50 citations · 60 across the 8 of their papers we have counts for
8 papers
End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting
Rushil Vohra, Ali Rajaei, Jochen L. Cremer
With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important. Th…
Exploring Operational Flexibility of Active Distribution Networks with Low Observability
Demetris Chrysostomou, Jose Luis Rueda Torres, Jochen Lorenz Cremer
Power electronic interfaced devices progressively enable the increasing provision of flexible operational actions in distribution networks. The feasible flexibility these devices c…
MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response
Jasper van Tilburg, Luciano C. Siebert, Jochen L. Cremer
This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limit…
Regularised Learning with Selected Physics for Power System Dynamics
Haiwei Xie, Federica Bellizio, Jochen L. Cremer +1
Due to the increasing system stability issues caused by the technological revolutions of power system equipment, the assessment of the dynamic security of the systems for changing…
Deep Statistical Solver for Distribution System State Estimation
Benjamin Habib, Elvin Isufi, Ward van Breda +2
Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. W…
Learning to run a power network with trust
Antoine Marot, Benjamin Donnot, Karim Chaouache +4
Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requir…
A Machine-learning based Probabilistic Perspective on Dynamic Security Assessment
Jochen L. Cremer, Goran Strbac
Probabilistic security assessment and real-time dynamic security assessments (DSA) are promising to better handle the risks of system operations. The current methodologies of secur…
Sample-Derived Disjunctive Rules for Secure Power System Operation
Jochen L. Cremer, Ioannis Konstantelos, Simon H. Tindemans +1
Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems. In this paper we move beyond the…