=Paper=
{{Paper
|id=Vol-1742/MRT16_paper_1
|storemode=property
|title=View-based and Model-driven Outage Management for the Smart Grid
|pdfUrl=https://ceur-ws.org/Vol-1742/MRT16_paper_1.pdf
|volume=Vol-1742
|authors=Erik Burger,Victoria Mittelbach,Anne Koziolek
|dblpUrl=https://dblp.org/rec/conf/models/BurgerMK16
}}
==View-based and Model-driven Outage Management for the Smart Grid==
https://ceur-ws.org/Vol-1742/MRT16_paper_1.pdf
View-based and Model-driven Outage Management
for the Smart Grid
Erik Burger, Victoria Mittelbach, Anne Koziolek
Institute for Program Structures and Data Organization, Chair for Software Design and Quality
Karlsruhe Institute of Technology, Karlsruhe, Germany
E-Mail: burger@kit.edu, koziolek@kit.edu
Abstract—The integration of renewable energy resources is Thus, the contribution of this paper is twofold: We have created
challenging the traditional electricity network. To manage this, a unified model of the smart grid that makes cross-domain
the smart grid has been defined as a cyber-physical system analyses possible, and in addition, we have created view types
consisting of a physical component, which is the electricity grid,
and a computational component consisting of a communication that implement these analyses. They offer insights into the
network, metering network, and software components. Therefore, smart grid that can only be gained by combining information
the smart grid can not just be seen as an electrical grid, but also from heterogeneous systems of the grid.
as a system of software systems. Currently, control centers of the For the evaluation of the prototype, we have evaluated the
smart grid use an outage management software system to react system using historical data of the the German electricity grid.
to reported outages.
In this paper, we present an extended outage management We have used the SAIDI index to demonstrate how the outage
system that solves one main problem of the smart grid: software management systems shortens the outage time per person per
systems of the different domains are using different standards. year. The results show that the average annual outage duration
Consequently, cross-domain data exchange and analysis are diffi- can be reduced by at least 2 min 28 s if our approach is applied.
cult. Therefore, we use the model-driven view-based V ITRUVIUS
approach to build a unified model of the smart grid. We then II. F OUNDATIONS
combine it with system stability analysis methods presented
as views on the model. The result is a model-driven run-time A. Smart Grid
monitoring, analysis and control framework to increase the
reliability of power supply. The evaluation with statistical data of The smart grid [3] integrates modern IT infrastructure into
the German power grid shows that outage time can be reduced the traditional physical infrastructure of the electricity network.
with our approach. It is thus a cyber-physical system. The motivation behind its
introduction are the integration of renewable energy resources
I. I NTRODUCTION into the network, improvement of efficiency, and the reduction
The smart grid is a cyber-physical system that spans the of transmission losses. Furthermore, new load profiles, such
physical structures of the electricity network and the system of as electrical vehicles and smart homes, shall participate in
software systems that monitor, control, and repair the system balancing the grid and its self-healing abilities [4–6].
in case of outages. Currently, many heterogeneous systems and Control centers manage the grid via the supervisory control
standards have to interoperate to achieve the desired reliability, and data acquisition system (SCADA), which monitors and
stability, and efficiency of the electricity network. Many of controls the technical processes of the electricity system.
these standards are based on UML and other metamodeling Smart meters are electronic devices that are installed at the
standards, or can at least be expressed as such. Thus, model consumers’ places. They measure electricity production and
management systems that were originally designed mainly consumption, and are connected in the advanced metering
for the development of software can be used to integrate infrastructure (AMI), which can also receive commands to
information from these heterogeneous systems. shape electricity consumption. The wide area monitoring
In this paper, we present a concept for an extended outage system (WAMS) connects metering data in real-time. The
management system (OMS) for the smart electricity grid. The WAMS consists of Phasor management units (PMU), which
extended OMS is used during run-time to monitor and control are high-precision sensors that measure electrical waves with
the smart grid, as well as for simulation. The goal of the a frequency of 10 to 30 times per second. A power outage is
introduction of the OMS is to increase the reliability of the grid a longer interruption in the electricity supply that can either
by detecting outages and by preventing outages by detecting of be planned or unplanned. The causes for unplanned outages
instabilities and imbalances. The extended OMS is realized with can be categorized into the following types [7]:
the view-based and model-driven V ITRUVIUS approach [1, 2] • Atmospheric Interferences are caused by events of nature,
and uses its capabilities for runtime analysis and control of the such as thunderstorms, sub-zero temperatures, avalances,
electricity grid. The system is prototypically implemented in the etc.
