=Paper=
{{Paper
|id=Vol-2019/docsymp_2
|storemode=property
|title=Model Driven Design of IT Systems for Smart Grids
|pdfUrl=https://ceur-ws.org/Vol-2019/docsymp_2.pdf
|volume=Vol-2019
|authors=David Oudart
|dblpUrl=https://dblp.org/rec/conf/models/Oudart17
}}
==Model Driven Design of IT Systems for Smart Grids==
https://ceur-ws.org/Vol-2019/docsymp_2.pdf
1
Model Driven Design of IT Systems for Smart
Grids
David Oudart∗†
∗ EDF R&D, Palaiseau, France
† SAMOVAR, CNRS, Université Paris-Saclay, Télécom SudParis, Évry, France
Abstract—Smart Grids are complex systems interfacing power the business processes have to be integrated to the simulations,
grids with information and communication technologies in order to synchronize the execution of the models and validate their
to automate decision making and balancing between production alignment to global and strategical concerns.
and consumption. In order to facilitate the IT engineering of these
systems, we aim to provide a framework relying on executable
modeling and simulation. Our main challenge is to interconnect II. R ELATED W ORK
models of the different business aspects of Smart Grids: physical
models of the Energy Management System (EMS), models In the electrical community, the challenge of simulating
of the telecommunication infrastructure and behaviour models Smart Grids is not new. [9] sets up an environment to cosim-
of the producers and consumers. Our solution complies with
Model Driven Engineering (MDE) principles. Indeed, we rely
ulate Smart Grids with distributed control. Their approach
on standards, like fUML for executable modeling and FMI for uses software and hardware-in-the-loop simulations, with real
cosimulation, and model transformations to improve reusability, controllers controlling a Matlab simulation of the plant through
scalability and consistency. Our framework will be tested on the UDP and TCP communications. They particularly addressed
case study of the EMS of an island power grid mixing battery the issue of the adaptation between event-driven and continu-
storage, renewable and fossil energy production.
ous components.
[5] explains how a power grid simulator should be designed
I. I NTRODUCTION in order “to accommodate the requirement for interoperabil-
A common way to see a Smart Grid is as an electric grid ity”. He developed his own power grid simulator based on
which is augmented by information and communication tech- numerical algorithms instead of only equations to combine
nologies, allowing prevention, better reactivity and improved discrete and continuous simulations in one engine. Sensors,
response to events such as electrical failures. As it involves controllers and electro-mechanical components are all mod-
many technical domains, a Smart Grid is a complex system eled and simulated with that engine and a component library
to design and simulation is therefore valuable to evaluate written in C++. The simulator also implements an interface
various behavioral assumptions. MDE principles are well- for time management and the injection of discrete data at
suited to address design and development issues of complex runtime. To integrate a communication simulator, the power
industrial systems by reasoning from executable models all grid models are wrapped in components for the OMNET++
along the life cycle of the system [3]. However there lacks or NS2 simulators. This approach is efficient but provides
a general approach to interconnect models from different a very specific solution for the cosimulation of smart grids
technical domains for complex systems. These models are de- and therefore lacks extensibility. New behaviors and control
signed using different tools (such as State machines, Activity equipments are defined by writing C++ code, so there is little
Diagrams, Modelica, Discrete Events, statistical models), and support for managing the refinement of the components during
the accuracy of the predictive value of the simulation depends design. Also, using code as models prevents the use of MDE
on the proper combination of their meaning and on the correct techniques for generating the wrappers used to interconnect
synchronization of their execution. It is therefore mandatory the different simulators and to check the consistency of the
to ensure the macroscopic alignment of the models with the whole simulation.
