Distributed Intelligence for Cost-Effective and Reliable Distribution Network Operation
Deliverable (D) No: 5.4
Standardisation assessment regarding canonical data models Author: OFFIS Date: 26.01.2015 Version: 3.0
www.discern.eu
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement
No. 308913.
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D5.4 Standardisation assessment regarding canonical data models
DISCERN_WP5_D5.4_150126_v3
Title of the Deliverable
Standardisation assessment regarding canonical data models
WP number WP title WP leader 5 Operational Process Integration Concept / Technical Specifications UFD
Task title T5.3 Developing the technical specifications for facilitating the implementation of DISCERN solutions at the demonstration sites, and for providing insights for economic analysis
Main Authors Rafael Santodomingo/ OFFIS Project partners involved
Erik Hamrin / ABB Andrés Honrubia / CIRCE Katrin Spanka / DNV KEMA Raúl Bachiller / IBDR Lars Nordström / KTH Carmen Calpe/ RWE Thomas Theisen/ RWE Sarah Rigby / SSEPD Ángel Yunta / UFD Miguel García / UFD Anders Johnson / VRD Fernando Castro / ZIV
Type (Distribution level)
PU, Public PP, Restricted to other program participants (including the Commission Services) RE, Restricted to other a group specified by the consortium (including the Commission Services) CO, Confidential, only for members of the consortium (including the Commission Services)
Status In Process In Revision Approved
Further information www.discern.eu
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Executive Summary
Deliverable D5.4 “Standardisation assessment regarding canonical data models” presents the
assessment of the canonical data models used in DISCERN solutions. Canonical data models are
aimed at defining the semantics of the information objects that must be exchanged between the
components of a system. The canonical data models promote, therefore, the semantic interoperability
or interoperability at the information layer. The objective is not only to enable components to receive
the data from other components, but also to understand the meaning of the information objects in
order to co-operate over complex control and management tasks.
The assessment performed in this deliverable followed a similar approach to the methodology utilised
in [D2-3.3] “Standard assessment regarding devices and communication architectures”; i.e. it
leverages the SGAM Information Layer (Canonical Data Model View) of the DISCERN SGAM models
with the aim of comparing the canonical data models used in DISCERN solutions with those
recommended by the European CEN-CENELEC-ETSI Smart Grid Coordination Group in the
Interoperability (IOP) Tool. This analysis results in recommendations to both DSOs, regarding existing
canonical data models that should be used to achieve semantic interoperability within Smart Grid
solutions, and standardisation bodies, regarding extensions and ambiguities identified in standard data
models during the project, as well as standardisation gaps affecting the DISCERN solutions.
The IEC TC57 Common Information Model (CIM) is the canonical data model recommended by the
European standardisation bodies in order to achieve semantic interoperability within the scope of
distribution management systems (DMS). Given that most of the DISCERN solutions focus on
automation and metering systems, and not on centralised management systems, the solutions do not
typically use the CIM to enable interoperability between the DMS applications. Nevertheless, in order
to promote the adoption of this data model at the DSOs different tasks have been carried out during
the project. This deliverable summarises these tasks, which comprise:
1) the use of the CIM Interface Reference Model as one of the sources for the development of the
DISCERN Actor and Function Libraries [D1.3] “Architecture templates and guidelines” and [D2-
3.2] “Tool support for managing Use Cases and SGAM models”,
2) the creation of the DISCERN Semantic Model in [D5.1] “Semantic model to transfer developed
solutions to DSOs and to facilitate their integration” and [D5.2] “DISCERN guide for facilitating the
replication and scalability of the solutions”, and
3) the definition of simulation scenarios in standard-based formats in [D6.1] “Identification of the
scenarios and distributed intelligence solutions”.
In addition to summarising experiences of the use of CIM in DISCERN, this deliverable presents a
novel methodology to go from SGAM models to the development of CIM message payloads,
which define the standard-based messages that must be used to exchange the SGAM information
objects in an interoperable manner. This methodology provides a structured approach to go from
high-level Smart Grid architectures to the definition of specific standard-based interfaces that
must be used within the solution; that is, the path to go from Smart Grid architectures to systems
engineering. This methodology is useful both for utilities, who can specify formats from a
representation of the solutions that is understood within and outside the company, and for
standardisation bodies, who can define standard formats from representative Use Cases
mapped into the SGAM model. Within the context of DISCERN, this methodology establishes a link
between the SGAM framework, widely used in WP 1, 2, 3, and 4, and the CIM used in WP5 and WP6.
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In summary, deliverable D5.4 contributes to one of the main goals of the project: promoting
interoperability in Smart Grid solutions. In particular, it focuses on one of the most challenging
tasks regarding interoperability, the semantic interoperability or interoperability at the
information layer.
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Table of Contents
Executive Summary .................................................................................................................................................................... 5 Table of Contents ........................................................................................................................................................................ 7 List of Figures ............................................................................................................................................................................. 8 List of Tables............................................................................................................................................................................... 9 Abbreviations and Acronyms ..................................................................................................................................................... 10 1. Introduction ..................................................................................................................................................................... 11
1.1. Scope of the document .......................................................................................................................................... 11 1.2. Structure of the document ...................................................................................................................................... 11
2. Canonical data models in DISCERN Smart Grid solutions ............................................................................................... 12 2.1. Methodology to assess DISCERN canonical data models ...................................................................................... 13 2.2. Assessment of canonical data models used in DISCERN ...................................................................................... 14
2.2.1. AMI Systems ..................................................................................................................................................... 14 2.2.2. Metering-related Back-Office systems ............................................................................................................... 18 2.2.3. Substation Automation Systems ........................................................................................................................ 22 2.2.4. DMS SCADA and GIS systems ......................................................................................................................... 28
3. Applications of the IEC TC57 Common Information Model (CIM) within DISCERN .......................................................... 34 3.1. Applications of the CIM in DISCERN so far ............................................................................................................ 34
3.1.1. CIM as basis for DISCERN libraries .................................................................................................................. 34 3.1.2. DISCERN Semantic Model ................................................................................................................................ 35 3.1.3. CIM for DISCERN simulations ........................................................................................................................... 37
