when required. For instance, an

asset management application has to communicate with all

the relevant assets assigned to it from the different domains

of generation, transmission, and distribution.

As an example, a utility that intends to roll out its smart

meters first and subsequently integrate an asset manage-

ment application over its vital system assets has to ensure

that the AMI system it is integrating will lend itself well

(as a set of distinct assets) to seamless integration with the

asset management application it will be rolling out in the

future. It goes without saying that it would not be accept-

able to have patchworks of individual asset management

tools for different categories of assets. In other words, no

utility would be happy using an asset management tool for

its smart meters, another for its relays, switches, reclosers,

and protection components, and yet another for its transmis-

sion equipment. One would therefore expect that a major

requirement for the selection of any AMI solution would

be its ability to interface with existing or future smart grid

functionalities, enabling on-demand or event-based report-

ing of the health, configuration, settings, and maintenance

schedule of all AMI assets, including meters, head ends,

and communication equipment. Similarly, a utility planning

to implement dynamic pricing and ToU tariffs has to ensure

that its AMI system is capable of handling and/or relaying

such real-time information for the systems relevant points

of termination.

Realities on the GroundThe approach advocated in Figure 4 is unfortunately not

the norm. It is probable that most utilities will attempt to

integrate smart grid functions with their operations start-

ing at two extreme ends of the system hierarchy: at the bot-tom of the chain through rollout of AMI systems and at


9/15


the top of the chain through adopting and integrating new

enterprise bus functions. That approach is understandable

and very much in line with the current constraints on util-

ity assets and organizational structures. In fact, the early

attempts to modernize the system had to take into account

the realities of a highly compartmentalized system and

operational hierarchy tasked with delivering a critical ser-vice to customers while meeting the challenges with which

most utilities grapple.

Figure 5 depicts the approach utilities in general have

taken in their integration of smart grid functions. The point

of entry of new functions into the hierarchical structure of

the utility system has been at the interface with customers

(e.g., smart meters), together with the associated support

functions within the enterprise bus, such as meter data man-

agement (MDM) systems. Patches of plumbing to connect

the two ends of the function (e,g., the required communi-

cation system to support the capture and exchange of data

for the purpose of billing and revenue management) are thusinserted within the appropriate information and communi-

cation technologies (ICT) layer of the system.

The question utilities have not answered here concerns what

other smart grid capability the chosen AMI technology can

support. The current integration of AMI systems across manyfigure 5.A hierarchical smart grid system integration map.

figure 6.Disjointednetwork domains in a legacy grid.

Smart GridEnterprise

Applications

Enterprise Bus

Information Technology Infrastructure

CommunicationInfrastructure

Utility Assets

Generation Transmission Distribution

PowerSystemL

ayer

ICTLayer

A

pplicationLayer

Security Regime

Smart GridEnterprise

ApplicationsSmart GridEnterprise

Applications

Public Switch Network

Feeder

M1

M2

M3 HAN#3

HAN#2

HAN#1

D-Sub#3

D-Sub#1

D-Sub#2

Utility Field Network

T-Sub#1T-Sub#2

PowerPlant#1

Power

Plant#2

Wide Area Network (WAN) Local AreaNetwork (LAN)

Home AreaNetwork (HAN)

T-Sub#3

T-Sub#4

D-Sub#4

SG-App#1

SG-App#3

SG

-App#4

SG

-App#5

Utility Core Network

SG-App#2


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jurisdictions in the world assumes a disjointed utility network

(assuming such networks exist at lower layers of the distribution

system, which is often not the case), parts of which can be con-

veniently bypassed. What typically happens in such installations

is that smart meters are grouped into a local-area network (LAN)

type of association (meshed or otherwise) and exchange their

data and commands through radio frequency (RF) or power linecarrier (PLC) links with a data aggregator unit (DAU) installed

on a pole in their vicinity. The DAU then employs a dedicated

communication link (often a proprietary wireless protocol) to

exchange the aggregated data and commands with an MDM

system within the utilitys enterprise bus.

As shown in Figure 6, the specification of the constituent

components of the system is therefore optimized to ensure

data exchange between smart meters and the associated

MDM system residing within the utilitys core network. The

AMI system as such is not only oblivious to anything that

happens in between, but it also has no provisions for han-

dling or carrying any other information or data, howevercritical or important such data may be for other smart grid

functions to be rolled out in the future.

This simply means that critical sensory data and informa-

tion produced at the downstream side of the network, which

may be critically required by middle layers of the system,

bypass such layers and end up in the upper layers of the

system for a specific function and/or purpose (e.g., billing).

