Introducing an Information Logistics Approach to Support the
Development of Future Energy Grid Management
Steffen Nienke, Oliver Zöllner, Violett Zeller and Günther Schuh
Institute for Industrial Management (FIR) at RWTH Aachen University,
Campus-Boulevard 55, Aachen, Germany
Keywords: Information Management, Information Logistics, Energy Grid, Information Logistics Notation, Grid Stability.
Abstract: Due to the drastically increasing amount of data, decision making in companies heavily relies on having the
right data available. Also because of an increasing complexity of structures and processes, quick and precise
flows of information become more important. This paper introduces a new approach for modelling
information flows, creating a basis for an efficient information management. It can be used to structure the
information requirements and identify gaps within the information processing. To display its benefits, the
proposed Information Logistics Notation (ILN) is applied to the information logistics of todays and future
energy market and grid stability management, both processes of increasing complexity.
1 MOTIVATION
During recent years, the industry is trying to replace
the currently prevailing experience-based decisions
with data-driven decisions to improve the decision
quality (pwc, 2016). However, due to the increasing
amount of data that is available nowadays, the
complexity of the decision-making processes
increases, too. Decision makers face an
overwhelming amount of data and information, which
usually leads to a neglect of some parts of the
available information (Saaty, 2008). In addition, the
subjective need for information differs from the
impartial need of information (Krcmar, 2015).
Therefore, a lot of potential to improve decision
quality remains unused. In order to reduce the
complexity, a consistent use of IT must be set up. The
IT system needs to collects the data, prepare
information and generate decision recommendations.
Such a system should be designed user-centred, so
that the user can focus on decision making instead of
searching for and preparing information. In other
words, the user can actually focus on his actual task
he was hired for.
To achieve such a system, many components on
three levels have to be considered. On the bottom
layer, there is a need for a fast and reliable data
acquisition and storage solution. On the middle layer,
it requires a data analytics software that is intelligent
enough to create decision recommendations. Finally,
on the top layer, the information needs to be presented
to the right person at the right time. Today, a lot of
research and development is carried out on all levels
and also on combining these levels. However, to
create such a holistic system, those developments and
approaches need to work together smoothly.
Consequently, it is essential to structure the whole
system to derive the necessary components and
determine where those components are required.
However, many existing approaches only consider
the business processes to structure the IT system.
Since every industry is striving to increase the use of
information, this is only half of the needed
requirements. In addition to the sequence of
performed tasks that are represented of a process, it is
important to determine who needs what kind of
information within each task. Therefore, an
information logistic model is required to structure the
flow of information and determine the impartial
information for each user. A Method for visualizing
the information logistics is the Information Logistics
Notation (ILN) that is presented in this paper.
To demonstrate the benefits of the information
logistics model, it will be applied in the context of
energy grid monitoring and stability management, a
subject most relevant with regard to rising renewable
generation. As the Climate Change 2014 Synthesis
Report points out, “Human influence on the climate
system is clear, and recent anthropogenic emissions
Nienke, S., Zöllner, O., Zeller, V. and Schuh, G.
Introducing an Information Logistics Approach to Support the Development of Future Energy Grid Management.
DOI: 10.5220/0006673202390246
In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 239-246
ISBN: 978-989-758-298-1
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
239

of green-house gases are the highest in history.”
(Pachauri and Mayer, 2015, p. 2).
However, renewable energy generation capacities
are neither reliable, nor centralized the way
conventional power plants are. Because of this, the
currently existing infrastructure for energy
distribution and information exchange is not prepared
to integrate large amounts of renewables. “The next
generation electricity grid, known as “smart grid”
[…], is expected to address the major shortcomings
of the existing grid” (Farhangi, 2010). A fully
developed smart grid continuously tries to balance
itself locally. To achieve the best possible balance,
consumers and other actors on the energy market are
constantly communicating and reacting, for example
reducing energy consumption to stabilize the grid.
Considering the many different players that need to
act interdependently on the energy market, the many
different measures that may have to be taken within
seconds, communication has to be extremely quick
and precise.
