Data-Driven Stochastic AC-OPF using Gaussian Processes
The thesis focuses on developing a data-driven algorithm, based on machine learning, to solve the stochastic alternating current (AC) chance-constrained (CC) Optimal Power Flow (OPF) problem. Although the AC CC-OPF problem has been successful in academic circles, it is highly nonlinear and computationally demanding, which limits its practical impact. The proposed approach aims to address this limitation and demonstrate its empirical efficiency through applications to multiple IEEE test cases. To solve the non-convex and computationally challenging CC AC-OPF problem, the proposed approach relies on a machine learning Gaussian process regression (GPR) model. The full Gaussian process (GP) approach is capable of learning a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertain inputs. The proposed approach uses various approximations for GP-uncertainty propagation. The full GP CC-OPF approach exhibits highly competitive and promising results, outperforming the state-of-the-art sample-based chance constraint approaches. To further improve the robustness and complexity/accuracy trade-off of the full GP CC-OPF, a fast data-driven setup is proposed. This setup relies on the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty.
Code (1)
Tasks
Gaussian ProcessesGPRMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel
Neural Processes (NPs) are a class of stochastic processes parametrized by neural networks. Unlike traditional stochastic processes (e.g., Gaussian processes), which require specifying explicit kernel functions, NPs impl…
DiversityGaussian ProcessesA probabilistic data-driven model for planar pushing
This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes …
Gaussian ProcessesmodelGlobal universality via discrete-time signatures
We establish global universal approximation theorems for non-anticipative and general path-dependent functionals on spaces of piecewise linear paths, stating that linear functionals of the corresponding signatures are de…
Random ReLU Neural Networks as Non-Gaussian Processes
We consider a large class of shallow neural networks with randomly initialized parameters and rectified linear unit activation functions. We prove that these random neural networks are well-defined non-Gaussian processes…
Gaussian ProcessesImproving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes
We propose a simple method that combines neural networks and Gaussian processes. The proposed method can estimate the uncertainty of outputs and flexibly adjust target functions where training data exist, which are advan…
Gaussian ProcessesVariational Inference