business processes, and to maintain the consistency of the [4] developed a framework called VPNet for Smart Grid
global model while refining the individual models toward an simulation. The VPNet framework provides a cosimulation
implementation. coordinator implemented in C# to interface the OPNET com-
This paper proposes a framework of methods and tools munication simulator with VTB, a simulator for power grids
based on MDE principles and focusing on computational with automated control. The two simulators are also both
and applicative development. IT models are integrated in extended with interface modules to allow exchanges with
simulations that execute all the models of the Smart Grid the coordinator. This approach takes into account only the
in order to perform predictive analysis. These models are communication and the power aspects of a grid. The control
refined towards their real implementation by successive model part is integrated in the electrical simulation and there is
transformations. To achieve these goals, it is necessary to no support for the IT aspect, which goes beyond “classical”
ensure that our models have consistent interfaces: they must control in smart grids. Moreover, this approach is tied to two
share a common meaning of the exchanged values. Moreover specific simulators while our goal is to build a framework
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allowing (with specific development effort) the integration
of any simulator. This is necessary because companies have
developed specific simulators for handling technical aspects
such as transients, harmonics and unbalanced networks. These
legacy tools capture business knowledge that can be used
during the design and analysis of a smart grid.
[6] notes that current Smart Grid simulation environments
generally focus on one domain of the system, and expresses the
need for a fully integrated environment handling multi-agent
control, and interactions between these agents and the power
system components. The paper presents a list of requirements
for appropriate smart grid simulation tools, such as using time-
stepped simulations, or allowing different paradigms in the
models that are integrated. The authors conclude that there
is a general lack of interoperability, and particularly of “a Figure 1. Business process controlling a smart grid simulation
standardized and lightweight simulator API to enable syntactic
interoperability (how is data exchanged), a standardized se-
mantic description to achieve semantic interoperability (what the description of a dynamic behavior, in practice from our
is the meaning of exchanged data), and a well-defined model- experience the language is not adapted to complex behaviors
independent scenario description language (what components manipulating a large set of variables, as is the case in smart
are to be simulated and how are they interconnected)”. grid applications. Model transformations would enable the
The existing solutions for the simulation of smart grids refinement of a behavior from a basic one, in the early design
show what is technically possible and the kind of results we phases, to a more precise and relevant one by switching to a
can expect. However, we would like to go further and to more suitable paradigm and a different language. Hence we
provide support for an evolving simulation of smart grids, looked for a solution to delegate an operation from a fUML
from abstract models with coarse physical behaviors, up to model encapsulated in an FMU to a process in another FMU
very detailed models of the power grid, the telecommunication (figure 1), by synchronizing the execution and the results.
network, and the “smart” control algorithms. The requirements Using the “plug & play” property of an FMU, we should be
in [6] are also part of our goal. able to execute the delegated behavior along its successive
transformations and refinements, and eventually test the final
code of an IT application.
III. P ROPOSED S OLUTION The validation of IT behaviors would be ensured by the
Our primary concern is to address the diversity of modeling global cosimulation. IT, communications and power grid sim-
tools and languages. We choose to consider the FMI standard ulators are wrapped with FMI, and a Master Algorithm of
[1] to perform cosimulations, instead of implementing our cosimulation is in charge of managing interactions between
own ad-hoc connectors between simulation engines, in order FMU and synchronizing the time-stepped simulations. [9]
to benefit from the reliability of a standard and to improve showed that the size of the step interval can have a significant
the reusability of our models and the modularity of our impact on the accuracy of the results in case one or more
cosimulations. Moreover, the interfaces rely on primitives models follow an event-driven paradigm. [8] proposes some
types which are basic enough to be implemented in a large additions to the FMI standard to build a Master Algorithm
variety of domain specific languages (DSL), and the particular with an event detection strategy in order to adapt the time
format of FMU protects the intellectual property, valuable in step at best. The authors implemented this functionality in
the industrial context of Smart Grid development. the DACCOSIM multi-simulation open source software, so
Previous work in our team proposed a framework to model we chose to use it as our cosimulation environment.
