-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 caonndsiasticnogmopfuatatpihonysailcaclomcopmopnoenntencto,nwsihsitcinhgisofthae ceolemctmriucnitiycagtrioidn, 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 Irneptohritsedpaopuetar,gews.e 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 VITRUVIUS approach to build a unified model of the smart grid. We then II. FOUNDATIONS 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 German power grid shows that outage time can be reduced with our approach.
The smart grid is a cyber-physical system that spans the physical structures of the electricity network and the system of software systems that monitor, control, and repair the system in case of outages. Currently, many heterogeneous systems and standards have to interoperate to achieve the desired reliability, stability, and efficiency of the electricity network. Many of these standards are based on UML and other metamodeling standards, or can at least be expressed as such. Thus, model management systems that were originally designed mainly for the development of software can be used to integrate information from these heterogeneous systems.
In this paper, we present a concept for an extended outage
management system (OMS) for the smart electricity grid. The
extended OMS is used during run-time to monitor and control
the smart grid, as well as for simulation. The goal of the
introduction of the OMS is to increase the reliability of the grid
by detecting outages and by preventing outages by detecting of
instabilities and imbalances. The extended OMS is realized with
the view-based and model-driven VITRUVIUS approach [
the traditional physical infrastructure of the electricity network.
It is thus a cyber-physical system. The motivation behind its
introduction are the integration of renewable energy resources
into the network, improvement of efficiency, and the reduction
of transmission losses. Furthermore, new load profiles, such
as electrical vehicles and smart homes, shall participate in
balancing the grid and its self-healing abilities [
Control centers manage the grid via the supervisory control and data acquisition system (SCADA), which monitors and controls the technical processes of the electricity system. Smart meters are electronic devices that are installed at the consumers’ places. They measure electricity production and consumption, and are connected in the advanced metering infrastructure (AMI), which can also receive commands to shape electricity consumption. The wide area monitoring system (WAMS) connects metering data in real-time. The
are high-precision sensors that measure electrical waves with a frequency of 10 to 30 times per second. A power outage is a longer interruption in the electricity supply that can either be planned or unplanned. The causes for unplanned outages can be categorized into the following types [7]:
such as thunderstorms, sub-zero temperatures, avalances, etc.
Internal Failures are caused by maloperation of systems, malfunction of network internal systems and all other VT3 MM1 MM2
MM3
VT4
VT1
Legend: VT MM
causes that are directly related to the operations of the network.
Outside Influences are not caused by network operators, but, e.g., by construction work, traffic accidents, etc.
network other than the one controlled by a specific energy supplier, but have an effect on the supplier’s network. In case that despite the monitoring and control systems, a 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 seen as a system of software systems that is connected by a communication network. A large number of standards exists, which cover the various aspects of the smart grid systems. The key standards will be presented in the following.
IEC 61970/61968: The IEC 61970 standard defines the
the physical components, measurement data, control and protection elements, and the SCADA system. It is defined in UML notation. The IEC 61968 standard is an extension of the CIM for the distribution network [8]. It is also called distributed CIM (DCIM)
IEC 61850: This is a series of standards for substations with the purpose of supporting interoperability of intelligent electronic devices (IED) in substation automation systems. It defines the Abstract Communication Service Interface with a mapping to concrete communication protocols, the XML-based
the Logical Node (LN) model, which describes power system functions [9].
Metering) is the international standard for data exchange for meter reading, tariff and load control in the domain of electricity metering. It works together with the Device Language Message Specification (DLMS). Together, they provide a communication profile to transport data from metering equipment to the metering system and to define a data model and communication protocols for data exchange [10].
for the mangement of heterogeneous models, i.e., models that are instances of different metamodels. It is based on the concept of a single underlying model (SUM) [11], which
Operator view view . . . uses view «instance-of» operate on
«instance-of» view uses
show information from
show information from view view . . .
represents all the information that is available about a system. The metamodel for this model is specific to the domain in which the VITRUVIUS approach is used. It combines several metamodels to form a modular SUM metamodel (see Figure 1). The metamodels are included non-intrusively and do not have to be adapted. To express the semantic relations between the elements of the metamodels, VITRUVIUS defines the consistency description language MIR (mapping/invariant/response). Since VITRUVIUS is a view-based approach, all information in the SUM can only be retrieved or manipulated via specialized views. A view is a special kind of model and conforms to a view type, i.e., its metamodel. For the definition of view types and views, VITRUVIUS uses the ModelJoin language [12]. VITRUVIUS has been implemented as a prototype1 in the Eclipse Modeling Framework and can thus be used with any Ecore-conforming metamodel. So far, it has been applied to software architecture models [13] and model-based representations of programming languages [14].