Eclipse Modeling Framework using the description languages • Internal Failures are caused by maloperation of systems,
for model correspondences and view generation of V ITRUVIUS. malfunction of network internal systems and all other
VT2
Control Center
VT1 Legend: Operator
MM1 VT View Type
MM3 uses
VT3
MM Metamodel
MM2 view view view . . .
MIR «instance-of»
VT4 ModelJoin Operator Viewtypes Extended OMS
operate on show information from
Figure 1. The Modular SUM Metamodel concept of V ITRUVIUS
Algorithms Smart Grid SUM Metamodel
show information from
causes that are directly related to the operations of the
System Viewtypes
network.
• Outside Influences are not caused by network operators, «instance-of»
but, e.g., by construction work, traffic accidents, etc. view view view . . .
• Supply Failures/Cascading Outages are caused by a uses
network other than the one controlled by a specific energy Smart Grid Software systems
supplier, but have an effect on the supplier’s network.
In case that despite the monitoring and control systems, a Figure 2. Concept for the extended OMS
disturbance causes a power outage, an outage management
system (OMS) becomes active to restore the power supply.
From an architectural viewpoint, the smart grid can be represents all the information that is available about a system.
seen as a system of software systems that is connected by The metamodel for this model is specific to the domain in
a communication network. A large number of standards exists, which the V ITRUVIUS approach is used. It combines several
which cover the various aspects of the smart grid systems. The metamodels to form a modular SUM metamodel (see Figure 1).
key standards will be presented in the following. The metamodels are included non-intrusively and do not have
IEC 61970/61968: The IEC 61970 standard defines the to be adapted. To express the semantic relations between the
Common Information Model (CIM), which is used to describe elements of the metamodels, V ITRUVIUS defines the consist-
the physical components, measurement data, control and ency description language MIR (mapping/invariant/response).
protection elements, and the SCADA system. It is defined Since V ITRUVIUS is a view-based approach, all information in
in UML notation. The IEC 61968 standard is an extension the SUM can only be retrieved or manipulated via specialized
of the CIM for the distribution network [8]. It is also called views. A view is a special kind of model and conforms to
distributed CIM (DCIM) a view type, i.e., its metamodel. For the definition of view
IEC 61850: This is a series of standards for substations types and views, V ITRUVIUS uses the ModelJoin language
with the purpose of supporting interoperability of intelligent [12]. V ITRUVIUS has been implemented as a prototype1
electronic devices (IED) in substation automation systems. It in the Eclipse Modeling Framework and can thus be used
defines the Abstract Communication Service Interface with a with any Ecore-conforming metamodel. So far, it has been
mapping to concrete communication protocols, the XML-based applied to software architecture models [13] and model-based
Substation Configuration Description Language (SCL), and representations of programming languages [14].
the Logical Node (LN) model, which describes power system III. A N EXTENDED O UTAGE M ANAGEMENT S YSTEM
functions [9]. In this section, we present the elements of the extended out-
IEC 62056: COSEM (Companion Specification for Energy age management system that are developed with V ITRUVIUS.
Metering) is the international standard for data exchange for
meter reading, tariff and load control in the domain of electricity A. Concept of the extended OMS
metering. It works together with the Device Language Message The Extended Outage Management System is a model-driven
Specification (DLMS). Together, they provide a communication analysis and control framework, which is used during run-time
profile to transport data from metering equipment to the of the electrical system to monitor and control the smart grid,
metering system and to define a data model and communication as well as for simulations. The framework enables network
protocols for data exchange [10]. stability analysis, grid balance analysis, failure detection and
B. Vitruvius location analysis, and direct controlling interactions with the
electricity system to correct faulty sections of the network.