In turn, the data exchange required by a smart grid
needs to be carefully organized with regard to
information flows which is a possible application area
for the Information Logistics Notation (ILN)
proposed in this paper. Thus, the logistics of
information in this sector is a crucial subject and a
suitable case to present the benefits of the ILN.
The paper is structured as follows: After
describing the motivation in this chapter, Chapter 2
clarifies the necessity for the new approach for
modelling information flows and describes the basics
for electricity grid operation. In Chapter 3, details on
the aim of the approach are given. Followed by a
detailed description of the components of the ILN and
how they are used. Afterwards, the ILN is
exemplarily applied to today’s and future electricity
grid operation in chapter 4. Finally chapter 5 sums up
the findings and provides an outlook on future work.
2 DEFINITIONS AND
FUNDAMENTALS
2.1 Information Logistics
In general, logistic is defined as controlling and
executing all processes within and outside a socio-
economic system (e.g., a company) that serve to
bridge time and space. These could be the primary
purpose of the company (e.g. logistics companies
such as DHL), but also the subsidiary tasks of a
company (e.g., transporting or storing within a
company, Koch, 1996). The definition of information
logistics varies in literature. In particular older
publications, information logistics is merely referred
to as the information linked to the flow of goods
(Immoor, 1998). However, with the increasing
importance of data and information, a broader
definition is used. In this definition, the information
is treated as a separate logistical asset (Krcmar, 2015;
Voß and Gutenschwager, 2001). This paper is
following the modern approach. As a result, the
information logic combines all the methods that deal
with the modelling, storage, distribution and
provision of information. Hence, information
logistics covers planning, control and monitoring of
information flows (Krämer, 2002). In short, the aim
of information logistic is to achieve the 5 R’s: get the
right information, to the right person, at the right time,
in the right amount and providing the right quality
(Krcmar, 2015).
2.2 Modelling Information Flows
In Literature, there are quite a few approaches on how
to model information and data itself. Notable
examples are the tools TOGAF (The Open Group
Architecture Framework), ArchiMate or DEMO
(Design & Engineering Methodology for
Organizations). These already offer possibilities to
model the information logistics structure of a
company. However, modelling of information flows
is usually not covered to the required extend of many
use cases. Especially the ability to model the time-
related exchange of specific objects of information
and distinguish between information push and
information pull is missing in those tools. As
described in Section 1, modelling the information
flow is getting more important since it enables entities
to analyse and coordinate the information movements
as well identify redundant information (Durugbo et
al., 2013). Modelling Information flows can be
complex, since an information exchange takes place
between individuals in one or more companies,
between departments or companies, and between
companies and their environment. Therefore, a
structured approach on modelling information flows
is crucial. In general, two methods of information
flow modelling can be distinguished: the diametric
modelling and the mathematical modelling (Durugbo
et al., 2013). The Diametric modelling usually results
in a human-readable representation and is thus
usually easier to understand e.g. by employees. The
mathematical modelling, on the other hand,
corresponds more to a machine-readable
representation of the information flows. However, the
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diametric and mathematical approach to information
flow modelling do not necessarily exclude but could
complement each other. Accordingly, combinations
of both approaches are often used (Durugbo et al.,
2013). In this paper, a diametric graph approach is
presented, which can easily be converted into a matrix
visualization and thus be turned into a mathematical
model.
2.3 Electric Grid Operation and
Expected Changes
Within this chapter, relevant aspects of the energy
market, power generation and power grid are
introduced to understand the ILN application area of
this paper.
2.3.1 Status Quo of Transmission and
Distribution Grids
The currently existing grid infrastructure was
“designed as a centralized system such that the
electric power flows unidirectional through
transmission and distribution lines from power plants
to the customer premises” (Gungor et al., 2013). The
networks themselves are radial, the top down power
flow is unidirectional and the security scheme simple
(Celli et al., 2004). This means that all distribution
grids simply pass the shortage on to higher grid
levels. This makes them relatively easy to maintain
but also limits them to their present use.
Engineers from a central network control station
manage the grid in real time. They have to foresee
bottlenecks or overloads (Schneiders, 2014).
