Ontological Integration of Semantics and Domain Knowledge in Energy

Scenario Co-simulation

Jan S

oren Schwarz and Sebastian Lehnhoff

Department of Computer Science, University of Oldenburg, Oldenburg, Germany

Keywords:

Co-simulation, Data Management, Energy Scenarios, Information Model, Ontology, Sustainability.

Abstract:

The transition of the power system to more decentralized power plants and intelligent devices in a smart grid

leads to a signiﬁcant rise in complexity. For testing new technologies before their implementation in the

ﬁeld co-simulation is an important approach, which allows to couple diverse simulation models from different

domains. In the planning and evaluation of co-simulation scenarios experts from different domains have to

collaborate. To assist the stakeholder in this process, we propose to integrate on the one hand semantics of sim-

ulation models and exchanged data and on the other hand domain knowledge in the planning, execution, and

evaluation of interdisciplinary co-simulation based on ontologies. This approach aims to allow the high-level

planning of simulation and the seamless integration of its information to simulation scenario speciﬁcation,

execution and evaluation. Thus, our approach intents to improve the usability of large-scale interdisciplinary

co-simulation scenarios.

1 INTRODUCTION

The intended transition from fossil to renewable ener-

gies in the power system poses many new challenges.

New technologies have to be developed to deal with

ﬂuctuating energy resources and available ﬂexibili-

ties. Additionally, the dependencies between differ-

ent domains become more and more important and

the power system can be considered neither detached

from the ICT system nor ecological, economic, or so-

ciotechnical systems. To handle this complexity in

the development of new technologies simulation is an

important approach. Especially, co-simulation is used

to couple diverse simulation models, which is ben-

eﬁcial because in different domains usually speciﬁc

software, programming languages, and paradigms are

used. The coupling allows to reuse existing simula-

tion models without the need for reimplementation

and allows to use sophisticated models of the different

domains.

Commonly, a simulation expert works together

with the experts of the different simulation models in

the planning of a co-simulation. This collaboration

of simulation and domain experts can be a complex

task, because they have to understand at least partly

the other domains. For example, in the discussion the

used terminology can be unclear, because the same

terms may be used for different concepts. Therefore,

it would be beneﬁcial for the development of energy

scenarios to directly integrate or reference external

domain knowledge.

Co-simulation scenarios, which describe an exe-

cutable co-simulation, are typically developed manu-

ally by the simulation expert. Central elements of this

process are the parameters, dependencies, and data

ﬂows of simulation models. An increasing number of

simulation models makes the planning more complex

and error-prone, when done manually. Therefore, it

is essential for the planning of complex co-simulation

scenarios to get assistance in this process, e.g. in get-

ting recommendations of suitable simulation models.

Often co-simulation is not used as standalone tool,

but is integrated in energy scenarios adding even

more complexity. Energy scenarios are used to de-

scribe possible future developments of the energy sys-

tem (Grunwald et al., 2016). Typically, the future

states are deﬁned, tested with simulation and eval-

uated afterwards. For this, a clear deﬁnition of the

parametrization, data ﬂows, and results is crucial.

Our approach introduces ontological representa-

tions of domain knowledge in co-simulation of energy

scenarios to address the described problems. Addi-

tionally, it uses Semantic Web technologies to struc-

ture the process of planning, execution, and evalua-

tion of co-simulation. It has been developed in the

project NEDS, which consists of an interdisciplinary

Schwarz, J. and Lehnhoff, S.

Ontological Integration of Semantics and Domain Knowledge in Energy Scenario Co-simulation.

DOI: 10.5220/0008069801270136

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 127-136

ISBN: 978-989-758-382-7

127

consortium from the domains of business adminis-

tration, computer science, economics, electrical engi-

neering, and psychology. In the project a process for

the integrated development of energy scenarios, their

simulation, and the evaluation of their sustainability

has been developed (Schwarz et al., 2019b) and ex-

ecuted for a future scenario for the German federal

state Lower Saxony. In this context, our proposed

ontology-based approach offers the following bene-

ﬁts:

Firstly, it enables the integration of knowledge

from external ontologies. This allows to reuse ex-

isting ontologies from different domains and inte-

grate deﬁnitions of used terms. Thus, the terminology

used in a co-simulation project can be made clear and

transparent.

