Data Analytics for Low Voltage Electrical Grids

Maria Stefan

, Jose G. Lopez

, Morten H. Andreasen

, Ruben Sanchez

and Rasmus L. Olsen

Electronic Systems, Aalborg University, Denmark

Energy Technology, Aalborg University, Denmark

Keywords:

Smart Grid, State Estimation, Historical, Near-real-time Analytics, Streaming Data.

Abstract:

At the consumer level in the electrical grid, the increase in distributed power generation from renewable energy

resources creates operational challenges for the DSOs. Nowadays, grid data is only used for billing purposes.

Intelligent management tools can facilitate enhanced control of the power system, where the ﬁrst step is the

ability to monitor the grid state in near-real-time. Therefore, the concepts of smart grids and Internet of Things

can enable future enhancements via the application of smart analytics. This paper introduces a use case for low

voltage grid observability. The proposal involves a state estimation algorithm (DSSE) that aims to eliminate

errors in the received meter data and provide an estimate of the actual grid state by replacing missing or

insufﬁcient data for the DSSE by pseudo-measurements acquired from historical data. A state of the art of

historical and near-real-time analytics techniques is further presented. Based on the proposed study model

and the survey, the team near-real-time is deﬁned. The proposal concludes with an evaluation of the different

analytical methods and a subsequent set of recommendations best suited for low voltage grid observability.

1 INTRODUCTION

At the beginning of the 21st century, a massive

improvement of Information and Communications

Technology (ICT) gave an opportunity for solving

some existing limitations of the electrical grid, while

also reducing the operational costs (Miceli et al.,

2013). This sparked people involved in the devel-

opment of the future energy market to think of new

concepts. Of these ideas, smart meters and smart grid

were the most popular, by adding ICT intelligence to

the system, wherever useful.

These ideas led many countries to support var-

ious research programs in the smart grid domain.

Denmark, already having a long tradition in the

green electricity market, published a set of recom-

mendations for implementing these concepts in the

report called Smart Grid in Denmark. One Dan-

ish ﬁnanced research program is ForskEL (Energinet,

2015), meant to support the development and inte-

gration of environmentally friendly power generation

technologies and grid connection.

One goal of ForskEL is to help the Distributed

System Operators (DSOs) in making sensible deci-

sions regarding future power grid planning and fault

diagnosis in near-real-time. This calls for the utiliza-

tion of intelligent methods for grid data visualization,

as presented in (Stefan et al., 2017).

The new challenge for the Danish DSOs arises

as more distributed power generation is introduced

at the low voltage grid level. This affects their abil-

ity to monitor the state of the power grid without en-

countering operational constraints. One of the DSOs’

primary tools are to obtain full observability of the

low voltage grid, by making use of scalable data an-

alytics, as intended with the Danish RemoteGRID

project (Martin-Loeches et al., 2017). Hence, high-

performance data processing and analytical methods

are fundamental for efﬁciently managing distribution

grid data.

Two relevant data types are considered in relation

to the power grid:

• Geographic data: electrical network structure (ca-

bles, transformers, substations, meters) and their

geographical coordinates;

• Measurement data: three-phased generic grid

measurements from each load or connection point

containing multiple loads (voltage, current, con-

sumption).

This paper introduces a study of analytical meth-

ods suitable for obtaining low voltage grid observ-

ability. The paper is organized as follows: Section

2 presents the ﬂow of data in a smart grid applica-

tion. The proposed study is presented in Section 3 and

Stefan, M., G. Lopez, J., H. Andreasen, M., Sanchez, R. and L. Olsen, R.

Data Analytics for Low Voltage Electrical Grids.

DOI: 10.5220/0006694802210228

In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security (IoTBDS 2018), pages 221-228

ISBN: 978-989-758-296-7

221

it underlines the advantages of pseudo-measurements

and state estimator for the smart grid scenario. In Sec-

tion 4, both generic and state of the art analytic meth-

ods will be presented. Given the chosen case study

and background, the most suitable analytics will be

emphasized in Section 5, along with the deﬁnitions

of bulk and stream data types. Section 6 will sum-

marize the aforementioned study requirements with

future action plans for testing the above concepts.

2 STUDY BACKGROUND

The underlying application structure is deﬁned based

on the requirements imposed by the analytical meth-

ods suitable for the state estimation algorithm intro-

duced in Section 3. In this study, the application struc-

ture is proposed as a client-server application, based

on the IEC 61868-100 standard (Commission, 2013).