Due to the emergence of decentralized power
generation in small, renewable power plants attached
to the distribution network, the grid becomes harder
to manage. The amount of information processed in
control centres is strongly increasing (Schneiders,
2014).
2.3.2 Trends and Expected Changes
Until now, the centralized approach on grid planning
and control allowed robust and scalable power
systems. The number of prosumers – persons who
consume and produce energy – will rise dramatically,
leading to a strong increase in renewable, variable
power generation. (Divan and Kandula, 2016)
Additionally, renewable power generation
quantities can only roughly be predicted (Su et al.,
2014). Communicating with energy suppliers to
adjust output to keep the grid balanced gets harder
because instead of a few companies, communication
would have to reach thousands or even millions of
households where a quick reaction would also have to
ensue.
Shifting to an inconsistent energy generation will
result in both energy surpluses and shortages that will
ultimately lead to a time-dependent energy price for
consumers (Hirth, 2013). Energy is cheapest when
renewable production is highest, thus increasing
energy consumption when the price is low disburdens
the grid.
As the power grid needs to be continuously
balanced with regard to power generation and
consumption, the fluctuating energy generation will
require all participants of the energy market to
become more flexible.
3 INFORMATION LOGISTICS
NOTATION (ILN)
The aim of the Information Logistics Notation (ILN)
is to visualize the information logistics within an
entity or in between various entities. The result can
also be described as information map that shows
where what information comes from and where each
information is directed. This notation will help to
solve the tasks of structuring the information flows
and therefore the whole information system. In fact,
the structured information flows can be used to
identify missing components or interfaces in
information systems as well as roles that do not
receive enough information to cover the objective
information need. While using this notation, there are
certain similarities to the use of conventional business
processes. Thus, in both cases, an actual state can be
recorded and compared with a desired state.
However, instead of presenting sequences of tasks,
the ILN displays information objects and their flows
between sources and sinks. Thus, an IL-model does
not replace the traditional recording of business
processes, but rather forms a new level, closing the
gap between modelling business processes and
modelling databases
3.1 Notation – Display of Information
Logistics Models
The following subchapter describe the components
and use of ILN.
3.1.1 Components of the ILN
In an IL-model, roles (or stakeholders) are the central
objects. These roles represent functions within
Introducing an Information Logistics Approach to Support the Development of Future Energy Grid Management
241

companies. Thus, they can symbolise persons,
departments or objects like sensors. As visible on the
left side of Figure 1, a role is visualised by an oval
form. A role is characterised by the information
generated (source) and required (sink). Every role
possesses a source as well as a sink; a vertical line
visually separates the two sides of a role.
Decision-making roles (or target roles) are
exceptional roles that have a certain demand for
information to reach a particular decision (Figure 1,
right side). The decision-making role is distinguished
by a filled margin. Additionally, the questions to be
answered are noted over the decision-making roles. It
is initially assumed that humans embody decision-
making roles, making decisions based on supplied
information. In future, due to increasing automation,
this assumption might change towards computer-
based roles.
Figure 1: Symbols for stakeholders and decision-making
stakeholders with target decision.
Some roles may not refer to single companies,
divisions or stakeholders, but rather to a group of
people. Taking the example of a power generation
company, a role within the ILN could be customers.
It is harder or sometimes impossible to exchange
information with all individuals of such a role.
Depending on how crucial the respective
communication is, a special infrastructure may be
required. Thus, roles that represent a big group are
visually accentuated by a group-symbol on the left
side of the vertical line (Figure 2).
Figure 2: Symbol for a group of stakeholders.
Apart from roles, systems are an important
element in mapping a flow of information. There are
two kinds of systems: on the one hand, database
systems are saving data and information (Figure 3,
left side), and on the other hand, processing systems
are aggregating, analysing and transforming
incoming information to new objects of information
(Figure 3, right side). Both kinds of system are
visualised by rectangles, labelled with a name and
tagged with a symbol to distinguish between
databases (database symbol) and processing systems
(gear wheels) as visible in Figure 3. Furthermore,
systems can be sources and sinks of information; the
object is divided by a vertical line analogue to
stakeholders. In contrast to a role, databases can store
series of data and can consequently give access to
historical data. A processing system can execute
complex analyses.
