VIMSEN: A Distributed Architecture for future Energy Market Frameworks

ABSTRACT

The current centralized framework of energy production and distribution prevents small electricity producers from participating actively in the electricity market. Their participation to larger energy associations so as to strengthen their position necessitates new Information & Communication Technologies (ICT), architectures and business models. The VIMSEN ("Virtual Microgrids for Smart Energy Networks") project proposes a highly dynamic and distributed framework for future energy markets, modifying the existing energy market ecosystem and introducing new market players. The framework is primarily based on the concept of Virtual Micro-Grids (VMGs) and on the active participation of renewable energy prosumers to the energy market while a number of ICT systems provide advanced functionalities at VMG aggregators' and prosumers' sides. This paper aims to present the high-level VIMSEN system architecture focusing on the identification of system entities, their functionalities and the required communication interfaces.

References

1. CEN-CENELEC-ETSI Smart Grid Coordination Group, Smart Grid Reference Architecture, November 2012. (online: http://ec.europa.eu/energy/sites/ener/files/documents/xpert_group1_reference_architecture.pdf)
2. DIRECTIVE 2009/72/EC of the European Parliament and of the Council, concerning common rules for the internal market in electricity and repealing Directive 2003/54/EC, July 13, 2009. OJ L 211, 14.8.2009, 55--93.
3. EU FP7-ICT-2013-11 VIMSEN STREP Project website, http://www.ict-vimsen.eu/
4. G. Lyberopoulos, E. Theodoropoulou, I. Mesogiti, P. Makris and E. Varvarigos, A Highly-Dynamic and Distributed Operational Framework for Smart Energy Networks, In Proceedings of the Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2014 IEEE 19th International Workshop, (Athens, Greece, December 01--03, 2014), 120--124. DOI= http://dx.doi.org/10.1109/CAMAD.2014.7033218
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Index Terms

• Computer systems organization

  • Architectures

    • Distributed architectures

  • Dependable and fault-tolerant systems and networks

• Computing methodologies

  • Machine learning

    • Machine learning approaches

      • Neural networks

• General and reference

  • Cross-computing tools and techniques

    • Performance

• Hardware

  • Emerging technologies

    • Circuit substrates

      • Cellular neural networks

• Networks

  • Network performance evaluation

• Software and its engineering

  • Software organization and properties

    • Software system structures

      • Distributed systems organizing principles

• Theory of computation

  • Theory and algorithms for application domains

    • Machine learning theory