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: Communication architectures and technologies for advanced Smart Grid Services —
Chap. 8 — 2017/10/22 — 16:38 — page 1
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Contents
8.1 Introduction 1
8.2 The Smart Grid communication architecture and infrastructure 4
8.2.1 DSO-based communications 5
8.2.2 Internet-based architectures 9
8.2.3 Next-generation Smart Grid architecture 10
8.3 Routing information in the Smart Grid 17
8.3.1 Routing family of protocols 17
8.3.2 Reactive routing protocol in a constrained network 25
8.4 Conclusion 30
8.1
Introduction
Over the past years, the demand for electricity has faced a drastic growth, as the num-
ber and heterogeneity of electrical devices are continuously increasing. In parallel,
the power sector is undergoing major changes, mostly by the switch from fossil to
renewable energies, the evolving energy policies and the emergence of less-reliable
renewable micro-generation. As stated in a 2015 Eurelectric survey [1], the grid
requires taking into consideration these modifications while ensuring secure, sus-
tainable, competitive and affordable energy for any individual and business.
Proper operation of the electrical network is based on the balance between produc-
tion and consumption, a great challenge for the network management. Actually, the
grid structure is evolving from a rigid and centralized architecture with large produc-
tion units at the top satisfying demand at the bottom, to a more distributed one with
individual premises equipped with local renewable production units. Electrical pro-
duction is therefore getting more decentralized but at the same time less predictable
as renewable sources are sporadic. In order to efficiently balance production and con-
sumption, real-time measurements, predictions and control capabilities are needed in
a widespread management system.
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Furthermore, electrical devices have also evolved in recent years. Some are now
mobile such as Electric Vehicles (EVs) making demand prediction more difficult;
others, such as connected heating/cooling devices, offer remote management capabil-
ities to their owners. Together, their growing numbers, and in particular the increas-
ing penetration of EVs, make the management of the system even more complex.
Today, private EVs are charged as soon as they are plugged into the grid, without any
management system. And most of these charging processes occur at peak hours, in
the evening, which is very challenging for the grid. While there are only few EVs,
the electrical grid can afford to provide this power. But, if EVs are generalized, we
will need to avoid charging all of them on peak hours and so, shift these demands on
a time window that will contain the needed power below a given threshold, and/or
align the consumption on the production periods. As a consequence, it is vital for the
Smart Grid to benefit from a control system of the charging periods to balance the
various energy demands over different period of time. As presented by Lefrancois
et al. [2], several mechanisms are required to provide smart-charging services in an
automated way. Moreover, EVs can be seen as mobile batteries from the electrical
network. In order to integrate such devices into the system, mobility mechanisms
are needed. Therefore, with appropriate ICT systems and services – such as smart
charging ones –, the Smart Grid could be able to efficiently use these batteries. For
instance, the electrical grid could encourage them to charge at given periods – and
compensate for overproduction – or it could ask them to re-inject their electricity
when and where it is needed [3].
Nevertheless, electrical devices, such as EVs or home appliances, are not compo-
nents of the Smart Grid yet. Their presences and types depend on users’ will. Even
though, utilities could benefit from managing these devices, the complexity arising
from them is out of their scopes [4]. As a consequence, a smarter and extended grid
is more than ever required to support utilities efficiently monitor and manage this
system [5], as well as “connecting” the grid with devices beyond.
The ongoing transition in the energy sector has propelled innovation forward. The
electrical grid is already getting smarter: it enables both automated meter reading
and indirect controlling demand with real-time pricing signals. Under a smarter sys-
tem, new business opportunities can be envisioned. Indeed, a system offering opti-
mized and automated energy production, distribution and consumption will enable
the appearance of flexible services (selling flexibility in consumption or production).
Together with appropriate incentives, this would lead to the creation of new energy
markets that would take place at various scales and levels.
These objectives can only be accomplished with a proper ICT architecture to first
interconnect all energy actors. As long as this interconnection is ensured, they will
be able to exchange energy information (consumption, production or storage) and
use available energy services. Such an architecture must address several require-
ments. First, it has to take into consideration the diversity of electrical devices that
are plugged into the grid. The architecture must be flexible enough to adapt to differ-
ent hardware constraints and software choices, and absorb their evolution over time.
Scalability is another must for the architecture, since the future smart grid involve a
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huge number of devices, including consumption devices and production sites. Final-
ly, real-time measurements, prediction and control capabilities are required to better
plan for the production and consumption alignment. Consequently, the architecture
must provide efficient mechanisms to 1) collect and process all the produced data, and
2) take decisions and control end-points accordingly. Note that some actors might
not require data in real-time, nor detailed data (ensuring data privacy). Lastly, it must
be secured in order to avoid any entity both taking control of a device without being
authorized, and collecting data without being granted to access it.
There are also several issues to solve before implementing such an innovative sys-
tem. While the remote control and coordination for electrical loads of homes, office
buildings and industrial premises have been possible for decades, such control is not
yet widely enough adopted as it involves handling the issue of control a large volume
of distributed nodes. Furthermore, such a system should deal with the challenges
of new electrical networks in which any party can act as an energy producer and/or
consumer hence the term “prosumer”. These challenges require to deal with the fol-
lowing issues:
•Finding a given party in a large structure;
•Accessing a given resource (data or control of nodes);
•Incentivizing for this access;
•Implementing technical compatibility with any system.
To properly cope with these problems, ICT is needed for energy actors to intercon-
nect and better manage energy usage. Future Smart Grid systems should provide the
tools for an efficient integration of the following:
1) local renewable energy production;
2) management systems to retrieve electrical production, consumption and storage
information; and
3) other energy actors to provide systems, mechanisms or services that use such
information for electricity management and control.
In order to achieve these goals, technology is required to:
•Offer automated access to any measurement point and load, in order to lower the
cost of control;
•Exchange data in such a way that it will allow data availability for large scale sce-
nario as well as to make possible application specific extensions in order to realize
the management of heterogeneous infrastructures.
In this chapter, we focus on presenting communication architectures, technologies
and protocols employed in Smart Grid environment to solve the problems such a
balancing system must face. In Section 8.2 we provide an overview of the existing
communication systems currently utilized in Smart Grid and then we present what is
required in order to meet a next-generation Smart Grid system. Before concluding,
Section 8.3 develops the routing issues and certain existing solutions to efficiently
transmit information while using constrained technologies.
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8.2
The Smart Grid communication architecture and infrastructure
In Smart Grids, the legacy communication architecture enabling data collection and
device management is called the Advanced Metering Infrastructure (AMI) [6, Chap.
7]. This architecture is an evolution of the Automated Meter Reading (AMR) by
adding bi-directional communication framework, which was deployed to facilitate
meter reading, billing and consumption planning. This two-way communication fea-
ture of AMI also offers additional operations on a network. It sure helps the utilities
better control their network but several other opportunities are foreseen with such ar-
chitecture especially if using high-speed Internet Protocol (IP)-based technologies.
In this section, we describe the AMI architecture that involves the main elements of
the electric supply chain (smart meters, distribution units, production units). While
AMI communications are operated by utilities and allow basic services such as mon-
itoring and some control, we will show that they are not enough to provide the
grid with fine-grained and optimized capabilities such as appliance-level demand-
response. For such advanced possibilities, we claim that other communication sys-
tems are needed, and that the currently developing paradigm [7] of the Internet of
Things (IoT) is an excellent candidate. Hence, we develop that view later in this
Section.