V ITRUVIUS [1, 2] is a view-based, model-driven framework The structure of the system, displayed in Figure 2, can
for the mangement of heterogeneous models, i.e., models be seen from two perspectives: From an inside perspective,
that are instances of different metamodels. It is based on
the concept of a single underlying model (SUM) [11], which 1 https://sdqweb.ipd.kit.edu/wiki/Vitruvius, retrieved 2016-09-13
Network for the electricity system provide possibilities for the grid to
Grid Control Topology
Viewtype Viewtype provide new measurement data, and make system changes to
Generator
VT VT and keep the smart grid model of the extended OMS system always
Generator Consumer up to date. Consequently, system and operator viewtypes are
Monitoring VT IEC 61850 Control
Viewtype
defined based on the smart grid SUM metamodel, and, in case
Viewtype
MIR VT of the operator viewtypes, on the analysis algorithms. Instances
System CIM/DCIM Control
of the operator viewtypes are the result of the evaluation of
Balance the viewtypes on the instance of the smart grid model. These
VT (IEC 61970/61968) VT Area
Analysis
Viewtype views are presented to the operator and the smart grid system
Viewtype MIR
for them to interact with the smart grid model.
COSEM Consumer
VT
(IEC 62056) VT Monitoring This leads directly to the structure as seen from the outside by
Consumer
Reachability Viewtype the operators and the electrical system. They see the complete
Viewtype VT analysis framework as a ‘black box’, and never interact directly
Three Phase Measure- with the models and algorithms inside, but only with the views
ment Matrix Viewtype presented to them. The operators can request certain views that
Figure 3. The smart grid SUM metamodel and view types for the extended
they need for monitoring, analysis, and controlling purposes,
outage management system which are delivered to them by the extended OMS system. The
extended OMS system presents them the data and results from
the smart grid system in a compact and consistently modeled
developers can see the inner structure of the system, whereas view, which contains only the information that they need at that
operators and existing software systems only have and outside special moment, to reduce the complexity for the operators.
perspective on the system.
The inner structure of the system consists of three elements: B. The Smart Grid SUM Metamodel
The first one is the Smart Grid SUM Metamodel, which is The modular SUM metamodel of the extended OMS consists
displayed in detail in Figure 3. Following the V ITRUVIUS of the IEC smart grid standards to model the four required
approach, it is a modular metamodel that combines four elements. The necessary elements are the advanced smart meter
separate elements of the electricity system: the physical network infrastructure modeled by the IEC 62056 COSEM standard, the
topology, the SCADA control system, the WAMS for grid wide area monitoring system for phasor measurements from
monitoring, and the AMI for customer monitoring. In the the grid modeled by a part of the IEC 61850 standard, the
metamodel, these elements are modeled using the IEC smart substation control functions modeled by another part of the
grid standards introduced in section II: The network topology IEC 61850 standard, and the network topology and overview,
model is based on the CIM and DCIM standards; the SCADA modeled by the two standards IEC 61970 and IEC 61968, also
control system uses the logical node model of the IEC 61850 known as the CIM and DCIM standards.
standard; the data collected from the WAMS are modeled COSEM for the Smart Meter System: The smart meter
applying the logical nodes for system measurements from the metamodel is not freely available as a digital UML model, so
IEC61850 standard; finally, the AMI smart metering data are we built it manually as an Ecore model in Eclipse, based on
modeled using the COSEM standard. Since these metamodels the IEC 62056 standard. An excerpt of the smart meter Ecore
overlap at certain points, they are complemented by MIR model can be seen in Figure 4: The smart meter is represented
correspondence rules to specify the relationships between them. by the PhysicalDevice class, which is identified by its ID. It is
Together, the four metamodels of the elements of the electricity associated with the ManagementLogicalDevice class and the
system and the correspondence rules form the modular SUM LogicalDevice class. The logical device class has a relation to
metamodel. The framework uses an instance of this metamodel each electricity-related COSEM object class. Each COSEM
as a basis for all further analysis and control actions. object class implements its interface class. For example, the
The second important element are the different outage and class ElectricityValues contains the attributes for current and
instability detection and location algorithms, which are used voltage measurement data of the power import and export. For
for the different types of failure and stability analysis. later modeling and containment purposes, the physical device
The third important element are the viewtypes, which define also references each COSEM object class.
