Model Driven Software Engineering for Grid Modeling, Optimization

and Simulation

Marcello Vitaletti

, Nicola Fontana

, Maurizio Giugni

and Gianluca Sorgenti degli Uberti

IBM Rome Software Laboratory, IBM Italia, Via Sciangai 53, 00144 Rome, Italy

Department of Engineering, University of Sannio, Piazza Roma 21, Benevento, Italy

Department of Hydraulic, Geotechnical and Environmental Engineering, University of Naples Federico II,

Via Claudio 21, 80125 Naples, Italy

ARIN S.p.A., Via Argine 929, 80147, Naples, Italy

Keywords: Model Driven Engineering, Domain Specific Languages, Model Transformations, Smart Grids.

Abstract: A three-year research project about water grid technology (“WATERGRID research project”) is led by

ARIN – the company managing water distribution in the city of Naples – in a partnership with IBM and the

University of Naples. Objectives of the project's initial phase include designing and prototyping a subsystem

for grid modelling, optimization and simulation (GMOS). The GMOS subsystem implements state-of-the-

art software kernels for the simulation of water distribution networks, including modules for the calibration

of the hydraulic model and for an optimal partitioning of the grid. This paper illustrates general findings in

applying model-driven software engineering to the architecture and design of the GMOS subsystem which

largely abstract from the specific nature of the distribution grid as they could equally apply to the modelling,

optimization and simulation of gas and electricity distribution networks.

1 INTRODUCTION

This paper illustrates general findings in applying

model-driven software development technology to

the architecture, design and implementation of a grid

modeling, optimization and simulation (GMOS)

subsystem in the context of a wider project (the

“WATERGRID research project”) aimed to an

intelligent management of water distribution

networks.

These findings largely abstract from the specific

nature of the distribution grid as they would likely

apply to the modelling, optimization and simulation

of gas and electricity distribution networks.

Introducing computer simulations for managing

and optimizing a smart grid generally comes with

the following requirements:

 The grid topology used in simulations must

reflect changes in the field which are stored in

a geographical information system (GIS) or in

enterprise asset management (EAM) systems.

 Parameter values characterizing the physical

model of grid components (e.g. tanks, pipes,

valves, pumps and turbines in a water grid)

can be derived from asset/management data

 but often they must be the output of a

calibration procedure.

 Automation and control logic installed on

field devices must be reflected in a

corresponding control model for use during

the simulation.

 Initial conditions to be used for predictive

simulations in a critical situation should be

created from sensor data collected in real-time.

 Boundary conditions must be provided as well

and they often include the estimated demand

(of water, gas or electricity) at distribution

nodes. Demands must be therefore obtained

by statistically analysing time series of end-

users consumption data.

 Computing the energy costs of grid operations

requires a model of energy consumption and

generation for different grid devices (e.g. for

pumps and turbines in a water grid).

It follows from the preceding requirements that

an effective use of computer simulations in the

operation of smart grids needs several independent

and complementary models. The following platform

independent models are relevant for water grids:

 Grid Topology model;

324

Vitaletti M., Fontana N., Giugni M. and Sorgenti degli Uberti G..

Model Driven Software Engineering for Grid Modeling, Optimization and Simulation.

DOI: 10.5220/0004028003240327

In Proceedings of the 7th International Conference on Software Paradigm Trends (ICSOFT-2012), pages 324-327

ISBN: 978-989-8565-19-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

 Physical model;

 Automation/Control model;

 Initial Conditions model;

 Boundary Conditions model;

 Energy model.

In order to ascertain which collection of model

instances a given simulation will be (or it was) based

upon, model instances corresponding to the different

parts (topology, physics, etc.) must have distinct

identities (name, version). Model instances

corresponding to a given part generally evolve

independently from the other parts.

The global model instance being used for grid

simulations in a specific context or at a given time is

therefore the composition of several sub-domain

model instances. As a most obvious example: the

same grid topology and physical model instances

may be employed in many simulations with different

initial and boundary conditions.