3.2. From SGAM architectures to CIM messages ......................................................................................................... 37 3.2.1. Step 1 – SGAM UML models ............................................................................................................................. 39 3.2.2. Step 2 – CIM Profiles ......................................................................................................................................... 41 3.2.3. Step 3 – CIM XML Schemas ............................................................................................................................. 42
4. Conclusions ..................................................................................................................................................................... 44 5. References ...................................................................................................................................................................... 46
5.1. Project documents ................................................................................................................................................. 46 5.2. External documents ............................................................................................................................................... 46
6. Revisions ......................................................................................................................................................................... 47 6.1. Track changes ....................................................................................................................................................... 47
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List of Figures
FIGURE 2-1 METHODOLOGY TO ASSESS THE DATA MODELS USED IN DISCERN .......................... 13
FIGURE 2-2. DISCERN_RWE_LEADER_B7BD – OBIS ............................................................ 14
FIGURE 2-3. DISCERN_SSEPD_LEADER_B7BD – COSEM ................................................... 15
FIGURE 2-4. DISCERN_IBDR_LEARNER_B7BD – COSEM ..................................................... 15
FIGURE 2-5. DISCERN_VRD_LEADER_B9A – PROPRIETARY DATA MODEL ............................. 16
FIGURE 2-6. DISCERN_UFD_LEARNER_B9A – COSEM ........................................................ 16
FIGURE 2-7. DISCERN_IBDR_LEADER_B9A – COSEM ......................................................... 17
FIGURE 2-8. DISCERN_SSEPD_LEADER_B7BD – CIM AND DNP3 ......................................... 19
FIGURE 2-9. DISCERN_IBDR_LEARNER_B7BD – STG-DC3.0 & PROPRIETARY ...................... 19
FIGURE 2-10. DISCERN_VRD_LEADER_B9A – PROPRIETARY DATA MODELS ......................... 20
FIGURE 2-11. DISCERN_UFD_LEARNER_B9A – STG-DC3.0 ................................................ 20
FIGURE 2-12. DISCERN_IBDR_LEADER_B9B – STG-DC3.0 ................................................. 21
FIGURE 2-13. DISCERN_UFD_LEADER_B6 – IEC 61850 & PROPRIETARY DATA MODEL ........ 23
FIGURE 2-14. DISCERN_RWE_LEADER_B6 – IEC 60870-5-104 & IEC 61850 ...................... 23
FIGURE 2-15. DISCERN_VRD_LEARNER_B6 – IEC 61850-8-4 & PROPRIETARY DATA MODEL 24
FIGURE 2-16. DISCERN_UFD_LEADER_B7BD –COSEM ....................................................... 24
FIGURE 2-17. DISCERN_SSEPD_LEADER_B7BD – DNP3 OVER GPRS ................................. 25
FIGURE 2-18. DISCERN_RWE_LEADER_B7BD – MODBUS TCP ............................................. 25
FIGURE 2-19. DISCERN_IBDR_LEADER_B7BD – PROPRIETARY DATA MODEL & COSEM ....... 26
FIGURE 2-20. DISCERN_UFD_LEADER_B6 – PROPRIETARY DATA MODEL ............................. 28
FIGURE 2-21. DISCERN_IBDR_LEADER_B6 – PROPRIETARY DATA MODEL ............................ 29
FIGURE 2-22. DISCERN_RWE_LEADER_B6 – PROPRIETARY DATA MODEL ............................ 29
FIGURE 2-23. DISCERN_VRD_LEARNER_B6 – PROPRIETARY DATA MODEL ........................... 30
FIGURE 2-24. DISCERN_UFD_LEADER_B7BD – PROPRIETARY DATA MODEL ......................... 30
FIGURE 2-25. DISCERN_IBDR_LEADER_B9B – PROPRIETARY DATA MODEL ........................... 30
FIGURE 2-26. DISCERN_UFD_LEARNER_B9B – PROPRIETARY DATA MODEL ......................... 31
FIGURE 3-1. CIM INTERFACE REFERENCE MODEL TO DISCERN ACTOR AND FUNCTION LIBRARIES35
FIGURE 3-2. LVSUPERVISOR AS DEFINED IN [D5.1] ................................................................... 36
FIGURE 3-3. LVSUPERVISOR AS DERIVED FROM CIM:REMOTEUNIT ............................................ 36
FIGURE 3-4. RELATIONSHIP BETWEEN SGAM FRAMEWORK AND CIM DATA MODEL ..................... 37
FIGURE 3-5. METHODOLOGY TO GO FROM SGAM ARCHITECTURES TO CIM MESSAGE PAYLOADS 38
FIGURE 3-6. DISCERN_IBDR_LEARNER_B7BD UML MODEL – BUSINESS CONTEXT VIEW ........ 39
FIGURE 3-7. INFORMATION OBJECTS FROM AMI HEAD END TO METER DATA MANAGEMENT
SYSTEM ......................................................................................................................... 40
FIGURE 3-8. MAPPING SGAM INFORMATION OBJECTS TO IEC TC57 CIM ................................. 40
FIGURE 3-9. CIM PROFILES OF SGAM INFORMATION OBJECTS (I) ............................................. 41
FIGURE 3-10. CIM PROFILES OF SGAM INFORMATION OBJECTS (II) .......................................... 42
FIGURE 3-11. CIM XML SCHEMAS DEFINING THE MESSAGE PAYLOADS OF SGAM INFORMATION
OBJECTS ........................................................................................................................ 43
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List of Tables
TABLE 0-1 ACRONYMS ............................................................................................................. 10
TABLE 2-1. EXTRACT OF IOP TOOL – INFORMATION LAYER + AMI SYSTEM ................................ 17
TABLE 2-2 CANONICAL DATA MODELS FOR AMI SYSTEMS .......................................................... 18
TABLE 2-3. EXTRACT OF IOP TOOL – INFORMATION LAYER + METER-RELATED BACK OFFICE
SYSTEM .......................................................................................................................... 21
TABLE 2-4. CANONICAL DATA MODELS FOR METERING-RELATED BACK-OFFICE SYSTEMS ............ 22
TABLE 2-5. EXTRACT OF IOP TOOL – INFORMATION LAYER + DISTRIBUTION SUBSTATION
AUTOMATION SYSTEMS ................................................................................................... 27
TABLE 2-6. CANONICAL DATA MODELS USED FOR SUBSTATION AUTOMATION SYSTEMS ............... 28
TABLE 2-7. EXTRACT OF IOP TOOL – INFORMATION LAYER + DMS SCADA AND GIS SYSTEM ... 32
TABLE 2-8. CANONICAL DATA MODELS USED FOR DMS SCADA AND GIS SYSTEMS ................... 33
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Abbreviations and Acronyms
Table 0-1 Acronyms
AMI Advanced Metering Infrastructure
BPL Broadband Power Line
CIM Common Information Model
CT Current Transformer
DMS Distributed Management System
DR Disturbance Records
DSO Distribution System Operator
EA Enterprise Architect
EPRI Electric Power Research Institute
EU M/490 European Mandate 490
IBDR Iberdrola Distribución (DISCERN partner)
ICT Information and Communication Technology
IEC International Electrotechnical Commission
IED Intelligent Electronic Device
IOP Interoperability
IT Information Technology
KPI Key Performance Indicator
LV Low Voltage
MDI Model-Driven Integration
MV Medium Voltage
QoS Quality of Service
RTU Remote Terminal Unit
RWE Rheinisch-Westfälisches Elektrizitätswerk (DISCERN partner)
SCADA Supervisory, Control, and Data Acquisition
SGAM Smart Grid Architecture Model
SGCG Smart Grid Coordination Group
TC57 Technical Committee 57
Tx.x Task
UFD Unión Fenosa Distribución (DISCERN partner)
UML Unified Modelling Language
VTF Vattenfall (DISCERN partner)
WPx Work Package x
XML eXtensible Markup Language
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1. Introduction
1.1. Scope of the document
Deliverable D5.4 is one of the outputs of task T5.3 “Developing the technical specifications for
facilitating the implementation of DISCERN solutions at the demonstration sites, and for providing
insights for economic analysis”.
This deliverable presents the assessment of the canonical data models used in DISCERN solutions
together with a summary of the application of the most widely used canonical data model in the
context of distribution management systems - the IEC TC57 Common Information Model - during the
project. The deliverable also presents a novel methodology that relates the SGAM frameworks with the
development of CIM-based messages.
1.2. Structure of the document
The document comprises the following main sections:
Section 1 introduces the document.
Section 2 carries out an assessment of the canonical data models used within DISCERN solutions.
Section 3 summarises the applications of the IEC TC57 Common Information Model (CIM) in
DISCERN, including a novel methodology to go from SGAM architectures to the definition of CIM
message payloads.
Finally, Section 4 concludes the results from the study.
