The Management System of INTEGRIS

Extending the Smart Grid to the Web of Energy

Joan Navarro

, Andreu Sancho-Asensio

, Agust´ın Zaballos

, Virginia Jim´enez-Ruano

David Vernet

and Jos´e Enrique Armend´ariz-I˜nigo

La Salle – Ramon Llull University, 08022 Barcelona, Spain

Universidad P´ublica de Navarra, 31006 Pamplona, Spain

Keywords:

Web-based Services, Intelligent Agents, Distributed Databases, Security, Smart Grids.

Abstract:

The recent growth experimented by the Internet has fostered the interaction of many heterogeneous technolo-

gies under a common environment (i.e., the Internet of Things). Smart Grids entail a sound example of such

situation where several devices from different vendors, running different protocols and policies, are integrated

in order to reach a common goal: bring together energy delivery and smart services. Latest advances on

this domain have led to effective architectures that support this idea from a technical perspective, but fail at

providing powerful tools to assist this new business model. Hence, the purpose of this paper is to present a

novel uniﬁed and ubiquitous management interface, driven by an intelligent system, that uses the advantages

featured by the Web of Things to manage the Smart Grid. Therefore, this work opens a new path between the

Internet of Things and the Web of Things resulting in a new concept coined as the Web of Energy.

1 INTRODUCTION

The ever growing set of features provided by the In-

ternet has led into a standard communications frame-

work that eases the deployment and development

of distributed and heterogeneous applications. Such

evolution has driven a rising interest in connecting

several gadgets to the Internet, ranging from mobile

phones to home appliances, including special sys-

tems such as trafﬁc lights or embedded devices. This

has led to a new form of distributed system, referred

to as the Internet of Things (IoT), that consists in

(1) uniquely identifying every object in the network,

(2) using the Internet as the communications infras-

tructure, and (3) providing a lightweight interface to

rapidly access everywhere.

Recent advances on Smart Grids have explored

the feasibility of considering the electricity distribu-

tion network as a particular case of the IoT (Guinard

et al., 2011; Zaballos et al., 2011). Certainly, this spe-

ciﬁc domain poses appealing challenges in terms of

integration, since several distinct smart devices (also

referred to as Intelligent Electronic Devices or IEDs)

from different vendors—often using proprietary pro-

tocols and running at different layers—must inter-

act to effectively deliver energy and provide a set

of enhanced services and features (also referred to

as smart functions) to both consumers and producers

(prosumers) such as network self-healing, real-time

consumption monitoring, and asset management (IN-

TEGRIS, 2011). Although the latest advances on the

IoT ﬁeld have deﬁnitely contributed to the physical

connection of such an overwhelming number of smart

devices, several issues have arisen when attempting to

provide a common management and monitoring in-

terface for the whole Smart Grid (INTEGRIS, 2011;

Aman et al., 2013).

Indeed, integrating the heterogeneous data gener-

ated by every device on the Smart Grid (e.g., wired

and wireless sensors, smart meters, distributed gen-

erators, dispersed loads, synchrophasors, windmills,

solar panels, communication network devices) into a

single interface has emerged as a hot research topic.

So far, some experimental proposals (Guinard et al.,

2011) have been presented to face this issue by using

the Web of Things (WoT) concept to access a mashup

of smart devices and directly retrieve their infor-

mation using reasonably thin protocols (e.g., HTTP,

SOAP) (Guinard et al., 2010).

However, the speciﬁc application of these ap-

proaches to real-world environments is fairly doubt-

ful because (1) they may open new security breaches

(Zeng et al., 2011; Bou-Harb et al., 2013) (i.e., end-

users could gain access to critical equipment), (2)

329

Navarro J., Sancho-Asensio A., Zaballos A., Jiménez-Ruano V., Vernet D. and Armendáriz-Iñigo J..

The Management System of INTEGRIS - Extending the Smart Grid to the Web of Energy.