the different possible perspectives on the smart grid model. CIM/DCIM (IEC 61970/61968): To use the CIM and DCIM
The viewtypes need to be differentiated between viewtypes standard as a part of the SUM metamodel, they both need to
for the system operator and such for the electricity system. be in the format of an Ecore model. The CIM User Group2
The viewtypes for the operator implement the algorithms for provides the two metamodels as an integrated Sparx Enterprise
balancing input and output of the network to stabilize the Architecture Model ready to download as an xmi file. These
voltage, and for failure detection analysis. They define views metamodels are based on the most current release of the two
on the network topology and control regions. Furthermore, standards in 2014. The CIM User Group offers the metamodel
they define views for the interaction with and control of the
physical equipment of the electrical system. The viewtypes 2 http://cimug.ucaiug.org, retrieved 2016-09-13
... only been defined for the combinations IEC 61850/CIM and
COSEM interface classes
CIM/COSEM (see Figure 3). An example for such a rule can
be seen in Listing 1, where the correspondence is defined for
metamodel elements that describe circuit breakers in both the
COSEM object classes ...
CIM and the IEC 61850 metamodel, which has the package
name “substation”. For a complete listing of all rules, we refer
the reader to [15].
LogicalDevice
PhysicalDevice D. Viewtypes
ID: EString
ID: EString
name: SAPAssignmentCurrent 0..*
...
The second part of the extended OMS are the viewtypes
... designed for the interaction with operators and the smart grid
software systems (cf. Figure 2). Each of the three metamodels in
ManagementLogicalDevice the modular SUM metamodel is exposed as a legacy viewtype
0..1 to support existing software and visualization methods, and for
data exchange with the systems of the smart grid. In addition,
Figure 4. The Smart Meter Ecore Model (Excerpt) nine specific view types have been defined using the declarative
ModelJoin language. Each viewtype combines information of
together with the open source Eclipse plugin “CIMtool”3 . With either a single or multiple sub-metamodels of the modular
this tool, it is possible to browse through the models and to SUM metamodel, as displayed in Figure 3.
export them as an Ecore model, which can then be directly For network monitoring, the Network Topology Viewtype
imported into the V ITRUVIUS environment. defines a view on the electricity system elements and their
IEC 61850 for Substation Control and PMU: Analogous to topology. The Control Area Viewtype focuses on the segment-
the CIM/DCIM metamodel, the IEC 61850 logical node model ation of the electricity system into control areas, where each
needs to be in the format of Ecore in order to be integrated into area is regulated by one control center.
the SUM. Our Ecore-based metamodel of the standard realizes Four viewtypes have been defined to support balancing of
parts 5 and 7 of the IEC 61850 standard. The metamodel the electricity grid: The System Balance Analysis Viewtype
has been created by adapting an Enterprise Architect UML compares production and consumption with predicted values to
model by ABB. ABB has donated this metamodel to the IEC detect fluctuations. The Generator Monitoring and Consumer
technical committee (TC) 57. It is accessible as a web-based Monitoring view types observe the current situation of generat-
UML model on their website.4 The web-based model can ors and consumers. They serve as a basis of decision-making
be browsed, but unfortunately, it cannot be downloaded. To on how to react to imbalances in the system. After this decision,
integrate the metamodel into the SUM Metamodel as an Ecore operators use the Generator and Consumer Control Viewtype
model, we have rebuilt it manually in Eclipse. Since the ABB to control the production and demand of electricity.
model is not completely compatible with Ecore, a few changes The Consumer Reachability Viewtype is used to detect
had to be made, which are described in detail in [15]. outages. The detection algorithm exploits the fact that when
a smart meter is cut off from power supply, it cannot send
C. Correspondence Rules any data. Together with the network topology viewtype, it is
map CIM.CIM.IEC61970.Wires.Breaker as Breaker possible to see the system in a tree structure, and to mark the
and substation.substationStandard.LNNodes.LNGroupX. nodes that failed by searching for the failed consumers in the
XCBR as XCBR { topology view. Thus, it is possible to detect the outage, and to
when-where {
Breaker.mRID == XCBR.NamePlt.IdNs locate the area of its origin. If a failure is detected, the Grid
Breaker.mRID = XCBR.NamePlt.IdNs Control Viewtype can be used to stabilize the situation again.
} Finally, the Three Phase Measurement Matrix Viewtype
}
combines measurement data from phasor measurement units
Listing 1. Example Mapping Rule for Breaker and smart meters. Thus, instabilities and disturbances in the
transmission and distribution network can be detected, so that
The correspondence rules for the extended Outage Management
outages can be prevented before they occur. For a complete
System (OMS) have been defined in the MIR language of
definition of all view types, we again refer the reader to [15].