Secondly, it allows to describe the semantics

of data in several steps of co-simulation: The

parametrization of simulation models, the exchange

of data between simulation models, and the results of

simulation. All of these different kinds of data can be

semantically annotated to make the interpretation less

error-prone.

Thirdly, Semantic Web technologies like RDF,

OWL, and SPARQL offer a well-known and

widespread structure for knowledge representation

and querying. Their usage permits the utilization of

manifold available tools and techniques. Especially,

the querying based on the ontological description of

dependencies and data ﬂows assists the planning of

simulation scenarios and enables the simulation ex-

pert on the one hand to check high-level scenarios for

completeness and missing models or evaluation func-

tions and on the other hand to verify simulation sce-

narios.

The remainder of this article is structured as fol-

lows: Section 2 gives an overview of the foundations

and related work. Section 3 describes the proposed

approach, gives some examples, and describes the

evaluation of the approach in a ﬁeld study. A con-

clusion is given in section 4.

2 FOUNDATIONS AND RELATED

WORK

In this section, we will give an overview of related

work using co-simulation and ontologies in the en-

ergy domain (see section 2.1) and introduce our previ-

ous work of a process for the planning and evaluation

of energy scenarios with an information model and a

catalog of components for co-simulation (see section

2.2).

2.1 Co-simulation and Ontologies in

Energy Domain

As stated in the introduction, the power system be-

comes more and more complex, because multiple do-

mains have to be considered. An approach for hand-

ling this issue is co-simulation, which is deﬁned as

“an approach for the joint simulation of models devel-

oped with different tools (tool coupling) where each

tool treats one part of a modular coupled problem”

(Bastian et al., 2011, p.1).

In energy domain, many different smart grid co-

simulation frameworks exist, which are developed

for different use cases. For example, the usage of

real-time and co-simulators for the development of

power system monitoring control and protection ap-

plications (Rehtanz and Guillaud, 2016), the coupling

with power ﬂow simulators (Lehnhoff et al., 2015),

the integration of power system and communication

networks (Mets et al., 2014), or a holistic view on

the power system (Schwarz et al., 2019b). Schl

ogl

et al. (2015) suggest a typiﬁcation for the available

co-simulation frameworks and Vogt et al. (2018) com-

pare many of them. However, Palensky et al. (2017)

state that challenges in co-simulation are still massive,

which is caused among other things by often missing

software interoperability in the modeling.

Although ontologies would offer many beneﬁts

for interoperability, the utilization in co-simulation

approaches is not common with two exceptions:

Teixeira et al. (2018) describe an approach for co-

simulation with integration of ontologies for the inter-

operability between different electricity market multi-

agent simulation platforms, which is called TOOCC

(Tools Conﬁguration Center). But the focus of this

approach seems to be mainly on energy markets and

building energy management and the data structure of

messages between simulation models. Another ap-

proach is CODES (Composable Discrete-Event scal-

able Simulation), described by Teo and Szabo (2008).

It contains the COSMO ontology, which supports the

classiﬁcation of components to allow component dis-

covery and reuse with a model repository, but it is lim-

ited to discrete-event simulation. As our focus is more

on the high-level scenario planning, the integration of

external domain knowledge, and the usability, our ap-

proach aims to be integrated in the established open-

source co-simulation framework mosaik

(Steinbrink

et al., 2019). It is focused on providing high usabil-

ity and ﬂexibility to enable interdisciplinary teams to

develop co-simulation scenarios. For the coupling of

simulation models mosaik provides an API, which is

https://mosaik.ofﬁs.de

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128

Figure 1: Structure of the SEP information model (Schwarz et al., 2019b).

available in several programming languages and can

also be accessed via network packages.

2.2 Process for Assisted Simulation

Planning for Co-simulation

In previous work we have introduced the Sustain-

ability Evaluation Process (SEP) and an information

model for the high-level planning of co-simulation

in the context of energy scenarios (Schwarz et al.,

2019b). The SEP describes an integrated process for

the sustainability evaluation of future scenarios based

on literature review and co-simulation. The ﬁrst step

of the SEP is the development of qualitative future

scenarios, which describe thinkable future states of

the power system – the energy scenarios. Afterwards,

these qualitative assumptions are quantiﬁed and used

as input for simulation. The last step is the evalua-

tion of the simulation results based on multi-criteria

decision making. The evaluation function for the SEP

is sustainability, but also other evaluation functions

could be deﬁned in the information model. The infor-

mation model links future scenarios and simulation

scenarios to the sustainability evaluation as shown in

ﬁgure 1. On the left-hand side the domains of interest

are modeled and described by attributes, which can

be deﬁned based on either future scenarios or sim-

ulation. On the right-hand side sustainability is de-

ﬁned as evaluation function and subdivided in facets

and criteria. The connection between the two sides

is established through transformation functions from

attributes to the sustainability criteria. The informa-

tion model aims to support the information exchange

in the SEP. It describes a structure for modeling sce-

narios and assists the users in the process.