Figure 3: Symbols for database systems and processing
systems.
The roles and systems exchange objects of
information via information flows. As shown in
Figure 4, objects of information are listed on the
source side of a role. To increase lucidity and
structure, datasets are pooled together in objects of
information and then listed one below the other.
Information flows connect objects of information
from the source to the designated sink of a role or
system. Each information is transmitted at a certain
transmission frequency that is specified next to the
respective information flow.
Figure 4: Visualization of objects of information and
information flows.
There are two categories of transmission
frequencies: On the one hand, information can be
exchanged on a regular basis (for example each
second or day), on the other hand the transmission can
be actively triggered. The transmission can either be
triggered from the side requiring information
(information pull) or from the information source in
case the information object changed (information
push). For large information logistics displays to be
more clearly arranged, the following shortcuts can be
used to label information flows with transmission
frequencies:
 s – every second;
 m – every minute;
 ¼ h – every quarter of an hour;
source
sink
Stakeholder Descision making stakeholder
Question to decide on?
Designation
Designation
- Objects of information
- …
- Objects of information
- …
Designation
- Objects of information
- …
- Objects of information
- …
DB-Designation
Process-
Designation
- Objects of information
- …
Database System Processing System
sink
source
Object of information 1
 Dataset 1.1
 Dataset 1.2
Object of information 2
 Dataset 2.1
 Dataset 2.2
Transmission
frequency
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 h – every hour;
 d – daily;
 iC – if changed;
 oD – on demand.
Apart from the notations’ basic elements that have
been described to this point, there are other optional
elements. They can display situations that are more
complex. Or-connections can model alternative or
redundant sources of information. As shown in Figure
5, such branches are pictured by a filled circle with an
“X” in the middle. Only the information flow directed
to the sink is labelled with the transmission
frequency, because it shows the necessary availability
of the respective information.
Additionally, it is possible to tag flows of
information with a weighting and the technology
used for transmission. Weighting flows of
information makes sense if they require prioritization
and the information system’s available resources are
possibly limited. Furthermore, priorities are the basis
for mathematically optimizing an information
logistics model.
Figure 5: Visualization of OR-connections, weighting and
transmission technology.
3.1.2 Rules and Approach for Modelling
Flows of Information
To model a flow of information in ILN it is usually
beneficial to go backwards along the information’s
flow. This means that the initial position is a
requirement for specific information resulting from
the need to make a certain decision within a period.
Demand for information and the question to decide
on are marked at the decision-making stakeholder.
From this starting position, one can follow the flow
of information along the stakeholders and systems to
the original source. As shown in Figure 6, a
processing system provides the required object of
information. To model the flow of information, the
arrow starts at the object of information and leads to
Figure 6: Exemplary presentation of logistics of
information following the proposed notation.
the designated sink of the following stakeholder or
system. Furthermore, systems or stakeholders that are
not part of the current examination but still contribute
information are coloured in grey.
3.2 Applying the ILN
To use the ILN for analysing the existing Information
system or to start a greenfield development, three
basic steps should be conducted:
3.2.1 Step 1: Situation Analysis and Target
Definitions
As a first step, the current situation and the target
dimension should be considered. To determine the
current situation of information flows, the ILN can be
used to describe the relationship of the required roles,
systems and information. Therefore, the actual state
is recorded on an information map that displays
which role provides and requires what kind of
information. The target situation should also be
defined by using the ILN. One possibility to create
such a target information map would be to use
reference ILN-Models to derive the individual needs.
The other possible solution is to create a target map
from scratch. In both cases, first, the relevant use
cases that should be covered by the information
system need to be identified. They can usually be
derived from the corporate strategy of a company. By
deciding about the use cases that should be
implemented, the involved roles and the decision that
each role hast to take are determined. These decisions
have to be made upon a solid and reliable information
basis. For example, relevant information can be
identified by interviewing the roles or by logic
assumptions. From that point, the information flows
can be designed. That means, information sources,
database-systems, processing-systems and the
exchange ways and rates need to be identified and put
into order.