The following section illustrates application

scenarios from the “WATERGRID research project”

providing motivations for the proposed modelling of

the problem domain.

Software engineering aspects of GMOS

development are discussed in the central section of

this paper which deals with partial models, cross-

references, model authoring and domain specific

languages.

2 APPLICATION SCENARIOS

This section illustrates two use cases from the

WATERGRID project providing motivations for the

proposed modelling of the problem domain.

2.1 Calibration of the Hydraulic Model

Calibration is the process by which the hydraulic

model parameters (generally the pipe roughness) are

estimated by using the available sensor data. The

simplest method is to calculate the covariance and

the sensitivity matrix of the state parameters by

assuming a preliminary estimate of the roughness,

which can be assigned according to the pipe material

and coating, age and water and soil characteristics.

The optimal sensor location problem (sampling

design) can be solved by running a simulation based

on the current estimate of the hydraulic parameters

and different boundary conditions e.g. corresponding

to minimum, maximum and average demand

patterns (Yu and Powell, 1994; Bush and Uber,

1998; Del Giudice and Di Cristo, 2003). Other

approaches (i.e. shortest path algorithms) calculate

the optimal location based on the network topology

(de Schaetzen et al., 2000). In this case, no initial

estimate of the parameters is required, and the sensor

location can be obtained without running any

simulation. Nevertheless, a certain objective

function has to be maximized (or minimized) and

optimization algorithms should be used. Data

collected from the sensors are then used to find the

best estimates for the unknown parameters

(roughness coefficients). To this aim, optimization-

based, explicit or implicit methods can be used

(Savic et al., 2009).

In summary, the calibration scenario is one

where simulations need to be repeatedly run for

different purposes. Model instances representing the

grid topology and initial conditions may be reused

without changes in these simulations with boundary

conditions chosen from a representative set of model

instances. Finally, the physical model is typically

updated at each step of an iterative process.

2.2 Running Predictive Simulations

In this scenario, the validated topology and physical

models are used with newly created models for the

initial and boundary conditions. The latter must be

created from field sensor data in order to support

reliable predictive simulations under new operating

conditions. For example, this is necessary when

analysing abnormal operating conditions which may

occur in a water distribution system in case of break

of transmission mains, for fire hydrant service, for

simulating the travel times of a pollutant

accidentally or intentionally injected into the system.

Moreover, predictive simulations can be used for

design of extension and/or rehabilitation of existing

water distribution networks.

3 PARTIAL MODELS, CROSS-

REFERENCES, AUTHORING

Cross-referencing across models is required to

combine the information contained in models of the

physics, energy, initial and boundary conditions of

grid objects with their topological relationships.

Simulation codes typically need to process all

this information at once in each run. Therefore, one

must always provide an exporter where cross-model

references are resolved and information is produced

in the format required for running the simulation. A

similar situation arises with authoring tools, because

different aspect of the same grid must be handled

together in a given authoring sessions.

Model Driven Software Engineering for Grid Modeling, Optimization and Simulation

325

3.1 Late Binding of Cross-References

The adopted model partitioning strategy is best

illustrated by considering a specific element of the

problem domain – a junction – and examining how

different attributes and relationships of this element

are distributed across the modelling packages.

The diagram in Figure 1 shows classes from the

topology model – in the yellow box – and from the

physical and boundary-conditions models in the red

and green boxes, respectively. The topology model

captures the fact that a Junction is a grid Node.

Figure 1: Modelling classes related to a grid junction.

The Junction class is not extended by classes

at the diagram's bottom because the latter only

capture specific complementary data. In the spirit of

model partitioning they cannot incorporate the

topological content expressed by the Node class.

With reference to Figure 1, it is desirable for an

instance of say JunctionModel to provide a

method returning a reference to the corresponding

Junction/Node instance. The requirement can be

satisfied by a form of late binding in which only a

“name reference” (string) to the corresponding

Junction object is included in JunctionModel.

While the non-topological JunctionModel

cannot extend Junction there are situations in

which it might be desirable for it to implement the

Node interface, for example to make instances

behave like ordinary nodes on a graphical map.