The IoT consists of a set of smart “things” that are able to connect to the Internet
– directly or via a gateway – and feed other devices with their collected information
(often referred as Big Data due to the high volume of them). As long as these things
can be uniquely identified and provide some empirical data on our environment –in
this case, electric information (consumption, production or even storage status)– any-
thing can be a “thing”. But they may have different capabilities in terms of hardware
and control (e.g. a sensor compared to an EV).
In smart grid communication systems, like other communication systems, we can
distinguish a core part and a last-mile1) part. The former involves large storage and
computational capabilities used to collect, organize and process data in order to co-
ordinate devices through remote management commands. The latter part consists
of uniquely identified and connected objects (e.g., sensors, actuators, smart devices,
etc.) as well as communication links between them, possibly by employing various
wired or wireless technologies.
This Section aims at detailing the characteristics and technologies of the differ-
ent communication networks that comply with Smart Grid requirements. We here
present the current state of advancement in this domain, as well as certain ongoing
efforts to make the grid smarter.
1) specifically, last-hop since some radio technologies allow communications over more than ten miles
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8.2.1
DSO-based communications
The legacy communication network used in Smart Grids is the aforementioned AMI.
The main elements for such infrastructure are the so-called smart meters, i.e. pow-
er meters embedded with communication capacities that are able to activate some
generic functions from remote control. These meters are deployed by utilities and
enable Distribution System Operators (DSOs) to both meter consumption and com-
mand end-points remotely. This bi-directional communication helps DSOs efficient-
ly manage their networks.
Hereafter, we describe the communication organization in such DSO-based smart
grids, as well as the limits that this structure imposes on the services that can be built.
8.2.1.1 The existing AMI organization
Energy distribution networks have a tree topology with large production units on top
producing most of the required energy, which is then transported via a widespread
distribution network towards consumers (called the end-points). Figure 8.1 illustrates
this top-down configuration in which consumers can be reached after passing through
different aggregating nodes, i.e. transformer. Utilities forecast the consumption of
end-points based on their historical consumption and adjust these forecasts based on
automated metering. These forecasts are therefore very sensitive to any modification
of end-points behavior.
Figure 8.1 The current distribution network topology.
Currently there are several notable trends taking place in the energy market. Some
examples are the shift away from fossil to renewable energies, the steadily increasing
number and heterogeneity of electrical appliances and devices, and the decreasing
price of various distributed energy production technologies.
DSOs require a bi-directional communication with smart meters in order to effi-
ciently manage their networks and face these trends. In fact, one consequence of
these upcoming changes is the growing demand at the consumer side. Therefore,
utilities have to plan such growth, increase the amount of electricity produced and
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transport this electricity towards the end-points. However, as the distribution net-
work capacity is limited, a question that arises is what will happen when reaching
this limit. Utilities should encourage end-points to balance their consumption during
a 24h period, hence the bi-directional communication with smart meters. In addition,
consumers are getting more and more equipped with micro-generation systems. As
a consequence, the grid evolves towards a system in which large energy production
facilities – dams, nuclear power plants, etc. – must co-exist with a myriad of smaller,
less reliable systems in the same network. Therefore, and without more information
from these “prosumer” – at the same time consumer and producer –, it will become
more and more difficult for DSOs to estimate consumption of such end-points. The
ideal solution in order to take into consideration these trends, would be to modify
the whole distribution network. First, by increasing its capacity and secondly, by
allowing prosumers to re-inject their production excess into the network – and have
smart meters metering it. However, these modifications are very costly and so, in-
conceivable for now or should be planned and spread over several years. For these
reasons, DSOs prefer to first set up means to efficiently meter as well as send signals
to individuals to balance their consumption.
Therefore, initial Smart Grid efforts, mainly directed by DSOs, use the AMI in or-
der to offer better management of a distributed energy production. And the smart me-
ter is thus a central piece of the needed communication framework. On the one hand,
it allows real-time monitoring in order to collect accurate data about the consump-
tion (and possibly the production). On the other hand, it allows receiving real-time
electricity pricing signals, which trigger a certain period of time where the energy is
cheaper. Relays can be used in user premises to activate or de-activate devices that
are plugged into a specific circuit (e.g. hot water tank, which could be switched on
during night).
Resident ial Comm ercia l Industri al
Utility3center
Figure 8.2 Illustration of an AMI.
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Technology Range Throughput Energy cost Monetary cost
802.15.4 [11, 12] up to 100m up to 250 kbit/s cheap cheap
802.11ah [8] up to 1km Tens of Mbit/s expensive cheap
LoRaWAN [9] 5km (urban) to
15 km (rural)
Tens of kbit/s cheap cheap
LTE-Cat M [10] 2km (urban) to
5km (rural)
≈200 kbit/s medium expensive
Narrowband PLC [13] several km up to 500 kbit/s cheap cheap
Wired Internet - up to 1Gbit/s cheap expensive
Table 8.1 Communication technologies for the AMI last-hop.
As illustrated in Figure 8.2, the AMI consists of a tree of smart meters, placed at
different positions of the network. Data from all these meters are collected, stored,
processed and analyzed by the utility center. Routing protocols are thus required from
the smart meters (i.e. nodes of the tree) to the utility center (i.e. root of the tree) in
order to forward the data hop by hop. Basic commands issued by the utility center
to announce the price in real-time are actually broadcasted to the whole network and
do not require unicast transmission.
8.2.1.2 Communication technologies used in the AMI
Currently, the communication taking place within the core system is based on Inter-
net technologies with IP above a variety of underlying technologies such as optical
fiber, cellular network or Power Line Communication (PLC). With some of them,
one aggregating node or a relay node can transfer data on behalf of hundreds of smart
meters behind it. To reach the core of the AMI, data issued by (or intended to) the
smart meters typically have different “hops” distance to perform. The distance to
cover in one hop will vary depending on the network configuration. The technology
used to communicate depends on this distance and the environment characteristics.
In particular, the technologies used for the last-hop heavily depend on a) the meter ca-
pabilities in terms of communication and processing power, b) the energy requires for
this communication, and c) the relative performance of the available options – such
as the distance with a wireless base station, the quality of PLC transmission, etc. A
non-exhaustive list of commonly used technologies is given in Table 8.1, along with
their advantages and drawbacks [8, 9, 10]. For wireless technologies, the “energy
cost” column refers to the energy consumption associated with the technologies: it
includes the transmission power, as well as the processing cost of the corresponding
protocol stack and the specific medium access control procedures. The “monetary
cost” reflects the cost of the hardware components needed to implement a technology,
including SIM cards, as well as potential spectrum license fees.
We need to bear in mind that all technologies listed in Table 8.1 are not always
available: due to hardware limitations and/or environmental constraints, the choice
can be limited.
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8.2.1.3 AMI Limitations
Up to now, on the demand side, the Smart Grid is mostly limited to the smart meters.
Their deployment within the AMI is mapped onto the distribution structure. Fur-
thermore, the intelligence in meters or in intermediate nodes is very limited, leaving
all the intelligence in the network core, i.e. the utility center. As a consequence,
the AMI lies into a centralized system as previously illustrated: all the data transit
through data-centers controlled by DSOs, where all storage and analysis actions are
performed: in other words, the AMI is fully operated and controlled by utilities.
With respect to this status, we see three kinds of limitations, all in favor of the use
of alternative communication systems.
First, we see issues in terms of incentives. Indeed, DSOs will not expect significant
gains from improving the communication capabilities, since those gains will go to the
entities exploiting them, such as flexibility operators. Conveying the data is not the
core mission of DSOs, and they will probably be reluctant to invest in communication
improvements if they do not get a return on investment.
