ARTÍCULOS ORIGINALES
ISSN 2389-8186
E-ISSN 2389-8194
Vol.7, No. 2-1
Julio-diciembre de 2020
doi: https://doi.org/10.16967/23898186.669
pp. 44-55
rpe.ceipa.edu.co
* This work is supported by the ITEA3 OPTIMUM project, ITEA3 SECUREGRID project and ITEA3 SCRATCH project, all of them funded by the
Centro Tecnológico de Desarrollo Industrial —CDTI—.
** Master Biomedical Engineering. Nimbeo Estrategia e Innovación S.L., Madrid, España. E-mail: mlagares@nimbeo.com.
ORCID: 0000-0003-1824-3740. Google Scholar: https://scholar.google.com/citations?view_op=list_works&hl=es&user=D6w06ewAAAAJ.
*** PhD in Computer Science. Nimbeo Estrategia e Innovación S.L., Madrid, España. E-mail: yperez@nimbeo.com.
ORCID: 0000-0002-2232-9582. Google Scholar: https://scholar.google.com/citations?user=1bF8dTwAAAAJ&hl=es.
**** PhD in Computer Science. Nimbeo Estrategia e Innovación S.L., Madrid, España. E-mail: alagares@gmail.com.
ORCID: 0000-0001-5310-301X. Google Scholar: https://scholar.google.com/citations?user=swVNmFoAAAAJ&hl=es.
***** PhD in Computer Science. Universidad Carlos III de Madrid, Getafe, España. E-mail: jmgberbi@inf.uc3m.es.
ORCID: 0000-0003-3902-9452. Google Scholar: https://scholar.google.com/citations?user=a2T86e0AAAAJ&hl=es.
ENERMONGRID: Intelligent Energy
Monitoring, Visualization and Fraud
Detection for Smart Grids*
MIGUEL LAGARES-LEMOS**
YULIANA PEREZ-GALLARDO***
ANGEL LAGARES-LEMOS****
JUAN MIGUEL GÓMEZ-BERBÍS*****
ISSN 2389-8186
E-ISSN 2389-8194
Vol.7, No. 2-1
Julio-diciembre de 2020
doi: https://doi.org/10.16967/23898186.669
COMO CITAR ESTE ARTÍCULO
How to cite this article:
Lagares-Lemos, M. et al. (2020).
ENERMONGRID: Intelligent Energy
Monitoring, Visualization and Fraud
Detection for Smart Grids. Revista
Perspectiva Empresarial, 7(2-1),
44-55.
Recibido: 20 de agosto de 2020
Aceptado: 07 de diciembre de 2020
ABSTRACT
The current obsolete electricity network is being transformed into net an
advanced, digitalized and more ecient one known as Smart Grid. The deployment of an
Automatic Metering Infrastructure will make an unseen quantity of rich information available
in near real-time, processed to make decisions for the optimal energy production, generation,
distribution, and consumption. This document presents an analysis of the ENERMONGRID
tool, a tool used for intelligent energy monitoring, data visualization and fraud detection
in electric networks.
KEY WORDS
Energy, monitoring, fraud detection, smart grids, data visualization.
ENERMONGRID: supervisión inteligente de la energía,
visualización y detección de fraudes para las redes
inteligentes
RESUMEN
La actual red eléctrica obsoleta se está transformando en una red avanzada,
digitalizada y más eciente conocida como Smart Grid. El despliegue de una infraestructura de
medición automática permitirá disponer de una cantidad inédita de información abundante
en tiempo casi real; procesada para tomar decisiones para la producción, generación,
distribución y consumo óptimos de energía. Este documento presenta un análisis de la
herramienta ENERMONGRID; una herramienta utilizada para la supervisión inteligente de
la energía, la visualización de datos y la detección del fraude en las redes eléctricas.
PALABRAS CLAVE
energía, supervisión, detección de fraude, redes inteligentes,
visualización de datos.