As a result of this step, two different information
flow maps are created. One that represent the actual
state of information flows and the other one shows the
desired information flows.
3.2.2 Step 2: Identifying Solutions
After identifying the actual and the desired state, a
matching of both states needs to be conducted. By
comparing the existing information needs and
demands to the desired ones, gaps of the information
transmission can be identified. For example, a worker
may not get the right parameters to set up a machine
Transmission frequency
X
(weighting / transmission technology)
(weighting / transmission technology)
(weighting / transmission technology)
Decider
Machine
(-components)
Machine-data
 Machine - ID
 Component - ID
Performance-data
 kW
 V
 mA
Metadata
 Timestamp
Energy-DB
History of Energy requirement per
component
 Timestamp
 Component-ID
 kW
Processing
system
Warning of failure
 Point in time
 Component-ID
 Probalility
Sensor-DB
History of other sensors
per component
iC
iC
s
s
When and why will a
machine or
component fail?
s
Introducing an Information Logistics Approach to Support the Development of Future Energy Grid Management
243

correctly. In addition, it is possible to determine
missing data storage and processing systems. Missing
processing systems could lead, for instance, to single,
unconnected pieces of information from various
sources that are far less valuable for the recipient than
suitably merged information could be. Moreover, the
current and the needed frequency of provided data
updates can be determined by comparing the two
states. This allows to derive the requirements for parts
of the information system. Overall, the second step
should result in an overview of identified gaps and
suggested solutions.
3.2.3 Step 3: Evaluation and Decision
Following the identification of the gap between actual
and target state, the proposed solutions need to be
evaluated to identify the best match. To do so, the
conceived solutions need to be transferred in an actual
specification list that can e.g. be used to approach
service providers. Subsequently, the evaluation of the
provided solutions and the service providers can be
done. This evaluation is based on individual
parameters for each companies and does not represent
the main focus of the presented work.
4 USING THE ILN TO MODEL
FUTURE GRID INFORMATION
LOGISTICS
In order to demonstrate its purpose, the ILN will be
used to model the information logistics of the energy
grid operators. This will be done twice, once for the
current situation and once for the future grid. The
examples will each focus on the task of network
monitoring and the procurement of the necessary
data, because maintaining network stability is most
crucial and has to be done continuously with the latest
data available. The diagrams have been created based
on the German grid. However, they can easily be
adapted to other developed electricity grids for
example in Europe or the USA.
4.1 Current Situation
Nowadays, network monitoring is a task to be
executed by the respective transmission network
operator. Even though decentralized energy
production is rising, the distribution grids are still
working unidirectional, conducting energy top-down
to customers. This kind of use makes them easy to
maintain (see chapter 2.3.1). Thus, the transmission
grids that conduct energy bi-directionally have to be
the focus of careful operation and maintenance today.
Apart from reacting to unforeseeable events like
disruptions, management of grid stability is based on
the amount of power that is to be delivered from the
various power plants’ grid connection points to the
distribution grid connection points. For this, the
transmission grid operator needs to save all standing
data of energy generation facilities from energy
suppliers in a database and receive updated
information in case of change. The database also
contains all historical data that links weather and
other factors to consumption and grid load and
automatically fetches live data from the grid. The
deliverable amount of energy is negotiated at energy
markets.
There are different markets where energy is
traded, usually a futures and options market where
future energy generation capacities are traded and a
spot market to balance supply and demand for the 24
hours to come. Because the current examination
focusses on grid stability management rather than
grid development, only the spot market is of interest.
Apart from the energy suppliers, the spot market also
communicates with companies buying energy (see
Figure 7) and other market actors that are not part of
the visualization. Every quarter of an hour, the spot
market informs energy suppliers and transmission
grid operator about the energy delivery schedule.
Figure 7: Todays logistics of information for grid stability
management.