Second, the current situation raises some privacy concerns: indeed, DSOs have
now access to a precise electricity consumption over the time, which may raise some
privacy concerns. And, if the infrastructure evolves in a way that even more infor-
mation is transfered to the DSO, e.g. with a per-device monitoring, such a situation
is unlikely to be accepted by users. On the contrary, they should decide which entity
(if any) to trust with those data, and at which granularity level.
Third, we envision some interoperability issues:
all the gathered data, which we recall are mainly used for billing and planning
purposes, are very sensitive. Therefore, DSOs will not likely let users, other orga-
nizations or entities, manipulate them. Consequently, the AMI is currently a closed
network where there is no interconnection with other systems and architectures –
resulting in a situation where users can only visualize their data. This missing in-
terconnection limits the possibilities to cross-reference or use information coming
from other systems. For instance, senior monitoring systems would be more accurate
if health information could be crossed with electric information, and DSOs could
benefit from having external information to better plan consumption or production.
Nevertheless, smart meters will probably embed more functionalities in the future,
such as data storage and decision / management mechanisms. However, adding such
capabilities will increase the cost to produce and deploy such meters. Therefore,
DSOs will have to determine a business model that will push them into deploying
such a costly new system. Although, this suggestion appears to be innovative, if
there is not a significant gain for DSOs, these “smarter” smart meters will never be
deployed.
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8.2.2
Internet-based architectures
As stated before, other communication networks used in Smart Grid are centered on
the Internet and, thus, employs various IoT standards. The main elements for such
architecture are the smart appliances, i.e. devices and/or appliances embedded with
communication capabilities, as well as actuators or sensors. These new connected
devices are “deployed” – or should we say, sold – by manufacturer and enable an
user to have controllable devices. This actually gives the users a better control and
knowledge of their appliances behavior – i.e. consume, produce or store. In addition,
it offers additional flexibility for users to change the way that their devices behave.
In the future, a large number of these smart appliances [14] is expected to appear
– consuming, storing and/or producing energy. These smart appliances provide their
energy information (consumption or production) along with controlling capabilities.
These smart devices are usually connected to the Internet, and if not – probably due
to specific constraints – they are still accessible via gateways. This enables users to
remotely access, control and/or monitor smart devices. For instance, almost all of
them come with a monitoring/controlling mobile application.
Furthermore, as the main purpose of the Internet is to interconnect systems, con-
nected devices have access to a plethora of services and information. As a result,
these smart appliances can use the Internet to have access to external information
such as weather information. Moreover, manufacturers can develop management
services on top of the Internet for users that prefer to leave the management of their
smart appliances to third parties. Such Internet-based communication networks offer
many possibilities for Smart Grid applications and very likely some we have not even
thought of yet.
An example of Smart Grid services that can be deployed over such architecture is
the automatic shedding of certain industrial loads during peak hours. In fact, with
such system, an operator could sell the flexibility of a pool of non-critical industrial
load as adjustment energy reserve for the grid. As a result, on grid demand – for
instance during peak hours–, this Flexibility Operator (FO) can switch-off a set of
its managed load in order to reduce the peak. This peak reduction enables utilities to
save money – e.g. avoiding to buy external electricity –, which in return pay the FO
for its assistance. Internet-based architecture enables a FO to aggregate industrial
load from different places and thus, to manage a large portfolio of flexibility. It can
be considered as a full win-win scenario as industry will also get a share of the FO’s
remuneration while decreasing their electricity bill.
In an Internet-based architecture, the core domain is the Internet with all the pro-
tocols and standards it provides. Communication within the core domain is therefore
IP based just like in the AMI. Moreover, last-mile communications also relies on IP
within the Local Area Network (LAN) – or Home Area Network (HAN), Premise
Area Network (PAN), Field Area Network (FAN) or even Business or Industrial
Area Network (BAN) –, or they might use gateways to connect to IP.
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The universal nature of the Internet and the flexibility of the IP stack provide lots
of opportunities. Such architecture enables any provider to offer various services
towards smart appliances – that might not be directly related to energy – as well as
enhancing Smart Grid possibilities.
8.2.2.1 IP-based architecture Limitations
Energy and Smart Grid are potential application domains for IoT among others such
as transportation, health-care or environmental monitoring. One issue of such an
Internet-based solution is that there are several ways to implement an architecture for
enhanced Smart Grid. In addition, there is not only one standard architecture, lead-
ing the standardization efforts to be scattered [15, 16]. Furthermore, energy-related
information is fragmented towards several standards and metrics, leading almost all
smart appliances to be manufacturer specific. This diversity leads to a fragmented
landscape of architecture models and isolated “silos” implementation. Interconnec-
tion of these silos is very difficult, if not impossible, as they often use their own
architecture, models and mechanisms – sometimes even between silos belonging to
the same application domain.
Another concern with this type of solution is the lack of security around the de-
ployed devices. Everything and everybody is now connected on the Internet, but not
all of them are benevolent. In fact, as most IoT architecture are not mature enough,
they do not include strong security mechanisms to protect underlying networks. As
a result, connected devices are more and more targeted in order to break in private
networks and user privacy2).
Last but not least, there is no actual interconnections with utilities networks apart
from a potential interaction with the centralized management system in order to re-
alize Demand Response (DR) mechanisms. Nevertheless, enabling crossed informa-
tion from both smart meters and smart appliances could greatly enhance Smart Grid
systems.
8.2.3
Next-generation Smart Grid architecture
As mentioned in the previous Sections, there are currently two types of Smart grid
systems, each using different types of communication technologies: 1) One relying
on smart meters and mostly used by utilities; 2) The other one relying on smart ap-
pliances and mostly used by end-users. However, each system has some drawbacks
and would benefit from the other. The next-generation grid communication network
has a future that is all planned out. It is expected to interconnect both systems in
order to provide an enhanced one as depicted in Figure 8.3. In this Figure, each site
has both a smart meter, an Energy Management System (EMS) (depicted as a gray
server), along with an Internet access. It receives information from the grid (solic-
itation or demand) via the smart meter (which is also used for billing). Each EMS
2) http://arstechnica.com/security/2016/10/double-dip- internet-of-
things-botnet- attack-felt- across-the- internet/
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aggregates data collected from both smart appliances and the smart meter in order for
the end-user to have an almost real-time visualization of its consumption, production
and storage (if any). An EMS can also inform the grid of its capabilities (production,
load shedding, etc.) via the smart meter. EMSs can operate collectively and depend
on a “higher” EMS to form a microgrid – a smaller managed grid, as depicted by the
gray cloud. This microgrid will also have its own EMS that will be able to coordinate
“lower” one in order to respond to the grid more efficiently and on a larger scale.
Utility¢er
Servic es
€
Microgrid
Figure 8.3 Illustration of the next-generation Smart Grid system.
This next-generation grid should involve more consumers and prosumers in the
management decisions of their own sites. In order to optimize consumption and pro-
duction both at the premises and on a global level, it is crucial to simultaneously
utilize inputs from a) the grid, such as pricing and demand; b) the user, providing its
needs and requirements; c) smart appliances, informing on their specificity and capa-
bilities – e.g. shedding potential. Consequently, all these data are combined in order
to determine the best usage for given smart appliances. Moreover, being connect-
ed to the Internet, this system will also have access to several services and informa-
tion coming from non-energy related connected objects (such as outside temperature,
weather forecasts, etc.) that might also help in the decision making. Nevertheless, in
order to reach such a complex system several challenges need to be addressed.