The data ﬂow is depicted in Figure 1.

Client

User

Analytics

DBMS

Server

AMI

Figure 1: Data ﬂow and exchange of automatic events ac-

cording to IEC 61968-100.

The IEC standard is meant to provide guidelines

regarding message exchange and interface speciﬁca-

tions for utility enterprise distribution systems. Con-

sequently, the key terms are clariﬁed as follows:

• Advanced Metering Infrastructure (AMI) (Uribe-

erez et al., 2016): main data source in a smart

grid, characterized by a large number of nodes

(meters) located at customer premises;

• Meter Data Management (MDM) (IEC/TC,

2013): software entity that involves the storage

and management of the AMI data. This includes

the Database Management System (DBMS);

• Enterprise Service Bus (ESB) (Neumann and

Nielsen, 2010): software-based integration layer

specifying a standardized communication inter-

face facilitating services (routing, mediation,

recording of data etc.) via standard event-driven

messaging. The ESB middelware works as an

adapter between different data formats and pro-

tocols in a Service Oriented Architecture (SOA).

Data is generated at the AMI (server entity) as

an encoded packet, which is then decoded at the

MDM level and sent to a database management sys-

tem (DBMS) for storage via XML messaging (Mc-

Morran, 2007). In this back-end architecture (Up-

work, 2017), the DBMS is deﬁned as an integration

feature, which provides the ESB middleware with raw

data to be sent to the analytics module for processing.

A processing unit in the analytics module extracts the

desired information to be displayed for the user (client

entity). In the smart grid context, the user is usually

located in the DSO control center. The event progres-

sion of data is storing - information extraction - infor-

mation display. These events take place in a cyclic

manner and thus, they are referred to as automatic

events.

The data exchange sequences are not solely one-

way. In case the client (user) detects unusual patterns

or missing information in a certain geographical area,

additional data from that speciﬁc area (or speciﬁc me-

ters) can be requested for enhanced monitoring pur-

User

Analytics

DBMS

Figure 2: Data exchange of interactive events based on

client request.

IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security

222

poses. If so, the data ﬂow is based on so-called inter-

active events, as shown in Figure 2. The client’s re-

quest for more detailed information is transmitted to

the DBMS via the ESB, to search if there is a match

for the requested data in the database. If a match is

found, a reply is sent to the client for display and vi-

sualization. If not, the data request may be forwarded

to the AMI, which will conﬁgure the meters to send

the required data. Timing is crucial in the DSOs deci-

sion making process and is notably affected by delays

in the transmissions from data collection to data dis-

play. Requesting certain information all the way from

the AMI will result in additional delays due to the in-

creased number of messaging sequences between en-

tities.

As a part of the analytics module, the next section

will introduce the Distributed System State Estimator

(DSSE).

3 STUDY OUTLINE: LOW

VOLTAGE GRID

OBSERVABILITY

Low voltage grids are undergoing a transformation

from a passive to a more active role in the electrical

network. Traditionally, conventional large gas or coal

power plants, among others, are the source of elec-

trical power generation (Trebolle et al., 2013). Af-

ter being transmitted at a high voltage level, the en-

ergy is distributed to supply the loads in the system.

Lately, the penetration of distributed generation, es-

pecially from Renewable Energy Resources (RES), at

the low voltage level has increased. It creates opera-

tional challenges for the DSOs since the low voltage

grid was not designed to operate under such condi-

tions. For example, generation peaks from RES do

not necessarily match peaks of consumption, intro-

ducing power ﬂows from the low to the high voltage

level.

In order to address operational concerns, the

DSOs require advanced management tools. Grid

monitoring is the ﬁrst step towards a more reliable

operational approach (Abur and Exposito, 2004). In

fact, nowadays, the low voltage grid electrical pa-

rameters are not monitored in the DSOs control cen-

ters. Monitoring the system allows DSOs to deter-

mine whether or not the system is operating under

normal conditions. A system is considered to oper-

ate under normal conditions if all the loads can be

supplied without violating any operational constrains

(Abur and Exposito, 2004).

