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Reliability data for smart grids: Where the real data can be found



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Reliability Data for Smart Grids:
Where the Real Data can be Found
Stanislav Chren, Bruno Rossi, Member, IEEE, Barbora B¨
uhnova, Member, IEEE, and Tom´
s Pitner
This is the accepted version of the paper Chren, S., Rossi, B., Buhnova, B., and Pitner, T. (2018). Reliability Data for Smart Grids: Where the Real Data
can be Found. In Smart Cities Symposium Prague (SCSP), 2018. IEEE
Abstract—Smart Grids play an important role in modern
society and for the sustainability of its wellbeing. However, the
undoubted advantages come at the cost of higher complex-
ity, especially at the level of information and communication
technologies that enhance the physical grid infrastructure. As
such, software quality requirements, such as reliability, resilience,
safety, security, privacy, and performance assume a more func-
tional facet. In this paper, we focus on software reliability
as one of the key qualities of a Smart Grid infrastructure,
which is however not yet well defined and understood. We
formulate relevant definitions of software reliability in the Smart
Grid context, categorize information necessary to quantify the
identified reliability views, and explore existing literature and
online resources to assess what datasets, necessary for reliability
quantification, are available to make the reliability assessment
Index Terms—Smart Grids, Power systems reliability, data
Information & Communication Technology (ICT)-enhanced
power grids, so called Smart Grids, are becoming the backbone
of modern society and also one of the key critical infrastruc-
tures. Although many quality perspectives are becoming the
center of attention during Smart Grid design and deployment
(including reliability, resilience, safety, security, privacy, and
performance [1], [2]), the concept of reliability still remains
bound mainly to the power distribution and the physical
infrastructure [3], [4], while software reliability is receiving
very little or no attention.
In this paper, we investigate the role of software reliability
in the Smart Grid domain by examining the Smart Grid
architecture, layer by layer, and identifying different views of
Smart Grid reliability that shall become the focus of software
reliability engineering in this domain. In this way, we also
provide insight into the specifics of Smart Grid reliability,
which is surprisingly strongly linked to the fulfillment of other
non-functional (rather than functional) requirements (such as
safety, security, performance or measurements accuracy) [5].
Such a study of reliability in the context of Smart Grid pro-
vides the basis for the main contribution of this paper, which
is the examination of types of datasets that carry the necessary
information for reliability engineering studying the identified
reliability directions, and the survey of the availability of such
datasets to reliability engineers, with the emphasis on real data.
All authors are with the Lab of Software Architectures and Information Sys-
tems (Lasaris),, Faculty of Informatics, Masaryk University,
Brno, Czech Republic, e-mail:{chren,brossi,buhnova,tomp}
In particular, we present the classification of information needs
for Smart Grids reliability, and identify information needs and
open datasets to support Smart Grids reliability analysis.
The paper is structured as follows. Section II provides an
overview of the Smart Grid Architecture. Section III discusses
different levels of reliability for the Smart Grid. Section IV
presents information needs for reliability analysis in the Smart
Grid and available datasets for the different needs. Section V
provides the discussion with the main findings, assumptions,
and limitations of the study. Section VI concludes the paper.
According to the Smart Grid Reference Model (SGAM) [6],
the Smart Grid architecture consists of multiple layers, namely
the business layer defining the market policies and business
models, the functional layer describing provided services, and
the communication layers detailing the protocols and standards
for information exchange between components. The lowest
layer consists of a variety of hardware and software com-
ponents, which cover the whole electrical energy conversion
chain comprising five parts (bulk generation,transmission,
distribution,distributed electrical resources (DER),customer
Bulk generation represents generation of energy in large
quantities, for example by fossil, nuclear or hydro power plants
that are connected to the transmission system. Transmission
represents the infrastructure and organization for long-distance
energy transportation. Distribution represents the infrastructure
and organization that distributes the electricity to customers.
DER describes the distributed electrical resources connected
to the public distribution grid. Customer premises include the
consumers of electricity and also the local producers.
