Modeling, Verification, and Control of Complex Systems for Energy Networks
Power networks (or generally energy networks) are systems of great societal and economic relevance and impact, particularly given the recent growing emphasis on environmental issues and on sustainable substitutes (renewables) to traditional energy sources (coal, oil, nuclear).
The aim of this Dagstuhl seminar is to survey existing and explore novel formal frameworks for modeling, analysis and control of complex, large scale cyber-physical systems, with emphasis on applications in power networks. Power networks also represent systems of considerable engineering interest, since:
- they can be large-scale and involve various devices interconnected in a complex manner,
- they are heterogeneous, that is they can be naturally modeled through a combination of continuous dynamical elements (to capture the evolution of quantities such as voltages, frequencies and generation output) and discrete dynamical components (to capture changes in the network topology, controller logic, state of breakers, isolation devices, transformer taps, etc.),
- they involve substantial stochastic components. Sources of uncertainty traditionally considered in power networks include hardware faults and unforeseen events, as well as stochasticity arising from continuous processes, particularly power demand. Furthermore, the increasing availability of renewable energy sources (e.g. photovoltaic panels, wind turbines, etc.) implies that uncertainty (for example, uncertainty in weather forecasts) also enters at the power supply side,
- some variables are only partially observable due to the absence of real-time sensing circuitry in large parts of the existing power distribution network.
Stochastic hybrid systems (SHS) stand for a mathematical framework that allows capturing the complex interactions between continuous dynamics, discrete dynamics, and probabilistic uncertainty. In the context of power networks, stochastic hybrid dynamics arises naturally: (i) continuous dynamics models the evolution of voltages, frequencies, etc.; (ii) discrete dynamics models controller logic and changes in network topology (unit commitment); and (iii) probability models the uncertainty about power demand, power supply from renewables and power market price.
The seminar will cover relevant approaches to modeling and analysis of stochastic hybrid dynamics, in the context of energy networks. It will thus foster cross-fertilization between techniques originating from disparate scientific communities, with the seminar hosting contributions from a number of different fields, such as the computer sciences, systems and control theory, power systems and probability theory. Bridging the gap and providing formal links between the different classes of methods will help with the goal of developing novel methodological approaches that are powerful enough to deal with complex, dynamical systems. The participation of practitioners and researchers from the field of power networks will enable exploration of the benefits offered by the use of such complex models and methods.
- Modelling / Simulation
- Optimization / Scheduling
- Semantics / Formal Methods
- Analysis / control / verification of complex stochastic systems
- Formal synthesis
- Reliability engineering and assessment
- Energy networks