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8.2.3.1 Technical issues for next-generation Smart Grids
The number of smart objects is expected to explode in the near future [17]. Many
of them will be electrical appliances, leveraging connectivity to provide daily-life
services to their owners and possibly to the grid (through load shedding or storage
for instance). This plethora of smart devices will use different type of access tech-
nologies, protocols and information format. As a consequence, this next-generation
system should be flexible, adaptable, dynamic and will enable automation.
In order to support scalability, this diversity of devices has to be handled locally.
It will first lower the complexity of such system and then, enable local management.
However, smart meters currently deployed in the AMI have not been designed for
managing and aggregating data on behalf of several appliances, nor for enabling such
elaborated services – i.e. collecting input from users and from other devices/services
in order to analyze data and control appliances.
8.2.3.2 Handing back the keys to the user: Energy Management should
be separated from the Smart Meter
We insist here on the distinction between the devices interacting with the user (to
take decision within its area) and the devices used by the utility to monitor and bill
the area consumption, as well as inform about the grid demands.
In order to enable a more effective energy management, end-users have to be more
involved with respect to their data as well as to make decisions about energy usage.
Their involvement should not anymore only be on a monitoring basis. They have to
understand how they consume (and produce), and at the same time how they can act
for both their own and the grid benefits. Therefore, they will be part of local decision-
making regarding the balance of their own consumption and production, and/or at
the same time they could participate in the balance optimization of the whole grid by
shifting, postponing or switching off their appliances upon request. Therefore, the
next-generation Smart Grid architecture should provide users with the proper tools
to control both their data and energy usage.
In order to realize all these actions a dedicated management equipment is required.
The smart meter could play this role if it can be equipped with new functionalities
(storage, analysis and user interactions). However, as previously mentioned this will
initially be more costly for the DSO and also implies that the user could possibly alter
the functioning of the smart meter through interactions. Let us recall that the main
purpose of smart meter is to ensure billing and planning for utilities, it is therefore
not acceptable to affect the communications with the utility management center. For
these reasons, it is very unlikely that the smart meter would act as a local EMS in the
future.
On the contrary, it is more probable to see users acquiring an independent EMS
in order to have access to energy services. Smart meters would still be in use, first
by the DSO to monitor, bill, and send grid-related requests (consumption reduction
or increase, load shifting). Furthermore, by the EMS in order to be aware of the site
global consumption and possibly to share certain data with the DSO (e.g. consump-
tion forecast). In any case, the per-device decisions should be left to the EMS, either
directly operated by the user, or by a specific service provider.
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This may enable new open markets for energy management in which the users
should be able to choose who will get access to their detailed energy data as well as
to the control of their smart appliances. Similarly, the users should be free to decide
whether to let a third party manage their consumption/production, and if positive,
to select one. Switching among “energy management service providers” should be
simple enough, so that market competition would lead to a desirable outcome.
8.2.3.3 To build an open market, use an open network
As previously stated, the communication network used for energy management
should be adaptive, dynamic, flexible and easily accessible to allow fair competition.
The Internet naturally appears as a good candidate as it is built on open standards that
provide almost all these features while adapting to smart grid applications through
various IoT standardization efforts. The openness of the Internet and its standards
seem preferable than letting energy operators define proprietary networks and pro-
tocols. The latter approach might potentially lead to several isolated platforms with
few potential interconnections. With the use of the right tools, the Internet will pro-
vide all the required flexibility to handle a large volume of distributed consumption
and production points.
As a result, the next-generation Smart Grid core network should be decentralized
and mainly focus on ensuring the interconnection of these end-points, while the in-
telligence should be distributed towards appliances, equipment, systems and aggre-
gating units as we will detail in the following Subsection.
8.2.3.4 Multi-level aggregation
In this next-generation model, an EMS can aggregate data from different devices
of a given site. Communications between EMS and appliances is based on the IoT
concept, which specified that all devices must be uniquely identified. An EMS can
therefore analyze and process collected data, which results in taking decisions and
controlling corresponding devices. The same principle could be performed at dif-
ferent levels (a set of EMSs could be managed by an other one). Actually, using
Internet-based technologies (such as peer-to-peer tools) simplifies the communica-
tion between different EMSs. As a result, it allows to define several types of aggre-
gation levels, corresponding to different scales for the appliance management grain
as well as for global energy balance and economic relationships.
For instance, a hierarchical and geographical aggregation can be defined, where
several individual users from the same area gather to form a microgrid. At the
individual site level, a fine-grained management can be parameterized, along with
an EMS, and accordingly to each individual preferences. These different sites –
with potential storage and/or production capabilities –, coordinate themselves, and
simultaneously provide their general energy information to an EMS taking care of
this microgrid. At the end, this smaller grid is seen as a single entity to the rest of
the grid. The microgrid EMS manages lower-level EMSs. It might receive demands
from the grid and determine if and how it can satisfy them, using available storages
within the microgrid or in turn sending demands to lower EMSs. Nevertheless, the
shedding potential of this sites’ gathering is more important than each individual site.
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Another example is to define an aggregation of appliances belonging to different
sites. In fact, controlling one appliance may not be significant at the grid level. How-
ever, remotely aggregating the flexibility supply from many appliances offers a great
potential – e.g. with a fleet of EVs.
The IoT paradigm is therefore allowing to gather altogether devices, from different
areas but with similar behavior. These aggregation mechanisms offer more signif-
icant potentials than what each individual could offer. Such next-generation grid
communication systems is encouraging the emergence of FOs that sell negative en-
ergy (also called “negawatts”) – i.e. selling flexibility in consumption, load shedding
capacity or vehicle-to-grid transfer system [5]. Additionally, an open network simpli-
fies the operation of new markets, since the layered structure of the Internet allows
to create new services upon existing ones. Actually, the proper incentives can be
computed based on the available data, and transmitted to actors at each envisioned
scale.
Therefore, the main elements for such model are the EMSs that have data storage,
analysis and management capabilities as well as decision and control functionalities.
They rely on available smart appliances and smart meter data in order to efficient-
ly optimize the energy balance of their managed group. The same communication
technologies that the one listed in Table 8.1 can still be implemented. However,
peer-to-peer communication as well as shared directories will be required to ensure
an efficient coordination of these elements while reducing the risks for a single point
of failure to affect the system.
8.2.3.5 Security concerns
We also stress the importance of security required in such Smart Grid systems. While
we still regularly observe new attacks occurring on the Internet, and recently target-
ing connected objects, the redundancy and resilience of the Internet, together with
various improvements in Internet security, still make it the best candidate for Smart
Grid applications. In fact, the Internet, and in particular the IoT, benefits from the
efforts of many specialists as regard to security issues [18].
In any case, this next-generation system will be required to use the following build-
ing blocks for providing security:
•Authentication: identifying a node before letting it access the system;
•Authorization: process to determine if a node has the authorization to perform
certain tasks;
•Access control: ensuring that a node can access to certain resources (data and/or
devices).
For instance, these mechanisms should help the system ensure that a given third
party is really who it claims to be, that it can interconnect with other parties of the
system and perhaps, that it can access data from a given EMS.
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8.2.3.6 Ongoing research efforts
Research efforts are required in order to study, standardize and test such next-
generation Smart Grid communication systems. Before being adopted it has to
demonstrate its benefits and above all that it is considered secure for both the end-
users and utilities.