The services of the Smart Grid are enabled by several
systems (Fig. 1). The most significant system is the Advanced
Metering Infrastructure (AMI) System [8]. The major AMI
components are: smart meters, meter data concentrator and
data central server [9]. A smart meter is a replacement for a
traditional electric meter which ensures the collection of de-
tailed data about customer power consumption and production
and also supports remote control of specific appliances as part
of demand-response program. A demand-response program,
which governs the strategies of power demand control and
management, is used for dynamic billing of customers and
load optimization in a power grid during peak hours. Data
concentrators are responsible for collection of data from smart
meters and they can be also used for temporary storage,
analysis or encryption of the data, and they can offer early
978-1-5386-5017-2/18/$31.00 c
2018 IEEE
Fig. 1. Smart Grid data and information flow. Adapted from [7]
information about outages. A data central server (also called
Demand-Response server) manages the AMI operation by
storing and analysing the data and handling the demand-
response program.
However, there are other systems that are needed to ensure
Smart Grid reliability:
Blackout Prevention System (WAMPAC) whose objec-
tive is to protect the grid from instabilities and failures.
It covers the whole power grid. It utilizes Phasor Mea-
surement Units (PMUs) to obtain relevant information
from the grid. PMU is a device which measures electrical
waves in a grid and helps to detect anomalies and failures.
Supervisory Control and Data Acquisition System
(SCADA) is one of the core systems of a Smart Grid
that provides support to operation activities and functions
in transmission automation, dispatch centers and control
rooms. In a SCADA system, a remote terminal unit (RTU)
collects data from devices in a substation and delivers
the data in packets, on command, to a central Energy
Management System (EMS).
Flexible Alternating Current Transmission System
(FACTS) is responsible for reliable and secure trans-
mission of power. It allows dynamic voltage control, in-
creased transmission capability and capacity, and supports
fast restore of the grid after failure.
Feeder Automation System is responsible for operation
of medium-voltage (MW) networks including fault detec-
Smart Grids use variety of communication technologies
for information exchange between components (Fig. 2). The
used technologies need to take into account communication
requirements of various usage scenarios, such as maximum
allowed latency, payload size, or frequency of data transfer.
In the smaller scope of Home Area Networks (HAN),
each device is expected to require bandwidth from 10 to 100
Kbps, conventionally adopting WiFi, ZigBee, and HomePlug
technologies. In Building/Business Area Network (BAN), a
wired technology named BACnet is the prominent communica-
tion protocol. The communication between consumer premises
and aggregation points is currently handled by Power-Line-
Communication (PLC). The Advantages of PLCs are their
low cost and expansion, and penetration in utility providers
territory. Their disadvantages include low bandwidth (up to
20Kbps), and data distortion around transformers which re-
quires bypassing transformer points using other techniques.
PLC is especially useful in remote locations where the num-
ber of nodes (consumers) is relatively low and no wireless
(cellular, GPRS) coverage is available. For cases when higher
bandwidth is required (e.g. in urban areas for advanced
demand-response services), Mesh networks are considered. In
Mesh networks, each node is responsible for collecting its own
data, as well as relaying information from other nodes in the
network [8].
Reliability is generally considered one of the most important
quality attribute in Smart Grids [1]. Due to the potentially
serious impact of a Smart Grid failure on our lives, the
electrical power grid is being considered the most critical in-
frastructure of modern society. Critical infrastructures require
specific treatment to ensure their reliability, which includes
also the legislation point of view. The handling of critical
infrastructures is described by the European Programme for
Critical Infrastructure Protection (EPCIP) [11], in the Eu-
ropean context. In the US, there is an organization—The
North American Electric Reliability Corporation (NERC)—
that supervises the reliability of the power grid.
The understanding of the reliability concept might vary
depending on the Smart Grid layer, namely communication,
power distribution, bulk power generation and transmission,
and software layers.
A. Communication layer
In the communication layer, reliability is typically defined as
a fraction of unsuccessfully delivered data to the whole volume
of the transfered data (i.e. probability of data loss). Moreover,
it can be specified in terms of other Quality of Service (QoS)
parameters. These include latency, payload size, bandwidth or
frequency of data transfers [1]. Any violation of the specified
QoS can be perceived as a failure in the Smart Grid system.
The exact requirements also depend on the individual use cases
and the communicating components.