They therefore do not contribute and/or enable future smart

grid functions that may benefit from access to such data. To

elaborate this issue further, the next section of this article

examines typical examples of such applications that are

deprived of access to these critical data as a result of inac-

curate smart grid integration maps.

Typical Issues with SmartGrid Integration MapsOne would certainly hope that utilities quest for infrastruc-

ture modernization will not come to a screeching halt. Utili-

ties will no doubt attempt to integrate additional smart grid

functionalities based on their particular priorities and road

maps. Two such functionsones many utilities intend toimplement nextare VVO and CVR. The U.S. Department

of Energys recent studies in energy conservation across the

United States indicated that CVR functions, if integrated

across less than half of U.S. feeders, could potentially yield

more than a 2% reduction in demand on the U.S. electri-

cal system. In fact, it is public knowledge that many utilities

regard VVO and CVR as high priorities for implementation

on critically congested feeders. It has been claimed that the

ROI ration for VVO and CVR integration is six to one, with

a payback period of two to three years. That is certainly a

great incentive to regard investment in advanced VVO and

CVR as the next item on the integration map of many utili-ties after AMI.

Prior to AMI, the VVO and CVR functions in distribu-

tion substations (if they existed at all) used a statistically

aggregated profile of feeder load to determine the settings

and configurations of capacitor banks, tab changers, volt-

age regulators, and other devices used to correct the feed-

ers power factor and to ensure compliance of the voltage

gradient across the entire feeder, from substation to the last

customer, with ANSI and IEEE requirements. Given the

fact that no real-time information about the actual voltage

samples across the feeder was available, the settings and

configurations for such VVO and CVR assets were either

ineffective or inefficient.

Data Power

Transformer

Substation

Area

Network

Recloser

Feeder ASCADA

WANInterface

LANInterface

AMI Headend

Substation EMS

Distribution Substation

Timing and

Syncronization

Substatio

nBus

DMZ

EMS

HAN

EMS

HAN

EMS

HAN

EMS

HAN

VR VRCaps Caps

Volt/Var and CVR

Optimization Engine

Field AreaNetworkInterface

RF/PLCI

nterface

DAU

figure 7.Client-server based VVO and CVR.


11/15


The advent of AMI changed that

situation. Engineers in charge of plan-

ning for new VVO and CVR functions

saw the opportunity to use real-time

voltage, current, and power factor

(V/I/PF) sample values from smart

meters at each customer node to builda realistic and accurate real-time view

of the load profile across any given

feeder and thus to optimize VVO and

CVR settings based on an accurate

voltage gradient across the feeder, cli-

matic conditions, and ToU. Such an

approach has been named adaptivereal-time VVO/CVR.

To implement adaptive real-time

VVO/CVR, two approaches have

been considered. One relies on cap-

turing smart meters sensory datathrough an interface with the MDM

system in the back office, then run-

ning VVO and CVR algorithms on

powerful enterprise servers using

the network model of the distribu-

tion system, and finally transferring

the new settings to the field VVO

and CVR assets through the SCADA

system. In other words, the VVO and

CVR functions are split into a client-

server configuration, with the server

operating in the back office and rely-

ing on MDM system databases to

continuously calculate new settings

for VVO and CVR clients in the field

and transfer the new configurations

to such assets through the SCADA

system. This approach is called cen-tralized VVO/CVR control.

Centralized VVO/CVR control,

depicted in Figure 7, seemed quite

attractive at first. The availability of

accurate network models, combined

with adequate processing power on the

enterprise bus and access to the DMS

system, could indeed result in highly

effective settings for VVO and CVR

assets. But further studies at BCIT (see

Figure 8 and the suggested readings

that follow this article) indicated that

the VVO and CVR functions could be

performed a lot more efficiently (and

at lower cost) if on-demand sample

values of V/I/PF from bellwether smart

meters could be made available muchmore frequently than they can be using

ToHVSubstation

SubstationHVBus

SubstationMVBus

MVBreaker

MV

Breaker

Distributed

GenerationSource

12.5

kV

Cap.

BankSwitch

CapacitorBanks

Unit

VoltageR

egulator

Switch

LineVo

ltage

Regulator

DistributionTransformer

12.5

kV/0.4

kv

Ca

pacitorBanks

Unit

MVBreaker

LoadBank

R

esidentialLoad-A

R

esidentialLoad-B

ResidentialLoad-H

Smar

tMeter-A

Sma

rtMeter-B

Sma

rtMeter-H

VVO/CVR

Opt.Engine M

CU

MCU

IA-IED

PLC

Modem

Cap.