DOI: 10.5220/0004950203290336

In Proceedings of the 4th International Conference on Cloud Computing and Services Science (CLOSER-2014), pages 329-336

ISBN: 978-989-758-019-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

there are no mature electric devices implementing

WoT-compliant standards available in the market (IN-

TEGRIS, 2011), and (3) industry is averse to include

foreign modules (i.e., web servers) on their histor-

ically tested and established—but poorly evolved—

proprietary systems (Gungor et al., 2013).

Therefore, the European project INTelligent Elec-

trical GRId Sensor Communications (INTEGRIS)

(INTEGRIS, 2011) has explored a new way to over-

come these issues and, thus, provide a manage-

ment interface for the Smart Grid inspired by the

WoT. More speciﬁcally, the aim of INTEGRIS is

twofold: on the one hand, it implements an ICT

infrastructure—based on the IoT paradigm—to han-

dle the Smart Grid security, storage, and commu-

nications requirements (Navarro et al., 2012). On

the other hand, it uses a cognitive-inspired intelli-

gent multi-agent system to manage the whole Smart

Grid and link it with end-users using a WoT-based ap-

proach, which results in a new bridge between the IoT

and WoT.

The purpose of this paper is to present this multi-

agent intelligent system that extends the WoT ap-

proach and implements the management system used

in the INTEGRIS’ real-world domain. This proposal,

which leads in a new form of the WoT coined as the

Web of Energy (WoE), is targeted to provide a secure,

context-aware, and uniform web-based novelenviron-

ment to effectively manage, monitor, and conﬁgure

the whole Smart Grid. Moreover, conducted devel-

opments proof the feasibility and reliability of our ap-

proach and encourage practitioners to further research

towards this direction.

The remainder of this paper is organized as fol-

lows. Section 2 reviews the related work and justiﬁes

the proposal. Then, Section 3 details the reference ar-

chitecture for the Smart Grid and Section 4 introduces

how it is integrated into the Web of Things. Finally,

Section 5 concludes the paper.

2 RELATED WORK

Service composition, heterogeneous devices interac-

tion, and a close contact with the real-life demands

are some of the features that have positioned Smart

Grids as an appealing landscape for deploying the

latest advances concerning the IoT and WoT. Over

the last years, practitioners have directed their efforts

on enabling communications between distinct types

of devices spread across the different network facil-

ities that compose the Smart Grid (Zaballos et al.,

2011). Certainly, the IoT and the WoT approaches

have promoted such advances in the sense that IEDs

are no longer considered as isolated entities but re-

ferred to as gears of a complex and heavily cou-

pled distributed system (Guinard et al., 2011; Guinard

et al., 2010). While the IoT has provided a mature

approach to enable hardware communication on the

Smart Grid (Zaballos et al., 2011; INTEGRIS, 2011),

management, monitoring, and grid conﬁguration re-

quirements envisage the need of using either central-

ized Network Management Systems (NMSs) (Gungor

et al., 2013) or WoT-based approaches (Guinard et al.,

2011; Priyantha et al., 2008).

Although centralized NMSs are rapid to deploy

and easy to maintain, their lack of scalability, sin-

gle point of failure exposure, and bottleneck effect

vulnerability make them unfeasible for real-world ap-

plications (Aman et al., 2013). These issues are

driving system designers to explore distributed solu-

tions for the Smart Grid domain such as the Repre-

sentational State Transfer (REST) and Web Services

(Guinard et al., 2010). On the one hand, REST-

ful strategies (Cubo et al., 2012; Luckenbach et al.,

2005) are targeted to ease the development of scal-

able services and applications by (1) uniformly iden-

tifying every smart object through a unique Uniform

Resource Identiﬁer (URI), (2) using HTTP stateless

communications, (3) representing resources through

standard human readable formats (e.g., XML), and (4)

using simple parsing algorithms—typically embed-

ded on tiny web servers—to interact with every IED

(Guinard et al., 2011; Duquennoy et al., 2009). On the

other hand, strategies based on Web Services pursue

the same goal but implement some advanced facilities

(e.g., UDDI server, WSDL, security) rather than using

light weight protocols (Guinard et al., 2010; Priyantha

et al., 2008).