Based on the information model, a process for the

planning of co-simulation was developed, which is

shown in ﬁgure 2 (Schwarz et al., 2019a). In this plan-

ning of a co-simulation, simulation models have to be

found, which can provide the results deﬁned in the in-

formation model. For this, a catalog of co-simulation

components was developed to give an overview of the

available components. The interfaces of the simula-

tion models are described in the catalog based on the

Functional Mockup Interface (FMI) standard, which

has been developed to allow the coupling of different

simulation models in industrial and scientiﬁc projects

(Blochwitz et al., 2009). A substantial part of FMI is

the deﬁnition of variables, which deﬁne the inputs and

outputs of simulation models (Modelica Association

Project FMI, 2013). Each variable can be described

by seven attributes in FMI. For example, the attribute

causality can have values like input, output, parame-

ter, or calculatedParameter or the attribute variabil-

ity characterizes time instants when a variable can

change its value and can have values like constant,

ﬁxed, discrete, or continuous.

3 APPROACH

Our approach for the ontological integration of do-

main knowledge in co-simulation is based on the in-

formation model and component catalog summarized

in the previous section. In previous papers, ﬁrst ideas

of this approach were described (Schwarz and Lehn-

hoff, 2018; Schwarz et al., 2019a,b), which will be

detailed in the following regarding the ontological in-

tegration.

The information model of the SEP aims to assists

the collaboration of a simulation expert and domain

experts, which provide the simulation models. It can

Ontological Integration of Semantics and Domain Knowledge in Energy Scenario Co-simulation

129

High Level Scenario

Definition

Instantiated

Information Model

Base Ontology

Co-Simulation

Component Catalog

Simulation

Scenarios

SPARQL Queries

External Domain

Knowledge

Figure 2: Overview of the approach for ontological integration of domain knowledge in energy scenario simulation (Schwarz

et al., 2019a).

be assumed that the simulation expert is a software

expert familiar with the co-simulation framework and

several programming languages and simulation tools,

but has only limited knowledge about all domains in-

cluded in simulation. The domain experts may also be

software experts, especially, if they provide simula-

tion models. But they could also have no background

in computer science or software development. There-

fore, an important requirement for our approach is to

facilitate the participation of domain experts in the

modeling without previous knowledge of Semantic

Web technologies. Thus, a semantic diagram in form

of a mind map is used for modeling the SEP informa-

tion model (see high level scenario deﬁnition in ﬁgure

2). This allows to start the planning of scenarios with

brainstorming in the project team and bringing the in-

formation step by step in the correct structure. Ob-

jects in the mind map can also be annotated directly

with additional information. For example, references

to external ontologies (see section 3.1), or the context

of the future scenarios can be annotated.

The map has to comply to the structure of the in-

formation model shown in ﬁgure 1 in the end. Other

methods for knowledge modeling with the graph-

based structure of concept maps also exist, as Simon-

Cuevas et al. (2009) describe it for example. We ar-

gue that a tree-based mind map is sufﬁcient for the

described use case and the superior ﬂexibility of a

graph-based concept map would distract the users.

For the ontological representation of the modeled

information, a base ontology representing the infor-

mation model structure has been developed (see sec-

tion 3.2). The mind map tool XMind

was used and

extended with a plug-in to instantiate the information

model ontology. Therefore, the scenario can be mod-

eled inside the mind map and be transformed to RDF

(see instantiated information model in ﬁgure 2).