V ITRUVIUS. They describe the semantic overlaps between the
metamodels of the modular SUM metamodel. Since it is not IV. E VALUATION
mandatory in the V ITRUVIUS approach to define rules for
all binary combinations, and since there were no semantic A. The SAIDI Index as Benchmark
correspondences between IEC 61850 and COSEM, rules have The overall goal of the extended Outage Management System
3 http://wiki.cimtool.org/HowToValidateCPSM.html, retrieved 2016-09-13 (OMS) is to reduce the outage duration and to lower the amount
4 http://www.nettedautomation.com/download/std/61850/uml/, retrieved 2016- of outages. To evaluate how well the system is performing in
09-13 doing this, the SAIDI index is used.
Outages Outages Consumers 49 600 000
Outage Type Low Med. Outages
Perc. Outages Low-Voltage 147 800
Voltage Voltage Total
Outages Medium-Voltage 26 000
Atmospheric 45.1 % 66 658 11 726 78 384 SAIDI Low-Voltage 2.19
Interferences SAIDI Medium-Voltage 10.09
Internal Failures 29.51 % 43 616 7673 51 289 SAIDI Total 12.28
Outside Influences 22.59 % 33 388 5873 39 261 Table II
Supply Failures, 2.8 % 4138 728 4866 A NNUAL O UTAGE AND SAIDI VALUES FOR G ERMANY IN 2014
Cascading Outages
Table I
O UTAGE T YPES OF AUSTRIA USED FOR G ERMANY 2014
the faulty section. In this case, the outage is repaired after 2
minutes. If this is not the case, the detection and restoration
of the failure takes up to one hour. Since these two cases
Definition 1. The SAIDI (System Average Interruption Dur-
cannot be differentiated here, an average outage restoration
ation Index) index is the average outage duration for each
duration is calculated based on them. Since this evaluation is
served customer. It is an indicator for the reliability of power
conducted in a modern smart grid, it is assumed that the first
supply of an electricity system. It is calculated as:
case happens slightly more often than the second one (60:40).
C Kj
Consequently, the average outage restoration duration is about
N j,k 30 minutes (using 7.5 minutes for the outage reporting time):
SAIDI = ∑ ∑ ϕ j,k
j=1 k=1 N 0.6 ∗ 2 min + 0.4 ∗ 60 min + 7.5 min = 31.5 min.
Besides these, some more general assumptions are made for
where C is the amount of areas the grid is divided into, K j all of the following evaluations.
is the number of annual outages in area j, and ϕ j,k is the
• The percentages of the types of outages from the Austrian
duration of the k-th outage in area j, and N j,k is the number
report will be used for the German system, since they
of consumers in area j affected by outage k, and N is the total
are neighbors with similar infrastructure and weather (see
number of consumers in the system [16].
Table I).
B. Datasets used for the Evaluation • The outage restoration duration is 30 minutes.
• Based on this duration and the SAIDI values in Table II,
To evaluate the extended outage management system in the
the average number of people affected by an outage in the
German smart grid, four main datasets are used:
low and medium voltage network is 24 in the low-voltage
1) Germany: Each year, the federal electricity agency of
network and 640 in the medium-voltage network.
Germany releases a monitoring report about the electricity
system. Besides data about production, renewable production C. Evaluation Views
and consumption, it includes numbers about the reliability of
power supply and the amount of outages per year. The report For the evaluation of our approach, we have analysed several
is freely available and will be used as a basis to calculate the properties of a smart grid using model and the views as
SAIDI index and for comparison, since it includes the SAIDI described in subsection III-D.
index for the current German system. For this evaluation, the 1) Grid Balancing: The main purpose of the viewtypes
report from 2014 will be used [17]. for grid balancing is to prevent outages by keeping electrical
2) Austria: Similar to the German report, E-Control pub- inflows and outflows at balance. We have recalculated the
lishes the outages and disturbances statistic for Austria each SAIDI index to measure the improvement gained from using the
year. The report includes data about the number and type of viewtypes. The calculation is based on the numbers from 2014
outages in Austria in 2014, together with the SAIDI index. in Germany and Austria and makes some specific assumptions:
Since the German and Austrian systems are neighbours, we • The fourth outage type Supply failures and cascading
will assume for the evaluation that the types of outages (as outages combines two kinds of outages, of which only
introduced in section II) are the same [7]. the supply failures are of interest. For this evaluation, it
3) entsoe: To analyze balancing the grid, statistical data is assumed that they have each a share of 50 % of the
about the actual and forecasted production and consumption for total 2.8 % of their appearance. This is 2069 outages in
2015 provided by entsoe is used. The dataset includes quarter the low-voltage network and 364 in the medium-voltage.