B. Distribution layer
At the distribution level, reliability is expressed with a
variety of performance indicators representing the availability
of the service from the consumer point of view. The most
common indicators are [12]:
Fig. 2. Communication infrastructure of the AMI [10]
System Average Interruption Frequency Index
(SAIFI): this indicator is estimated by dividing the
number of customer interruptions by the total number
of customers served. Thus, it is a measure of the num-
ber of outages experienced by users. In UK, Customer
Interruption is used instead, computed as 100*SAIFI.
System Average Interruption Duration Index
(SAIDI): this indicator is computed by dividing the sum
of long duration interruptions (i.e., longer than 3 minutes)
by the total number of customers. Thus, it is a measure
of the average amount of time when customers encounter
interruptions in electricity supply. In UK, it is called
Customer Minutes Lost (CML).
Customer Average Interruption Duration Index
(CAIDI): this indicator is computed as the ratio of
SAIDI to SAIFI and is expressed in terms of minutes
per interruption.
These three reliability indicators are often prescribed in
the legislation and are monitored by the Energy Regulatory
Authority (ERA). The companies that provide power genera-
tion/distribution services need to obtain a license from ERA
which binds them to maintain the minimum values for SAIFI,
Further indicators include system availability, number of
incidents, excursions beyond set voltage limits or excursion
beyond set frequency limits. Additionally, within the DIS-
CERN project the Key Performance Indicators (KPI) related
to individual Smart Grid use cases were surveyed [13]. Those
related to reliability are, for example, improvement of SAIDI,
improvement of voltage quality, peak load reduction, or suc-
cess index in meter reading.
C. Generation and transmission layer
On the transmission grid, reliability can be described by the
following [12]:
Energy Not Supplied (ENS): total amount of energy
that would have been supplied if there were no interrup-
Average Interruption Time (AIT): amount of time that
the electricity power supply is interrupted.
The indicators on the distribution and transmission layers
are useful when benchmarking the reliability performance
of the power grid, for example when we want to compare
multiple systems or when we want to determine whether
conditions of the service contract have been met. On the other
hand, they are not suitable to make predictions about future
system behaviour [14]. For example, the occurrence of failures
is stochastic in nature. Therefore, for prediction of system
failures, the probabilistic indicators, such as probability of
loss of load are recommended. Probability of loss of load
represents the probability that there will not be enough power
load to satisfy the demand, i.e. the outage occurs.
D. Software layer
In literature, the reliability of Smart Grids focuses on the
hardware components [1]. However, since a Smart Grid also
relies on various software systems, the potential failures in
their software components might also have a significant impact
on the Smart Grid operation [1], [15]. For example, there might
be a failure during the reading of the smart meter, resulting
in obtaining incorrect consumption data, and thus wrongly
calculating the price for power consumption. Although such
failure does not cause a power outage, it might have negative
impact on the associated stakeholders and possibly on other
use-cases as well (e.g. wrong information from smart meters
might influence the load management algorithms).
In software engineering, reliability can be defined as the
probability of successfully running software operations, or its
intended functions, for a specified period of time in a specified
environment [31]. There are several factors which influence
software reliability, such as inadequate testing, operations
Dataset Year Source Type Characteristics URL
REFIT [16] 2017 UK LOLP house aggregate loads / 9 individual appliance measure-
ments at 8-second intervals per house / 20 houses / 2
-4e95-a093-e0a9536983 c4
Cadiz-PQ [17] 2017 ESP PD signals recordings from the power network of the Uni-
versity of Cadiz / 5 years
real-life- power-quality-sags
µPMU [18] 2016 US LOLP data from 3 PMUs / voltage and current on 3 phases,
magnitude and phase angle / 3 months
EPRI-DMD [19] 2015 US LOLP,
aggregates multiple datasets / health index for grid de-
vices / power load disturbances
UK-Dale [20] 2015 UK LOLP electricity usage data at a sample rate of 16 kHz for the
whole-house and at 1/6 Hz for individual appliances / 5
houses, 655 days
PLAID [21] 2014 USA LOLP 1094 current and voltage records from 11 different ap-
pliance types collected at 30 KHz
GREEND [22] 2014 ITA/AUT LOLP electricity usage data / 1 year data / 8 houses / 9 sensors
/ 1Hz resolution
AMPds [23] 2013 USA LOLP electricity usage data / 1 year data / one house / 21 smart
BERDS [24] 2013 USA LOLP energy usage & climate data Campus buildings UC
(not online)
iAWE [25] 2013 IND LOLP energy & water usage, climate information for 3 houses,
73 days, 33 sensors
Smart* [26] 2012 USA LOLP electricity usage data / high resolution dataset from 3
homes and low resolution dataset from 400 anonymous
homes / 1Hz resolution /multiple sensors & weather
BLUED [27] 2012 USA LOLP electricity usage data / 8 days / 1 house / 12 Khz
ECO [28] 2012 SWI LOLP electricity usage data & occupancy data / 8 months / 6
houses / 1Hz resolution
Tracebase [29] 2012 GER LOLP energy usage 1,000 event traces / 31 appliances
REDD [30] 2011 USA LOLP electricity usage data / 10 homes, 119 days
errors, different operating conditions, failures of underlying
hardware, low quality source code, interaction with external
services or applications, random events, and issues related to
the operational environment [32].