To build an executable co-simulation based on

this, a co-simulation component catalog was imple-

https://www.xmind.net/

mented (Schwarz et al., 2019a). For this, a Semantic

MediaWiki (SMW) (Kr

otzsch et al., 2007) was used

to collect available simulation models. It is used to fa-

cilitate the participation of users without experiences

in Semantic Web technologies. With the page forms

extension

it offers intuitive usable forms to add new

models to the catalog. The SMW allows to import

vocabularies from external ontologies and to export

the content to RDF or to use directly a triplet store

as database. Thus, the catalog can directly be inte-

grated in the instantiated ontology of the information

model and the user can be assisted in ﬁnding the suit-

able simulation models for his purpose. Some ex-

ample queries showing this assistance are shown in

section 3.3. For the integration of the approach in

co-simulation a prototype for the framework mosaik

has been developed. It allows to use the information

from the information model and the component cata-

log to assist the simulation expert in the development

and validation of executable simulation scenarios. Fi-

nally, the integration in data management is shortly

described in section 3.4 and the evaluation of the ap-

proach in section 3.5.

3.1 Referencing External Ontologies

The ontological modeling allows to reference exist-

ing external ontologies in different manners. Exem-

plary ontologies for the following relevant use cases

are described in this section. On the one hand, the

objects of interest for the simulation (domain objects)

and objects of evaluation (sustainability criteria) in

the information model can be mapped to external on-

tologies to deﬁne their meaning. On the other hand,

external ontologies can be used for the deﬁnition of

units of measurement for different kinds of attributes

in the information model.

https://www.mediawiki.org/wiki/Extension:Page Forms

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Figure 3: Mapping of individuals of the instantiated information model from project NEDS to external ontologies.

3.1.1 Deﬁnition of Terms

In the energy domain the Common Information

Model (CIM) is widespread to facilitate interoperabil-

ity in the power system. It contains a data model in

form of a domain ontology, various interface speci-

ﬁcations, and mappings between technologies. Thus,

it enables automated communication between compo-

nents of smart grids. For our approach mainly the ﬁrst

use case for CIM described by Uslar et al. (2012) is

of interest, which is CIM as a large domain ontology

providing a vocabulary. This vocabulary can be used

to map objects to deﬁnitions in the CIM. The CIM is

deﬁned as an UML model, but the complete model or

subsets (so-called proﬁles) can be transformed to Web

Ontology Language (OWL) with the CIMTool

As in the SEP sustainability is evaluated, it is

examined here as example as well. The United

Nations deﬁned Sustainable Development Goals

(SDGs), which should be fulﬁlled until the year 2030

(UN General Assembly, 2015). To reference these

goals and their indicators the SDG Interface Ontol-

ogy

(SDGIO) based on the Environment Ontology

(ENVO) (Buttigieg et al., 2016) is under develop-

ment. It contains deﬁnitions of indicators for the mea-

surement of the SDGs deﬁned by the United Nations.

The mapping to external ontologies allows inte-

grating deﬁnitions of terms to make clear their mean-

ing in an interdisciplinary simulation or to relate in-

ternal evaluation criteria to external criteria. Exam-

ples of mapping to the external ontologies CIM and

SDGIO are described as follows (see also ﬁgure 3).

The CIM ontology contains deﬁnitions for objects of

the power system like CIM-generic#EnergyMarket,

CIM-generic#HydroPowerPlant, and CIM-generic#

GasPrice, which are mapped to domain objects and

attributes of the information model in the project

NEDS. Additionally, the domain object attribute foot-

print in NEDS is mapped to the class material foot-

http://wiki.cimtool.org

https://github.com/SDG-InterfaceOntology/sdgio

print (sdg/SDGIO 00010057) and its deﬁnition in the

SDGIO. Another example is the sustainability crite-

rion Percentage of income used for energy in NEDS,

which addresses the SDG 7: “Ensure access to afford-

able, reliable, sustainable and modern energy for all”

(UN General Assembly, 2015, p. 21), which is repre-

sented by the individual sdg/ SDGIO 00000041 in the

SDGIO.