The Extended Outage Management System is a model-driven analysis and control framework, which is used during run-time of the electrical system to monitor and control the smart grid, as well as for simulations. The framework enables network stability analysis, grid balance analysis, failure detection and location analysis, and direct controlling interactions with the electricity system to correct faulty sections of the network.
The structure of the system, displayed in Figure 2, can be seen from two perspectives: From an inside perspective,
VT VT
Monitoring VT Viewtype for the electricity system provide possibilities for the grid to provide new measurement data, and make system changes to keep the smart grid model of the extended OMS system always up to date. Consequently, system and operator viewtypes are defined based on the smart grid SUM metamodel, and, in case of the operator viewtypes, on the analysis algorithms. Instances of the operator viewtypes are the result of the evaluation of the viewtypes on the instance of the smart grid model. These views are presented to the operator and the smart grid system for them to interact with the smart grid model.
This leads directly to the structure as seen from the outside by the operators and the electrical system. They see the complete analysis framework as a ‘black box’, and never interact directly with the models and algorithms inside, but only with the views presented to them. The operators can request certain views that they need for monitoring, analysis, and controlling purposes, 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 view, which contains only the information that they need at that special moment, to reduce the complexity for the operators. developers can see the inner structure of the system, whereas operators and existing software systems only have and outside 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 VITRUVIUS 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 2http://cimug.ucaiug.org, retrieved 2016-09-13 COSEM interface classes
LogicalDevice ID: EString name: SAPAssignmentCurrent . . .
ManagementLogicalDevice 0..* 0..1
PhysicalDevice ID: EString . . . together with the open source Eclipse plugin “CIMtool”3. With this tool, it is possible to browse through the models and to export them as an Ecore model, which can then be directly imported into the VITRUVIUS environment.
IEC 61850 for Substation Control and PMU: Analogous to the CIM/DCIM metamodel, the IEC 61850 logical node model needs to be in the format of Ecore in order to be integrated into the SUM. Our Ecore-based metamodel of the standard realizes parts 5 and 7 of the IEC 61850 standard. The metamodel has been created by adapting an Enterprise Architect UML model by ABB. ABB has donated this metamodel to the IEC technical committee (TC) 57. It is accessible as a web-based UML model on their website.4 The web-based model can be browsed, but unfortunately, it cannot be downloaded. To integrate the metamodel into the SUM Metamodel as an Ecore model, we have rebuilt it manually in Eclipse. Since the ABB model is not completely compatible with Ecore, a few changes had to be made, which are described in detail in [15].
map CIM.CIM.IEC61970.Wires.Breaker as Breaker and substation.substationStandard.LNNodes.LNGroupX.
XCBR as XCBR { when-where {
Breaker.mRID == XCBR.NamePlt.IdNs
Breaker.mRID = XCBR.NamePlt.IdNs } }
System (OMS) have been defined in the MIR language of VITRUVIUS. They describe the semantic overlaps between the metamodels of the modular SUM metamodel. Since it is not mandatory in the VITRUVIUS approach to define rules for all binary combinations, and since there were no semantic correspondences between IEC 61850 and COSEM, rules have
4http://www.nettedautomation.com/download/std/61850/uml/, retrieved 201609-13 only been defined for the combinations IEC 61850/CIM and 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 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].
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 the modular SUM metamodel is exposed as a legacy viewtype to support existing software and visualization methods, and for data exchange with the systems of the smart grid. In addition, nine specific view types have been defined using the declarative ModelJoin language. Each viewtype combines information of either a single or multiple sub-metamodels of the modular SUM metamodel, as displayed in Figure 3.
For network monitoring, the Network Topology Viewtype defines a view on the electricity system elements and their topology. The Control Area Viewtype focuses on the segmentation of the electricity system into control areas, where each area is regulated by one control center.
Four viewtypes have been defined to support balancing of the electricity grid: The System Balance Analysis Viewtype compares production and consumption with predicted values to detect fluctuations. The Generator Monitoring and Consumer Monitoring view types observe the current situation of generators and consumers. They serve as a basis of decision-making on how to react to imbalances in the system. After this decision, operators use the Generator and Consumer Control Viewtype to control the production and demand of electricity.
