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Found 6 papers, 1 papers with code
A Convergence Predictor Model for Consensus-based Decentralised Energy Markets
This letter introduces a convergence prediction model (CPM) for decentralized market clearing mechanisms.
Gaussian Process-based Approach for Bilevel Optimization in the Power System -- A Critical Load Restoration Case
Then the two layers in the bilevel problem can be solved separately by the power system and its followers.
A Gaussian Process-based Price-Amount Curve Construction for Demand Response Provided by Internet Data Centers
For a Demand Response (DR) program with internet data centers (IDC), the Price-Amount curve that estimates how the potential DR amount depends on the DR price determined by power systems is crucial.
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.
Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function -- Part I: Theoretical Framework and Off-line Study
This paper introduces a novel framework to construct the region of attraction (ROA) of a power system centered around a stable equilibrium by using stable state trajectories of system dynamics.