4.2 Future Situation
As stated before (see chapter 2.3.2), the development
of the energy distribution system is directed towards
Data on insolation and wind
velocity (weather data)
 Live data
 Forecast data
External
Data of energy generation
facilities
 Live power output
 Power output (planned)
 Ability to redispatch
 Controlling power range
 Upper and lower power
limit
 If applicable: Problems
Standing data of energy
generation facilities
 Name
 Type
 Installed output power
 Network connection point
Energy
supplier
Energy prices for the next 24 hours,
derived from expected supply and
demand
Spot Energy
Marketplace
transmission grid operator
Database
Historical data on consumption
and renewable feed-in
Live data from grid
s
iC
s
¼ h
oD
How can grid stability be maintained?
Grid stability
management
s
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244

a smart grid. The future grid chosen to be examined
in this paper is set after significant changes with
regard to renewable energy generation have taken,
though some time before total automation takes over
by means of smart grid.
It has to be mentioned that the design of the future
situation modelled here is hypothetical. It is
guesswork in what sequence and manner the various
smart grid traits will be implemented, but current
developments like the EU’s smart meter rollout lead
to some educated guesses for an intermediate level
between now and the fully developed smart grid:
The spot market will still have the same function
(compare chapter 4.1), but will handle a lot more
traffic, as consumers and prosumers will become
stakeholders on the market by means of smart meters
(see Figure 8). While consumers can offer DSM-
potential, prosumers also provide energy (e.g. in
groups as virtual power plant).
The operation of transmission and distribution
grids will still be separated. Distribution grid
operators will use local weather forecast data and the
data made available by smart meters to forecast
internal bottlenecks as well as power surplus and
deficit that will reach the transmission grid over the
course of the next day. To compute the anticipated
course of the day, measuring point operators collect
all necessary data from grid, energy suppliers,
external sources and smart meters of consumers and
prosumers. Measuring point operators can be
contractors or departments of the distribution grid
operator. There is no equivalent role in transmission
Figure 8: Future logistics of information for grid stability management.
Live consumption
If applicable:
 Consumption forecast
 Battery and/or DSM data
(available potential)
Consumer
Live consumption and
production
If applicable:
 Consumption and production
forecast
 Battery and DSM data
(available potential)
Prosumer
Data on insolation and wind
velocity (weather data)
 Live data
 Forecast data
External
Data of energy generation
facilities
 Live power output
 Power output (planned)
 Ability to redispatch
 Controlling power range
 Upper and lower power
limit
 If applicable: Problems
Standing data of energy
generation facilities
 Name
 Type
 Installed output power
 Network connection point
Energy
supplier
Live data from grid
 Frequency
 Stability indicators
Aggregate data
(preprocessed for
distribution grid operator)
Measuring point
operator *
* Possibly independent from grid operator
Database
Aggregate data
Historical data
 Consumption and feed-in from
renewables over time, related
to weather, date (and other
factors)
distribution grid operator
Processing system
Aggregate course of deficit or surplus
over the next day within the
distribution grid (including expected
influences due to weather, date, etc.)
If applicable:
 Recommendation for optimized use
 Probabilities of failures
Energy prices for the next 24 hours,
derived from expected supply and
demand
Spot Energy
Marketplace
How can grid stability be maintained?
Grid stability
management
transmission grid operator
Database
Aggregate data from all
distribution grids
Historical data on consumption
and renewable feed-in
Live data from grid
s
s
s
s
h
s
iC
s
h
s
h
h
¼ h
iC
h
oD
oD
How can grid stability be maintained?
Grid stability
management
s
Introducing an Information Logistics Approach to Support the Development of Future Energy Grid Management
245

grid operation because the main task of retrieving
smart meter data does not apply there. The database
automatically collects live data from the grid.
After pre-processing, the data is passed on to the
distribution grid operator’s database, which is the
basis for the processing system. The database
archives all received data, so that the processing
system can also rely on historical data when
computing the surplus and deficit forecast. The spot
market, the distribution grid stability management
and the transmission grid operator receive the
progression of necessary supply or generated surplus
from the processing system. The transmission grid
operator’s database saves the accumulated data and
passes them on to the associated grid stability
management, while the spot market uses the data to
set the energy prices accordingly.
5 CONCLUSIONS
The proposed ILN approach helps to structure
overwhelming amounts of information, as the given
examples show. By means of visualizing the flows of
information, one can determine the – for a certain task
– actually required information and involved
stakeholders. By that, the presented notation supports
the implementation of IT systems and therefore
reduces the costs of digitalization and automation.