Despite the aforementioned beneﬁts featured by

Web Services and RESTful frameworks, the estab-

lished electric industry is still far from widely adopt-

ing them as a standard solution for the Smart Grid

domain (INTEGRIS, 2011). In addition, both solu-

tions may incur in some critical security issues that

are not affordable in this domain (Zeng et al., 2011;

Bou-Harb et al., 2013). Therefore, in the INTEGRIS

project we have split the Smart Grid into two isolated

layers: a lower layer that encompasses IEDs’ com-

munications following the IoT schema—described in

(Navarro et al., 2012)—, and an upper layer—herein

presented—that covers prosumers interactions with

the grid following the WoT approach. To success-

fully bridge both layers and meet the stringent se-

curity and scalability constraints demanded by the

Smart Grid (Aman et al., 2013; Navarro et al., 2012),

we have implemented a multi-agent intelligent system

that (1) is aware of all heterogeneities of the lower

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layer (Zaballos et al., 2011), (2) predicts future situa-

tions (Gama, 2010), (3) builds a comprehensive sys-

tem model (Navarro et al., 2012), and (4) delivers it to

a uniform interface for user interaction using a REST-

ful approach. The following section reviews the key

parameters to be extracted from the lower layer and

presents the internals of this cognitive-inspired intel-

ligent subsystem.

3 A REFERENCE MODEL FOR

SMART GRIDS

Actually, splitting the ICT system that holds the am-

bitious requirements of Smart Grids (Aman et al.,

2013) into the two aforesaid layers enables (1) provid-

ing an extra security barrier by hiding electric-devices

and grid topology from unauthorized end-users, (2)

building scalable mechanisms to collect data from

IEDs, (3) addressing the electric functions (e.g., volt-

age monitoring)separately from user-drivenfunctions

(e.g., power ﬂow management) and communication

issues (i.e., overlay networks (Zaballos et al., 2011)),

(4) integrating heterogeneous protocols and technolo-

gies, and (5) providing a uniform management inter-

face (INTEGRIS, 2011). The purpose of this section

is to present the architecture that supports this idea

and announce the key parameters used to link both

layers.

From our real-world experiences collected dur-

ing the INTEGRIS project, we have found that di-

viding the Smart Grid into these logical layers poses

some critical difﬁculties arisen from the fact that typi-

cally, IEDs are closed devices that do not allow im-

plementing custom developments (e.g., security or

information-exchange protocols)—as novel experi-

mental devices do (Guinard et al., 2011). Therefore,

we proposed a new device coined as I-Dev (Navarro

et al., 2012) that behaves as a frontier between these

two layers and implements (1) a communications sub-

system that allows heterogeneous network coexis-

tence, (2) a security subsystem that provides a reli-

able and secure low layer communications infrastruc-

ture, (3) a distributed storage subsystem that smartly

stores all data generated by IEDs, and (4) an intelli-

gent cognitive-inspired subsystem that is aware of all

events arisen from any subsystem of the network.

In order to avoid the bottleneck effect and thus

deploying a scalable ICT infrastructure, two distinct

roles have been assigned to I-Devs: the Perception

Action Agent (PAA) and the Domain Management

Agent (DMA). Therefore, the whole Smart Grid is

spread into several regions (e.g., low voltage substa-

tions) referred to as I-Domains (Navarro et al., 2012),

where in each I-Domain a DMA and several PAAs

interact with deployed IEDs using the IoT approach

(Zaballos et al., 2011).

As depicted in Fig. 1, PAAs—placed in the bot-

tom layer—(1) gather information from their associ-

ated IEDs, (2) report their ﬁndings to their assigned

DMA, and (3) execute the commands ordered by it as

a result of a collaborative decision between all DMAs

in the Smart Grid. Hence, I-Devs behaving as DMAs

bridge the bottom layer and the top layer while en-

suring scalability and a safe isolation between both

sides. Likewise, the upper layer is committed to in-

terface with the management side of all the smart

functions provided by the Smart Grid (e.g., supervi-

sory control and data acquisition (SCADA), advanced

distributed automation (ADA), distributed energy re-

source (DER), automatic meter reading (AMR), ad-

vanced metering infrastructure (AMI), NMS, or qual-

ity of service) taking beneﬁt from the ubiquitous fea-

tures provided by the WoT approach.