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2. Canonical data models in DISCERN Smart Grid solutions
The term Smart Grid is nowadays synonymously used for the future power system. It describes the
worldwide ongoing changes in the overall power grid infrastructure and thus also in the underlying
architectures. The former centralized infrastructure with its unidirectional power flow from large power
plants via different voltage level grids and transformers to the consumers, turns into a complex and
fully-meshed topology including bidirectional power flows. The new decentralized system includes
distributed power plants like wind, solar, hydro, and biomass generation. Furthermore, due to novel
technologies like electric mobility and the installation of sensors, new stakeholders are participating in
Smart Grids, for instance, experts in computer science, telecommunication and automation. Many
definitions of Smart Grids exist, but most of them agree in some common characteristics and
requirements. Some of these requirements are: the integration of Distributed Energy Resources
(DER); a full-scale smart power supply system based on modern and advanced Information and
Communication Technologies (ICT); integration of automated protection and control systems; an
efficient and sustainable power supply; use of decentralized network operation technologies; and
enabling new energy market products and services.
The development of a suitable ICT-infrastructure was identified as a necessary solution, so that Smart
Grids will consist of a physical infrastructure covering the power flow and an ICT-infrastructure
covering the information/data flow. However, the aforementioned requirements for Smart Grids raise
many challenges for the ICT-infrastructure, especially in terms of interoperability issues. An
established means of managing interoperability aspects is standardization. Therefore, many national
and international roadmaps and studies analyse the ICT-standardization environment. In [Rohjans et
al. 2010] and [Uslar et al. 2010] an overview on the most important approaches is given, summarizing
the consolidated results in order to present a set of core ICT-standards for the realization of Smart
Grids.
As explained in the reference architecture defined by the International Electrotechnical Commission
Technical Committee 57 (IEC TC57), previous standardization efforts were focused on the definition of
protocols for transporting the data [IEC 62357]. Nevertheless, the increasing use of object modelling
techniques and Model-Driven Integration (MDI) architectures has shifted the focus to the
interoperability at semantic level. This means that devices and applications from different vendors not
only have to exchange data, but they also have to share a common understanding on the semantics of
such data in order to interoperate with each other. Thus, semantic integration has become a key
enabler of future Smart Grids.
Many ICT standards for the electric systems include canonical data models, which define specific
domain terms for information exchange. Canonical data models significantly reduce the integration
efforts [SGCG-SGAM]. However, due to the diversity of applications, vendors and benefits associated
with different approaches, it is not possible in practice to define a unique canonical data model valid for
all the systems having to interact in the Smart Grids [IEEE P2030]. This section analyses the data
models used in DISCERN Smart Grid solutions, compares them to the recommended standard data
models by the European standardisation bodies, and highlights the standardisation gaps regarding
data models that affect the DISCERN solutions.
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2.1. Methodology to assess DISCERN canonical data models
The methodology used to assess DISCERN canonical data models is similar to the approach followed
in [D2-3.3] to analyse the communication standards of DISCERN solutions. Following the Leader,
Learner, Listener approach described in [D1.1], the solutions proposed by Leaders (i.e., the DSOs with
good knowledge about the functionalities gained from previous research projects) are presented in
[D4.2], whereas the solutions developed by Learners (i.e., the DSOs that will implement these
functionalities during project at the demonstration site) are presented in [D4.3].
The SGAM models (in this case, the SGAM Information Layer – Canonical Data Model View) of the
DISCERN solutions are compared with each other and with the data models recommended by the
CEN-CENELEC-ETSI Smart Grid Coordination Group in the IOP Tool (Figure 2-1). This comparison is
then in turn analysed by the DSOs and results in a set of recommendations for both:
the DSOs regarding canonical data models that promote semantic interoperability in this
domain and
the standardisation bodies regarding: a) standardisation activities that should be started in
order to resolve standardisation issues, and b) extensions in the IOP Tool.
Figure 2-1 Methodology to assess the data models used in DISCERN
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2.2. Assessment of canonical data models used in DISCERN
As explained in [D2-3.3], the IOP Tool classifies the standards in “SGAM Domain Specific Systems”.
The domain specific systems that apply to the functionalities proposed in DISCERN are:
AMI Systems; that is, Advanced Metering Infrastructure systems covering the devices and
communications to collect smart meter readings and to send commands to smart meters
located at customer premises. This domain specific system covers also the end point monitors
owned by suppliers in the UK to monitor electricity for the purposes of LV visibility of per-
premises consumption.
Metering-related Back-Office systems; that is, systems managing meter-related data at
operation and enterprise level.
Substation Automation Systems; that is, devices and communications that automatically
monitor, protect and control the substations and communicate with centralised SCADA
applications. This domain specific system refers also to the network monitoring systems
aiming at collecting voltage and current measurements in MV and LV networks.
DMS SCADA and GIS systems; that is, Distribution Management Systems at operation and
enterprise level managing field data regarding operation and operation-related information.
What follows summarises the data model assessment performed in this study. The analyses were
grouped in the four domain specific systems described above.
2.2.1. AMI Systems
This section focuses on the data models used for the communication between Smart Meters and data
concentrators placed at station level or AMI Head End systems, typically, at operation level. These
communications include reports from Smart Meters or End Point Monitors (meter readings, events,
alarms) as well as commands sent to the Smart Meters by operation applications.
DISCERN_RWE_Leader_B7bd “Real time monitoring of LV grid”
The solution proposed by RWE for functionality B7bd “Real time monitoring of LV grid” uses the data
model Object Identification System (OBIS) defined in the standard IEC 62056-6-1.
Figure 2-2. DISCERN_RWE_Leader_B7bd – OBIS
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DISCERN_SSEPD_Leader_B7bd “Real time monitoring of LV grid”
The solution proposed by SSEPD for functionality B7bd “Real time monitoring of LV grid” uses the
Companion Specification for Energy Metering data model (COSEM) standardised in the IEC 62056-5-
3.
Figure 2-3. DISCERN_SSEPD_Leader_B7bd – COSEM
DISCERN_IBDR_Learner_B7bd “Real time monitoring of LV grid”
The solution proposed by IBDR as Learner of functionality B7bd “Real time monitoring of LV grid” will
use the COSEM data model (IEC 62056-5-3) for the communications between the Smart Meters and
the Station aggregators (IED).
Figure 2-4. DISCERN_IBDR_Learner_B7bd – COSEM
DISCERN_VRD_Leader_B9a “Optimized AMR data collection and analysis using physical
concentrators”
The solution proposed by VRD for functionality B9a “Optimized AMR data collection and analysis
using physical concentrators” uses a proprietary data model to define the semantics of the messages
exchanged between the Smart Meter and the Meter Data Concentrator over the Open Smart Grid
Protocol (OSGP).
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Figure 2-5. DISCERN_VRD_Leader_B9a – Proprietary Data Model
DISCERN_UFD_Learner_B9a “Optimized AMR data collection and analysis using virtualized as
well as physical concentrators”
The solution proposed by UFD as Learner for functionality B9a “Optimized AMR data collection and
analysis using virtualized as well as physical concentrators” will use IEC 62056-5-3 COSEM for the
communications between the Smart Meters and the Station concentrators (physical Meter Data
Concentrator) and also with the Virtual Meter Data Concentrator.
Figure 2-6. DISCERN_UFD_Learner_B9a – COSEM
DISCERN_IBDR_Leader_B9b “Calculation and separation of non-technical losses”
The solution proposed by IBDR for functionality B9b “Calculation and separation of non-technical
losses” uses the IEC 62056-5-3 COSEM data model for the communications between Smart Meters
and Meter Data Concentrators.
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Figure 2-7. DISCERN_IBDR_Leader_B9a – COSEM
IOP Tool
Table 2-1 shows an extract of the standards proposed by the IOP Tool when we select the filters for
Information Layer and AMI Systems. As can be seen, the IOP Tool developed by CEN-CENELEC-
ETSI Smart Grid Coordination Group proposes the IEC 62056 standard series “Electricity metering –
data exchange for meter reading, tariff and load control”, which define the IEC 62056-5-3 COSEM data
model.