In the IoT paradigm, several groups are trying to standardize a common IoT ar-
chitecture. Standardization and uniformity are required in order to be able to collect
information from different application domains, and cross them in order to enhance
the system. For instance, machine learning mechanisms can be used to determine
predictive information based on gathered data. But, as aforementioned, the IoT land-
scape is currently fragmented and standardization efforts are scattered. As a matter
of fact, most of the IoT architectures currently used are deployment-specific and do
not enable simple interconnection. In the following, we cite certain IoT architectures:
•oneM2M: A global organization composed of standardization bodies as well as
industries. It was created to provide technical specifications addressing the need
for a common architecture to connect the myriad of Machine-to-Machine (M2M)
devices. oneM2M Functional Architecture [19] mainly focuses on the collection
of data from field nodes and interconnection of M2M systems. This group is still
active and have on-going research on semantics, which should provide automa-
tion to their Functional Architecture. However, it does not plan to have end-users
interacting with end-nodes.
•Internet of Things - Architecture (IoT-A): A European research project that aims at
providing a reference architecture model for IoT applications and business. This
project develops guidelines (common understanding, common grounding, stan-
dardized interfaces and best practices) and a reference model for building compli-
ant IoT solutions. The resulting Architecture Reference Model (ARM) [20] helps
to build and interconnect systems. However, it carries on the silos model.
•Industrial Internet Consortium (IIC): An organization composed of several compa-
nies created to promote open standards and interoperability for technologies used
in industrial and M2M environments. They designed an architecture, Industrial
Internet Reference Architecture (IIRA) [21], enabling to set up industrial IoT sys-
tems, which will be compatible and interoperable with other industrial systems. It
is based on specific publish-subscribe mechanisms that helps the interconnection
ensure reliability, performance and scalability. However, IIRA supposes that all
deployments have a centralized management domain.
•Smart Energy Aware Systems (SEAS): A European project aiming to provide the
ICT tools to interconnect energy actors in order to better manage, coordinate and
optimize energy consumption, production and storage. The proposed SEAS Ref-
erence Architecture Model (S-RAM) [22] derives from oneM2M and is based on
distributed core services to interconnect both energy actors and management sys-
tems. These core services should take care of the following:
– finding other parties in a simple way;
– automatically learn from them using semantics;
– ensuring security of the system;
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– ensuring monetary compensation for compliance to commitments.
This model defines a way to divide the system in various groups, which therefore
provides different management levels.
•Alliance for Internet of Things Innovation (AIOTI): An alliance initiated by the
European Commission based on the observation that there was no common Euro-
pean IoT market. The aim of this alliance is to strengthen interaction among IoT
players in Europe, and to contribute to the creation of a dynamic European IoT
ecosystem. In order to meet this target, they suggest to define an High Level Archi-
tecture 3), providing minimal requirements, using semantics and compatible with
previous architectures.
All the above approaches attempt to provide a common reference model for IoT-
related solutions, apart from SEAS, which is dedicated to the energy domain – but
S-RAM could be used for other application domains.
The next-generation Smart Grid systems that would interconnect AMI with an
Internet-based communication network might probably rely on one of these architec-
tures. The most promising solutions from the one cited previously would be a) IoT-A
but it will not provide the openness required to support open energy market; and
b) SEAS as it appears to provide all the required building blocks.
No matter which solution is chosen to support such next-generation Smart Grid
systems, it would have to demonstrate the advantages of such a complex system
through research and test-bed implementations. In the next part, we give more de-
tails on the communication framework, and especially the routing part that nowadays
allows smart meters to communicate with the utility center in an efficient and reliable
way.
3) No document published at the time this chapter was written
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8.3
Routing information in the Smart Grid
In smart grid networks, the majority of smart meters are located in the Neighborhood
Area Network (NAN), without having direct communication with the data concen-
trator. As a result, routing paths need to be established for smart meters to reach the
data concentrator (sink) of the network.
An AMI connects the Wide Area Network (WAN) and the NANs, acting as a gate-
way between the smart meters and the utilities. As it was presented in Section 8.2.1.2,
those networks mostly utilize technologies that are sensitive to perturbation and could
be considered as Low power and Lossy Networks (LLNs). Considering the fact that
most of the smart meters that are used in AMI networks come with a single com-
munication interface, the path to the the sink needs to be carefully designed as no
redundancy exists.
In Narrowband PLC and wireless networks, most of the nodes that exist in the
infrastructure cannot communicate directly with the sink or with other nodes due
to the limited transmission capacity (long distance, external interference and noise).
Therefore, the nodes need to collaborate together to forward the data packets to the
final destination. Similarly, in a smart grid network, the nodes are the smart meters
that route metering information to the data concentrator.
Typically, a routing protocol constructs and maintains the best paths in the net-
work for the packets to be routed toward the destination. To do so, routing protocols
propagate routing information message using either proactive or reactive models.
High number of hops degrades the network performance as it introduces additional
delay in reactive routing or additional overhead in proactive approaches. To minimize
the impact of routing, it is essential to minimize the number of hops in the network.
However, it is also important to carefully select the optimal path to the destination
according to an objective function and appropriate metrics. Note that the shortest
path is not always the optimal solution, i.e., the Expected Transmission Count (ETX)
is a popular metric in IoT networks.
8.3.1
Routing family of protocols
Prior the emergence of LLNs, several routing protocols have been presented and stud-
ied. However, none of them was meeting the requirements of such wireless and lossy
networks. Levis et al. show in[23] that most of the routing protocols do not fulfill
the requirements of LLN such as footprint or Maximum Transmission Unit (MTU)
limitation [24]. In smart grid network community, mainly two routing protocols are
emerged, and are widely studied and deployed. J.Yi et al.[25] present how criti-
cal the routing protocol is for smart grid networks, and how Routing Protocol for
LLNs (RPL) and Lightweight On-demand Ad hoc Distance-vector Routing Proto-
col Next Generation (LOADng) could tackle the specifics of smart grid applications.
These protocols have routing metrics that deal with the characteristics of LLN that
make them specifically suitable for smart grid networks. For instance, the link re-
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liability metric is not used in the Internet routing protocols because technologies
employed in such networks are extremely reliable and fast recovery mechanisms ex-
ists for failure. But in LLN, taking into account the reliability of the links to build
the path is significant because link quality quickly changes over time.
In addition, using the node’s energy consumption as a metric allows to consid-
er how the node is powered and what is its remaining lifetime. Such metric is a
key-enabler to enhance the lifetime of a wireless network where devices are mostly
battery-operated. Hereafter, we provide a detailed description of two leading family
of routing protocols, based on the propagation of the routing information in the net-
work, namely the proactive and the reactive routing protocols, respectively. More-
over, we present a performance comparison of the most popular routing protocols
such as RPL, Ad Hoc On-Demande Distance Vector Protocol (AODV) and LOADng
for LLNs.
8.3.1.1 Proactive routing protocol
In proactive routing protocols, routes are built a priori and, as a result, all nodes in
a network are aware of the routes to any destination at any time. Thus, a node may
transmit a data packet to any destination at no delay, since all routes are stored in the
routing tables. However, periodic routing-related control packets need to be trans-
mitted to maintain the routing table updated. Furthermore, to control the network
overload, the periodicity at which these control packets must be accurately defined.
RPL[26] is today the main protocol in the proactive family of routing protocols
chosen in LLN. It is actually a distance vector routing protocol specified by the
Internet Engineering Task Force (IETF) ROLL working group [27]. RPL is defined
as Link-layer agnostic, so it can operate over Wireless or PLC networks for example.