As the lower layer has been broadly addressed in

(Navarro et al., 2012; Zaballos et al., 2011), the re-

mainder of this section is devoted to (1) summarize

the functionalities of every subsystem running in any

I-Dev and (2) specify the key monitoring and conﬁg-

uration parameters to be delivered to the herein pro-

posed upper layer.

3.1 Distributed Storage Subsystem

Smart Grids intrinsically generate vast amounts of

heterogeneous information (i.e., every IED may have

its own proprietary data format) that need to be ef-

fectively stored in order to be later processed by

the aforementioned smart functions. In this context,

data are generated at different points of the grid and

need to be reliably replicated in order to afford site

failures and boost their availability through a dis-

tributed storage system. Typically, classical relational

databases are unable to handle neither the dynamic

nature, nor the scalability requirements, nor the strin-

gent computing and storage capabilities of smart de-

vices (Aman et al., 2013). Hence, we developed our

own distributed storage architecture running a custom

replication protocol that smartly placed data to opti-

mize the performance of smart functions being exe-

cuted at the I-Devs layer. This replication protocol is

aimed to provide a consistent view of any datum—

stale but consistent versions are useful for non critical

functions such as average consumption monitoring—

at any situation. To achieve this commitment data

is replicated and partitioned following a hierarchi-

cal hybrid approach between state-machine and syn-

chronous primary copy replication that attempts to

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Figure 1: Deployed reference architecture for Smart Grids.

keep system scalability by epidemically updating data

across I-Devs (Navarro et al., 2012). This strategy

converts the storage infrastructure composed by I-

Devs as a dynamic set of nested onion layers that store

eventually consistent versions of data.

Manually conﬁguring and monitoring this dis-

tributed system is unfeasible because (1) there is an

overwhelming number of devices involved on the

replication process, (2) there are several interdepen-

dencies with other subsystems, and (3) the system

conditions may change abruptly. Therefore, we pro-

pose to deliver the following key performance metrics

and conﬁguration parameters to an intelligent system

who is aware of the status of the whole Smart Grid, in

order to get a reliable set of actions to be performed on

the storage domain. Speciﬁcally, we need to track the

amount of read/write operations, response time, repli-

cation depth, required consistency degree, and repli-

cation hierarchy layout (Navarro et al., 2012). The

following subsection describes the communications

and security subsystem and details which metrics are

delivered to the intelligent system.

3.2 Communications and Security

Subsystem

ICTs, trust management, and technological integra-

tion play an essential role when coordinating all IEDs

to solve every smart function. However, they have to

be carefully addressed since the electrical distribution

infrastructure encompasses aerial and underground

areas that current communications technologies are

unable to reach (Zaballos et al., 2011). Current ap-

proaches in this domain rely on the Internet network

to boost their performance but inherit the same threats

and critical risks in terms of cyber-security(Bou-Harb

et al., 2013). Therefore, communications and security

in Smart Grids are crucial for the survival and fea-

sibility of the global electricity distribution concept

(Navarro et al., 2012). So far, very few standards con-

cerning security (e.g., IEC62351, NISTIR7268) have

been proposed to address the speciﬁc issues posed by

Smart Grids, which still leaves the network and its

links open to cyber-security attacks that may produce

Denial of Service (DoS) and eavesdropping of critical

network management messages. Cyber attacks in the

communications network are aimed to bring the max-

imum damage, exploit the greatest beneﬁts and take

advantage of the network structure or from protocols

vulnerabilities for malicious purposes (e.g., extended

power outages and destruction of power equipment

(Bou-Harb et al., 2013)). Latest advances on network

resilience protocols (Chen et al., 2012) implemented

on I-Devs permit them to sustain from temporal node

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failures or disconnections as long as countermeasures

can be activated.

