=Paper=
{{Paper
|id=None
|storemode=property
|title=Construction of Data Streams Applications from Functional, Non-Functional and Resource Requirements for Electric Vehicle Aggregators. The COSMOS Vision
|pdfUrl=https://ceur-ws.org/Vol-1160/paper20.pdf
|volume=Vol-1160
|dblpUrl=https://dblp.org/rec/conf/apn/BanaresTTACC14
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==Construction of Data Streams Applications from Functional, Non-Functional and Resource Requirements for Electric Vehicle Aggregators. The COSMOS Vision==
https://ceur-ws.org/Vol-1160/paper20.pdf
Construction of Data Streams Applications from
Functional, Non-Functional and Resource
Requirements for Electric Vehicle Aggregators.
The COSMOS Vision
J.A. Bañares, R. Tolosana-Calasanz, F. Tricas, U. Arronategui, J. Celaya, and
J.M. Colom
Depto de Informática e Ingeniería de Sistemas - Universidad de Zaragoza
banares@unizar.es
Abstract. COSMOS, Computer Science for Complex System Model-
ing, is a research team that has the mission of bridging the gap between
formal methods and real problems. The goal is twofold: (1) a better
management of the growing complexity of current systems; (2) a high
quality of the implementation reducing the time to market. The COS-
MOS vision is to prove this approach in non-trivial industrial problems
leveraging technologies such as software engineering, cloud computing, or
workflows. In particular, we are interested in the technological challenges
arising from the Electric Vehicle (EV) industry, around the EV-charging
and control IT infrastructure.
Keywords: Continuous Data Streams, Executable High-Level Specification, Schedul-
ing, Non-functional specifications, Distributed Systems, Resource Allocation Systems,
Cloud Computing, Smart Grid, Electric Vehicle
1 Introduction
Electric Vehicles (EVs) give rise to computational challenging problems. EVs
will be plugged into the electric infrastructure (distribution networks) for the
re-charging of their batteries, and they will share capacity with other users of
electricity. On other hand, electricity distribution and generation infrastructures
are currently turning into Smart Grids for more efficient management. From a
computational point of view, this involves demand forecast methods, state es-
timation techniques, and real-time monitoring, leading to communication and
control of residential and commercial areas taking place at a fine granularity.
Critical characteristics of the involved computational problems are related to
large volumes of data being generated in a distributed, stream fashion, and
real-time –which is often agnostic of demand variation in a given geographical
area. The main scientific and technical objectives are: 1) The development of a
methodology for the construction of applications for Continuous Data Streams
Processing. It must cover all Software Engineering phases of the life cycle, and
334 PNSE’14 – Petri Nets and Software Engineering
must be able to address functional and non-functional requirements together
with the specification of the execution infrastructure and the involved resources.
2) The previous methodology requires the definition of an executable specifica-
tion language across all the software architectural levels. It will support modular
and hierarchical specification of these types of applications, together with the as-
sociated tools, analysis, verification, simulation, implementation, execution and
monitoring. A set of mechanisms, taking into account different architectural con-
figurations that can be used in the implementation, will be designed to support
the studied policies and mechanisms. 3) Analysis, design and development of
a proof of concept Autonomic Smart Energy Management System for Electric
Vehicles infrastructure management.
2 Expected Scientific Impact
Recent studies estimate that uncontrolled re-charging processes of EV batter-
ies can lead to significant increase in the electricity demand peaks. Moreover,
they anticipate that EVs will impact the local level, where hotspots will be cre-
ated depending on how EVs cluster within a particular geographical location.
These hotspots may eventually overload the low voltage distribution networks.
The re-charging of EV batteries will generate large-scale volumes of informa-
tion, in a distributed fashion, that need to be processed. This project looks for
computational solutions to manage the large amounts of re-charging informa-
tion coming from the EVs. It uses a single computational infrastructure that
enforces an established Service Level Agreement, while adapting the computa-
tional power for the processing of the information. From a socioeconomic point of
view, the expected impact is: (1) The computational solutions can be exploited
for the managing of EVs in the electric infrastructure. Large-amounts of infor-
mation streamed by smart meters can be required to be processed in real-time
when EVs re-charge their batteries. (2) As a result, there will be a substantial
reduction of computational resources and, as a consequence, of energy in their
management as well. Additionally, a substantial reduction of human intervention
is expected, saving costs to companies. (3) The project falls within the social
challenge 4 of the Horizon 2020 program of the European Union: "Smart, Green
and Integrated Transport." (4) The results of the research shall also apply to
decision-making systems of healthcare systems. (5) The results of the research
may also apply to different data processing applications with real-time needs,
that can be of interest for highly qualified SMEs, enabling them to become a
media company to data process.
3 Collaboration
In this endeavour, we are looking for partners that can help us to construct solid
collaboration structures in order to apply for an European project proposal
within the EU Horizon 2020 framework.