These situations are addressed by a variant of the

well known Decorator pattern, where a class

(JunctionModel) delegates the implementation of

an additional interface (Junction) to an object

natively implementing that interface. The solution is

illustrated in Figure 2 for a junction grid object: at

runtime, a reference to the Junction object is

Figure 2: A variant of the Decorator pattern.

injected into the decorated name reference and used

for delegating methods exposed by the topological

(Junction) interface.

In summary, name references implement the

necessary late binding between model objects

capturing different dimensions of a domain element

and the corresponding topology object (Node or

Link). Additionally, name references may be

decorated to make a non-topological object behave

in a GUI like its node (or link) counterpart.

3.2 Model Authoring

Authoring tools are used to manually fill-in some

missing data or to freely define all dimensions of an

experimental test case. Dealing with multiple

underlying model instances or files in the same

logical session is impractical, therefore GMOS

editors use a single-file representation of the

information which would be otherwise distributed

over instances of the partial models discussed in the

previous section. This is obtained by introducing a

an authoring model where all dimensions of a grid

element are represented by a single class (logically

an adapter). Transformations are also provided for:

 Merging model instances from the persistence

layer into an instance of the authoring model;

and

 Exporting an instance of the authoring model

into instances of the partial models for storing

changes in the persistence layer.

A wealth of EMF and related Eclipse tooling can

be exploited with the GMOS domain models. In

particular Graphical Editing Framework (GEF), the

Graphical Modeling Framework (GMF) and Xtext

are well known open-source frameworks by which it

is possible and practical to develop domain specific

languages (DSL).

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3.2.1 Graphical DSL Editors in GMOS

A convenient approach implemented in GMOS

separates graphical editing of the whole topology

from (textual or graphical) editing of information

related to grid elements (nodes or links). The spatial

arrangement of grid objects (nodes) and connections

(pipes) is rendered by adapting the grid topology

model for display as a graph with GEF/GMF.

Data associated to all dimensions (including

spatial data already captured by the graph editor) are

presented in a “grid object editor”. This is a form-

based multi-tab editing panel with one field for each

attribute of the corresponding grid object.

3.2.2 Text DSL Editors in GMOS

Text based editors extend the form-based object

editor described in the previous section. In this case

different text editors are defined, each one targeted

to a specific object type (pump, valve, etc.) but

covering all dimensions (including spatial data). A

grammar for each editor is generated in Xtext from

the corresponding object type in the authoring model.

The following complication is inherent and worth

noting in this approach: being the editor's scope a

single object, the editor's target resource (text file) is

only a temporary object whose content must be

initialized from (and saved back to) the underlying

instance of the authoring (global) model.

4 CONCLUSIONS

This paper illustrates some peculiarities emerging

from the use of model driven software engineering

in applications aimed to the modelling, simulation

and optimization of water grids. The proposed

approach is motivated by the requirement – likely

applying to other smart grids – of supporting an

independent life-cycle of model instances associated

to different modelling dimensions. Significant

impacts of this requirement were found in the areas

of modelling, authoring and persistence.

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Steinberg, D., Budinsky F., Paternostro M., Merks E.,

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Gronback, C. R., 2009. Eclipse Modeling Project – A

Domain-Specific Language (DSL) Toolkit, Addison

Wesley.

Yu, G., Powell, R. S., 1994. Optimal design of meter

placement in water distribution systems, International

Journal of Systems Science 25 (12), 2155–2166.

Bush, C. A., Uber, J. G., 1998. Sampling design methods

for water distribution model calibration, Journal of

Water Resources Planning and Management 124 (6),

334–344.

Del Giudice, G., Di Cristo, C. (2003). Sampling design for

water distribution networks, Proceedings of II Int.

Conference on Water Resources Management, Las

Palmas, WIT-Press, pp.181-204

Del Giudice, G., Di Cristo, C. (2003). Nodal sensitivity

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Savic, D. A., Kapelan, Z., Jonkergouw, P. M. R., 2009.

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