Bank

Switch

Sub.

Capacitor

BanksUnit

AFE

AFE

MCU

PLC

Modem

AFE

MCU

PLC

Modem

AFE

HVBreaker

Substatio

nTransformer

138-k

v/12.5-kV

TransformerOn-Load

Tap

Changer

. . . .

PLC

Modem

figure 8.Substation-based VVO and CVR.


12/15


current AMI systems and their

MDM system interfaces. To achieve

this, VVO and CVR algorithms have

to be processed locally within each

substation, using the local sensory

data associated with each individ-

ual feeder. This approach is calleddecentralized VVO/CVR control.

Although research in decentral-

ized VVO/CVR control algorithms

is ongoing, early results indicate

that a decentralized approach is

more efficient and cost-effective for

such applications. The difficulty in

realizing a decentralized VVO/CVR

control strategy is that although it

requires the AMI system to supply

it more data more frequently, as a

substation-based function it has nodirect access to the AMI system.

This means that such data must be

extracted from the MDM system on

the enterprise bus and transported

down to the substation through the

SCADA system. The issue there is

that current AMI systems (which

interface directly with an MDM

system in the back office) are not

typically designed to supply such

large quantities of real-time data

from the field to the MDM system

without the risk of network conges-

tion. Second, most SCADA systems

are incapable of transferring such

massive amounts of time-sensitive

information from the back office to

field devices without depriving other

critical functions of access to their

allocated bandwidth. Third, given

the fact that the VVO and CVR

functions are feeder-bound (i.e., the

required inputs and outputs are all

local), there is very little rationale

for involving upper-layer enterprise

functions in their operation.

This example is a clear indica-

tion of how critical a smart grid

integration map can be to the

realization of the smart grid. If a

utilitys integration map fails to

accommodate access to time-sen-

sitive data for upper-layer-based

smart grid functions, it will either

have to give up implementingfuture smart grid functionalities figure 9.A substation-based EMS.

HV/MVSubstation

EMS

Server

CVR

Server

SubstationTransform

er

withTap-Changer

Substation

Capacitor

Bank

Feeder

Capacitor

Bank

Fe

eder

Cap

acitor

B

ank

VarInje

ction

Poin

t-1

V

arInjection

Point-2

VarInjection

Point-3

VarInjection

Point-4

VVO

Engine

IEC

61850

CommunicationFlow

Dist.NetworkFlow

N2SmartMeters

N5-1Sm

art

Meters

N5-2Smart

Meters

Net

Meter

NetMeterD1

EV

DK

SmartInverter

DGSource

A1

C2

Cj

T1

T3

N=1

N

=2

T2

T4

N3Smar

tMeters

B1

B2

Net

Meter

SmartInverters R

oo

ftop

PVs

N

=3

N

=4

N

=5

A8

A2

C1

D2

Bi


13/15


or force its valuable assets (in this case, its communication

systems) to haul time-sensitive data back and forth across

various layers of the ICT hierarchy, thereby risking system

inefficiency and by design failure.

Another example of how critical forward-looking smart

grid integration maps are arises out of the requirement to

achieve tighter and more meaningful interfaces with cus-tomer-based assets. It is believed that in the not very distant

future, substation-based VVO and CVR functions will need

to extend their reach beyond smart meters and include some

coordination (or even command and control of) customer-

side generation assets and loads.

As depicted in Figure 9, such assets will include rooftop

PV modules and electric cars. In fact, recent reports about

the negative impact of uncoordinated rooftop solar cells on

the stability of feeder voltage levels are quite discouraging.

The unpredictable and intermittent behavior of such distrib-

uted generation assets cannot be entirely mitigated with util-

ity field assets (e.g., capacitor banks). And even if such costlyassets could be effectively used to help stabilize voltage lev-

els, their useful life spans (and health) could be considerably

compromised by these frequent anomalies (e.g., by voltage

levels pushing outside the ANSI band due to intermittent

generation from customer rooftop PV modules).

The voltage stability issues caused by electric cars used in

their vehicle-to-grid (V2G) mode may be far less severe than

those from rooftop PV modules, but this is still a problem for

which utilities need to make adequate provision. Even though

electric car manufacturers may not enable V2G functionality

for their cars for the foreseeable future due to their concerns

about the cost of battery warranties, utilities need to plan and

be ready for such issues should V2G become a reality.