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ARTÍCULOS ORIGINALES
MIGUEL LAGARES-LEMOS, YULIANA PEREZ-GALLARDO, ANGEL LAGARES-LEMOS, JUAN MIGUEL GÓMEZ-BERBÍS
Revista Perspectiva Empresarial, Vol. 7, No. 2-1, julio-diciembre de 2020, 44-55
ISSN 2389-8186, E-ISSN 2389-8194
ENERMONGRID: supervisão inteligente da energia,
visualização e detecção de fraudes para as redes
inteligentes
RESUMO
A atual rede elétrica obsoleta está se transformando numa rede avançada,
digitalizada e mais eciente conhecida como Smart Grid. A implantação de uma
infraestrutura de medição automática permitirá dispor de uma quantidade inédita
de informação abundante em tempo quase real; processada para tomar decisões
para a produção, geração, distribuição e consumo ótimos de energia. Este documento
apresenta uma análise da ferramenta ENERMONGRID; uma ferramenta utilizada para
a supervisão inteligente da energia, a visualização de dados e a detecção de fraude
nas redes elétricas.
PALAVRAS CHAVE
energia, supervisão, detecção de fraude, redes inteligentes,
visualização de dados.
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ARTÍCULOS
MIGUEL LAGARES-LEMOS, YULIANA PEREZ-GALLARDO, ANGEL LAGARES-LEMOS, JUAN MIGUEL GÓMEZ-BERBÍS
Revista Perspectiva Empresarial, Vol. 7, No. 2-1, julio-diciembre de 2020, 44-55
ISSN 2389-8186, E-ISSN 2389-8194
Introduction
The electrical grids of today managed by
utility companies are complex systems with many
different aspects that require expertise to operate
successfully, such as grid management, data
visualization, load prediction, loss prediction, and
fraud prevention. The complexity of this domain is
not only architectural, due to many different devices
communicating and interoperating amongst the
network, but there also exists a logical complexity,
given the fact that incredible amounts of data must
be properly managed in order to optimize the
that can arise when dealing with energy load
estimates, loss estimates, as well as fraud
detection and prevention, the ENERMONGRID
tool was developed to aid those entities in charge
of managing an electric network, with the focus of
doing so in Smart Grids.
This paper is made up of the following parts: (i)
the introduction; (ii) a review of related works; (iii)
the ENERMONGRID system is analyzed, detailing
elements, the restrictions and considerations that
were taking into account when developing and
testing the system, the anomalies that were found
in the electric grid once the system was put into
the conclusions.
Related Work
According to studies by the Galvin electricity
initiative, in the United States the technologies Smart
Grid will lower the costs of power supply and reduce
the need for massive infrastructure investment in
at least the next twenty years with a larger capacity
electric grid. In the environmental aspect there
is a great interest of the countries in developing
policies and regulations that encourage the creation
of social awareness with respect to consequences
of greenhouse gases. The problem lies in the fuel
used by traditional power generation plants and
is produced during demand peaks that force the
activation of special plants to be able to supply those
additional energy requirements (García, Beltrán y
Núñez, 2010). These plants are used only during
these periods, with the resulting cost overruns
—which have a direct impact on bills—. A very
country, 40 % of carbon dioxide emissions come
from electricity generation; while that only 20
% are caused by transport. This presents a huge
challenge for the electricity industry in terms of
climate change global.
There are currently many parallel activities
related to standardization of Smart Grid networks.
Since these activities are relevant to the same topic, it
is some overlap and duplication of them is inevitable
(Cleveland, 2008). There are several development
agencies and standardization, among them:
(i) IEC Smart Grid Strategy Group: The
International Electrotechnical Commission —
IEC— is the natural focal point for the electrical
industry. It aims to provide a unique reference
source for the many projects of Smart Grids that
are being implemented around the world. It has
developed a framework for standardization that
includes protocols and reference standards for
achieve interoperability of Smart Grid systems and
devices (Díaz y Hernández, 2011).
(ii) National Institute of Standards and
Technology —NIST—: It is not a body of
standardization but has been designated by the
government of the United States to manage the
project of selecting a set of standards for the Smart
Grid network in that country.
(iii) EU Commission Task Force for Smart Grids:
Its mission is to assist the Commission and guidelines
steps towards the implementation of Smart Grid in
the provision of the third energy package (Gordon
and de Bucs, 2000).
