What is MoVeS ?

Transportation systems, power networks, biological systems, communication networks and other large-scale systems often involve the interaction between continuous, discrete and stochastic phenomena. In systems theory and computer science, stochastic hybrid systems have been developed as an ideal framework for capturing the intricacies of complex, large-scale systems.

Considerable research effort has been devoted to the development of modeling, analysis and control methods for stochastic hybrid systems, both in computer science (giving rise to theorem proving and model checking methods) and in control engineering (giving rise to optimal control and randomized methods).

Despite several success stories, however, none of the methods currently available are powerful enough to deal with real life, large-scale applications. We believe a key reason for this is that different communities have developed these methods for various applications in relative isolation. As a consequence, potential synergies between these methods have never been fully explored.

In October 2010, the MoVeS project received three-year funding from the European Commission's Seventh Framework Programme to develop methods and computational tools to support the modeling, analysis and control of stochastic hybrid systems. Specifically, our aims are to establish links between existing methods of model checking, theorem proving, optimal control and randomized methods, and to leverage their complementary strengths to enable novel applications for large-scale, complex systems.

In this context, common case studies from the area of power networks will provide a fertile testing ground for the fundamental developments.

Stable operation of the electric power system is crucial to any advanced society. And yet the safety, stability and efficiency of our power networks are affected by many uncertainties, including fluctuating demands, unforeseen events such as natural failures or acts of malice, and, more recently, uncertainty in power production due to the push to integrate renewable sources of energy and distributed generation into existing grids.

This combination of continuous dynamics (e.g. the evolution of voltages, frequencies), discrete dynamics (e.g. changes in network topology), and probability (e.g. uncertainty about power demand and supply) make power systems an ideal testing ground for stochastic hybrid systems methods.

The MoVeS project will explore the potential impact of our novel methodological developments on this important application.

Key Deliverables

  • Report on modeling framework, model composition, and bisimulation notions
  • Final report on model predictive control for stochastic hybrid systems
  • Final report on modeling, analysis, impact and potential exploitation