Table 2-1. Extract of IOP Tool – Information Layer + AMI System
.
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2.2.1.1 Conclusions and recommendations
The SGAM information layer (canonical data model view) of the DISCERN solutions show that the
COSEM data model standardised in the IEC 62506 series is widely used in real implementations
of AMI systems in the context of DISCERN project. This canonical data model is recommended
by the CEN-CENELEC-ETSI Smart Grid Coordination Group to promote semantic interoperability in
the context of Smart Metering.
Moreover, as explained in [D2-3.3], the IEC TC57 WG19 is developing a technical specification to
map the IEC 62056-5-3 COSEM data model to the OSGP (Open Smart Grid Protocol) used by VRD
in Figure 2-5 (TS 50586).
Table 2-2 summarises the canonical data models used in DISCERN solutions for AMI systems.
Table 2-2 Canonical data models for AMI systems
Data Models used for AMI systems in DISCERN
Sub-functionality DSO Data Model
B7bd "Real time monitoring of LV grid" RWE OBIS (IEC 62056-6-1)
B7bd "Real time monitoring of LV grid" SSEPD COSEM (IEC 62056-5-3)
B7bd "Real time monitoring of LV grid" IBDR COSEM (IEC 62056-5-3)
B9a "Optimized AMR data collection and analysis using virtualized as well as physical concentrators" VRD Proprietary over OSGP
B9a "Optimized AMR data collection and analysis using virtualized as well as physical concentrators" UFD COSEM (IEC 62056-5-3)
B9b "Calculation and separation of non-technical losses" IBDR COSEM (IEC 62056-5-3)
2.2.2. Metering-related Back-Office systems
This section focuses on the data models used for the communications within centralised systems at
Operation / Enterprise level in relation to Smart Meter data.
DISCERN_SSEPD_Leader_B7bd “Real time monitoring of LV grid”
The solution proposed by SSEPD for functionality B7bd “Real time monitoring of LV grid” uses the CIM
data model for the exchange of meter-related (as well as operation-related) data between the
centralised applications, although the Distributed Management System (DMS) uses a data model
based on DNP3 protocol.
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Figure 2-8. DISCERN_SSEPD_Leader_B7bd – CIM and DNP3
DISCERN_IBDR_Learner_B7bd “Real time monitoring of LV grid”
The solution proposed by IBDR as Learner of functionality B7bd “Real time monitoring of LV grid” will
use STG-DC3.0 data model defined by the PRIME alliance to the AMI Head End, and a proprietary
data model for the communications between the AMI Head End and the Meter Data Management
System.
Figure 2-9. DISCERN_IBDR_Learner_B7bd – STG-DC3.0 & Proprietary
DISCERN_VRD_Leader_B9a “Optimized AMR data collection and analysis using physical
concentrators”
The solution proposed by VRD for functionality B9a “Optimized AMR data collection and analysis
using physical concentrators” uses a proprietary data model for the communication of meter-related
data with Enterprise-level applications.
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Figure 2-10. DISCERN_VRD_Leader_B9a – Proprietary Data Models
DISCERN_UFD_Learner_B9a “Optimized AMR data collection and analysis using virtualized as
well as physical concentrators”
The solution proposed by UFD as Learner for functionality B9a “Optimized AMR data collection and
analysis using virtualized as well as physical concentrators” will use the STG-DC3.0 data model
defined by the PRIME alliance for the exchange of meter-related data from Station level to the
centralised Meter Data Management System, and a proprietary data model for the communication
between the Virtual Meter Data concentrator and the Meter Data Management System.
Figure 2-11. DISCERN_UFD_Learner_B9a – STG-DC3.0
DISCERN_IBDR_Leader_B9b “Calculation and separation of non-technical losses”
The solution proposed by IBDR for functionality B9b “Calculation and separation of non-technical
losses” uses the STG-DC3.0 data model for the communications between the AMI Head End and the
centralised Meter Data Management System.
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Figure 2-12. DISCERN_IBDR_Leader_B9b – STG-DC3.0
IOP Tool
Table 2-3 shows the data model recommended by the IOP Tool when we select the filters for
Information Layer and Meter-related Back Office systems. As can be seen, the IOP Tool developed by
CEN-CENELEC-ETSI Smart Grid Coordination Group proposes the IEC 61968-9 standard, which
defines the CIM profiles for exchanging “meter reading and control” data.
Table 2-3. Extract of IOP Tool – Information Layer + Meter-related Back Office system
2.2.2.1 Conclusions and recommendations
As can be seen from the IOP tool, the Common Information Model (CIM) is the main canonical
data model for meter-related back office systems. In particular, the profile defined in the standard
IEC 61968-9 “meter reading and control” is recommended by the IOP Tool in order to achieve
semantic interoperability within this domain. Nonetheless, given that the focus of DISCERN
demonstration sites is not on the centralised systems at Operation/Enterprise levels, the
communications in most DISCERN solutions in this context are based on proprietary data models.
The analysis of the Information Layers confirms the need for harmonizing the STG-DC3.0 data
model defined by the PRIME Alliance and the IEC TC57 CIM.
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Furthermore, the DISCERN SGAM models highlight the need for harmonizing the CIM with field-
related data models, such as IEC 62056-5-3 COSEM. The IEC TC57 WG 19 and TC13 PT 62056-8-6
are already working on that issue.
Table 2-4 summarises the canonical data models used in DISCERN solutions for Metering-related
Back-Office systems.
Table 2-4. Canonical data models for Metering-related Back-Office systems
Data Models used for Metering-related Back-Office systems in DISCERN
Sub-functionality DSO Data Model
B7bd "Real time monitoring of LV grid" SSEPD CIM (IEC 61968/61970)
B7bd "Real time monitoring of LV grid" IBDR STG-DC3.0 & Proprietary
B9a "Optimized AMR data collection and analysis using virtualized as well as physical concentrators" VRD Proprietary
B9a "Optimized AMR data collection and analysis using virtualized as well as physical concentrators" UFD STG-DC3.0 & Proprietary
B9b "Calculation and separation of non-technical losses" IBDR STG-DC3.0
2.2.3. Substation Automation Systems
This section is focused on the communications within substation automation systems; that is, mainly,
sensors and Intelligent Electronic Devices (IED).
DISCERN_UFD_Leader_B6 “Enhanced monitoring and control of MV/LV network”
The solution proposed by UFD for functionality B6 “Enhanced monitoring and control of MV/LV
network” uses the data model IEC 61850-7-4 for the communications within the substation automation
systems (FPI, IED, Data Aggregator), and a proprietary data model to communicate with the SCADA
application using the standard IEC 60870-5-104.
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Figure 2-13. DISCERN_UFD_Leader_B6 – IEC 61850 & Proprietary Data Model1
DISCERN_RWE_Leader_B6 “Enhanced monitoring and control of MV/LV network”
The solution proposed by RWE for functionality B6 “Enhanced monitoring and control of MV/LV
network” uses the data model IEC 61850-7-4 for the communications of IEDs, Data Aggregator, and
the Automatic Tap Changer Controller.
Figure 2-14. DISCERN_RWE_Leader_B6 – IEC 60870-5-104 & IEC 61850
1 This figure shows a new version of the Information Layer – Canonical Data Model View presented in [D4.2]. The figure
includes now the IEC 61850-7-4 data model, which was omitted in the figure shown in [D4.2]
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DISCERN_VRD_Learner_B6 “Enhanced monitoring and control of MV/LV network”
The solution proposed by VRD for functionality B6 “Enhanced monitoring and control of MV/LV
network” will use the data model IEC 61850-7-4 for the communications within the substation
automation system; that is, IED, Fault Analysis Tool, and Remote Terminal Unit, and a proprietary data
model to communicate with the centralised SCADA application based on the IEC 60870-5-104
protocol.