(iv) IEEE P2030: It is an IEEE working group for
the development of a guide for the interoperability
of Smart Grid in the operation of energy technologies
and information technology with the electrical
power system —EPS— and the loads and end-user
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ARTÍCULOS ORIGINALES
MIGUEL LAGARES-LEMOS, YULIANA PEREZ-GALLARDO, ANGEL LAGARES-LEMOS, JUAN MIGUEL GÓMEZ-BERBÍS
Revista Perspectiva Empresarial, Vol. 7, No. 2-1, julio-diciembre de 2020, 44-55
ISSN 2389-8186, E-ISSN 2389-8194
applications. Many demonstration projects are
currently underway, and some results are available.
Smart Grid are present in the United States, Europe,
Japan and China.
ENERMONGRID
The ENERMONGRID tool is a tool used for
intelligent energy monitoring, data visualization
and fraud detection in electric networks. It is
responsible for collecting data from the meters
and transformation centers of the measurement
system deployed in the metering infrastructure,
also known as AMI, which stands for Advanced
Metering Infrastructure (van der Meijden,
Veringa and Rabou, 2010). This information is
processed through the MDM module (Meter Data
Management) that collects, consolidates, and
manages this information.
The MDM module, among other tasks, also
provides security through the anonymization
of data, as well as advanced security measures;
offers information to external systems through
the communications module, through WEB REST
services; manages the topological, cartographic and
electrical information of the network; preprocesses
The ENERMONGRID tool, via the use of
algorithms developed by the different project
members, calculates estimates, energy losses in the
network, predictions, as well as energy balances.
These results are treated as new reports, which
have been designed for the project following the
STG standard.
Architecture
The system architecture can be described with
Figure 1. System Architecture. Source: author own elaboration.
The information flow of the architecture is
given by three main elements: the meters, the
transformation centers, and the BackOffice. The
role of each one of these elements will be briefly
described.
Meters
A meter is the device typically used to gather
information regarding the energy consumption
inside of a household or other location that
consumes electrical energy. Most meters are
analogical, but companies and technological
drive have made it possible for these meters to be
electrical devices in and of themselves, coining the
term “Smart Meter.” Smart Meters are especially
important in the context and domain of Smart Grids
and Smart Cities.
The role of the meter is to accrue the
of time. Smart Meters can perform this data
gathering duty over non-traditional methods such
as OTA (over the air) by establishing a wireless
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MIGUEL LAGARES-LEMOS, YULIANA PEREZ-GALLARDO, ANGEL LAGARES-LEMOS, JUAN MIGUEL GÓMEZ-BERBÍS
Revista Perspectiva Empresarial, Vol. 7, No. 2-1, julio-diciembre de 2020, 44-55
ISSN 2389-8186, E-ISSN 2389-8194
connection with smart appliances. If this is not the
case, however, a more standard approach is followed,
which is simply to take measures at the power lines
feeding the household or location. Once the meter
has the information regarding energy consumption,
it sends this information to a Transformation Center.
This communication can be achieved via several
different communication protocols, such as PLC,
Ethernet, WiFi, or Zigbee.
(i) Transformation Center: The transformation
center —TC—, is the element in charge of aggregating
the data obtained from a group of meters and then
A single transformation center obtains
information from many meters, so the relationship
be a 1-N relationship.
The TC can be seen as the intermediate step
tasked with grouping lots of small measurements
taken at meters and sent over low-throughput
communication protocols, to sending great amounts
of data through higher-throughput communication
protocols and allowing the data to reach its ultimate
destination.
The communication protocols used to relay
the data from the transformation center to the
WiMAX, or ADSL (Mowbray, 2013).
The ENERMONGRID tool allows to visualize the
Figure 2. Energy flow of transformation centers. Source:
author own elaboration.
meters to the transformation centers, aggregated,
the consumption data from each meter.
Types of data and reports
For ENERMONGRID to work the way it does
and allow data prediction, visualization, and fraud
chart of the different types of reports is presented in
Figure 3. Types of reports and data dependencies. Source:
author own elaboration.
original reports. These are reports provided by the
utility companies. These reports contain hourly
data with information regarding the consumption
its own way of reporting this information, so two
On the next level, we have the estimation
layer. This layer takes as input the original reports
provided by the utility companies and processes the
information in a way that allows to create estimates
regarding further values. This process allows for
a preliminary view at how data might change in
upcoming moments.