In this section, we describe the types of data that might serve
as the basis for deriving the reliability parameters discussed
in Section III. Additionally, we provide insights into our
methodology for dataset search, with the summary overview
of the datasets found, summarized in Table I.
A. Information Needs
In order to obtain network quality data at the communication
layer—such as latency or probability of data loss—we can
use the captured packet data (PCAP data). This kind of data
is often collected for SCADA systems. However, the main
purpose in that context is the detection of cyber-security
violations rather than reliability analysis.
At the distribution layer, the performance indicators de-
tailed in Section III.B are available in regular reports that
are required from the distribution companies. Other relevant
information could be collected from the sensor components
monitoring low level grid parameters, such as voltage or signal
The transmission layer indicators can be found in the
company reports. However, unlike the distribution reports, they
are typically not publicly available.
The data about failures in the individual software modules,
as well as hardware components, can be collected from event
logs. Software components in the Smart Grid systems log
their changes in state and behaviour into event records. Other
useful information can be timeseries of failure occurrences or
mean-time-between-failures (MTBF). They are often available
during the testing periods of the software and hardware
components and they can be used to derive the components
probability of failure.
To calculate the loss of load probability, data about the
actual load of the network, that is the volume of power
production and consumption, are necessary. The load profiles
can differ in the level of granularity and scope. They can range
from per minute to hourly, weekly or monthly values and
they can represent the load profiles of individual appliances,
households or full geographic areas.
B. Search Method
To set-up a search for datasets related to Smart Grid reliabil-
ity and to make it more systematic, we used the following strat-
egy. First, we used Google Scholar and Google search engines
with the keywords from Section III and IV followed by the
string “(Smart Grid Case Study) OR (experience report) AND
dataset”. Given the type of search, we privileged more gray
literature as a source of relevant datasets. Second, we searched
in open dataset repositories for Smart Grid analysis for existing
available datasets (e.g. OpenEI1). Next, we looked at resources
of real Smart Grid projects, since some of them provide related
datasets on their web pages2. Finally, since there is a high
demand for real datasets among researchers, we monitored
scientific community portals, such as Researchgate3, Zenodo4
and Figshare5for responses to dataset availability requests.
C. Datasets for Smart Grids
We report the datasets according to different type of infor-
mation needs (Table I). We divided the datasets in three main
categories, depending on the information that is available: Loss
of Load Probability (LOLP), Power Distribution (PD) and
Hardware (HW). Datasets range from year 2011 to 2017, and
the general motivation for the release of the datasets was due
to research needs, to allow for replicability of research results.
For this reason, there is a variety of very heterogeneous LOLP
datasets, in terms of number of residential units available,
appliances monitored, sensors, environmental information, but
also in terms of the measurement interval. The table summa-
rizes the main characteristics of the datasets according to these
These datasets can be used for reliability prediction in loss
of load probability. Information about the load profiles has
been used in past research in several studies as input to various
analytical approaches, such as regression [33] or Bayesian
models [34].
The first aspect we noted during the study is the scarce
availability of specific failure information for Smart Grids
components (for example, in form of events logs). Our as-
sumption is that event logs analysis is very common in
software/system engineering, but such datasets are likely to
be available for general reliability analysis, non-specific to the
Smart Grid. Finding openly available failure event logs for
different Smart Grids components is challenging.
Another aspect worth noting is that gathering data about
Smart Grids networking communication was very demanding,
generally raw datasets are very rare. However, there is good
availability of datasets from generic SCADA systems, aimed
often more at security concerns, such as intrusion detection.