The Consumer Reachability Viewtype is used to detect outages. The detection algorithm exploits the fact that when a smart meter is cut off from power supply, it cannot send any data. Together with the network topology viewtype, it is possible to see the system in a tree structure, and to mark the nodes that failed by searching for the failed consumers in the topology view. Thus, it is possible to detect the outage, and to locate the area of its origin. If a failure is detected, the Grid Control Viewtype can be used to stabilize the situation again.
combines measurement data from phasor measurement units and smart meters. Thus, instabilities and disturbances in the transmission and distribution network can be detected, so that outages can be prevented before they occur. For a complete definition of all view types, we again refer the reader to [15].
Interferences Internal Failures Outside Influences Supply Failures, Cascading Outages Perc. 45:1 % 29:51 % 22:59 % 2:8 % Outages Low Voltage 66 658
ation Index) index is the average outage duration for each served customer. It is an indicator for the reliability of power supply of an electricity system. It is calculated as:
C Kj SAIDI = å å j j;k NNj;k
j=1 k=1 where C is the amount of areas the grid is divided into, K j is the number of annual outages in area j, and j j;k is the duration of the k-th outage in area j, and N j;k is the number of consumers in area j affected by outage k, and N is the total number of consumers in the system [16].
Outages Low-Voltage Outages Medium-Voltage
SAIDI Low-Voltage SAIDI Medium-Voltage
SAIDI Total 49 600 000 147 800 26 000 2:19 10:09 12:28 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 cannot be differentiated here, an average outage restoration duration is calculated based on them. Since this evaluation is conducted in a modern smart grid, it is assumed that the first case happens slightly more often than the second one (60:40). Consequently, the average outage restoration duration is about 30 minutes (using 7.5 minutes for the outage reporting time): 0:6 2 min + 0:4 60 min + 7:5 min = 31:5 min.
Besides these, some more general assumptions are made for all of the following evaluations.
The percentages of the types of outages from the Austrian report will be used for the German system, since they are neighbors with similar infrastructure and weather (see Table I).
The outage restoration duration is 30 minutes.
Based on this duration and the SAIDI values in Table II, the average number of people affected by an outage in the low and medium voltage network is 24 in the low-voltage network and 640 in the medium-voltage network.
German smart grid, four main datasets are used:
1) Germany: Each year, the federal electricity agency of 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 [
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 indeed help to improve the reliability of power supply. VI. CONCLUSION
using model-driven methods can be grouped in two categories: Approaches that treat the problem of improving the reliability of power supply, and approaches that examine the problem of combining information of the smart grid using model-driven or related methods.
There is quite an extensive amount of research about the reliability of electrical systems, also in connection with the smart grid. E.R. Brown [18] examines the challenge of a reliable power distribution system, and D. Elmakias introduces in his book methods to examine and improve the reliability of an electrical system [19]. Chowdhury et al. [20] and Waseem et al. [21] focus especially on the impact of distributed generation on system reliability, since the integration of distributed generation is a new challenge for the power system. Related to power distribution, Russell et. al [22] work on improving the reliability of the distribution system equipment in their research.
Since the unification of different smart grid elements in
one model has been identified early in the NIST smart grid
roadmap [
The Electric Power Research Institute (EPRI) has harmonized the CIM model with the IEC 61850 substation equipment with a CIM-based unified UML model that includes the physical elements of both standards [23–27]. In contrast, the approach presented in this paper leaves the existing smart grid models unchanged and builds mappings to combine them. The EPRI approach includes only the substation equipment from the IEC 61850 SCL model, while our research focuses on the substation functions in the LN model.
Andrén et. al. use model-driven QVTo transformations to transform IEC 61850 LN models into IEC 61499 models, a standard for process automation. This is done to automate the substation processes. The authors use model transformations to combine the two standards, like it is done in this research. However, they focus on those two standards and do not have the goal to enhance smart grid integration [28].
Byunghun et. al. present in their work mappings between datatypes from the CIM and the IEC 61850 standard. The two standards can only be unified if their datatypes match. The IEC 61850 however defines special datatypes for the standard that need to be mapped onto the datatypes defined in the CIM model. For deeper research about the mapping of the two standards, this paper is of importance [29].
Another different approach to map the two standards CIM and IEC 61850 is the use of ontologies and semantic web
based framework, called the extended outage management system, for run-time analysis and control of the smart grid.
The main purpose of the system is to increase the system reliability by faster detection of outages and by preventing the outages through the detection of system instabilities and imbalances. The system is built in VITRUVIUS, a model-driven and view based model management framework, which was originally created for software development purposes, but can be used with any kind of metamodel-based data. This paper has shown that the mechanisms and languages of VITRUVIUS for defining correspondences and viewtypes can successfully be applied to domains that do not purely concern software.
The framework has been evaluated using the SAIDI index.
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