However, these countermeasures may prevent

other subsystems to behave properly (e.g., the dis-

tributed storage system may be unable to collect data

from a given PAA) and thus drive the whole Smart

Grid to a panic situation. Therefore, our proposal

sends the following key communications and security

metrics of every I-Dev to an intelligent system that

owns a global view of the Smart Grid: connectivity—

the number of links to the Smart Grid control center—

, security performance—number of packet errors,

number of encryption errors, number of unsuccess-

ful access attempts, number of I-Dev reboots, num-

ber of network overload situations and other DoS at-

tacks, and number of packet retransmissions—, qual-

ity of service performance—amountof ﬂow rates, dis-

carding probabilities and queue lengths—, and de-

lay performance—average link delays. Then, the in-

telligent subsystem examines these parameters and

selects the best security and communications action

(e.g., isolating an I-Dev) in order to achieve the best

Smart Grid overall performance.

3.3 Intelligent Subsystem

Although designing and modeling every subsystem of

the Smart Grid separately eases the development pro-

cess, we have observed that it is unfeasible to allow

them running on their own (i.e., without considering

the whole grid status) (Navarro et al., 2012). Con-

sidering the vast amount of data and distinct events

continuously arisen from the grid, the management of

such a system cannot fully rely on an expert. There-

fore, we have chosen an intelligent multi-agent sys-

tem to interprete the aforesaid key parameters of ev-

ery subsystem in the grid in order to build a com-

prehensive model of the whole system. Speciﬁcally,

this context requires an online learning scheme able

to (1) put together the parameters of each subsystem,

(2) rapidly adapt to the constant changes of the Smart

Grid, (3) provide an accurate estimation concerning

the whole grid status, and (4) come up with the best

conﬁguration at every subsystem to reach the optimal

overall performance. To this regard, we have selected

an enhanced version of the eXtended Classiﬁer Sys-

tem (XCS) (INTEGRIS, 2011) that diligently meets

these requirements as shown in what follows.

In fact, XCS, a cognitive-inspired algorithm, is

targeted to evolve a population [P] of classiﬁers. At

the end of the learning process, the population is ex-

pected to acquire a high quality model using these

classiﬁers. Each one consists of a production rule—

composed by an antecedent and a consequent—and

Figure 2: Intelligent subsystem learning architecture.

DMAs use the Web of Energy to share their experiences

collected at every I-Domain and build a global Smart Grid

knowledge model.

a set of parameters that evaluate the quality of the

rule. The antecedent part of the rule contains the input

variables collected by PAAs from every subsystem

and related to the environment (i.e., what the algo-

rithm “senses”). Likewise, the consequent part con-

tains the action predicted for the future status of the

Smart Grid. The main parameters that evaluate the

quality of every rule are (1) an estimation of the re-

ward that will be received if the predicted status is

triggered, (2) the expected error, (3) the experience of

the classiﬁer, and (4) the ﬁtness of the classiﬁer.

Thoroughly, XCS follows the online learning pro-

cess depicted in Fig. 2 at the DMA: it starts with an

empty population and it learns by sampling new train-

ing examples. This learning process creates the match

set [M] containing all the classiﬁers that match with

the current example (INTEGRIS, 2011). If [M] does

not contain a minimum user-deﬁned threshold of dif-

ferent actions, the system generates new ones arbitrar-

ily. Then, the action to be proposed to the actuators

(i.e., PAAs) is selected via a ﬁtness-weighted average

of all matching classiﬁers in [M], forming the action

set [A]. All classiﬁers in [A] predict the same action

and hence they share the evaluation payoff in a nich-

ing scheme. At the end of the iteration, a genetic al-

gorithm is applied at [A] to discover new promising

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rules, for instance if data response time ∈ [10, 100]

ms and ... and reconnections ∈ [40, 60] then block

I-Dev3.

As the XCS is a reinforcement learning approach,

training and scalability need to be properly consid-

ered: First, as it is unfeasible to manually label all

examples of the training set, we have used an on-

line clustering approach based on k-Nearest Neigh-

bors (Navarro et al., 2012) to label them and thus let

the system autonomously learn from the environment.

