distributed intelligence for cost-effective and reliable distribution network operation€¦ ·...

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.

Confidential (Y / N): N

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


DISCERN_WP5_D5.4_150126_v3 of 47

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.



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.



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



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



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



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



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.



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.



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



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



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).



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.



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

.



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.


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.



Figure 2-8. DISCERN_SSEPD_Leader_B7bd – CIM and DNP3


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.



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



losses” uses the STG-DC3.0 data model for the communications between the AMI Head End and the

centralised Meter Data Management System.



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


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.



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


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.



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]



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




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




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.



Table 2-5. Extract of IOP Tool – Information Layer + Distribution Substation Automation Systems


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.



Table 2-6. Canonical data models used for Substation Automation Systems

Data Models used for Substation Automation Systems in DISCERN


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.



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.



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


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).



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



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



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


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.



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


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



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.

http://www.discern.eu/project_output/tools.html



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.



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



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



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/

http://www.sparxsystems.com/

http://www.en-trust.at/downloads/sgam-toolbox/



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).



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



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).



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/

http://cimug.ucaiug.org/Meetings/Melbourne2014/Presentations/Day%202%20-%20Wednesday,%2026%20February/Modsarus_Presentation%20EN.pdf

http://www.cimea.org/



Figure 3-11. CIM XML Schemas defining the message payloads of SGAM Information Objects



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



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).



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.



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

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