There are many datasets that can be used mainly to test
different intrusion detection algorithms.
We reported that there are vast amounts of datasets for load
control with different levels of granularity, and with sources
from disparate number/types of sensors. As highlighted in this
paper, such datasets can be relevant for reliability analysis, for
example for the prediction of over-current situations.
For distribution/transmission, the indicators we reported are
generally available in public reports, while the only ones
we found, covered in some datasets, are related to Phasor
Measurements (PM).
Finally, when reviewing literature for sources, a vast number
of papers dealing with data analysis in Smart Grids is using
more synthetic data from simulations, rather than using real
datasets. However, the availability of many datasets (Table I)
brings the possibility of better comparative research, allowing
for better research replicability and reproducibility, as all
datasets are openly available.
A. Assumptions and Limitations
A first limitation of the study is given by the fact that the
reliability in Smart Grid is not clearly defined, which is why
we needed to define different reliability views and categories.
As reported in the architecture overview, the complexity
and different layers of the Smart Grids pose challenges in
the definition of reliability, and very often aspects such as
resilience, availability, security are used interchangeably.
The second limitation is related to the exhaustiveness of the
search approach. However, although we might have missed
relevant resources due to their industrial rather than academic
context, we combined different sources with the aim of im-
proving the quality of the search result. As such, Table I is
meant more as a starting point for reliability engineers to
gather insights about datasets according to different reliability
categories, rather than a definitive table listing all existing
resources. In some of the categories, however, it is close
to being exhaustive. These are namely information about
software failures and detailed software and hardware event
logs, where hardly any work is publicly available, due to the
potential sensitive data included. On the other hand, there are
likely more industry-based datasets from companies available,
which have not been included as we have mainly focused on
datasets that have already been part/used in existing research.
The complexity of Smart Grids renders software quality
requirements such as reliability, resilience, safety, security,
privacy, and performance of key relevance. Software reliability
is one of the key qualities of Smart Grid infrastructures.
However, the different layers of the Smart Grid make the
concept more complex than in traditional software reliability.
With the aim of increasing the understandability of relia-
bility in the Smart Grid context, we categorized information
needs for different reliability views, and explored different
open datasets available for reliability analysis. Overall, we
found many datasets that can be used for reliability analysis.
These can be useful to improve comparability across research
studies, as they represent an openly available source of data to
improve replicability and reproducibility of current research.
The work was supported from ERDF/ESF ”CyberSecurity,
CyberCrime and Critical Information Infrastructures Center of
Excellence” (No. CZ.02.1.01/0.0/0.0/16 019/0000822).
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... Software reliability is a vital aspect of the smart grid infrastructure. In [21], the smart grid infrastructure's complexity is explored to explore and quantify reliability views in the context of software reliability. The analysis is performed using different datasets, for example, failure logs, intrusion detection algorithms, industry-based datasets from companies, etc. available online. ...
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With the transformation from a traditional, rural, and agrarian society to a secular, urban, and industrial society and competitive environment, the rise in the power demand and supply makes it difficult to meet the customer’s expectations and needs. It has become necessary that the electric utility industry make sure they have accurate information about system performance and reliability. The paper presents four different cases through which the reliability of the electric distribution system is studied by calculating customer-oriented indices and load-oriented indices. The assessment is carried out using Modified Reliability Assessment Method (MRAM) on IEEE 24 bus system using MATLAB simulation, and the indicators of reliability analysis such as System Average Interruption Frequency Index (SIAFI), System Average Interruption Duration Index (SAIDI), Customer Average Interruption Duration Index (CAIDI), Average Service Availability Index (ASAI), Average Service Unavailability Index (ASUI), Energy Not Supplied index (ENS), Average Energy Not Supplied (AENS), Annual Customer Interruptions (ACI), and Customer Interruption Duration (CID) are evaluated. Moreover, the impact of the inclusion of cyber networks in the traditional system is also taken into consideration to determine the system’s reliability. Also, the result shows that the parallel combination is more superior to the other one. © 2022 Lalit Tak, Atul Kumar Yadav, Neeraj Kumar Singh, Mahshooq Abdul Majeed and Vasundhara Mahajan.