3.1 Low Voltage Grid State Estimation -

LV DSSE

Grid observability depends on where the measure-

ment points are placed along the electrical grid. In

the case of low voltage grids, these measurements are

provided by the smart meters. However, the informa-

tion extracted from the meter’s data contains errors

due to various factors, such as communication issues

or measurement deviations in the devices. Thus, as a

ﬁrst step in control centers, efﬁcient data analytics are

required to properly determine the state of the electri-

cal grid. The state is deﬁned as ”known” if the volt-

ages and phase angles with respect to a certain voltage

and angle reference are known at every node (point

where two or more circuit elements meet) (Abur and

Exposito, 2004).The process in charge of eliminat-

ing errors and providing the best estimate of the sys-

tem state in distribution systems is the so-called dis-

tribution system state estimation (DSSE) (Alimardani

et al., 2015).

Figure 3: Evaluating observability based on the ﬁeld near-

real-time measurements.

Figure 3 shows the block diagram of the observ-

ability analysis performed based on the raw measured

data. This analysis determines if the system state can

be estimated based on the set of acquired near-real-

time readings. For example, few or non-existing mea-

surements are sometimes provided from a speciﬁc ge-

ographical area of the system. This implies that the

available data is insufﬁcient to successfully estimate

the state of the system. In that case, other data an-

alytics methods are needed, where the unavailable

near-real-time measurements are substituted by the

Data Analytics for Low Voltage Electrical Grids

223

so-called pseudo-measurements obtained from histor-

ical data (Khodabakhshian et al., 2017).

3.2 Pseudo-measurements

Traditionally, pseudo-measurements have been ob-

tained from standardized daily load and generation

proﬁles (DLP-DGP). Those are created for different

customer classes based on socio-demographic factors

(Krsman et al., 2016). However, other approaches

seeking more precise accuracy have been developed

in the literature. Artiﬁcial Neuronal Networks (ANN)

are used in (Do Coutto Filho et al., 1999). Besides,

different clustering techniques were utilized as it is

the case of k-means (Ben

ıtez et al., 2014), princi-

ple component analysis (Abreu et al., 2012), spectral

clustering method (Albert and Rajagopal, 2013) or ﬁ-

nite mixture model (Stephen et al., 2014), among oth-

ers.

New solutions are to be studied in order to provide

robust pseudo-measurements for low voltage grid ap-

plications based on the utilization of AMI data. Un-

predictable behavior from RES is a challenge where

efﬁciency in terms of the amount of stored data needs

to be considered given the large number of nodes at

the low voltage level.

4 ANALYTIC METHODS

AMI data is by deﬁnition part of the Internet of

Things (IoT) umbrella, in the sense that smart me-

ters act as sensors in the electrical grid infrastructure.

IoT data analytics is characterized by autonomous

or semi-autonomous examination of data, employing

sophisticated techniques and tools, typically beyond

those of traditional Business Intelligence (BI). These

techniques help to reduce complex data sets into ac-

tionable insights, enhance and empower BI decision

support systems. By this token, some traditional an-

alytics and algorithms include data mining, machine

learning, pattern matching, forecasting, visualization,

semantic analysis, sentiment analysis (Gartner Sum-

mits, 2017).

Analytics are classiﬁed by two main categories:

historical and near-real-time analytics.

• Historical analytics: based on the past data values.

Data-at-rest corresponds to batch data processing;

• Near-real-time analytics: based on the present.

Data-in-motion equals stream data processing.

4.1 Historical Analytics

Four traditional types of historical analysis are pre-

sented in the following subsections. They are a trade-

off between the provided information value and the

implementation difﬁculty. This is illustrated in (CI

and T, 2014).

4.1.1 Descriptive

This type of data analysis is used to provide insight

into past events, by identifying overall themes and

patterns. Descriptive analytics is commonly classiﬁed

as BI and is the de facto standard analytics methodol-

ogy. Typical outputs include dashboards, reports and

status emails stating historical observations by sum-

marizing raw data for human interpretation. These are

mainly obtained through methods such as data mining

and data aggregation.

An example of descriptive analytics can be found

in (Liu et al., 2016), where daily proﬁle of consump-

tion trends are obtained by means of data aggrega-

tion. This helps to understand daily habits of con-

sumers and, at the same time, to insure the privacy

of end users through data anonymization (Diaman-

toulakis et al., 2015).

4.1.2 Diagnostic

Diagnostic analysis helps answering questions like

”Why was a certain event triggered”, by providing a

deep understanding of a limited problem space via in-

depth data analysis, discovering the root causes and

characteristics of an event. Advancing from aggre-

gate and summary information to detailed data, based

on speciﬁc focus attribute(s), is done via selection and

querying of data sets. Data granularity deﬁnes the

limit for the analytic level of detail. The resulting out-

put is typically an analytic dashboard.