and execute business processes in [7]. This framework relies
on the fUML standard, which is an executable subset of UML. IV. P LAN FOR VALIDATION
Class diagrams are used for data entities, and activity diagrams To validate our framework, we want to use it for the
for dynamic behaviors. They developed specific extensions for design and development of an EMS for the “Île de Sein”
the execution engine Moka allowing to integrate software in island power grid use case. Île de Sein is a french island
the loop, driven by the business process seen as an orchestrator, which has a power grid independent from the mainland, with
and then ensure Business and IT synchronization. its own production equipments. A diesel power plant is the
In our solution, the business process represents the whole main energy producer, and is complemented by a photovoltaic
dynamic behavior of the IT and its interactions with physical farm and battery storage to balance the production with the
models. We iterate on previous work by using FMI for the industrial and residential consumptions. Besides distributed
interactions between models, instead of specific connectors. control, the smartness of the grid is handled by a central EMS
Business processes are written in fUML and exported to which can make decisions based on the knowledge of the state
FMUs through Papyrus and its executable engine Moka, of all the components of the grid. One of the main purposes
as described in [2]. Although fUML theoretically supports of the software is to optimize the costs and production, by
3
minimizing diesel energy over renewable energy. The Telecom [3] J. Hutchinson, M. Rouncefield, and J. Whittle. Model-driven engineering
network links all the grid control systems to the EMS. IT practices in industry. In 2011 33rd International Conference on Software
Engineering (ICSE), pages 633–642, May 2011.
systems should be modeled starting from the business process, [4] W. Li, A. Monti, M. Luo, and R. A. Dougal. VPNET: A co-simulation
with as much automation as possible in order to ease the framework for analyzing communication channel effects on power sys-
implementation of the applications, to ensure business and IT tems. In 2011 IEEE Electric Ship Technologies Symposium, pages 143–
149, April 2011.
experts can work together. Then, the relevance of the models [5] J. Nutaro. Designing power system simulators for the smart grid:
should be analyzed through cosimulations of business, IT, Combining controls, communications, and electro-mechanical dynamics.
Telecom and electrical models. Finally, we want to validate the In 2011 IEEE Power and Energy Society General Meeting, pages 1–5,
July 2011.
consistency between IT models and their implementation, by [6] S. Rohjans, S. Lehnhoff, S. Schütte, F. Andrén, and T. Strasser. Require-
replacing in the cosimulation the EMS FMU by the final code ments for Smart Grid simulation tools. In 2014 IEEE 23rd International
of the EMS, itself wrapped in an FMU. In each cosimulation Symposium on Industrial Electronics (ISIE), pages 1730–1736, June 2014.
[7] Rachida Seghiri. Modeling and simulation of an Entreprise Architecture-
case, the global results of the cosimulation are our main Application to Smart Grids (in French). PhD thesis, Université Paris-
criteria to analyze and validate the behavior of the business and Saclay, July 2016.
IT components. It implies to consider that the physical models [8] J.-P. Tavella, M. Caujolle, S. Vialle, C. Dad, C. Tan, G. Plessis, M. Schu-
mann, A. Cuccuru, and S. Revol. Toward an Accurate and Fast Hybrid
are correct and that their behavior is known and determined. Multi-Simulation with the FMI-CS Standard. In 21 st International
Conference on: Emerging Technologies and Factory Automation (ETFA-
2016), Berlin, Germany, September 2016.
V. E XPECTED C ONTRIBUTIONS [9] C.-H. Yang, G. Zhabelova, C.-W. Yang, and V. Vyatkin. Cosimulation
Environment for Event-Driven Distributed Controls of Smart Grid. IEEE
The expected contributions of our work are a methodology Trans. Industrial Informatics, 9(3):1423–1435, 2013.
for designing and modeling smart grids in an executable way,
and tooling to support this methodology. The methodological
aspects aim to guide the designers in the construction of the
models of the different parts of the grid and in the definition
of the interfaces between these models. The tooling aspect
handles the executability of the models and the proper orches-
tration of their executions. It also handles the consistency of
several refinements of a model to allow the simulation of the
whole grid at different levels of detail.
VI. C ONCLUSION AND C URRENT S TATUS
So far we have mostly worked on the tooling aspects and
focused on a cosimulation involving only the power grid
and the business/IT models. Electrical components and their
automated control are modeled in Modelica, with a load-flow
approach (balance between power production and consump-
tion), and wrapped with FMI (OpenModelica and Dymola im-
plement the model export to FMU). We managed to simulate a
basic control of the grid FMU with a fUML process wrapped
in another FMU, which amounts to interfacing an event-based
process with a continuous physical system over FMI. This
work is necessary to determine what kind of information must
be available in the models, which has consequences on the
methodology used for their design. However, we have also
worked on some methodological aspects to take into account
the separation of different business types of knowledge in the
design of these models.
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