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Found 7 papers, 2 papers with code
End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting
For the first time, MM is combined with E2E training of the model that minimises the expected total system cost.
MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response
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 limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption.
Regularised Learning with Selected Physics for Power System Dynamics
To address the computational time problem of conventional dynamic security assessment tools, many machine learning (ML) approaches have been proposed and well-studied in this context.
Deep Statistical Solver for Distribution System State Estimation
While data-driven alternatives based on Machine Learning models could be a choice, they suffer in DSSE because of the lack of labeled data.
Learning to run a power network with trust
We first advance an agent with the ability to send to the operator alarms ahead of time when the proposed actions are of low confidence.
A Machine-learning based Probabilistic Perspective on Dynamic Security Assessment
Probabilistic security assessment and real-time dynamic security assessments (DSA) are promising to better handle the risks of system operations.
Sample-Derived Disjunctive Rules for Secure Power System Operation
Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems.