8.3.1.2 Topology management under RPL
In a LLN, the topology is not predefined and, thus, RPL is in charge of discovering
and carefully selecting nodes in order to construct optimal routes. The topology is
organized based on a Directed Acyclic Graphs (DAG), a graph where the connec-
tions between nodes have a direction and a "non-circular" property. Based on the
"acyclic" nature of the DAG, the graph comprises at least one root, a node with no
outgoing edge. In Figure 8.4 (a), a DAG composed of ten nodes and three DAG roots
is illustrated. To construct a routing topology, RPL employs an extension of DAG:
the Destination Oriented DAG (DODAG) which is similar to DAG with single DAG
root. In a smart grid scenario, the root of a RPL network could be the data concentra-
tor that gathers the metering information. Figure 8.4 (b) depicts a DODAG topology
that consists of eight nodes with one root.
To establish and maintain routes, RPL uses three different types of ICMPv6 control
packets:
•DAG Information Object (DIO)
•DAG Information Solicitation (DIS)
•Destination Advertisement Object (DAO)
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Figure 8.4 Example of a DAG and a DODAG.
The upward route construction, the one used between smart meters and the core
network, is managed by transmitting DIO messages in multicast. DIO messages con-
tain information that allows discovering a RPL instance, calculating its own rank and
choosing parents in the DODAG. The rank contained in the DIO message is the rank
of the node sending the DIO message and determines the relative position of a node
in the DODAG. The rank is computed by the objective function using routing met-
rics and its purpose is to avoid loops. The downward route construction, which is
optional in RPL, is managed by the DAO messages to propagate information about
the destination in the upward direction. To construct the downward routes, there are
Storing and Non-Storing mode. Finally, DIS control packets are utilized to solicit a
DIO message from a RPL node.
8.3.1.3 Routing table maintenance under RPL
As previously stated, DIO messages are periodically transmitted to build and main-
tain the RPL DODAG. However, if the network is stable, the DIO message frequency
is decreased to reduce the overhead of signaling messages. On the contrary, if the
condition of the network is not stable, more DIO messages have to be transmitted.
This timing function is called Trickle timer [28]. If a received DIO message does
not imply any change on the receiver in terms of rank, parent set or preferred parent,
the DIO is considered consistent. As long as consistent messages are received, the
interval between DIO messages is exponentially doubled to reduce the overhead of
periodic messages. Conversely, when the network is not stable and DIO messages
are inconsistent with the known topology, more DIS and DIO messages are needed
to update the node routing tables. Messages such as multicast DIS without a solicited
information option or DIO messages containing infinite rank are considered incon-
sistent, and cause the trickle timer to reset, and the interval time is set to its minimum
value. The Trickle algorithm allows to be reactive in case of a change or failure in
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the network while minimizing the overhead when the network is stable.
For the downward route construction, a DelayDAO is sent to govern the emission
of the DAO messages. At each transmission of a DAO message, a random interval is
chosen before the actual transmission.
8.3.1.4 Routing strategy: metrics and constraints
A metric in RPL is a quantitative value, and it is used to evaluate the path cost.
Vasseur et al. [29] define two kinds of metrics that can be used for path calculation:
•The link metric that concerns the link’s attributes e.g., Link Quality Level (LQL),
ETX, latency, throughput.
•The node metric that takes into account the Node State and Attribute (NSA) such
as energy (remaining energy, power source) or min-hop (number of hops to the
root).
RPL supports also a constraint-based routing where the constraint may be applied
on both link and nodes. If a link or a node does not satisfy a constraint, it is discarded
from the parent set.
This constraint is used to include or eliminate a link or a node that not meet a spe-
cific criteria. For instance, the objective function will not choose a path that traverses
a node that is battery-powered or a link with low ETX. RPL objective function could
combine metrics and constraints to compute the best path.
8.3.1.5 Path computation under RPL
To compute the optimal path, the objective function plays a major role in RPL pro-
tocol. To this aim, the two following algorithms have to be defined:
•the computation of the node’s rank according to one or several metrics
•the parent selection operation according to metrics and constraints
Two objective functions have been defined by the ROLL working group: Ob-
jective Function Zero (OF0) and Minimum Rank with Hysteresis Objective Func-
tion (MRHOF) that are presented next.
The Objective function zero
The OF0 [30] works by computing the rank based on the addition of a scalar, rep-
resenting the link properties to the rank of the preferred parent. The scalar value is
normalized between 1 and 9 for expressing the link properties with 1 for excellent,
and 9 for very poor. Note that any kind of metric could be used for the scalar value.
This objective function allows for finding the closest grounded root (a root that of-
fers connectivity to the application goal) by selecting a preferred parent and a backup
successor if available.
The rank computation is given by the algorithm below:
R(N) = R(P) + rank_increase (8.1)
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rank_increase = ((Rf ∗Sp +S r)∗M inHopRankI ncrease)(8.2)
where:
•R(P) is the preferred parent’s rank
•Sp (the step_of_rank), Sr (stretch_of_rank) and Rf (rank_factor) are respectively
the expression of the link properties normalized between 1 and 9, the maximum
augmentation to the step_of_rank of a preferred parent to allow the selection of
an additional feasible successor and a value used to increase the importance of the
link properties.
•MinHopRankIncrease is a multiplying factor that plays a major role in the rank
computation by reflecting the impact of the metric on the rank increase. The default
value is 256 as it is described in[26].
OF0 parent selection is governed by several rules (see Section 4.2.1 of [30]), but
the most important is that the selected parent must be the one that causes the lesser
resulting rank for the node. This selected parent become the "preferred" parent.
The Minimum Rank Hysteresis Objective Function
MRHOF [31] optimizes the path to the root that minimizes a defined metric. How-
ever, it avoids to change this path frequently. Light metrics variations cause changes
in the network that are decreased by introducing an hysteresis. MRHOF works with
additive metrics and introduces the path cost for the rank computation, that specifies
the property of the path to the root regarding the employed metric. The path cost is
calculated by the sum of the path cost advertised by the parent and the link metric
cost to the parent.
The rank computation for MRHOF is given by the algorithm below:
pathcost =parentpath_cost +link_cost (8.3)
rank =f unc(pathcost )(8.4)
where:
•parentpath_cost is advertised by the parent and represents the pathcost of the parent.
•link_cost is the cost associated with the parent’s link regarding to the selected met-
ric.
MRHOF parent selection is governed by an hysteresis function given by the equa-
tion below where P1path_cost and P2path_cost being respectively the path cost to Parent
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Figure 8.5 Example of an Upward route construction with RPL.
1 and Parent 2. PP is the selected parent designated as Preferred Parent. P1 is the
current best parent and P2 is a candidate parent.
P P =(P2if P 1path_cost +T hreshold > P 2path_cost
P1else (8.5)
where Threshold is the hysteresis function, i.e., the minimum difference between
the cost of the path through the preferred parent and the cost path of a candidate parent
to trigger the selection of a new preferred parent. This objective function allows for
selection of the route towards the root with the lowest path cost, e.g., minimum hop
counts if the hop-count metric is used.