Figure 2-15. DISCERN_VRD_Learner_B6 – IEC 61850-8-4 & Proprietary Data Model
DISCERN_UFD_Leader_B7bd “Real time monitoring of LV grid”
The solution proposed by UFD for functionality B7bd “Real time monitoring of LV grid” uses the data
model IEC 62056-5-3 COSEM for exchanging LV measurements, events, alarms, and power quality
indexes from field IEDs to station Data Aggregators.
Figure 2-16. DISCERN_UFD_Leader_B7bd –COSEM
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DISCERN_SSEPD_Leader_B7bd “Real time monitoring of LV grid”
The solution proposed by SSEPD for functionality B7bd “Real time monitoring of LV grid” uses a data
model based on the DNP3 protocol for the communications between the substation IEDs and the Data
Repository.
Figure 2-17. DISCERN_SSEPD_Leader_B7bd – DNP3 over GPRS
DISCERN_RWE_Leader_B7bd “Real time monitoring of LV grid”
The solution proposed by RWE for functionality B7bd “Real time monitoring of LV grid” uses a data
model based on the Modbus protocol for the communications between field devices and sensors (Tap
Changer, Battery Controller, and Switch Controller) with the Station Controller and the Smart Operator
(Station Controller).
Figure 2-18. DISCERN_RWE_Leader_B7bd – Modbus TCP
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DISCERN_IBDR_Learner_B7bd “Real time monitoring of LV grid”
The solution proposed by IBDR as Learner for functionality B7bd “Real time monitoring of LV grid” will
use a proprietary data model for the communications between sensors and IED and the COSEM data
model between the IEDs and Meter Data Concentrator.
Figure 2-19. DISCERN_IBDR_Leader_B7bd – Proprietary Data Model & COSEM
IOP Tool
Table 2-5 shows an extract of the canonical data models proposed by the IOP Tool when we select
the filters for Information Layer and Distribution Substation Automation System. As can be seen, the
IOP Tool developed by CEN-CENELEC-ETSI Smart Grid Coordination Group recommends the IEC
61850-7-4, which defines the Logical Node data model, as well as the extensions of such a model for
Hydroelectric plants (IEC 61850-7-410) and Distributed Energy Resources (IEC 61850-7-420). In
addition, it also recommends the CIM IEC 61968 standards, although these are more focused on
management systems rather than automation systems.
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Table 2-5. Extract of IOP Tool – Information Layer + Distribution Substation Automation Systems
2.2.3.1 Conclusions and recommendations
The IEC 61850 data models (IEC 61850-7-4) are widely used within DISCERN solutions to
achieve semantic interoperability in substation automation systems. These data models are
also recommended by the CEN-CENELEC-ETSI Smart Grid Coordination Group. Nevertheless, it
should be noted that the COSEM data model can also be used to exchange LV measurements, as
well as related alarms and events. The standard IEC 61850-80-4 provides the mappings between
both data models. Moreover, the harmonisation between the data model widely used in
automation systems (IEC 61850) and the data model used in distribution management systems
(CIM) is carried out by the IEC TC57 WG19. The on-going development of the IEC 61850 data
models in UML, which is the formal modelling language used in the CIM, will facilitate this
harmonisation.
Table 2-6 summarises the canonical data models used in DISCERN solutions for Substation
Automation Systems.
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Table 2-6. Canonical data models used for Substation Automation Systems
Data Models used for Substation Automation Systems in DISCERN
Sub-functionality DSO Data Model
B6 "Enhanced monitoring and control of MV/LV network" UFD IEC 61850-7-4
B6 "Enhanced monitoring and control of MV/LV network" RWE IEC 61850-7-4
B6 "Enhanced monitoring and control of MV/LV network" VRD IEC 61850-7-4
B7bd "Real time monitoring of LV grid" RWE Modbus
B7bd "Real time monitoring of LV grid" UFD COSEM (IEC 62056-5-3)
B7bd "Real time monitoring of LV grid" SSEPD DNP3
B7bd "Real time monitoring of LV grid" IBDR COSEM (IEC 62056-5-3)
2.2.4. DMS SCADA and GIS systems
This section analyses the communication standards used to achieve interoperability between DMS
applications (SCADA, GIS, etc.) regarding operation-related data.
DISCERN_UFD_Leader_B6 “Enhanced monitoring and control of MV/LV network”
The solution proposed by UFD for functionality B6 “Enhanced monitoring and control of MV/LV
network” uses a proprietary data model within the DMS, which comprises only the SCADA application
and a GUI.
Figure 2-20. DISCERN_UFD_Leader_B6 – Proprietary Data Model
DISCERN_IBDR_Leader_B6 “Enhanced monitoring and control of MV/LV network”
The solution proposed by IBDR for functionality B6 “Enhanced monitoring and control of MV/LV
network” uses a proprietary data model within the DMS, which comprises the following applications:
Network Operation Statistics and Reporting, Network Operation Simulation, and Asset Investment
Planning.
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Figure 2-21. DISCERN_IBDR_Leader_B6 – Proprietary Data Model
DISCERN_RWE_Leader_B6 “Enhanced monitoring and control of MV/LV network”
The solution proposed by RWE for functionality B6 “Enhanced monitoring and control of MV/LV
network” uses the proprietary communications within the centralised DMS system, which comprises
only an Automatic Controller and a GUI.
Figure 2-22. DISCERN_RWE_Leader_B6 – Proprietary Data Model
DISCERN_VRD_Learner_B6 “Enhanced monitoring and control of MV/LV network”
The solution proposed by VRD for functionality B6 “Enhanced monitoring and control of MV/LV
network” will use a proprietary data model within the centralised DMS system, which comprises only
an SCADA application and GUI.
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Figure 2-23. DISCERN_VRD_Learner_B6 – Proprietary Data Model
DISCERN_UFD_Leader_B7bd “LV monitoring for future power quality analysis”
The solution proposed by UFD for functionality B7bd “LV monitoring for future power quality analysis”
uses a proprietary data model within the centralised system; that is, the DMS and the MDMS.
Figure 2-24. DISCERN_UFD_Leader_B7bd – Proprietary Data Model
DISCERN_SSEPD_Leader_B7bd “Real time monitoring of LV grid”
As explained in Section 2.2.2, the solution proposed by SSEPD for functionality B7bd “Real time
monitoring of LV grid” uses the CIM data model for the exchange of meter-related (as well as
operation-related) data between the centralised applications (see Figure 2-8).
DISCERN_IBDR_Leader_B9b “Calculation and separation of non-technical losses”
The solution proposed by IBDR for functionality B9b “Calculation and separation of non-technical
losses” uses a proprietary data model within the centralised system, which comprises only one Meter
Data Management System and a GUI.
Figure 2-25. DISCERN_IBDR_Leader_B9b – Proprietary Data model
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DISCERN_UFD_Learner_B9b “Calculation and separation of non-technical losses”
The solution proposed by UFD for functionality B9b “Calculation and separation of non-technical
losses” will use proprietary data models within the centralised system, which comprises: Network
Information System, Meter Data Management System, Power Analysis Tool applications and GUI.
Figure 2-26. DISCERN_UFD_Learner_B9b – Proprietary Data Model
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IOP Tool
Table 2-7 shows an extract of the standards proposed by the IOP Tool when we select the filters for
Information Layer and DMS SCADA & GIS systems. As can be seen, the IOP Tool proposes the IEC
61968 standard series that define the CIM profiles for different distribution management functionalities;
such as IEC 61698-3 (network operations), IEC 61968-4 (asset management), IEC 61968-6
(maintenance and construction), IEC 61968-8 (customer support), IEC 61968-9 (meter reading and
control), and IEC 61968-13 (exchange of distribution network models).
