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Found 5 papers, 0 papers with code
Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies
This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies.
A Convergence Predictor Model for Consensus-based Decentralised Energy Markets
This letter introduces a convergence prediction model (CPM) for decentralized market clearing mechanisms.
Graph-Structured Kernel Design for Power Flow Learning using Gaussian Processes
This paper presents a physics-inspired graph-structured kernel designed for power flow learning using Gaussian Process (GP).
Gaussian Process Learning-based Probabilistic Optimal Power Flow
In this letter, we present a novel Gaussian Process Learning-based Probabilistic Optimal Power Flow (GP-POPF) for solving POPF under renewable and load uncertainties of arbitrary distribution.
Non-parametric Probabilistic Load Flow using Gaussian Process Learning
In this work, we propose a non-parametric probabilistic load flow (NP-PLF) technique based on the Gaussian Process (GP) learning to understand the power system behavior under uncertainty for better operational decisions.