Last, in order to meet the intrinsic scalability require-

ments of the Smart Grid, a unique centralized thinking

unit is avoided by deploying a DMA and its associated

PAAs on every I-Domian.

However, the distributed nature of this cognitive

subsystem entails further concerns on how integrat-

ing all the knowledge to an accurate set of human-

readable rules, which enables to think globally and

act locally. The following section is devoted to detail

how these rules are delivered across the upper layer of

the herein presented WoT approach.

4 FROM THE IoT TO THE WoE

In fact, distributing the knowledge building layer of

the cognitivesubsystem (i.e., DMAs) to meet the scal-

ability constraints posed by the Smart Grid deﬁni-

tively hampers the learning process. As shown in

Fig. 2, refusing a centralized architecture forces lo-

cally collected rules at every I-Domain to be shared

among all DMAs of the Smart Grid—the underlying

hypothesis in rule sharing is that similar structures

(i.e., I-Domains) require similar conﬁgurations (i.e.,

knowledge model)—, which may lead to some con-

ﬂicting situations arisen from the fact that the cogni-

tive subsystem is unable to reach 100% of accuracy

(Navarro et al., 2012). Note that rules shared be-

tween I-Domains are those with a classiﬁcation accu-

racy greater than a user-deﬁned minimum threshold.

Although the dynamics of this on-line learning archi-

tecture allow removing these conﬂicting rules without

shutting down or reseting the affected DMA, a reli-

able decision process to conduct this action is manda-

tory.

Therefore, as depicted in Fig. 2, we have intro-

duced a new role on the Smart Grid: the expert sys-

tem; which is the combination of the aforesaid soft-

ware entities and a specialist that owns enough expe-

rience related to the electricity domain. To this regard,

this specialist is continuously analyzing the results

provided by the intelligent system and thus, learning

from the Smart Grid in order to supplement software

suggestions by deciding which rules must be deleted

from every DMA (also referred to as conﬂict resolu-

tion). Likewise, this expert can introduce new knowl-

edge based on his expertise to the cognitive subsystem

by forcing the usage of new rules. We have found (IN-

TEGRIS, 2011; Navarro et al., 2012) that this expert

system greatly enhances the performance of the intel-

ligent system because it ensures that critical decisions

are taken consistently.

However, the feasibility of this approach relies

on a uniform interface that ﬁts with the intrinsic

distributed nature of the Smart Grid and enables

its effective management. So far, existing prelimi-

nary solutions (INTEGRIS, 2011) achieve such com-

mitment by using centralized management systems

(e.g., SCADA) that are unable to fully integrate the

long-term requirements and applications posed by the

Smart Grid domain (Gungor et al., 2013). Hence, we

have deployed a WoT-inspired infrastructure—coined

as WoE—on top of the Smart Grid that links all I-

Domains and permits a bidirectional communication

between electricity domain (i.e., bottom layer in Fig.

1) and the application domain (i.e., top layer in Fig.

1).

To this concern, every I-Dev in the Smart Grid has

been labelled with an URI, which enables specialists

and all entities residing at the top layer of Fig. 1 using

asynchronous communications to (1) incorporate new

knowledge or erase existing rules that enter in conﬂict

with previously discovered ones, (2) reset and manip-

ulate the conﬁguration parameters that control every

subsystem—especially the cognitive one—, and (3)

obtain accurate statistics from every DMA, and thus

from the whole Smart Grid as depicted in Fig. 3.

More speciﬁcally, Fig. 3 depicts some of the web

interfaces that integrate the heterogeneous framework

found on the bottom layer of the Smart Grid—and

linked using a IoT approach—to a uniform environ-

ment. The top left screenshot in Fig. 3 shows the

conﬁguration interface of the distributed subsystem

(i.e., replication depth, cache size, number of layers

(Navarro et al., 2012)) running at I-Domain 6. The

top right screenshot in Fig. 3 shows the conﬁguration

of the communications and security subsystem run-

ning at I-Domain 3. Finally, the bottom screenshots

in Fig. 3 are devoted to depict the two layers of the

cognitive system.