Table 2-7. Extract of IOP Tool – Information Layer + DMS SCADA and GIS system
2.2.4.1 Conclusions and recommendations
In most DISCERN solutions the centralised DMS systems use proprietary data models, because, as
stated previously, the focus of most DISCERN demonstration sites is not on the centralised systems.
Hence, the DMS systems are simple solutions to collect the field data; that is, they comprise an
application and a GUI, and the interoperability requirements for these solutions are not relevant for the
main purpose of the functionalities. The main standard data model to achieve semantic
interoperability in distribution management systems is the CIM, specifically IEC 61968. The
CEN-CENELEC-ETSI Smart Grid Coordination Group promotes the adoption of this data model,
highlighting the profiles from the CIM that select the classes, relationships and attributes that
must be used to exchange DMS-related information.
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Table 2-8 summarises the canonical data models used in DISCERN solutions for DMS SCADA and
GIS systems.
Table 2-8. Canonical data models used for DMS SCADA and GIS systems
Data Models used for DMS SCADA and GIS systems in DISCERN
Sub-functionality DSO Data Model
B6 "Enhanced monitoring and control of MV/LV network" UFD Proprietary
B6 "Enhanced monitoring and control of MV/LV network" IBDR Proprietary
B6 "Enhanced monitoring and control of MV/LV network" RWE Proprietary
B6 "Enhanced monitoring and control of MV/LV network" VRD Proprietary
B7bd "Real time monitoring of LV grid" RWE Proprietary
B7bd "Real time monitoring of LV grid" UFD Proprietary
B7bd "Real time monitoring of LV grid" SSEPD CIM (IEC 61968/61970)
B7bd "Real time monitoring of LV grid" IBDR Proprietary
B9b "Calculation and separation of non-technical losses" UFD Proprietary
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3. Applications of the IEC TC57 Common Information Model
(CIM) within DISCERN
As explained in the previous section, there are numerous standard data models in the context of Smart
Grids, but the IEC TC57 Common Information Model (CIM) is seen as the most important one for
Distribution Management Systems (DMS) by the European standardisation bodies CEN-CENELEC-
ETSI. Hence, these standardisation bodies recommend the use of the CIM in order to achieve
semantic interoperability (i.e. interoperability at information layer) within the scope of DMS systems.
Currently, the CIM is not widely used in DISCERN solutions, since they are more focused on achieving
interoperability in substation automation systems (where IEC 61850 are the most suitable ones) as
well as in metering systems (where IEC 62056-5-3 COSEM is the most relevant data model). The
recommendation to DSOs (within and beyond DISCERN project) is, therefore, to leverage the
CIM as the canonical data model to promote interoperability also in the management systems.
The activities carried out in WP5 “Operational Process Integration Concept / Technical Specifications”
are directed towards this aim. Different tasks within this Work Package promoted the adoption of the
CIM in DISCERN DSOs with practical experiences based on their solutions. Furthermore, WP6
“Technical evaluation and replicability assessment of the solutions” applied the CIM to facilitate
interoperability with simulation tools. Section 3.1 summarizes the applications of the CIM in the
DISCERN project so far. Thereafter, section 3.2 presents a novel methodology developed within the
project to establish a link between the SGAM framework and the CIM interfaces.
3.1. Applications of the CIM in DISCERN so far
The following sub-sections summarise the main applications of the CIM in DISCERN project. These
applications were mainly aimed at promoting the adoption of the CIM as a tool towards semantic
interoperability in distribution management systems and at defining a common terminology within the
project.
3.1.1. CIM as basis for DISCERN libraries
In DISCERN, the SGAM framework and Use Case methodology have been used to provide a common
framework for facilitating knowledge sharing among the partners. Nevertheless, the SGAM and Use
Case templates developed within DISCERN are not sufficient themselves in order to achieve a
common understanding within the project. It is also necessary to create libraries of terms that must be
used in the SGAM models and Use Cases; that is, libraries of: actors, technical functions, and
requirements. These libraries were developed during the project. As explained in [D1.3], the CIM was
one of the main sources for creating the actor and function libraries. In that way, many actors of the
DISCERN Actor Library refer to Business Functions and Sub-Functions defined in the CIM Interface
Reference Model (IRM) and many functions of the DISCERN Function Library refer to the Abstract
Components of the same reference model (Figure 3-1). The final libraries developed in the project are
available at the website of the project: http://www.discern.eu/project_output/tools.html.
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Figure 3-1. CIM Interface Reference Model to DISCERN Actor and Function Libraries
3.1.2. DISCERN Semantic Model
The deliverables [D5.1] and [D5.2] developed a DISCERN Semantic Model based on the CIM. The
main contributions of these deliverables in relation to semantic interoperability in the context of DSOs
are: a) learning process by the DSOs; the DISCERN partners had a deep look into the structure and
usefulness of the CIM as a tool to achieve interoperability in distribution management systems; b)
assess whether the CIM can be used to represent their solutions.
It is worth noting that, as stated in [D5.2], the DISCERN semantic model was not used during the
project to define the interfaces between the applications at the distribution management systems.
However, the analysis carried out in those deliverables helped identify possible extensions and
ambiguities that should be resolved in the model.
In addition to the analyses carried out in [D5.1] and [D5.2], what follows presents an ambiguity in the
model regarding the representation of one of the concepts included in the DISCERN solutions.
The “LVSupervisor” class defined for the solution DISCERN_UFD_Leader_B6 derives from the class
cim:Meter. In that way, it represents the voltage and current measurements as instances of
cim:MeterReading associated with an cim:UsagePoint, which in turn is associated with LVSupervisor
as class derived from cim:Meter. The representation of the measurements per phase can be given by
the class cim:ReadingType.
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Figure 3-2. LVSupervisor as defined in [D5.1]
The approach followed in DISCERN for representing LV supervisors is perfectly valid according to the
CIM data model. Nevertheless, during the project it was observed that the CIM enables users to
represent the same element by using completely different classes. For example, the “LVSupervisor”
could be defined as a class derived from cim:RemoteUnit. This class is associated with
cim:RemoteSource, which in turn is linked to cim:MeasumentValue. The cim:AnalogValue class
derived from cim:MeasurementValue can represent voltage and current measurements by means of
its association with cim:Analog, which contains the attribute cim:measurementType (to represent the
type of measurement) and cim:phases determining the phases of the measurement, and is associated
with a cim:PowerSystemResource, which can be an electric power equipment or device (Figure 3-3).
Figure 3-3. LVSupervisor as derived from cim:RemoteUnit
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As can be seen, the same concept (LVSupervisor) can be represented in CIM in two completely
different (but valid) ways. Therefore, the recommendation for the standardisation bodies in charge of
maintaining the CIM (i.e. IEC TC57 WG13, WG14, WG16) is to analyse which of the two approaches
should be followed in case there is a need to represent LV supervisors in CIM. Otherwise, the
representation of LV supervisors would lead to ambiguities within the model, since different classes
and relationships can be used to represent the same concept, which may result in mismatches
between different representations of the same concepts in the same model.
3.1.3. CIM for DISCERN simulations
One of the issues identified during the project in relation to the definition of the simulation scenarios
was the lack of a common electronic format for representing the electricity networks of the DSOs. Due
to this lack of a common format, it was necessary to collect all the data of the networks and covert it
manually into the format of the simulation tools. With the aim of avoiding this time-consuming task in
the future, WP6 “Technical evaluation and replicability assessment of the solutions” is analysing how
the CIM can be utilised for that purpose. This is still on-going work in the project and will be presented
as an annex of deliverable [D6.2] “Simulation tests of DISCERN solution”.
