Search Results for author:
Found 9 papers, 0 papers with code
Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems
To this end, this paper presents the Physics-informed Actor-Critic (PI-AC) algorithm for coordination of Virtual Inertia (VI) from renewable Inverter-based Resources (IBRs) in power distribution systems.
Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systems
The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification.
Multi-energy Grid Expansion Planning Under Uncertainty: A Robust Optimization Approach
In response to these challenges, this paper proposes a novel approach to apply robust optimization methods in the integrated planning of multi-energy grids, to reduce the risk of investment in grid expansion and to optimize the use of different carbon-neutral energy carriers.
Towards a more comprehensive open-source model for interdisciplinary smart integrated energy systems
In order to make the integration of renewable energies as cost-effective, secure and sustainable as possible and to develop new paradigms for the energy system, many energy system models have been developed in research in the past to evaluate the solutions.
Optimized utilization of decentral flexibility for the operational management of cellular multi-modal distribution grids
Aim is to provide flexibility with a cell by optimally distributing a flexibility request to subordinate prosumers changing their active and reactive power.
Integrated Planning of Multi-energy Grids: Concepts and Challenges
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 increased use of renewable energy sources and employing hydrogen as a clean energy carrier.
Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems
Bayesian Physics-Informed Neural Networks (BPINNs) combine the advantages of Physics-Informed Neural Networks (PINNs), being robust to noise and missing data, with Bayesian modeling, delivering a confidence measure for their output.
Roadmap for Edge AI: A Dagstuhl Perspective
Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI.
A Survey on Predictive Maintenance for Industry 4.0
Production issues at Volkswagen in 2016 lead to dramatic losses in sales of up to 400 million Euros per week.
