Related papers: Modeling and Control of Multi-Energy Dynamical Sys…
The rapid development of modern energy applications drives an urgent need to enhance the dielectric strength of energy storage dielectrics for higher power density. Interface design is a promising strategy to regulate the crucial charge…
The challenging problems, in the field of control of chaos or of transition to chaos, lie in the domain of infinite-dimensional systems. Access to all variables being impossible in this case and the controlling action being limited to a few…
Coupling constitutes a foundational mechanism in the Earth system, regulating the interconnected physical, chemical, and biological processes that link its spheres. This review examines how emerging artificial intelligence (AI) methods…
Hydrogen energy plays an important role in the transformation of low-carbon energy, and electric hydrogen coupling will become a typical energy scenario. Aiming at the operation flexibility of low-carbon electricity hydrogen coupling system…
The paradigm of layered networks is used to describe many real-world systems -- from biological networks, to social organizations and transportation systems. Recently there has been much progress in understanding the general properties of…
Various studies have been conducted in the fields of sustainable operations management, optimization, and wastewater treatment, yielding unsubstantiated recovery. In the context of Europes climate neutrality vision, this paper reviews…
Buildings account for a substantial portion of global energy consumption. Reducing buildings' energy usage primarily involves obtaining data from building systems and environment, which are instrumental in assessing and optimizing the…
The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy…
The quantification of controllability and observability has recently received new interest in the context of large, complex networks of dynamical systems. A fundamental but computationally difficult problem is the placement or selection of…
Integration of electronics-based residential appliances and distributed energy resources in homes is expected to rise with grid decarbonization. These devices may introduce significant harmonics into power networks that need to be closely…
In order to meet ever-stricter climate targets and achieve the eventual decarbonization of the energy supply of German industrial metropolises, the focus is on gradually phasing out nuclear power, then coal and gas combined with the…
Transitioning to sustainable and resilient energy systems requires navigating complex and interdependent trade-offs across environmental, social, and resource dimensions. Neglecting these trade-offs can lead to unintended consequences…
Smart energy networks provide for an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for deep decarbonisation of energy production. However, given the variability of…
Maintaining energy balance and economical operation is significant for multi-energy systems such as the energy hub. However, it is usually challenged by the frequently changing and unpredictable uncertainties at different timescales. Under…
Effective control requires knowledge of the process dynamics to guide the system toward desired states. In many control applications this knowledge is expressed mathematically or through data-driven models, however, as complexity grows…
Modeling and evaluation of automated vehicles (AVs) in mixed-autonomy traffic is essential prior to their safe and efficient deployment. This is especially important at urban junctions where complex multi-agent interactions occur. Current…
Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with…
Control of a dynamical system without the knowledge of dynamics is an important and challenging task. Modern machine learning approaches, such as deep neural networks (DNNs), allow for the estimation of a dynamics model from control inputs…
We investigate the possible performance gains of power control in multi-layer cellular systems where microcells and picocells are distributed within macrocells. Although multilayers in cellular networks help increase system capacity and…
Integrating the heterogeneous controllers of a complex mechanical system, such as a mobile manipulator, within the same structure and in a modular way is still challenging. In this work we extend our framework based on Behavior Trees for…