3.2. From SGAM architectures to CIM messages
The CEN-CENELEC-ETSI Smart Grid Architecture Model (SGAM) is being used as a common
framework to facilitate knowledge sharing among DISCERN partners and to carry out analysis on
interoperability aspects (see section 2 and [D2-3.3]) as well as IT security issues (see [D3.5]). The
SGAM is a key outcome of the EU Mandate M/490’s Reference Architecture Working Group. It
provides a structured approach for developing Smart Grid architectures in three dimensions. The first
two dimensions refer to the domains in the energy conversion chain and to the hierarchical zones of
power system management. The third dimension represents five layers (Business, Function,
Information, Communication, Component) addressing different interoperability aspects. The study
described in this section focuses on the Information Layer, which defines the information objects to be
exchanged within the Smart Grid systems along with the associated canonical data models (e.g. the
CIM).
Figure 3-4. Relationship between SGAM framework and CIM data model
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In particular, the objective of the methodology proposed in this section is to guide users from the
development of high-level Smart Grid architectures (SGAM models) towards the definition of specific
system interfaces between the components of the Smart Grid solution (CIM message payloads). That
is, this methodology contributes to filling the existing gap between the definition of architectures and
systems engineering. The methodology comprises three steps, summarised here and detailed below:
1. In the first step of the methodology, the SGAM architectures are represented in UML including
the information objects and the associated canonical data models. For that purpose, the
available Enterprise Architect (EA)2 SGAM Toolbox
3 developed by the Salzburg University has
been leveraged. The main advantage of the SGAM UML models is that they are represented
in the same formal software engineering language as existing canonical data models, such as
the CIM. In fact, the CIM is maintained as a UML model in Enterprise Architect (EA); that is,
the SGAM UML models are represented in the same formal language and platform as the
CIM, which facilitates the mapping between SGAM Information Objects and CIM classes.
2. In the second step, the SGAM information objects are mapped to the corresponding CIM
classes by defining the corresponding CIM profiles; that is, users follow the profiling
methodology utilized in the IEC TC57 in order to choose the CIM classes, relationships, and
attributes that represent the selected SGAM information objects
3. In the last step, the CIM-based XML Schemas are created from the CIM profiles of the SGAM
information objects. These XML Schemas define the message payloads that enable
interoperability within the Smart Grid system represented in the SGAM.
Figure 3-5. Methodology to go from SGAM architectures to CIM message payloads
2 Sparx Systems Enterprise Arhictect (EA) is an UML tool avaialble at: www.sparxsystems.com.
3 The SGAM Toolbox is available at: http://www.en-trust.at/downloads/sgam-toolbox/
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With this approach, SGAM information objects and CIM elements are formally related in a common
modelling language (UML) and in the same platform (EA), which enables users to easily develop CIM-
based interfaces from the high-level SGAM architectures of the Smart Grid systems.
3.2.1. Step 1 – SGAM UML models
The first step of the methodology refers to the creation of the SGAM UML models by using the EA
SGAM Toolbox. Figure 3-6 shows the SGAM UML model created in this study for the
DISCERN_IBDR_Learner_B7bd solution. As can be seen, the model structures the SGAM diagrams
in separate packages for each layer: SGAM Business Layer, SGAM Function Layer, SGAM
Information Layer, SGAM Communication Layer, and SGAM Component Layer. In particular, Figure
3-6 shows the diagram “Business Context View” within the SGAM Information Layer, which represents
the information objects exchanged across the components (devices and applications) that take part in
the solution.
Figure 3-6. DISCERN_IBDR_Learner_B7bd UML model – Business Context View
Figure 3-7 is zoomed in on the area that shows the information objects exchanged between the AMI
Head End and the Meter Data Management System. According to the SGAM model, the AMI Head
End sends two information objects to the Meter Data Management System: SmartMeterReadings (that
is, the readings of the Smart Meters at customer premises) and LVMeasurements (that is, the current
and voltage measurements collected by sensors at the LV network).
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Figure 3-7. Information Objects from AMI Head End to Meter Data Management System
In addition to representing the information objects between the components, the SGAM Information
Layer of the UML model represents the mappings between the information objects and the
corresponding canonical data model that should be used to achieve semantic interoperability within
the solution; that is, to make sure that the components represent the information objects in the same
manner. For instance, Figure 3-8 shows an example where the information objects mentioned before
(SmartMeterReadings and LVMeasurements) are mapped to the IEC TC57 Common Information
Model (CIM).
Figure 3-8. Mapping SGAM Information Objects to IEC TC57 CIM
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3.2.2. Step 2 – CIM Profiles
Once the SGAM UML models are created and the links between the SGAM information objects and
the canonical data models are formally represented in the model, it is possible to identify those
information objects associated with canonical data models available in UML, like the CIM. In that way,
using the same modelling language and platform, the corresponding canonical data model can be
opened in Enterprise Architect in order to create the profiles for each information object connected to it
in the SGAM UML model. Following the example presented previously, Figure 3-9 shows the creation
of the CIM profile for the SGAM information object SmartMeterReadings.
Figure 3-9. CIM profiles of SGAM Information Objects (I)
The profile of a canonical data model contains the classes, relationships and attributes of the data
model that are required for a particular purpose. Therefore, the CIM profile for the SGAM information
object SmartMeterReadings comprises the CIM classes, relationships and attributes that should be
used to represent the readings of the Smart Meters at customer premise (Figure 3-10).
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Figure 3-10. CIM profiles of SGAM Information Objects (II)
The SGAM information objects typically include a description provided by the user explaining the
content of the information objects in further detail. Taking advantage of these descriptions and
leveraging freely available EA plug-ins to select the classes of the model (e.g., the Modsarus tool
developed by EDF4, or the CIM EA by eXtensible Solutions
5), the CIM profiles can be created for all
the SGAM information objects associated with the CIM represented in the SGAM UML model.
3.2.3. Step 3 – CIM XML Schemas
From the CIM profiles containing the classes, relationships, and attributes of the SGAM information
objects it is possible to create the XML Schemas defining the structure of the XML messages that
must be used to exchange the SGAM information objects in an interoperable way. For this purpose,
the available EA-plugins mentioned in the previous step (Modsarus or CIM EA) can be used. Given
that we are using the same modelling language (UML) and platform (EA), once the XML Schemas are
automatically generated they can be dragged and dropped next to the corresponding information
object within the SGAM UML model. In that way, it is possible to develop a structured representation of
the Smart Grid solutions going from the high-level architectures representing the information objects
that must be exchanged within the solution to the specific XML Schemas defining the formats of the
standard-based messages that must be used to achieve interoperability (Figure 3-11).
4 A description of Modsarus can be found here: link to Modsarus presentation
5 CIM EA is available at: http://www.cimea.org/
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Figure 3-11. CIM XML Schemas defining the message payloads of SGAM Information Objects
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4. Conclusions
Deliverable D5.4 is one of the outputs from work undertaken in task T5.3 “Developing the technical
specifications for facilitating the implementation of DISCERN solutions at the demonstration sites, and
for providing insights for economic analysis”. It presents the assessment of the canonical data models
used in the DISCERN solutions.
Canonical data models define the semantics of the information objects exchanged between the
components of a solution. They promote, therefore, the semantic interoperability within the systems;
that is, they make it possible for the components not only to receive data from other components, but
also to understand the content of the information. Many ICT standards for the electricity systems
include canonical data models defining specific domain terms for information exchange. However, due
to the diversity of applications, vendors and benefits associated with different approaches, it is not
possible in practice to define a unique canonical data model valid for all the systems having to interact
in the Smart Grids. What follows summarises the main conclusions of the assessment carried out of
the DISCERN canonical data models:
The Companion Specification for Energy Metering data model (COSEM) standardised in
the IEC 62506-5-3 is recommended by the European standardisation bodies for
achieving semantic interoperability in metering systems. The analysis performed in this
deliverable based on the SGAM Information layer of DISCERN solutions, shows that the
COSEM is indeed widely used in real implementation of Advanced Metering
Infrastructures (AMI) within the context of DISCERN project. Moreover, the IEC is working
on the harmonisation of this data model in other protocols, such as the Open Smart Grid
Protocol (OSGP) used in VTF’s solution.