What is interesting is that both of these threats could be

converted into opportunities for the utility if appropriate

provisions are made in the utilitys smart grid integration

map to take advantage of the availability of such down-

stream assets and integrate them with future substation-

based EMSs. As depicted in Figure 9, such an EMS would

incorporate various command, control, and processing func-

tions, using global system attributes and local feeder data to

configure all of its assets (inside and outside the substation)

so as to achieve its energy management goals.

Obviously, the demands such a level of integration would

place on the AMI system are even heavier than in the pre-

vious example. Here, the AMI system would work as the

conduit of communication and coordination between the

substation-based EMS engine and customer-owned cogen-

eration resources placed behind the meter. As such, it would

be critical to ensure that smart grid integration maps require

AMI systems to support such functionalities without major

engineering and overhaul.

Litmus Test

As discussed earlier, a forward-looking smart grid integrationmap is critical for the realization of a smart grid. And given the

cost involved in integrating new technologies and functional-

ities into the existing grid, the smart grid integration map could

prove to be either the savior or the Achilles heel of a utilitys

smart grid program. In making that judgment, every utility

has to review the operational requirements of its medium- and

long-term smart grid functions and determine if its smart grid

integration map supports a seamless transition from where it isnow to where it would like to be in the future.

In addition to the examples discussed at length above,

there are several other commonly identified smart grid capa-

bilities that may be considered as a litmus test for ascer-

taining the suitability of a utilitys smart grid integration

map. These include:

Distributed generation: As discussed earlier, con-

cerns about cogeneration synchronization, var control,

voltage stability, and so on have convinced utilities of

the need to achieve a level of integration (notwith-

standing the regulatory impediments that exist in

various jurisdictions across North America) betweenfeeder assets and behind-the-meter, customer-owned

equipment. Given the fact that the point of common

coupling between the utility and the customer is the

smart meter, such a level of integration must be facili-

tated by the utilitys smart grid integration map.

Sensor networks on the low-voltage (LV) side of the

distribution system:Although such sensory data on the

LV side (such as those from phasor measurement units)

have not yet been established as a critical requirement,

one should assume that should that become a necessity,

the AMI infrastructure could be the primary means of

supporting such real-time data (through an auxiliary

channel) and transporting them to the substation. The

alternative to using the existing AMI assets for such

data would be to construct a dedicated, low-latency

communication system with a universal communication

protocol and mission-critical availability and resilience,

together with secure and intrusion-resistant multitier

access, as the carrier of such data for the upper layers of

the system. That could be quite costly. Again, no matter

what the chosen architecture for the implementation of

sensor networks, a utilitys smart grid integration map

must include provisions for supporting additional data

networks going forward.

Customer-side EMS: EMSs on the customer side

of distribution systems are often regarded as killer

apps enabling accurate, reliable, real-time, and end-

to-end energy management functions. Given the

trend toward designing distribution substations as

energy hubs in charge of achieving cost-effective

management of power and services transactions

between producers and consumers (prosumers),

it is paramount to move away from a broadcast-

based, global utility pricing and tariff-signaling

system to a real-time, substation-based, local pric-ing signal. Just as the price of gas is never the same


14/15


MDMSServer

BillingServer

DistAutoServer

DRServer

EnterpriseApps

Servers

Data

UtilityData

Warehouse

Outage

Management

Server

Asset

Management

Server

EnterpriseBus

Firewall

Firewall

Protocols:IEC61850/CIM/WebServicesforEnd-to-EndCommandandControl

Trans

port:TCP/IPOverFiber,Wi-Max,Microw

ave,etc.

Transport:TCP/IPOverFiber,Wi-Max,etc.

Proprietary

Protocol

RF/PLC

SEP2.0

ZigBee

Backhaul

Networks

Distribution

Networks

Agent

BP00

Agent

BP01

Agent

BP10

Agent

BP11

Agent

BP10

Agent

BP11

Agent

BP20

Agent

BP21

Agent

BP20

Agent

BP21

Agent

BP20

Agent

BP21

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BP20

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HAN

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HAN

HAN

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OperationalIntelligentAgents

OrganizationalIntelligentAgents G

eographicalIntelligentAgents

figur

e

10.

Integratednetworkdomainsin

asmartgrid.


15/15

6 & i /j 2014

across all the gas stations in a town, so the price of

electricity should vary from substation to substation

in a given jurisdiction. In other words, every substa-

tion should be able to price its services based on a

host of local parameters such as load congestion,

the demand profile, and the energy available from

the grid and from prosumers. To achieve this, a util-itys smart grid integration map should facilitate the

required integration between the energy hub (the

substation) and its termination points (prosumers).

