Involved People : Bruno Sareni, Xavier Roboam
This topic aims to develop design approches that integrate in a structured way (either sequentially or simultaneously) the fundamental features of systems:
Architecture: the system and subsystem topology, the nature of the components (e.g. the type of storage element) as well as the technology associated with a given component
Sizing: scale effects, both in geometric and energy terms
Energy Mangagement: strategies for planning and controling power flows within the system
Environement: external factors influencing the system’s behavior (e.g. temperature, wind, or solar resources), which are inherently stochastic and intermittent, as well as the mission the system must accomplish (typically defined by the load profile to be satisfied)
A comprehensive and integrated design that accounts for all the couplings between these different aspects inevitably encounters a high level of complexity, which justifies the development of ‘systemic’ approaches. This complexity can be expressed along three dimensions:
The static complexity, related to the size of multi-source, multi-load systems, to the heterogeneity of the elements to be combined, and to the coupled disciplinary domains. This level of complexity also includes the numerous constraints and criteria that are now expected to be considered in system design: energy efficiency, integration (mass and/or volume), reliability and operational safety, lifespan, environmental impact, and economic cost.
The dynamic complexity, related to the dispersion of modes within systems, ranging from:
– Fast electrical modes (from less than a second to several minutes), affecting the control and management of dynamic storage devices such as electrochemical systems (batteries, supercapacitors) or flywheels.
– Electro-matter modes (from a few hours to several days), influencing material transfers (water or hydrogen) in large-scale storage devices (e.g., pumped hydro storage, redox flow batteries).
– Slow environmental evolution modes (from a few days to several months), associated with environmental cycles the system is exposed to (e.g., diurnal cycles and seasonal variation of solar irradiation). These modes are also characteristic of component aging processes within systems.
The resolution complexity, arising in particular from the increase in computation times of simulation models, is especially critical in an optimization context that requires a large number of system evaluations within its environment and mission framework. This resolution complexity is directly linked to the complexity of the models themselves, whose level of granularity is generally variable, thus determining the trade-off between accuracy and computational cost in the design process..