The IEC 61850 data models are recommended by the European standardisation bodies
to promote interoperability in substation automation systems (IEC 61850-7-4); and also
in hydroelectric power plants (IEC 61850-7-410) and Distributed Energy Resources (IEC
61850-7-420). The Logical Node data model for substation automation systems defined in the
standard IEC 61850-7-4 is widely used within DISCERN solutions. It should be noted also
that the IEC 62056-5-3 COSEM data model can be used to exchange LV measurements,
as well as related alarms and events, and this is also done in the project. The standard
IEC 61850-80-4 provides the mappings between both IEC 61850 and IEC 62056-5-3
COSEM data models.
The main standard data model to achieve semantic interoperability in distribution
management systems (DMS) is the IEC TC57 Common Information Model (CIM). The
European standardisation bodies promote the adoption of this data model, highlighting the
profiles from the CIM that define the classes, relationships and attributes that must be used to
exchange DMS-related information. In most DISCERN solutions the centralised DMS
systems use proprietary data models, because the focus of most DISCERN
demonstration sites is not on the centralised systems. Hence, the DMS systems are
simple solutions to collect the field data; that is, they comprise an application and a GUI, and
the interoperability requirements for these solutions are not relevant for the main purpose of
the functionalities. However, it is recommended by DISCERN that the DSOs should
consider the CIM as the main canonical data model for DMS applications in the future.
Given that the CIM is seen as the main canonical data model for DMS applications, it is
necessary to work on the harmonisation of this data model with other widely used data models
at operation, station, and field level. The IEC is already working on the harmonisation with the
IEC 61850 and COSEM data models. Furthermore, the analysis on the SGAM Information
D5.4 Standardisation assessment regarding canonical data models
DISCERN_WP5_D5.4_150126_v3 Page 45 of 47
Layer performed in this study confirms the need identified in [D2-3.3] regarding the
harmonisation of the STG-DC3.0 data model defined by the PRIME Alliance for
exchanging meter-related data in centralised systems and the IEC TC57 CIM.
In order to promote the adoption of the CIM by European DSOs, different tasks within the project
have applied this data model for different purposes. These applications are summarised as follows:
The CIM Interface Reference Model was one of the main sources for the development of
the DISCERN Actor and Function Libraries [D1.3], [D2-3.1]. In that way, it was used to
agree on the common terminology for representing the actors and technical functions of
DISCERN solutions.
The DISCERN Semantic Model developed in [D5.1] and [D5.2] was based on the CIM.
This semantic model was not used during the project to define the interfaces between the
applications at the distribution management systems, because, as explained previously, the
focus of DISCERN solutions is not on centralised systems. However, the analysis carried out
in those deliverables helped identify possible extensions and ambiguities that should be
resolved in the model. In addition to the analyses carried out in [D5.1] and [D5.2], this
deliverable described an additional ambiguity regarding the representation of LV
Supervisors in the CIM.
The CIM is also being used in WP6 for expressing the simulation scenarios in a standard
electronic format. One of the problems identified in DISCERN regarding the definition of
simulation scenarios was the lack of a common electronic format for representing the
electricity networks of the DSOs. With the aim of avoiding this time-consuming task in the
future, [D6.2] will include an annex analysing how the CIM can be utilised for that purpose in
the context of large Smart Grid projects.
In addition to the applications of the CIM in previous DISCERN tasks, this deliverable presents a
novel methodology to go from the SGAM framework to the development of CIM message
payloads, which define the structure of the messages that must be used to exchange the SGAM
information objects in an interoperable manner. The methodology leverages freely available tools and
plug-ins for Enterprise Architect (EA), such as the SGAM Toolbox developed by the University of
Salzburg for creating SGAM UML models, and the Modsarus tool developed by EDF for creating CIM
message payloads. In that way, it enables the development of an organised representation of the
Smart Grid solutions going from the high-level architectures representing the information
objects that must be exchanged within the solution to the specific XML Schemas defining the
formats of the standard-based messages that must be used to achieve interoperability. In the
context of DISCERN, this methodology establishes a link between the SGAM models used in WP1, 2,
3, and 4 with the CIM data model used in WP5 and WP6. Beyond the project, it provides a structured
approach to go from high-level Smart Grid architectures to the definition of standard-based interfaces
in order to achieve interoperability within solutions; that is, a path to go from Smart Grid architectures
to system engineering. This is useful both for DSOs (who can specify formats from a representation of
the solutions that is understood within and outside the company); and for standardisation bodies (who
can define standard formats from representative Use Cases mapped into the SGAM model).
D5.4 Standardisation assessment regarding canonical data models
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5. References
5.1. Project documents
[D1.3] – DISCERN Deliverable 1.3: “Architecture templates and guidelines”
[D2-3.1] – DISCERN Deliverable 2-3.1: “Catalogues and requirements for distributed devices and
communication architectures”
[D2-3.3] – DISCERN Deliverable 2-3.3: “Standard assessment regarding devices and communication
architectures”
[D4.2] – DISCERN Deliverable 4.2: “New system functionality”
[D4.3] – DISCERN Deliverable 4.3: “Preferable general system architecture, integrations and user
interface”
[D5.1] – DISCERN Deliverable 5.1: “Semantic model to transfer developed solutions to DSOs and to
facilitate their integration”
[D5.2] – DISCERN Deliverable 5.2: “DISCERN guide for facilitating the replication and scalability of the
solutions”
[D6.2] – DISCERN Deliverable 6.2: “Simulation tests of DISCERN solution”
5.2. External documents
[IEEE P2030] – IEEE P2030™/D5.0 1 Draft Guide for Smart Grid Interoperability of Energy
Technology and Information Technology Operation with the Electric Power System (EPS), and End-
Use Applications and Loads, IEEE Standards Association Department, February 2011.
[SGCG-SGAM] – “Smart Grid Reference Architecture”, CEN-CENELEC-ETSI Smart Grid Coordination
Group, November 2012
[Rohjans et al. 2010] – S. Rohjans, M. Uslar, R. Bleiker, J. González, M. Specht, T. Suding, and T.
Weidelt, “Survey of Smart Grid Standardization Studies and Recommendations,” in Proc. 1st IEEE
International Conference on Smart Grid Communications, vol., no., pp.583-588, 4-6 Oct. 2010.
[Uslar et al. 2010] – M. Uslar, S. Rohjans, R. Bleiker, J. González, M. Specht, T. Suding, and T.
Weidelt, “Survey of Smart Grid Standardization Studies and Recommendations - Part 2,” in Proc. IEEE
Innovative Smart Grid Technologies Europe, pp.1-6, 11-13 Oct. 2010.
D5.4 Standardisation assessment regarding canonical data models
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6. Revisions
6.1. Track changes
Name Date
(dd.mm.jjjj) Version Changes
Subject of change page
Rafael Santodomingo / OFFIS 19.12.2014 1.0 First version
Rafael Santodomingo / OFFIS 14.01.2015 2.0 Comments from KTH and WP2&3 members added
Carmen Calpe / RWE Thomas Theisen/ RWE
20.01.2015 2.1 First revision, comments were added and actions proposed
Carmen Calpe/RWE 26.01.2015 3.0 Final revision/ approval