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MIGUEL LAGARES-LEMOS, YULIANA PEREZ-GALLARDO, ANGEL LAGARES-LEMOS, JUAN MIGUEL GÓMEZ-BERBÍS
Revista Perspectiva Empresarial, Vol. 7, No. 2-1, julio-diciembre de 2020, 44-55
ISSN 2389-8186, E-ISSN 2389-8194
On the third level, we have the process in charge
of predicting the curves of load on the system as
well as potential losses. This sets the threshold for
sampled values, allowing the detection of possible
fraud-like activities in the network.
Finally, the last process involves the calculation
of energy balances by using the previous layer’s
process output as a starting point.
It should also be noted that the tool internally
processes information of real data (not estimated,
nor of prediction), through its algorithms to have
measurements of real energy balances, which can
be compared with the estimates and predictions, as
well as algorithms for the generation of alerts when
certain conditions are met.
Through its web interface the tool allows
access to all the information handled in it, both the
monitoring and control of the network, the status of
the various reports, the information of each report,
cartographic information, consumption, voltages
in sections, energy balances, alerts generated by
the system, etc.
interface which analyzes different metrics and KPI’s
of various types of reports detailed above. In this
Figure 4. Analysis of energy balances. Source: author own elaboration.
Considerations and restrictions
In this section, some considerations and
restrictions that have been present during the
energy analysis process using the ENERMONGRID
tool will be detailed.
(i) Samples with a quality bit other than ‘00’
have not been considered: Samples with a quality bit
of ‘00’ have been, however, considered to calculate
the Reading rate.
certain scenarios when many of the samples of the
meters come without the quality bit at ‘00’ at a certain
moment, when not taken into account, the total sum
can produce very low values, throwing losses greater
than those that actually exist in the network and
which would be obtained by the calculations when
the quality bit is taken into account. An example of
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MIGUEL LAGARES-LEMOS, YULIANA PEREZ-GALLARDO, ANGEL LAGARES-LEMOS, JUAN MIGUEL GÓMEZ-BERBÍS
Revista Perspectiva Empresarial, Vol. 7, No. 2-1, julio-diciembre de 2020, 44-55
ISSN 2389-8186, E-ISSN 2389-8194
Figure 5. Side eect of discarding measurements with quality bit other than ‘00’. Source: author own elaboration.
The opposite situation can also occur, as in the
following example in which one of the supervisors
of one of the TC6 trades having values of Bc other
than ‘00’ is not taken into account, giving lower
values of the sum of the supervisors compared to
the sum of the meters. Resulting in negative losses,
Figure 6. Apparent prots. Source: author own elaboration.
(ii) Meters that did not have a registered
location in the system have not been considered
in the analysis: The values obtained from meters
not associated to a location were only used to adjust
the reading rate value. This change arises because
the estimator and the loss calculation algorithm do
not take them into account and therefore, in order
to compare with the real values, they have also been
discarded inside the algorithms.
Given this condition, it can be observed that
the consumption of the sum of meters is lower and
therefore apparently increases the differences with
the value of the supervisor, therefore yielding higher
values of total losses.
Real-time data anomalies
In the following section, the real-time data
anomalies that were observed during the analysis
period of the ENERMONGRID tool will be detailed.
(i) Supervisor values set to 0: There are certain
scenarios (11.99 % of cases in the UTLA network
and 1.58 % for UTLB for the entire project) in which
the supervisor shows consumptions equal to 0,
when there is consumption in the meters associated
with them. All these values are associated with
quality bits other than ‘00’ for UTLB, but not for
UTLA, where in some cases, not in the majority, the
quality bit offers values to ‘00’.
If we do this TC analysis for the UTLA network,
in TC3 and TC4, whenever the supervisor gives a
null value, it has the quality bit marked other than
‘00’. It is not the same case for TC 1 and 2. It should
also be noted that this is a case that has not been
presented since April 2, 2014.
(ii) Abnormally high values: In some cases,
abnormally high values have been detected in
some meters that caused the balance to provide
erroneous calculations and that did not allow
to render in detail the data visualization graphs
containing the balances of the TC.
