Modeling and Control of Multi-Energy Dynamical Systems: Hidden Paths to Decarbonization
This paper points out some key drawbacks of today's modeling and control underlying hierarchical electric power system operations and planning as the hidden roadblocks on the way to decarbonization. We suggest that these can be overcome by enhancing today's information exchange and control. This can be done by revealing and utilising inherent structure-preserving features of complex physical systems, and, based on this, by establishing multi-layered energy modeling. Each module (component, control area, non-utility-owned entities) can be characterized in terms of its interaction variable, and higher level models can be used to understand the interaction dynamics between different modules. Once the structure is understood, we propose nonlinear energy control for these modules which supports feed-forward self-adaptation to ensure feasible interconnected system. Based on these technology agnostic structures it becomes possible to expand today's Balancing Authorities (BA) to multi-layered interactive intelligent Balancing Authorities (iBAs) and to introduce protocols for flexible utilization of diverse technologies over broad ranges of temporal and spatial conditions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Controlled oscillation modeling using port-Hamiltonian neural networks
Learning dynamical systems through purely data-driven methods is challenging as they do not learn the underlying conservation laws that enable them to correctly generalize. Existing port-Hamiltonian neural network method…
Distributed component-level modeling and control of energy dynamics in electric power systems
The widespread deployment of power electronic-based technologies is transforming modern power systems into fast, nonlinear, and heterogeneous systems. Conventional modeling and control approaches, rooted in quasi-static …
Implicit energy regularization of neural ordinary-differential-equation control
Although optimal control problems of dynamical systems can be formulated within the framework of variational calculus, their solution for complex systems is often analytically and computationally intractable. In this Let…
Cognitive Energy Modeling for Neuroadaptive Human-Machine Systems using EEG and WGAN-GP
Electroencephalography (EEG) provides a non-invasive insight into the brain's cognitive and emotional dynamics. However, modeling how these states evolve in real time and quantifying the energy required for such transiti…
Comparing Approaches to Distributed Control of Fluid Systems based on Multi-Agent Systems
Conventional control of fluid systems does not consider system-wide knowledge for optimising energy efficient operation. Distributed control of fluid systems combines reliable local control of components while using syst…
Deep Reinforcement LearningModel Predictive Control