... The reliability of the power system is commonly used to encompass all these metrics. The authors of References [69, [110][111][112][113][114][115][116][117][118][119][120][121] have intensively investigated the reliability assessment for smart grid purposes. The reliability is often measured differently at the transmission, generation system, and distribution levels. ...
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The smart grid is an unprecedented opportunity to shift the current energy industry into a new era of a modernized network where the power generation, transmission, and distribution are intelligently, responsively, and cooperatively managed through a bi-directional automation system. Although the domains of smart grid applications and technologies vary in functions and forms, they generally share common potentials such as intelligent energy curtailment, efficient integration of Demand Response, Distributed Renewable Generation, and Energy Storage. This paper presents a comprehensive review categorically on the recent advances and previous research developments of the smart grid paradigm over the last two decades. The main intent of the study is to provide an application-focused survey where every category and sub-category herein are thoroughly and independently investigated. The preamble of the paper highlights the concept and the structure of the smart grids. The work presented intensively and extensively reviews the recent advances on the energy data management in smart grids, pricing modalities in a modernized power grid, and the predominant components of the smart grid. The paper thoroughly enumerates the recent advances in the area of network reliability. On the other hand, the reliance on smart cities on advanced communication infrastructure promotes more concerns regarding data integrity. Therefore, the paper dedicates a sub-section to highlight the challenges and the state-of-the-art of cybersecurity. Furthermore, highlighting the emerging developments in the pricing mechanisms concludes the review.
... With the growing popularity of the Internet of Things (IoT), Big Data technologies have emerged as a critical tool bringing a better understanding of crossdomain knowledge within IoT infrastructures. Smart cities (Piro et al., 2014;Walletzky et al., 2018) are one example, covering many sub-domains, such as smart buildings, smart mobility, smart grids, and others (Gesvindr et al., 2017;Rossi et al., 2016;Chren et al., 2018). ...
... State analytics deals with the status of the smart grid. The most used algorithms and methods used for this analytics are regression, wavelet transform, SVM and K-Means for some clustering purposes [51]. Operational analytics focused on the renewable energy generation forecast, managed mostly by regression analysis. ...
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Abstract Big data is one of the fields which is affecting almost all the scientific field. Be it medical, robotics, smart grid, smart cities big data is playing a vital role in the analysis of data and decision making. It has the potential to unlock the numerous opportunities to enhance all the fields. To date, substantial work has been carried by the researcher in the field of Smart Grid data analytics. They have already discovered the usage of big data analytics in the analysis of smart grid data in the area of planning and operations of the smart grid with a proven record. The widespread use of Advanced Metering Infrastructure (AMI) generates a huge amount of data and it is expected to increase shortly. Due to the complex nature of smart grid data, real - time data transformation, and huge data, the impl ementation of big data analytics has become challenging. To understand the smart grid analytics process and future challenges, a review is conducted in this paper. This paper discusses the architecture of the smart grid for data collection, stages of data processing and a review of analytical activities in a smart grid for four categories namely, event analytics, state analytics, operational analytics, and customer analytics. In addition to this, the paper also discusses the open research issues of big data analytics in the smart grid.
... With the growing popularity of the Internet of Things (IoT), Big Data technologies have emerged as a critical tool bringing a better understanding of crossdomain knowledge within IoT infrastructures. Smart cities (Piro et al., 2014;Walletzky et al., 2018) are one example, covering many sub-domains, such as smart buildings, smart mobility, smart grids, and others (Gesvindr et al., 2017;Rossi et al., 2016;Chren et al., 2018). ...
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Digitalization of our society brings various new digital ecosystems (e.g., Smart Cities, Smart Buildings, Smart Mobility), which rely on the collection, storage, and processing of Big Data. One of the recently popular advancements in Big Data storage and processing are the graph databases. A graph database is specialized to handle highly connected data, which can be, for instance, found in the cross-domain setting where various levels of data interconnection take place. Existing works suggest that for data with many relationships, the graph databases perform better than non-graph databases. However, it is not clear where are the borders for specific query types, for which it is still efficient to use a graph database. In this paper, we design and perform tests that examine these borders. We perform the tests in a cluster of three machines so that we explore the database behavior in Big Data scenarios concerning the query. We specifically work with Neo4j as a representative of graph databases and PostgreSQL as a representative of non-graph databases.