3.1.2 Units of Measure

The Ontology of units of Measure (OM) is an OWL

ontology of the domain of quantities and units of mea-

sure described by Rijgersberg et al. (2013). It aims to

“support making quantitative research data more ex-

plicit, so that the data can be integrated, veriﬁed and

reproduced” (Rijgersberg et al., 2013, p. 1). In the

OM every measure is deﬁned by a unit of measure,

which can have a preﬁx. These units of measure are

deﬁned by a quantity and each quantity has a dimen-

sion. For example, the measure “3 meters” would be

deﬁned by the unit “meter”, which could be deﬁned

by the quantity “length” or “height”, which both are

in the “length dimension”. Additionally, a java library

for conversion of units based on the ontology is avail-

able

Units play an important role in the attributes,

transformation functions, and sustainability criteria in

the information model as well as in simulation models

and co-simulation. All connections have to be vali-

dated in consideration of their unit to ensure the func-

tionality. Therefore, the OM is used to add references

to the units of attributes and criteria annotated in the

information model. Additionally, the OM is used to

assist the user in annotating directly in the mind map,

comparable to the assistance with an Excel add-in de-

scribed by Rijgersberg et al. (2013). With this infor-

mation the OM allows to validate the connections. In

the case of problems, a conversion can be added or

the user be warned. The information can also be used

https://github.com/dieudonne-willems/om-java-libs

Ontological Integration of Semantics and Domain Knowledge in Energy Scenario Co-simulation

131

within the co-simulation scenarios to check for cor-

rectness of connections between simulation models.

3.2 Ontological Representation

Three base ontologies have been developed and are

imported in an additional ontology for integration.

This modularity enables the reuse of the ontologies.

The ﬁrst ontology represents the structure of the in-

formation model for the high-level scenario planning.

The second ontology represents the structure of the

component catalog and the FMI-based speciﬁcation

of variables. The third ontology represents the struc-

ture of a simulation scenario modeled in mosaik.

Such a scenario consists of multiple simulation com-

ponent with their parametrization and the connections

between simulation component and the exchanged at-

tributes between them. Based on these three ontolo-

gies the available data is described and can be used

for queries.

3.3 Example Queries

The usage of ontologies provides a structure for

querying the data of the planning in the information

model, the component catalog, and the mosaik sce-

nario with SPARQL to assist the development of sim-

ulation scenarios. In the following, two examples are

given, which show the opportunities of the ontolog-

ical representation in the planning of executable co-

simulation scenarios. For both examples the SPARQL

code and a visualization of the query are shown. The

following preﬁxes are used: The preﬁxes wiki and fmi

are referencing the component catalog in the SMW.

The preﬁxes imDB, imDom, and im are referencing

the information model base ontology. The preﬁx om

is referencing the OM. In the visualization (see ﬁg-

ures 4(b), and 5(b)) the data source is indicated by the

background color and label.

SELECT DISTINCT ? d e r A t t r ? u n i t ? omUnit ? di m e n s i o n ? comp o nent ? f miV ar

? f m i U n i t

WHERE {

? d e r A t t r r d f : t y p e imDom: D e r i v e d D o m a i nO b j ec t A tt r i b u t e ; imDB : u n i t ? u n i t

? omUnit r df : t y p e om: U n i t ; om: s y mbol ? symb o l

FILTER( ? symbo l = ? u n i t )

? omUnit om: h as Di me n s i on ? d i m e n s i o n

? co mpon e nt r d f : t y p e w i k i C a te go ry : Component ; wi ki : f m i V a r i a b l e s ? fmi V ar

? fmiV ar r df : t y p e w i k i C at eg or y : F MI Va r i a bl e

? fmiV ar fmi : u n i t ? fm i U n i t ; fmi : c a u s a l i t y ? f m i C a u s a l i t y

FILTER( ? f m i C a u s a l i t y = ’ o u t p u t ’ )

? omUnit2 r d f : t y p e om: U n i t ; om : s ymbol ? sy m bol2

FILTER( ? symb o l2 = ? fm i U n i t )

? omUnit2 om: h a s D i m en si on ? d i m e n s i o n }

(a) SPARQL code

information model

ontology of units of measure

wiki

?derAttr

rdf:type

imDom:DerivedDomainObjectAttribute

imDB:unit ?unit

om:symbol

?omUnit

rdf:type

om:Unit

om:hasDimension ?dimension

om:hasDimension

?omUnit2

rdf:type

om:unit

om:symbol

?fmiUnit

fmi:unit?fmiVar

rdf:type

wikiCategory:FMIVariable

fmi:causality

FILTER(’output’)

wiki:fmiVariables

?component

rdf:type

wikiCategory:Component

(b) Visualization

Figure 4: Query 1 – Simulation models from wiki providing output for derived attributes of information model.