ConclusionsThe central theme in all of the examples discussed in this

article is the need to have a forward-looking smart grid

integration map that empowers utilities to add incremental

functionalities to their existing grid, if and when required,

without the need to redo any of their previous investments.

Given the examples discussed abovewhich cannot by any

stretch of the imagination be considered comprehensivethe utilities have to be extremely careful about the initial

investments they make in this regard. That does not appear

to be always the case, however, as some of the choices that

have already been made in the early stages of the process

have not been encouraging.

The AMI model implemented in many jurisdictions

across North America, for example, relies on local data

collection units (often referred to as DAUs) as the primary

interface between smart meters and MDM system applica-

tions in the back office. In such a model, the local distribu-

tion substation will either be totally disconnected from the

AMI system that monitors the customers feeding off its feed-

ers or if there is any communication between smart meters

and substation equipment, the data will have to go through

the round robin of being captured by DAUs locally, passed

on to the appropriate MDM system in the remote back office,

and handed over to the SCADA head end in the back office

before finding its way through the SCADA network from the

back office down into the substation.

It goes without saying that such long delays in data and

command communication would make it nearly impossible

to efficiently run any number of smart grid capabilities

that rely on distributed command and control and as such

require local analytics and decision making. Such appli-

cations are by default substation-resident, with a stringent

need for unimpeded access to real-time data from smart

meters, sensors, and other termination points associated

with that substation. In other words, smart meters should

ideally be substations over-the-fence intelligent elec-

tronic devices (IEDs), fully engaged in real-time data and

command exchange with substation-resident functions;

failing this, they are nothing more than an interim solution

for automating billing and revenue management.

Finally, a utilitys smart grid integration map must support

the realization of the utilitys integrated network domains,

as depicted in Figure 10, which emphasizes the need for a

distributed command and control system (using a system of

intelligent agents) running across multiple domains of the

utility network and providing end-to-end communication

and data exchange among all utility assets. In that regard,

no single smart grid asset should be planned as fulfilling an

outlying function divorced from the utilitys existing andplanned operations and capabilities. If it is, one can seriously

doubt the business justification for acquiring such expensive

assets, as well as that utilitys ability to actually implement

cost-effective and efficient smart grid capabilities.

For Further ReadingU.S. Department of Energy. (2012, Dec.). Application of

automated controls for voltage and reactive power man-

agement, initial results. [Online]. Available: https://www.

smartgrid.gov/sites/default/files/doc/files/VVO%20Re-

port%20-%20Final.pdf

U.S. Department of Energy. (2012, Mar.). Visioning the21st century electricity industry: Strategies and outcomes

for America. [Online]. Available: http://energy.gov/sites/

prod/files/Presentation%20to%20the%20EAC%20-%20Vi-

sioning%20the%2021st%20Century%20-%20William%20

Parks.pdf

M. Nasri, H. Farhangi, A. Palizban, and M. Moallem,

Application of intelligent agents in smart grids for volt/VAr

optimization and conservation voltage reduction, in Proc.IEEE Canada Electrical Power and Energy Conf. London,Ontario, Oct. 2012.

M. Manbachi, H. Farhangi, A. Palizban, and S. Arzan-

pour, Real-time adaptive optimization engine algorithm for

integrated volt/VAr optimization and conservation voltage

reduction of smart microgrids, in Proc. CIGR CanadaConf., Montreal, Sept. 2012.

H. Farhangi, Smart grid and ICTs role in its evolution, in

Green Communications: Theoretical Fundamentals, Algorithmsand Applications, J. Wu, S. Rangan, and H. Zhang, Eds. BocaRaton, FL: CRC Press, 2012.

G. Stanciulescu, H. Farhangi, A. Palizban, and N.

Stanchev, Communication technologies for BCIT smart

microgrid, in Proc. IEEE PES Innovative Smart Grid Tech-nologies Conf.,Washington DC, Jan. 2012.

M. Manbachi, M. Nasri, B. Shahabi, H. Farhangi, A. Palizban,

S. Arzanpour, M. Moallem, and D. C. Lee, Real-time adap-

tive VVO/CVR topology using multi agent system and IEC

61850-based communication protocol, IEEE Trans. Sus-tainable Energy, vol. PP, no. 99, p. 1, Oct. 2013.

BiographyHassan Farhangiis with the British Columbia Institute of

Technology, Vancouver, Canada.

p&

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Documents

Roadmap to integration