hourly data for every day and can be used to detect differences • Since the views currently only focus on production and
between forecasted and actual production and consumption. consumption and no models for the electricity market
The duration of the system restoration after an outage is an and special forecasts are included it is assumed that the
important aspect. According to literature [4, 16], two cases need views will not perform very well in preventing imbalance
to be differentiated: In both cases, the detection of an outage outages that currently occur. Therefore it is assumed, that
relies on customer feedback. On average, an outage is reported only 30 % of the outages can be prevented. This makes
after 5 to 10 minutes. In the first case, the system has an 629 prevented outages in the low-voltage network and
implemented infrastructure to automatically detect and restore 109 in the medium voltage. The total amount of outages
would then be 147 151 in the low-voltage network and Outages Outages
Outage Type Low- Medium- Outages
25 891 in the medium-voltage. Voltage Voltage Total
The results for the low- and medium-voltage system are: Atmospheric 66 658 11 726 78 384
147151
4 4 Interferences
24
SAIDI low = ∑ ∑ 30 min ∗ 49 600 000 = 2.14 min,
Internal Failures 21 808 3837 25 645
j=1 k=1 Outside Influences 33 388 5873 39 261
25891 Supply Failures, 2061 364 2425
4 4
640 Cascading Outages
SAIDI medium = ∑ ∑ 30 min ∗ 49 600 000 = 10.02 min
j=1 k=1 Table III
Thus, the total is 12.16 min with an improvement of 0.16 min. O UTAGES OF THE TOTAL EXTENDED OMS
2) Outage Detection: The main objective of these views
is to detect outages automatically and faster, since currently,
this relies on customer feedback, which can take up to 10
is 2069 outages in the low-voltage network and 364 in
minutes. Since in the modern smart grid with automatic remote
the medium-voltage.
control, it is possible to restore the power supply after an
• It is assumed that the external system to compare the
outage already after two minutes, it is important to reduce the
values from the views with historic data has been built.
time for detection in order to lower the total outage restoration
• Internal failures, especially if a device is broken, cannot
duration.
always be prevented even if the disturbance is detected.
The evaluation is based on the following assumptions:
However, due to the N-1 criterion it should be guaranteed
• The low-voltage networks attached to a medium-voltage
in most of the cases that the outage can be prevented. But
network do all have the same amount of customers. to estimate it carefully it is assumed, that only 50 % of
• Each smart meter sends data once every 30 minutes.
the internal outages can be prevented.
• The smart meters in a medium-voltage network segment
• Concerning cascading outages, since they can be prevented
do not send all at the same time, but are distributed equally by separating the faulty section from the rest of the grid,
over the 30 minutes. They are rotating through the low- it is assumed that 70 % of them can be prevented.
voltage networks attached to the medium-voltage.
The results for the low- and medium-voltage system are:
• To differentiate between short temporal outages (< 1 min) 124 539
4 4
and long permanent outages, the detection time needs to SAIDI low = ∑ ∑ 30 min ∗ 49 600 24
000 = 1.81 min,
be longer than 1 minute. j=1 k=1
21 909
We use the formula of [15, section 5.3.4] to calculate the 4 4
640
SAIDI medium = ∑ ∑ 30 min ∗ 49 600 000 = 8.48 min.
outage detection time based on the number of customers in the j=1 k=1
640 30 min Thus, the total is 10.29 min with an improvement of 1.99 min.