The bottom left screenshot in Fig. 3 depicts the

management interface of the intelligent subsystem

running at I-Domain 9; ﬁrst, it shows a list of three

PAAs, below it depicts the predicted I-Domain status

and the selected action in [P]. Additionally, the bot-

tom right screenshot in Fig. 3 depicts the collected

data at PAA 2 from I-Domain 7. In this way, special-

ists can monitor the (1) effects of applying a given ac-

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Figure 3: The Web of Energy. A uniform interface to support smart functions and manage the associated distributed, commu-

nications, security, and intelligent subsystems.

tion, (2) set of locally and globally collected rules, (3)

parameters perceived at every PAA, and (4) training

accuracy at a glance.

Thoroughly, the herein presented approach pro-

vides a uniform web-based interface referred to as

WoE that safely isolates the electricity grid domain

from its applications. More speciﬁcally, this sys-

tem includes an ad-hoc distributed storage architec-

ture to support the massive amount of data gener-

ated by the grid, that delivers these data through a se-

cured communication network to a multi-agent—able

to face the intrinsic dynamic nature of Smart Grids—

intelligent subsystem that processes the events and

changes arisen in this domain.

5 CONCLUSION

This paper presents a particular application of the

WoT to the concrete scenario of power networks.

We have conducted our experimentation over the sys-

tem presented in (Navarro et al., 2012), where an

IoT-based infrastructure enabled machine-to-machine

interactions between small and resource-constrained

devices on the Smart Grid domain. Thus, we have

extended the IoT concept by providing a bidirec-

tional human-to-machine interface—inspired by the

WoT—that results in a ubiquitous energy control and

management system coined as Web of Energy. This

proposal combines the web-based visualization and

tracking tools with the Internet protocols, which en-

ables a uniform access to all devices of the Smart

Grid.

In order to provide such an effective and reliable

management interface aimed to address the hetero-

geneous nature of devices residing on the grid, we

have deployed an intelligent subsystem devoted to (1)

learn from the real-world events, (2) predict future sit-

uations, and (3) assist on the decision making pro-

cess. This intelligence layer is composed by means of

a multi agent system to meet the scalability require-

ments of the Smart Grid. Moreover,it builds a knowl-

edge model in terms of production rules, implements

its own apportionment of credit mechanism (i.e., uses

reinforcement learning), and has an ad-hoc rule dis-

covery technique based on a genetic algorithm. How-

ever, we have seen that the intelligent system is un-

able to cope with the complexities of the Smart Grid

that hamper the optimal learning performance (i.e.,

100% accuracy). Therefore, we have successfully

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included the role of the specialist in the presented

WoE approach. This entity is targeted to dynami-

cally rectify the intelligent subsystem outcomes and

improve the global grid performance. Note that the

aforesaid WoE framework eases the specialists com-

mitment considerably, in the sense that they are able

to interact with the different modules that control the

Smart Grid through a pervasive and user friendly web-

based interface rather than traditional roughly com-

mand lines.

Finally, we have demonstrated the feasibility of

our proposal by running it on the real-world sce-

nario deﬁned by the INTEGRIS project (INTEGRIS,

2011). Our collected experiences show that this uni-

form management interface—depicted in Fig. 3—

plays a key role in the process of development and

standardization of current and new smart functions.

Certainly, this paper encourages practitioners to con-

duct future work in this direction by deﬁning new

electric applications following this layered scheme.

ACKNOWLEDGEMENTS

The research leading to these results has received

funding from the European Union European Atomic

Energy Community Seventh Framework Programme

(FP7/2007-2013 FP7/2007-2011) under grant agree-

ment 247938 for Joan Navarro, Agust´ın Zaballos by

Generalitat de Catalunya for its support under grant

2013FI

B2 00089 for Andreu Sancho-Asensio, and

by the Spanish National Science Foundation (MEC)

(grant TIN2012-37719-C03-03) for Jos´e Enrique Ar-

mend´ariz-I˜nigo.

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