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Revista Perspectiva Empresarial, Vol. 7, No. 2-1, julio-diciembre de 2020, 44-55
ISSN 2389-8186, E-ISSN 2389-8194
Twenty-nine abnormally high values have been
detected over the entire duration of the project for
the UTLA network.
are certain times when the total losses are negative,
that is, the subtraction of the consumption data
recorded by the supervisor minus the sum of the
consumption record of all the meters associated
with said supervisor (or TC) is less than 0.
moments (schedules, that they have been calculated
through S02) where this circumstance occurs for
the TC of the UTLA and UTLB network:
CT1
CT2
CT3
CT4
Negative losses moments
Moments negative losses without counting values Bc!=
'00' Moments negative losses with all the perfect data
3587
3182
33
2657
3588
3085
31
1722
0
2988
0
953
Figure 7. Data from TC1-TC4. Source: author own elaboration.
CT5
CT6
CT7
Negative losses moments
Moments negative losses without counting values Bc!= '00'
Moments negative losses with all the perfect data
158
11575
433
158
11564
425
0
10024
105
Figure 8. Data from TC5-TC7. Source: author own elaboration.
where total negative losses are recorded, which
considering the number of records taken represent
0.20 % of cases for UTLA and 0.18 % for UTLB.
The second row shows the moments with
negative losses when values with a quality bit
other than ‘00’ are not taken into account. Its value
is slightly lower, which does not seem to have a
relevant effect and simply reduces the number of
moments due to the fact that these data are not
taken into account in the sum.
The last row represents those moments with
negative losses when all the data coming from the
network have the quality bit at ‘00’. This anomaly
is still occurring (except in TC5), which should be
studied further to determine the cause (meters
or supervisors that measure incorrectly, meters
that do not belong to this TC and are transmitting
values to it).
Reading rates of original reports
In this section, the different reading rates
obtained from analyzing reports across the different
layers are detailed.
(i) UTLA: The reading rates obtained for the
hourly reports (S02) are shown in the following
September:
CT1
CT2
CT3
CT4
September
88.16 %
79.73 %
10.42 %
95.52 %
Figure 9. Reading rates of September. Source: author own
elaboration.
As can be observed, the reading rate is around 80
% in September. The lowest rate can be found in TC3
given that it experienced technical communication
problems during these dates, and the maximum
values belong to TC4, which coincides with the
TC that does not have any meter of industrial or
commercial type (type 3 or 4), although this should
not have a direct relationship, because these are not
taken into account to establish the reading rate. If
we do not consider the TC3 due to breakdown, the
total rate is around 89 %.
Considering that TC3 has suffered a breakdown,
this is what the ENERMONGRID tool provides in
terms of data visualization for a fully operational
TC, namely TC1, and the faulty TC3:
Figure 10. Reading rate of TC1. Source: author own
elaboration.
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MIGUEL LAGARES-LEMOS, YULIANA PEREZ-GALLARDO, ANGEL LAGARES-LEMOS, JUAN MIGUEL GÓMEZ-BERBÍS
Revista Perspectiva Empresarial, Vol. 7, No. 2-1, julio-diciembre de 2020, 44-55
ISSN 2389-8186, E-ISSN 2389-8194
Figure 11. Reading rate of TC3. Source: author own
elaboration.
(ii) UTLB: The reading rates obtained for the
hourly reports (S02) are shown in the following
August and September:
CT5
CT6
CT7
August
95.10 %
88.41 %
45.07 %
September
69.91 %
65.29 %
34.61 %
Figure 12. UTLB Readings. Source: author own elaboration.
As we can observe, the reading rate is around
47 % in September, while in August it was 63 %.
Analyzing the availability of reports in
for the lowest percentage during the month of
September, is due to the lack of availability of
reports on certain days.
Figure 13. Availability of reports. Source: author own
elaboration.
KPIs of Energy Balances
(i) UTLA: The UTLA values corresponding to
the energy balances for September for UTLA and
for August 2014 for UTLB are shown below (since
the reading rate is better in that month). The KPIs
are based on the real hour data (S02), except for
the case of technical losses that are obtained from
the estimated ones. These data can be consulted
through the ENERMONGRID tool.
Figure 14. UTLA analysis. Source: author own elaboration.