8.3.1.6 Summary of the RPL DODAG construction
Figure 8.5 shows an example of the upward route construction using hop-count met-
ric. Once the trickle timer is expired, RPL root will broadcast a DIO message, con-
taining its rank. Nodes in the coverage area of the root (i.e., yellow circles) will
receive the DIO message and process it. If the DIO message had been corrupted, it
would have been discarded. Since the root is the sink of the network, nodes 1 and 2
can not be closer to the root so they will add the root as their preferred parent and
compute their rank. To test if a candidate neighbor is eligible to be a preferred par-
ent, a node will verify if the rank contained in the received DIO message added to
a RPL parametric value (min_hop_rank_increase) is less than its rank. Then node
1 and 2 will broadcast their own DIO message with their new computed rank. Note
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that since the root has a smaller rank than the one advertised in nodes 1 and 2 DIO
messages, nodes 1 and 2 will not be considered as potential parents for the root. It
is worth mentioning that ranks shown under node names in this example depends
on the objective function and values shown beside edges represents the link quality
(i.e., ETX). The arrows between nodes represents the upward route and when a node
installed at least one of them, it is considered to have joined the DODAG. It has to be
noted that a node may either stay silent and wait for a DIO message or it may send a
DIS message during the initialization process.
8.3.1.7 Reactive routing protocol
In a reactive-based routing protocols, routes are built and maintained only when they
are requested, which means that there is no need to maintain a route if there is no
traffic. Thus, a delay is added before transmitting a data packet due to the route
construction. Contrary to proactive protocols, reactive protocols do not need to send
routing information periodically and, thus, will require less energy or CPU resources.
However, the quantity of routing messages will greatly depends on the frequency of
the traffic in the network.
8.3.1.8 Topology management under AODV
AODV [32] is a well known reactive routing protocol designed for use in Mobile
Ad Hoc Networks (MANET). It floods the network with broadcasted Route-request
messages when a needed route that does not exist.
To establish and maintain routes, AODV uses five type of messages:
•RREQ: Route request
•RREP: Route reply
•RERR: Route error
•RREP-ACK: Route Reply Acknowledgment
•HELLO: Link status monitoring
When a source node expects to establish a route to a destination, it broadcasts a
RREQ packet. Once the destination is reached (or an intermediate node that knows
the route to the destination), a RREP message is sent back to the RREQ sender which
ends the route discovery process. If a RREP message is received, the route discovery
operation is over. Otherwise, after certain period, it repeats the RREQ message and
increases the waiting period. If there is no RREP message, this process can be re-
peated several times (by default, RREQ_RETRIES = 2). If there is still no response
after three attempts, the route search process is aborted. Consequently, a new route
request will be initiated after ten seconds. A node receiving a RREQ packet will
send a RREP (route reply) packet if it is the destination or if it has a route to the
destination with a sequence number greater or equal to the RREQ packet, otherwise
it rebroadcasts the RREQ packet. Each node keep a trace of the source IPs and the
identifiers of the RREQ packets. In case of receiving a RREQ packet that they have
already processed, they delete it.
Once the source has received the RREP packets, it can start sending data packets
to the destination. If the source subsequently receives a RREP containing a higher or
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Figure 8.6 Example of an AODV route detection between node A and G.
equal sequence number but with a smaller number of hop, it will update its routing
information to that destination and start using the best route. A route is maintained
as long as it continues to be active, in other words, as long as data traverse between
the source and the destination. The link expires when there is no more data in transit
on the link and after a pre-defined delay. If the link is cut, the end-node sends a
RERR (Route Error) packet to the source node to warn that the destination is currently
unreachable. If the source node still wants to get a route to that destination, it must
start the route discovery process again.
Concerning the routing table, each entry contains nine fields. In addition to IP ad-
dress of the destination node, the fields contain routing information and information
related to the qualitative state of the route for maintenance purposes. Unlike other
protocols, AODV only maintains information about the next hop in the route, not the
entire routing list. This saves memory and decreases overhead for route maintenance.
The routing table also contains information enabling the host to share information
with other nodes when link states change. To ensure the information is the latest one
available in the route table entry, a sequence number for the IP address is included in
the message. This sequence number is called the "destination sequence number". It
is updated each time a node receives a RRER, RREP, RREQ message.
To offer connectivity information, nodes that are part of an active route, can broad-
cast local HELLO messages. Every HELLO_INTERVAL, the node will check if it
has sent a broadcast message during the last interval, and if it has not, it will broad-
cast a RREP message with a TTL set to 1. Within a dedicated period, if a node that
has received Hello message from a neighbor, does not receive any packet from that
neighbor, the node will assume the link is lost, and will send a RRER route error
message. Figure 8.6 shows a route search on the initiative of the node A in the direc-
tion of J. The RREQ message is broadcasted from node A to all its neighbors. When
node G receives the message, it returns a RREP message to node A through node E.
•The RREQ route request message is sent to search for available routes, it is made
of frame size 24 bytes in length.
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•The RREP route response to demand message is sent to indicate available routes
to the originator of the demand, the frame consists of 20 bytes .
•The RRER is sent to report routes with potential errors to the originator of the
demand, it consists of 20 bytes.
A Route Reply Acknowledgment (RREP-ACK) message is sent in response to a
RREP message with the ’A’ bit set to 1when there is danger of unidirectional links
preventing the completion of a Route Discovery cycle. It indicates that another avail-
able route is already used.
8.3.2
Reactive routing protocol in a constrained network
Several proposals emerged to simplify and adapt AODV for LLNs. In 2011 and 2012,
with the use of an adaptation of AODV in G3-PLC standard in smart grids networks,
a single LOADng specification emerged, as the next version of AODV.
6LoWPAN Ad Hoc On-Demand Distance Vector Routing (LOAD) and Lightweight
On-demand Ad Hoc Distance-vector Routing Protocol Next Generation (LOADng)
are both routing protocols based on AODV reactive routing protocol. LOADng is the
latest version of LOAD where many features have been reviewed to make LOADng
more efficient and extensible. In LOADng several extensions have been included
to improve the performance under specific scenarios such as LLN, by reducing the
network overhead. Thus, LOADng, LOAD and AODV share many common points:
1. A node that has data to transmit to a destination but has not any information
related to this destination in its routing table: it sends a RREQ message. If the mes-
sage is received by a node that has been already transmitted by itself, it will discard
the message to avoid breaking existing routes. Intermediates nodes constructs the
reverse route upon the reception of RREQ message.
2. When the destination node receive the RREQ message, it can generate a RREP
message immediately and, thus, minimize the time to establish the path). It could
also wait to receive several RREQ messages with better metric to optimize the path
at the cost of a longer path establishment delay.
3. To detect broken or asymmetric links, intermediates nodes can request for an
acknowledgment during the forward route to the destination construction.
4. Except for LOADng, when a node is no longer able to forward packets to the
next hop, a local repair mechanism is triggered to solve the problem. In case the local
repair mechanism fails, a RRER message is sent to the originator of the message.
In Figure 8.7 an example of route construction using LOADng routing protocol is
illustrated.
8.3.2.1 Performance evaluation
As LOAD and RPL are both specifically designed for LLN, hereafter, we will present
a performance evaluation comparison of these two protocols.
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Figure 8.7 Example of a route construction with LOADng.
Comparing RPL and LOAD will mainly depend on the topology (e.g., density). In
a stable network, the round-trip time for a data request (i.e., end-to-end delay) will
tends to be better with RPL, due to the time needed to build the path using the LOAD
RREQ message. Thus, thanks to the trickle timer, RPL will decrease significantly
the control traffic as long as the network stays stable. When the condition of the
network evolves unexpected (i.e., a node loses its parent, a path cost change), RPL
will reset the trickle timer and send more DIO messages to recompute the DODAG.
This explains the additional control traffic in RPL, and may results in a broadcast
storm, caused by the issue of DIO messages with increased DODAG version number
(Global repair). A global repair of the DODAG triggered by the root. It re-computes
the DODAG and increases the end-to-end delay where LOAD will be less impacted
by the network variation. In smart grid applications, end-to-end delay tolerance could
vary from below than 10ms to more than two seconds (e.g., smart meter reading). On
the other hand, a teleprotection for instance, which ensure the protection of network
equipment from severe damage by managing the grid load, requires fast signals to
pilot protective relays, no more than 10ms [33].