This becomes more relevant as future IT systems, like
the smart grid, will be significantly more complex and
flexible. Especially for the design of independent
subsystems like nodes in a smart grid, the ILN can
help to predefine open systems interconnections.
The ILN has been developed within the research
project eSafeNet. In the context of this project the
Notation has been successfully applied to the
information logistics of involved project partners.
The ILN can generally be applied to enhance complex
information logistics, for example internal
information exchange in industrial companies. Thus,
the next step should be to create a software tool to
simplify the application of the ILN in further use
cases.
ACKNOWLEDGEMENTS
This article arose during the work of the authors,
within the context of the research project eSafeNet
(project number: 03ET7549A) funded by the Federal
Ministry for Economic Affairs and Energy in
Germany.
REFERENCES
Celli, G., Pilo, F. and Pisano, G. (2004), Meshed vs. Radial
MV Distribution Network in Presence of Large Amount
of DG: 10 - 13 Oct. 2004, [New York, NY], IEEE
Operations Center, Piscataway, NJ.
Divan, D. and Kandula, P. (2016), “Distributed Power
Electronics. An Enabler for the Future Grid”, CPSS
Transactions on Power Electronics and Applications,
Vol. 1 No. 1, pp. 57–65.
Durugbo, C., Tiwari, A. and Alcock, J.R. (2013),
“Modelling information flow for organisations. A
review of approaches and future challenges”,
International Journal of Information Management,
Vol. 33 No. 3, pp. 597–610.
Farhangi, H. (2010), “The Path of the Smart Grid”,
IEEE power & energy magazine, No. january/february
2010, pp. 18–28.
Gungor, V.C., Sahin, D. and Kocak, T. (2013), “A Survey
on Smart Grid Potential Applications and
Communication Requirements”, IEEE Transactions on
Industrial Informatics, No. Vo. 9, NO. 1, February
2013, pp. 28–40.
Hirth, L. (2013), “The market value of variable
renewables”, Energy Economics, Vol. 38, pp. 218–236.
Immoor, K.H. (1998), “Informationslogistik ais
Dienstleistung fur vernetzte Unternehmen”, Logistik im
Unternehmen, Vol. 12 No. 3, pp. 48–50.
Koch, U. (1996), Bewertung und
Wirtschaftlichkeitsermittlung logistischer Systeme: Zur
Bedeutung von Informationen in der Logistik, Gabler
Edition Wissenschaft, Dt. Univ.-Verl; Gabler Verlag,
Wiesbaden, Wiesbaden.
Krämer, K. (2002), Automatisierung in Materialfluss und
Logistik: Ebenen, Informationslogistik,
Identifikationssysteme, intelligente Geräte, Informatik,
1. Aufl, Dt. Univ.-Verl., Wiesbaden.
Krcmar, H. (2015), Informationsmanagement, 6., überarb.
Aufl., Springer, Berlin, Heidelberg.
Pachauri, R.K. and Mayer, L. (Eds.) (2015), Climate
change 2014: Synthesis report, Intergovernmental
Panel on Climate Change, Geneva, Switzerland.
pwc (2016), “PwC's Global Data and Analytics Survey
2016. Big Decisions” (accessed 13 October 2017).
Saaty, T.L. (2008), “Decision making with the analytic
hierarchy process”, International journal of services
sciences, Vol. 1 No. 1, pp. 83–98.
Schneiders, C. (2014), Visualisierung des Systemzustandes
und Situationserfassung in großräumigen elektrischen
Übertragungsnetzen, 1. Aufl., Mainz, G, Aachen.
Su, W., Wang, J. and Roh, J. (2014), “Stochastic Energy
Scheduling in Microgrids With Intermittent Renewable
Energy Resources”, IEEE Transactions on Smart Grid,
Vol. 5 No. 4, pp. 1876–1883.
Voß, S. and Gutenschwager, K. (2001),
Informationsmanagement: Mit 25 Tabellen, Springer,
Berlin [u.a.].
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