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

132

3.3.1 Query 1

This query (see ﬁgure 4) assist the user in common

use cases for the development of a co-simulation sce-

nario. If the simulation models are not predeﬁned,

the simulation expert has to ﬁnd simulation models

matching the goal of the simulation. This can be a

complex task, because there can be a vast amount of

available simulation models, which were usually not

developed by the simulation expert. Therefore, the

simulation expert does not know all details about the

simulation models and is assisted by querying the in-

formation model and the speciﬁcation of the simula-

tion models in the component catalog.

Query 1 shows simulation models, whose output

can be used in the information model. These de-

rived attributes in the information model are by def-

inition the output of a simulation. In the query the

derived attributes (?derAttr) in the information model

are mapped to the variables (?fmiVar) of simulation

models (?component) in the component catalog. The

variables are ﬁltered by their fmi:causality, which has

to be output. To ﬁnd suitable combinations of de-

rived attributes and variables the units annotated in

the mind map are used. In this query the units are not

compared directly, but the OM is used to reference the

dimension (?dimension) of the unit, e.g., the unit “me-

ter” is in the length dimension. Hence, differences in

unit preﬁxes or the system of measurement (imperial

or metric system) are of no importance for mapping.

3.3.2 Query 2

This query searches for simulation models that use the

output of another simulation model as input(see ﬁgure

5). In the information model such kind of connection

is modeled via a derived attribute, but it is usually re-

alized by a direct coupling of the two simulation mod-

els in co-simulation. For these cases, the query checks

the technical interfaces and characteristics of the sim-

ulation models for compatibility based on the compo-

nent catalog. In the query, the ﬁrst simulation model

(?simMod1) is mapped to the derived attributes (?de-

rAttr) and the second simulation model (?simMod2).

If two simulation models in the information model are

modeled this way, the technical characteristics of the

SELECT DISTINCT ? simMod1 ? fmiV ar1 ? f m i U n i t 1 ? d e r A t t r ? fmi V ar2 ? f mi U n i t 2

? simMod2

WHERE {

? simMod1 im : f e e d s D e r i v e d A t t r i b u t e ? d e r A t t r

? d e r A t t r im : f e e d s S i m u l a t i o n M o d e l ? simMod2

? co mpon e nt1 r df : t y p e w i k i C at eg or y : MosaikM odel

? co mpon e nt1 w ik i : name ? simMod1 ; w ik i : f m i V a r i a b l e s ? fmi V ar1

? fmiV ar1 r d f : t y p e w ik iC at eg or y : F M I V ar ia b l e

? co mpon e nt2 r df : t y p e w i k i C at eg or y : MosaikM odel

? co mpon e nt2 w ik i : name ? simMod2 ; w ik i : f m i V a r i a b l e s ? fmi V ar2

? fmiV ar2 r d f : t y p e w ik iC at eg or y : F M I V ar ia b l e

? fmiV ar1 fmi : t y p e ? fmi V ar1T y pe ; fmi : v a r i a b i l i t y ? fm i V a r 1 V a r i

? fmiV ar2 fmi : t y p e ? fmi V ar1T y pe ; fmi : v a r i a b i l i t y ? fm i V a r 1 V a r i

? co mpon e nt1 w ik i : ti m e Domain ? comp1time Domain

? co mpon e nt2 w ik i : ti m e Domain ? comp1time Domain

(a) SPARQL code

information model

wiki

?simMod1

wiki:name

?component1

wiki:fmiVariables

?fmiVar1

wiki:name

?component2

wiki:fmiVariables

?fmiVar2

im:feedsDerivedAttribute

?derAttr

im:feedsSimulationModel

?simMod2

wiki:timeDomain wiki:timeDomain

?comp1timeDomain

rdf:type rdf:typewikiCategory:MosaikModel

fmi:type fmi:type?fmiVar1Type

fmi:variability fmi:variability

?fmiVar1Vari

(b) Visualization

Figure 5: Query 2 – Finding suitable simulation models for coupling with another simulation model.

Ontological Integration of Semantics and Domain Knowledge in Energy Scenario Co-simulation

133

simulation models can be checked for compatibility

based on the component catalog.

In the example, the characteristics fmi:variability

and fmi:type of the FMI variables (?fmiVar1 and ?fmi-

Var2) are compared. Additionally, the characteristics

wiki:timeDomain of the simulation models (?compo-

nent1 and ?component2) are compared. This charac-

teristic can have values like “discrete”, “continuous”,

or “stationary” and addresses the common problem

of different timing in simulation models. This query

can also be adapted to ﬁnd suitable simulation mod-

els based on these characteristics, if one of them is

missing in the information model.