... Additionally, IQ is also of great importance for the efficient performance of some systems that are essential for 1 Data and information are synonyms in our work the reliability of Smart Grid such as Wide Area Monitoring, Protection, and Control (WAMPAC), Supervisory Control and Data Acquisition Systems (SCADA), Flexible Alternating Current Transmission System (FACTS), and Feeder Automation System [5]. Despite this, the current IQ related issues in SG are still addressed in an ad-hoc manner [4]. ...
Conference Paper
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Nowadays, most developed countries need to optimize their electricity production and consumption, which has led to the development of the Smart Grid (SG) concept. SG has a main objective of optimizing the generation, consumption, and management of electricity via information and communication technology. However, the vast amounts of information generated and processed in SG environments raise the issue of Information Quality (IQ). Accordingly, IQ has become an increasingly prominent issue in SG, since IQ can directly affect the services quality, reliability, and availability of an electric power supply. Despite this, the current IQ related issues in SG are still addressed in an ad-hoc manner. Without considering IQ requirements during the design of SG, it will be vulnerable to faults arising from depending on low-quality information, which may influence the dependability, reliability and efficient performance of SG. In this track of research, we aim at tackling this problem by developing a model-based approach for modeling and analyzing IQ requirements for SG.
... Thus, appropriate tests are necessary to evaluate reliability, integration, and performance of different layers of these systems [5], [6], [7]. However, there is no ad-hoc process model for SG testing, a model that can take into account the peculiarities of the domain. ...
... There is a number of systems in smart grids that ensure reliability of the power supply and the availability of critical services and which rely on high quality data collected from smart meters or PMUs [9]: (1) Blackout Prevention Systems protect the grid from instabilities and failures. They cover the whole power grid, using the data from PMUs to obtain relevant information from the grid. ...
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New devices in smart grid such as smart meters and sensors have emerged to become a massive and complex network, where a large volume of data is flowing to the smart grid systems. Those data can be real-time, fast-moving, and originated from a vast variety of terminal devices. However, the big smart grid data also bring various data quality problems, which may cause the delayed, inaccurate analysis of results, even fatal errors in the smart grid system. This paper, therefore, identifies a comprehensive taxonomy of typical data quality problems in the smart grid. Based on the adaptation of established data quality research and frameworks, this paper proposes a new data quality management framework that classifies the typical data quality problems into related data quality dimensions, contexts, as well as countermeasures. Based on this framework, this paper not only provides a systematic overview of data quality in the smart grid domain, but also offers practical guidance to improve data quality in smart grids such as which data quality dimensions are critical and which data quality problems can be addressed in which context.
Cities continue to face significant challenges that test their capacity for resilience. With the development of smart cities, there needs to be a better understanding of how the introduction of smart technologies will affect urban resilience. To address this issue, this article presents a critical review of the literature on smart cities and smart technologies focussing on representations of resilience. The findings reveal that discussing resilience in relation to smart city components of the data layer, digital technologies and the physical city can provide some degree of clarity despite the existence of a multiplicity of definitions and interpretations. Furthermore, the analysis indicates that the nature of relationships between ‘smartness’ and ‘resilience’ remains contested, and largely dependent on the perceived role of digital technologies in resilience-building processes. This in turn is influenced by how these technologies are used and what the intention and expectations are in relation to their use. In order to address these issues, we conclude that further interdisciplinary research, extending to the physical, social and environmental systems of cities, is needed to better understand the relations between smartness and resilience.
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Data analytics and data science play a significant role in nowadays society. In the context of Smart Grids (SG), the collection of vast amounts of data has seen the emergence of a plethora of data analysis approaches. In this paper, we conduct a Systematic Mapping Study (SMS) aimed at getting insights about different facets of SG data analysis: application sub-domains (e.g., power load control), aspects covered (e.g., forecasting), used techniques (e.g., clustering), tool-support, research methods (e.g., experiments/simulations), replicability/reproducibility of research. The final goal is to provide a view of the current status of research. Overall, we found that each sub-domain has its peculiarities in terms of techniques, approaches and research methodologies applied. Simulations and experiments play a crucial role in many areas. The replicability of studies is limited concerning the provided implemented algorithms, and to a lower extent due to the usage of private datasets.