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27
Protocol LOADng RPL
Type On-Demand Proactive
Algorithm Distance Vector Distance Vector Source Routing
Local repair Yes Yes
Mobility Static, Mobile Static, Mobile
Scalability High High
Supported traffic P2P P2P, P2MP, MP2P
Table 8.2 LOADng / RPL comparison.
02468
0
20
40
60
80
100
End-to-end delay (second)
CDF [%]
LOAD Urban
LOAD Rural
LOAD Mixed
RPL Urban
RPL Rural
RPL Mixed
Figure 8.8 End-to-end delay comparison.
Since in LLNs nodes have constrained memory, smart meters will only store a
dozen of entries whereas the routing table usually contains hundred of thousands
routes in IP core routers. As a consequence of flooding, each smart meter in a LOAD
network receiving a RREQ message will install a route towards the sender resulting
in a large number of unnecessary routing entries. The same issue occurs when a node
is situated on a route of a RREP message. In contrary, most routers in RPL network
have the default entry towards the preferred parent. However, when RPL operates in
storing-mode, nodes that are chosen as preferred parent have to store the downward
route and may cause critical issues such as loops, in case a node runs out of energy.
Concerning the path efficiency, since RPL compute a DODAG from a sub-topology
of the physical network, the traffic has to follow paths along the DODAG even if
a more optimal path exists in the physical world. Those protocols produce a sub-
optimal solution, which can be improved by carefully select parameters for the met-
rics used to arbitrate the chosen links. For instance, LOAD uses the LQI (Link Qual-
ity Indicator) of the 6LoWPAN physical layer in addition to the Hop distance.
Figure 8.9 shows the Root Data Delivery Ratio of a 100 nodes topology for RPL
and LOAD, respectively, after two hours of simulation. As the traffic is set to start
when the simulation initiates, RPL demonstrates additional delay before the actual
data packet reception. However, RPL attains high performance once the DODAG is
established. Concerning LOAD, results indicate that data is received quickly once
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28
0 2,000 4,000 6,000
0
0.2
0.4
0.6
0.8
1
Simulation time (second)
Data Delivery Ratio RPL[%]
(a) RPL
0 2,000 4,000 6,000
0
0.2
0.4
0.6
0.8
1
Simulation time (second)
Data Delivery Ratio LOAD[%]
(b) LOAD
Figure 8.9 Data delivery ratio comparison.
the network is initiated, however, it takes time for LOAD to reach the same DDR like
RPL does.
In RPL, packets are sent only after the DODAG is constructed. Thus, if the metric
chosen for constructing the DODAG is hop-count, then RPL will compute a DODAG
with minimum hops. Due to the flooding mechanism of LOAD, nodes construct the
path using the first RREP message arrived, which is not necessarily the optimal one
in terms of hops. The packet will follow a non-optimal route until subsequent RREP
message reception to update the path.
In terms of overhead, differences between the protocols will mainly depend on the
implementation and employed parameters. The stability of the network has a sig-
nificant impact on RPL protocol, since its parameters should be carefully selected
to handle specific network circumstances. In LOAD, Route Hold Time (RHT) will
greatly impact the frequency of the flooding and, consequently, will increase the over-
head. The number of nodes in the network is also critical in LOAD since high density
in the network will increase the overall overhead. In RPL, we expect the maximum
overhead at the beginning of the DODAG construction, and then a reduction as the
network become stable, due to the behavior of the trickle algorithm.
8.3.2.2 Summary on routing protocols
Choosing between reactive and proactive routing protocols in a smart grid network
depends on multiple factors. The application, which identifies the type of traffic,
has a major role in choosing the routing protocol and its corresponding parameters.
Several parameters will also depends on the density of smart meters and the type of
topology, i.e., number of maximum hops to the root. Furthermore, the priority of the
traffic has an impact on the routing strategy as well, i.e., if the application is tolerant to
high end-to-end delay. However, each protocol has different implementation issues
attaining, thus, different performances. As a result, several parameters need to be
properly configured in order to satisfy the requirements of the considered network
and application.
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29
RPL for instance is known to work well in Multipoint to Point application, a typical
scenario in smart grid network, where data concentrators will receive the data from
a large amount of smart meter in the NANs. LOADng address 6LoWPAN Ad Hoc
On-Demand Distance Vector Routing (LOAD) Multipoint-to-Point issue and offer a
similar performance to RPL at the cost of delay for the route discovery process. In
smart grid scenario, for typical monthly readings, the delay could not be a critical
issue, so both protocols could be chosen depending on the acceptable overhead. The
frequency of the traffic is also a major issue in smart grid networks, where a stable
routing graph, such as RPL constantly maintained DODAG will greatly impact the
delay at the cost of energy consuming. On the other hand, if the smart grid traffic is
sparse, the need of maintaining a routing graph at the cost of high control traffic is
not essential. For example, millions of smart meters using RPL have been installed
in California mandated by California Public Utilities Commission (CPUC) while
French Enedis has chosen LOADng for the widely deployed Linky smart meters in
France.
Furthermore, today Smart Grid devices have multiple heterogeneous communica-
tion interfaces that leads to hybrid networks. Such a feature allows to enhance reli-
ability and robustness by taking advantages of all available technologies (i.e., PLC
and 802.15.4) [34].
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30
8.4
Conclusion
This chapter was devoted to present and discuss both infrastructures and communi-
cation architectures as well as technologies and protocols employed in Smart Grid
systems. We especially detailed the necessary requirements next-generation Smart
Grid systems should address in order to enable dynamic and evolving architectures.
In addition, we compared certain existing routing families that efficiently fulfill the
requirements of a constrained-based Smart Grid environment.
Current Smart Grid systems, which rely on Advanced Metering Infrastructure
(AMI), are facing several issues as prosumers demands are constantly growing while
simultaneaously production capacity is less predictable due to the increasing pop-
ularity of local renewable production units (i.e. a decentralized energy production
system). In order to have a better management of the network, utilities require tools
to forecast both consumption and local production of end-points, as well as mecha-
nisms to remotely control them. As a consequence, current Smart Grid systems need
to open up to Internet-based architectures. They will therefore move from a central-
ized to a distributed configuration in which end-users could be more involved and
that might have different management levels.
Such configuration requires dedicated Energy Management System (EMS), that
will collaborate with both end-users and smart meters deployed by DSO, in order
to locally handle the management of a given set of nodes. Using such EMSs will
help any provider deploy new energy services while ensuring users’ data privacy.
However, security concerns arise from this configuration and must be considered
along with its deployment.
Regardless of the configuration choosen, Smart Grid networks mostly remains on
constrained devices used in large topologiesAs a result, we also insist in this chapter
in presenting two families of routing protocols that efficiently manage the routing
issues in such topologies. Proactive routing protocols, such as RPL, construct routes
a priori, i.e. before they are required, and all nodes are perfectly aware of the path
to any destination. Applications that need low end-to-end delay will benefit from
such a proactive protocol, as once the routing graph is built, traffic could be sent
without any additional time. On the other hand, reactive routing protocols, such as
LOAD, construct the routes only when required. Therefore, these protocols allow
applications to overcome any failure in the network without waiting for a complete
reconstruction of the routing graph. However, they add delay in the data traffic.
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31
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