Correlation methods are part of obtaining a diag-

nosis analysis. The review in (Raza and Khosravi,

2015) proposes a method for characterizing power

system loads by the correlation between load demand

and weather variables.

4.1.3 Predictive

Predictive analytics is about foreseeing the future

based on historical data patterns. Future predictions

and scenarios come from data mining, machine learn-

ing and statistical modeling of raw data. Thus, action-

able insights are obtained via plausible estimates of

future outcomes. Typical deliverables are in the form

of predictive forecasts based on probabilistic and cor-

relation analysis.

Load forecasting is a common use case of pre-

dictive analysis in smart grids (Diamantoulakis et al.,

2015). The study made in (Abu-El-Magd and Findlay,

IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security

224

Collect

Advanced

Metering

Infrastructure

Diagnose &

Predict

Querying

Correlating

Statistical modelling

Forecasting

Drill-down/through

Prescribe

Business rules

Optimization

Visualization

Big Data

Describe &

Pre-process

Filtering

Aggregation

Mining

Storage - Database Management System

Figure 4: Proposal of streaming analytics architecture for low-voltage electrical grids (Vitria, 2015).

2003) approaches a forecast method which is based

on a combination of ANNs and time series data mod-

els. Load forecasting can be achieved using not only

correlations, but also through machine learning solu-

tions, such as the MapReduce processing model (Riz-

vandi et al., 2011) (GJSissons, 2014). MapReduce

allows for massive scalability across a cluster of com-

puters, for large data sets (in the range of Terabytes),

which is a suitable solution in case of AMI infrastruc-

tures.

4.1.4 Prescriptive

The primary focus of prescriptive analytics is to pro-

vide real-world recommendations. Datasets are eval-

uated via analytical models and the preferred cause of

action for each speciﬁc event is selected. Then the re-

sult, in the form of explicit actionable information, is

presented for human interaction, typically making the

ﬁnal decision on acceptance or rejection. Hence, pre-

scriptive analytics takes a step further than predictive

analytics by reducing complex data and algorithms to

non-technical descriptors for immediately recogniz-

able advice on predicted future outcomes. The anal-

ysis aids the decision-making process, having the po-

tential to both maximize positive outcomes as well

as prevent undesirable events (Halo Business Intelli-

gence, 2017).

Simultaneous utilization of multi-source datasets

includes historical and real-time data, transactional

and big data analytics, that affect marketing strategies

(Daki et al., 2017). For example, one signiﬁcant tool

to help utility companies navigate towards a smart

grid platform is the Vitria IoT Analytics Platform, re-

ported in (Vitria, 2015). This white paper states that a

combination of prescriptive analytics and smart deci-

sions provide the highest throughput in the analytics

value chain.

4.2 Near-real-time Analytics

The resilience of the power grid is part of the future

requirements for evolving towards intelligent grids.

The main motivation for near-real-time analytics lies

in the lack of limited grid functionality to timely de-

tect and prevent failures. This extends to the discov-

ery of natural disasters or criminal actions that might

have caused the failures. Therefore, these can be pre-

vented by making use of real-time intelligence (Vitria,

2015).

4.2.1 Streaming Analytics

Near-real-time decision support can be provided via

data-in-motion pre-database processing, inspection,

correlation and analysis. It enables instantaneous

management, monitoring, and continuous statistical

analysis of data. Introducing real-time KPI overview,

immediate access to metrics, and reporting, improves

reaction time and accelerates decision-making.

Streaming analytics provide value from the data

in a similar manner as traditional historical analytics.

The value of streaming data decreases non-linearly

over time, meaning that events should be reacted upon

quickly, in near-real-time. The progression from his-

torical methods comes as analytics are no longer per-

formed ”at-rest”. Instead, data is processed before it

is stored and therefore the decision-making process

becomes timely and more efﬁcient (Gutierrez, 2016)

(Techopedia, 2017). A summary of the modules in-

volved in the data streaming based on the surveyed

analytics types is shown in Figure 4. This ﬁgure

shows that the same principles as in historical ana-

lytics can be applied to streaming data.

Data Analytics for Low Voltage Electrical Grids

225

Table 1: Advantages and disadvantages of using historical and near-real-time analytics for providing data to the DSSE.