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A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. In essence, smart grids bring profound changes in the information systems that drive them: new information flows coming from the electricity grid, new players such as decentralized producers of renewable energies, new uses such as electric vehicles and connected houses and new communicating equipments such as smart meters, sensors and remote control points. All this will cause a deluge of data that the energy companies will have to face. Big Data technologies offers suitable solutions for utilities, but the decision about which Big Data technology to use is critical. In this paper, we provide an overview of data management for smart grids, summarise the added value of Big Data technologies for this kind of data, and discuss the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context.
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Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data.
Conference Paper
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There is a large number of European Union (EU) projects that deal with Smart Grids research and deployment. Overall, they provide a substantial amount of knowledge that can be mined to gain useful insights for future projects and on-going roll-outs of Smart Grid related utilities. In the current paper, we focus on Smart Meters and we evaluate different communication-related architectural styles within an Advanced Metering infrastructure (AMI). In particular, we derive from the Joint Research Centre (JRC) Smart Grids projects review three different layouts for Smart Meters two-way communication: i) mobile Peer-to-Peer (P2P), ii) data concentrator-supported, and iii) gateway-supported. After the discussion about the architectural styles, we look at how common such choices are within EU projects deployments. Overall, we found predominance of both gateway / data concentrator architectures over P2P mobile communication layouts. The main outcome of the paper is a mapping of the three architectural styles to the deployments within the selected EU projects. Based on the map, we debate about the implications of such deployments within the current and future Smart Grids context.
We introduce the Plug-Level Appliance Identification Dataset (PLAID), a public and crowd-sourced dataset for load identification research consisting of short voltage and current measurements (in the order of a few seconds) for different residential appliances. The goal of PLAID is to provide a public library for high-resolution appliance measurements that can be integrated into existing or novel appliance identification algorithms. PLAID currently contains measurements for more than 200 different appliance instances, representing 11 appliance classes, and totaling more than a thousand records. In this demo we summarize the existing dataset, demonstrate how new records can be added to the library using a web interface and, finally, walk through a live example of how the library can be integrated into an existing non-intrusive load monitoring (NILM) algorithm framework.
Smart metering systems generally referred to as the next-generation power measurement system, is considered as a revolutionary and evolutionary regime of existing power grids. More importantly, with integration of advanced computing and communication technologies, the smart meter (SM) is expected to greatly enhance efficiency and reliability of future power systems with renewable energy resources, as well as distributed intelligence and demand response. Different electrical energy metering standards are point of concern for power/energy measurements. As measurement standards are formed, systems built around them can become interoperable from a standards point of view but still have incompatible configurations or different maturity levels, or include non-standardized functions. Even in areas that are standardized, there are sometimes implementation decisions that can result in different measurement and security behavior. With this paper we make three contributions: firstly, we identify various 1-channel and 3-channel metrology integrated circuits (ICs), which are mandatory for the standard measurement of distributed and renewable electricity generation. Secondly, we describe harmonics effect on metrology, which impacts on reliability of widespread smart metering infrastructure. Finally, we develop and describe a comprehensive set of security issues for SMs. Specifically, we focus on reviewing and discussing smart metrology meter (SMM) applications (i.e. metrological functions and real-time monitoring functions), security requirements, network vulnerabilities, attack countermeasures, secure communication protocols required in smart grid (SG) architectures. This review will enable the researchers, public policy makers and stakeholders to open the mind to explore possible in an evolving energy domain as well as beyond this area.
Conference Paper
Non-intrusive load monitoring (NILM) is a popular approach to estimate appliance-level electricity consumption from aggregate consumption data of households. Assessing the suitability of NILM algorithms to be used in real scenarios is however still cumbersome, mainly because there exists no standardized evaluation procedure for NILM algorithms and the availability of comprehensive electricity consumption data sets on which to run such a procedure is still limited. This paper contributes to the solution of this problem by: (1) outlining the key dimensions of the design space of NILM algorithms; (2) presenting a novel, comprehensive data set to evaluate the performance of NILM algorithms; (3) describing the design and implementation of a framework that significantly eases the evaluation of NILM algorithms using different data sets and parameter configurations; (4) demonstrating the use of the presented framework and data set through an extensive performance evaluation of four selected NILM algorithms. Both the presented data set and the evaluation framework are made publicly available.