As can be observed the non-technical losses
are high, especially in TC1 and TC2, which have
type 3 and type 4 meters, which, being not tele-
managed and registered in the system, virtually
increase non-technical losses. This can be seen in
graph (in purple) and the sum of the meters (blue),
in addition to the technical losses (red). The graph
of the supervisor is far above the consumption
reported by the meters.
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ISSN 2389-8186, E-ISSN 2389-8194
(ii) UTLB: The UTLB values corresponding to
the energy balances for September for UTLA and
for August 2014 for UTLB are shown below (since
the reading rate is better in that month). The KPIs
are based on the real hour data (S02), except for
the case of technical losses that are obtained from
the estimated ones. These data can be consulted
through the ENERMONGRID tool.
Figure 15. UTLB analysis. Source: author own elaboration.
As can be observed the non-technical losses are
more moderate than in the UTLA network, since
there is no existence of type 3 or 4 meters. The TC7
offers the best performance values and the TC6
registers apparently high values for this type of TC.
you can see the supervisor’s graph (in purple), the
sum of the meters (blue), and the technical (red)
and non-technical (white) losses.
It is relevant that the TC7 in which the lowest
reading rates were presented, offers the best results.
considers are probably meters written off or that
are not yet operating.
Alerts
In the following section, details regarding the
alerts set off b the ENERMONGRID will be detailed,
namely, the UTLA and UTLB alert values will be
analyzed.
(i) UTLA: The system controls the tensions in the
nodes and the saturation in the sections to generate
alerts when the marked thresholds are passed: 7 %
deviation in voltage at nodes in the 230 V reference
voltage; 90% saturation limit in sections.
Since September 2014, 3917 alerts have been
registered, 13 regarding the saturation in sections
and the rest due to deviations.
(ii) UTLB: The system controls the tensions in
the nodes and the saturation in sections to generate
alerts when the marked thresholds are passed: 7 %
deviation in voltage at nodes in the 230 V reference
voltage; 90 % saturation limit in sections.
Since November 22, 2014, 737 alerts have been
registered, all due to the tension in nodes.
Work approach
The project will be validated with several
procedural challenges that we will detail below:
(i) Testing with various stakeholders to see
how the system is affected by various people or
businesses it is understood that not all participants
need to understand and address the needs and
functionality of the system.
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Revista Perspectiva Empresarial, Vol. 7, No. 2-1, julio-diciembre de 2020, 44-55
ISSN 2389-8186, E-ISSN 2389-8194
(ii) Checking the complexity of the system, in
addition to checking the good performance of the
system. Some aspects will be sensitive to human
response and interaction, while others will require
instant and automated responses.
(iii) The security of the cybernetic systems
will be checked to validate that the system is
safe. Information security technologies are not
risk assessment and training are not applied.
(iv) The intelligent network is an evolving goal,
so you have to study all the capabilities it has.
As discussed in this article, there are many areas
of work, but especially for ICTs necessarily involved
in this transition.
There are numerous factors to consider and
steps that can be taken now to get ready. The
interesting thing is that there is a lot to do and no
country leads to the moment a very marked lead
ENERMONGRID.
Conclusions
The ENERMONGRID management tool complies
very closely with the objectives presented in the
project. It is true that the tool does not manage the
network, because it was not intended to operate on
the network, since access was restricted. However,
the functionality that has been provided in other
cases exceeds the initial claims, because in addition
to collecting, pre-processing and analyzing all
information safely through the MDM module, it
also serves as a tool to establish communications
with external systems and their visual function
has been seen working on many other aspects not
As future work we will seek to increase the
completeness of ENERMONGRID to help more
entities in charge of managing an electricity network,
with the focus on creating a large smart grid. We will
analyze the ENERMONGRID system, considering
the elements involved, the restrictions that were
taken into account when developing and testing the
system, solving the anomalies that it presented and
being able to develop a competitive system that can
be used by any entity.
References
Cleveland, F. (2008). Cyber security issues for
Advanced Metering Infrasttructure (AMI). In
IEEE Power and Energy Society General Meeting
— Conversion and Delivery of Electrical Energy
in the 21
st
Century, Pittsburgh, USA.
Díaz, C.A. y Hernández, J.C. (2011). Smart Grid:
las TICs y la modernización de las redes de
energía eléctrica – Estado del Arte. Sistemas &
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