Analytics Pros Cons

Historical

(context

awareness)

• provide insight by uncovering data

patterns and trends

• accuracy and realiability dependent on

time

• quickly accessible and detailed (available

and veriﬁed data)

• most machine learning algorithms do

not deal with temporal effects

• clarity by presentation of reduced

complex data sets - thorough presentation of

large data sets

• reliance on batch processing and

consequently limited by the resulting

update intervals

Near-real-time

(situation

awareness)

• detects gross errors - accuracy • highly dependent on the delays in the

communication network

• avoid latency from ﬁltering disk data • difﬁcult to adapt to platform and

hardware requirements

• detect emerging correlations between

multiple data sets

• risk of incorrect analysis via

implementation dependency

• immediate pre-database data availability

5 MAIN FINDINGS AND

DISCUSSION

The study presented above emphasized the impor-

tance of introducing analytical methods to monitor the

status of low voltage electrical grids and to plan future

grid reinforcements. Historical data is used to cre-

ate pseudo-measurements, aiming to ﬁll in missing or

erroneous data received from a smart grid infrastruc-

ture.

Given the back-end client-server architecture pre-

sented in Section 2, the automatic ingestion of data

can be deﬁned as a ”stream of data”:

Near-real-time measurements are characterized as a

continuous, fast changing and voluminous data ﬂow,

commonly known as stream.

To support the above-mentioned deﬁnition, the

notion of near-real-time data can be given in the con-

text of the data ﬂow architecture in Section 2 and the

use case presented in Section 3:

Assuming that the data packets sent from the low volt-

age grid arrive consecutively with a ﬁxed period of

time, then a near-real-time data stream can be deﬁned

as: a data packet characterized by the arrival granu-

larity and received in a timely manner at the user side.

Timing is then relative to the types of events involved

in the data ﬂow: automatic or interactive.

The analytical methods involved in the DSSE al-

gorithm are based on both historical and near-real-

time data. Due to their timely nature, the near-real-

time measurements are more reliable and accurate

than the historical ones. Therefore, the DSSE needs

near-real-time data, that should be pre-processed in

order for the estimator to ”understand” it, equivalently

to the streaming analytics procedures shown in Fig-

ure 4. There are typically not enough near-real-time

measurements available to successfully perform the

DSSE. Therefore, there is not enough data to pro-

vide full grid observability. In order to ﬁll in the gaps

of missing information, pseudo-measurements can be

created by requesting raw data that has been previ-

ously stored in a database. The requested informa-

tion can therefore be extracted by means of ﬁltering,

mining or querying, making it comprehensible for the

DSSE. In this case, the most suitable analytics are de-

scriptive.

A summary of pros and cons of the aforemen-

tioned analytics for the DSSE is presented in Table 1.

The novelty of this study is based on the integration

of traditional analytics into the energy-related ﬁeld,

which consists of the DSSE algorithm. As historical

based analytics are useful to build periodic reports for

strategic and long-term decisions, they are also lim-

ited by the temporal effects. Historical data may not

give a true pattern of a data trend, if this has changed

with time. While near-real-time analytical tools can

address the temporal dependency, they are also plat-

form sensitive.

IoTBDS 2018 - 3rd International Conference on Internet of Things, Big Data and Security

226

6 CONCLUSION

This study addresses the challenges for choosing suit-

able data analytics methods in the domain of low volt-

age smart grids. DSSE is an analytical method for

providing a reliable source of information related to

the state of the grid, by ﬁltering the raw data and

detecting gross errors. Ideally, DSSE makes use of

near-real-time data to provide a successful estima-

tion. In many cases, this data is insufﬁcient or non-

available, so pseudo-measurements generated from

historical data will ﬁll in for the lack of information.

Traditional historic analytics can build predictive out-

puts useful for the DSSE, but there is a higher error

probability in the pseudo-measurements.

By this token, the data analytics module should

be built on a platform that can accommodate for both

historical and near-real-time analysis. The next step

in this research is to test the functionality of a DSSE

algorithm and analyze the capabilites of processing

large amounts of historical batch data. At the same

time, the test aims to characterize the performance

and bottlenecks of parallel processing of both stream

and batch data types, taking into account parame-

ters such as memory usage, processing time and in-

memory processing behavior.

ACKNOWLEDGEMENTS

This work is ﬁnancially supported by the Danish

project RemoteGRID, which is a ForskEL program

under Energinet.dk with grant agreement no. 2016-1-

12399.

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