system: 3 ∗ 25 ∗ 640 = 3.6 min. As explained in the general
assumptions, there are now two cases of outage restoration. D. The SAIDI Index of the overall extended OMS
The same restoration times will now be combined with the new
detection time. With 60 %, the restoration time is 2 minutes, The different views evaluated above are all used together
and with 40 %, it is 60 minutes, which gives an average outage in the extended outage management system. Therefore their
detection and restoration time of 0.6 ∗ 2 min + 0.4 ∗ 60 min + functionalities can be combined to combine outage detection
3.6 min = 28.8 min. This duration will become lower the higher with prevention. This will further improve the SAIDI index of
the share of remote controls in the grid gets. the extended OMS, since the single improvements are added
The results for the low- and medium-voltage system are: up. In order to do that, this section summarizes the single
4
147800 improvements from the previous sections and calculates a
4
24 combined SAIDI index. The main achievements are:
SAIDI low = ∑ ∑ 28.8 min ∗ 49 600 000 = 2.06 min,
j=1 k=1
26000
• Reduction of supply failure outages by 30 %, which are
4 4
640
SAIDI medium = ∑ ∑ 28.8 min ∗ 49 600 000 = 9.66 min. 629 prevented outages in the low-voltage network and
j=1 k=1 109 in the medium voltage.
Thus, the total is 11.72 min with an improvement of 0.56 min. • Detection of outages in 3.6 minutes with a total reduction
3) Instability and Cascading Blackouts detection: The main of average outage detection and restoration time to 28.1.
objective of these views is to prevent outages due to instabilities • Reduction of internal failure outages by 50 %, which
in the system and to detain cascading outages. It is the goal to means 21 808 prevented outages in the low-voltage net-
prevent outages of two types: internal outages and cascading work and 3837 in the medium voltage.
outages. To evaluate the functionality of these views, some • Reduction of cascading outages by 70 %, which are 1448
further assumptions have to be made: prevented outages in the low-voltage network and 255 in
• The fourth outage type Supply failures and cascading the medium voltage.
outages combines two different kinds of outages of which This results are displayed in Table III. The results for the
only the cascading outages are of interest for these views. extended OMS are:
123 915
For this evaluation it is assumed that they each have a 4 4
24
share of 50 % of the total 2.8 % of their appearance. This SAIDI low = ∑ ∑ 28.8 min ∗ 49 600 000 = 1.72 min
j=1 k=1
21 800
4 4 methods. This can be found several times in literature [30–33].
640
SAIDI medium = ∑ ∑ 28.8 min ∗ 49 600 000 = 8.10 min.
j=1 k=1 Since currently the mapping of signals between the CIM
In total, this is 9.82 min with a total improvement of 2.46 min, and IEC 61850 standards needs to be performed manually,
which is 2 minutes and 28 seconds. these papers suggest a mapping between the SCL and the
This means if the extended OMS is used in the German smart CIM configuration file with ontology matching. This approach
grid, the average annual outage duration for each consumer follows the same purpose like the model-driven approach
could be reduced by at least 2 minutes and 28 seconds. presented in this research, however using a different method.
Consequently, the analysis and control framework built can
VI. C ONCLUSION
indeed help to improve the reliability of power supply.
In this paper, we have presented a model-driven and view-
V. R ELATED W ORK based framework, called the extended outage management
Related work on improving the reliability of the power supply system, for run-time analysis and control of the smart grid.
using model-driven methods can be grouped in two categories: The main purpose of the system is to increase the system
Approaches that treat the problem of improving the reliability reliability by faster detection of outages and by preventing
of power supply, and approaches that examine the problem of the outages through the detection of system instabilities and
combining information of the smart grid using model-driven imbalances. The system is built in V ITRUVIUS, a model-driven
or related methods. and view based model management framework, which was
There is quite an extensive amount of research about the originally created for software development purposes, but can
reliability of electrical systems, also in connection with the be used with any kind of metamodel-based data. This paper
smart grid. E.R. Brown [18] examines the challenge of a reliable has shown that the mechanisms and languages of V ITRUVIUS
power distribution system, and D. Elmakias introduces in his for defining correspondences and viewtypes can successfully
book methods to examine and improve the reliability of an be applied to domains that do not purely concern software.
electrical system [19]. Chowdhury et al. [20] and Waseem et al. The framework has been evaluated using the SAIDI index.
[21] focus especially on the impact of distributed generation on Using statistical data of the German power grid, a possible
system reliability, since the integration of distributed generation improvement of 2 minutes and 28 seconds in annual outage
is a new challenge for the power system. Related to power time. The system could be further improved by including further
distribution, Russell et. al [22] work on improving the reliability data sources, such as real-time data from the energy market.
of the distribution system equipment in their research.
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