3.4 Data Management

In the SEP, values from future scenarios, simulation

scenario parametrization, and simulation results have

to be managed and are directly integrated in the infor-

mation model. The information model provides one

central storage for the semantics of all relevant data

in the complete process. Thus, also the data manage-

ment is integrated with the information model.

As brieﬂy mentioned in Schwarz et al. (2019b) the

information model was used to generate the schema

for a data store, which was implemented in a re-

lational database (RDB) in the NEDS project. To

facilitate the collaboration of different domain ex-

perts, different views were deﬁned on the schema.

To integrate the data from a relational database again

Ontology-based Data Access (OBDA) could be used.

It is based on a three-level architecture containing

an ontology, data sources, and a mapping between

them Daraio et al. (2016). Thus, OBDA faces the

challenge of data heterogeneity by replacing a global

scheme in data management with the ontology de-

scribing the domains. It allows also to integrate data

from other sources like CSV, XML, and XLSX di-

rectly in SPARQL queries, which can be helpful in

the interdisciplinary environment of co-simulation.

To reduce the complexity the direct usage of a

triple store would be preferable compared to a RDB

with ODBA, but is not always possible. However, the

usage of a triple store or OBDA would allow to access

data based on the information model ontology and to

integrate the data store directly in SPARQL queries.

3.5 Evaluation

The proposed approach aims to support users in the

modeling and management of information in the de-

velopment of co-simulation scenarios. For its evalua-

tion, the process was used in a ﬁeld study in the inter-

disciplinary project team of the project NEDS. Alto-

gether, 28 scientiﬁc researchers participated and used

the information model to model a simulation scenario,

which integrated several simulation models from dif-

ferent domains and to evaluate the results of simula-

tion. The members of the project team came from the

domains energy, computer science, business admin-

istration, economics, electrical engineering, and psy-

chology. 29 domain objects, 231 attributes, and 19

sustainability criteria where modeled with their de-

pendencies and data ﬂows in the information model

and transformed to RDF automatically for further us-

age. Based on the RDF representation of the informa-

tion model SPARQL queries where used to check for

completeness and correctness of the modeled infor-

mation. As the project partners were mostly not from

computer science, we deﬁned the SPARQL queries to

get the needed information. The implementation of a

GUI to enable the users to do this themselves would

be interesting future work.

The ﬁeld study showed that the process was help-

ful to include domain experts in the design of simula-

tion scenarios. The use of the information model al-

lowed easy participation and offered a central model.

Also, the semantic diagram was supportive as tool

for discussion in the project team. The ﬁeld study

showed that not all participating domains have ontolo-

gies which could be referenced for deﬁnition of terms.

However, the modeling of the information model im-

proved the processes in the interdisciplinary project

team during the development of energy scenarios and

helped signiﬁcantly making clear the terminology.

4 CONCLUSION

In this paper, we described an approach for the on-

tological integration of semantics and domain knowl-

edge in the process of planning, execution, and eval-

uation of interdisciplinary co-simulation of the en-

ergy system. Our approach incorporates the SEP and

its information model representing the process and

providing the ontological structures for the modeling

of energy scenarios using co-simulation. It can be

instantiated in collaboration of interdisciplinary do-

main experts and allows to integrate external ontolo-

gies for deﬁnition of terms and referencing external

works. The modeling of the scenarios in the informa-

tion model allows also the integration in data manage-

ment of scenario parametrization and results.

Also, a catalog of simulation components in a

SMW was integrated to assist the simulation expert

ﬁnding suitable simulation model during the planning

of co-simulation. The integration of the approach in

the co-simulation framework mosaik has been imple-

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134

mented prototypical. This prototype uses the informa-

tion model and simulation model speciﬁcation from

the SMW to validate simulation scenarios. Based on

this validation, also wrong connections of simulation

components should be found in the future. These

could be corrected by automatically added conver-

sions.

ACKNOWLEDGEMENTS

The research project ’NEDS Nachhaltige Energiev-

ersorgung Niedersachsen’ acknowledges the support

of the Lower Saxony Ministry of Science and Culture

through the ’Nieders

achsisches Vorab’ grant program

(grant ZN3043).

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