Data-driven optimal power flow: A physics-informed machine learning approach
This paper proposes a data-driven approach for optimal power flow (OPF) based on the
stacked extreme learning machine (SELM) framework. SELM has a fast training speed and …
stacked extreme learning machine (SELM) framework. SELM has a fast training speed and …
A survey on applications of machine learning for optimal power flow
F Hasan, A Kargarian… - 2020 IEEE Texas Power …, 2020 - ieeexplore.ieee.org
Optimal power flow (OPF) is at the heart of many power system operation tools and market
clearing processes. Several mathematical and heuristic approaches have been presented in …
clearing processes. Several mathematical and heuristic approaches have been presented in …
Real-Time Optimal Power Flow: A Lagrangian Based Deep Reinforcement Learning Approach
High-level penetration of intermittent renewable energy sources has introduced significant
uncertainties and variabilities into modern power systems. In order to rapidly and …
uncertainties and variabilities into modern power systems. In order to rapidly and …
Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods
Abstract The Optimal Power Flow (OPF) problem is a fundamental building block for the
optimization of electrical power systems. It is nonlinear and nonconvex and computes the …
optimization of electrical power systems. It is nonlinear and nonconvex and computes the …
Deep reinforcement learning based real-time AC optimal power flow considering uncertainties
Modern power systems are experiencing larger fluctuations and more uncertainties caused
by increased penetration of renewable energy sources (RESs) and power electronics …
by increased penetration of renewable energy sources (RESs) and power electronics …
A data-driven method for fast ac optimal power flow solutions via deep reinforcement learning
With the increasing penetration of renewable energy, power grid operators are observing
both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and …
both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and …
Feasibility constrained online calculation for real-time optimal power flow: A convex constrained deep reinforcement learning approach
Due to the increasing uncertainties of renewable energy and stochastic demands, quick-
optimal control actions are necessary to retain the system stability and economic operation …
optimal control actions are necessary to retain the system stability and economic operation …
DeepOPF: A feasibility-optimized deep neural network approach for AC optimal power flow problems
To cope with increasing uncertainty from renewable generation and flexible load, grid
operators need to solve alternative current optimal power flow (AC-OPF) problems more …
operators need to solve alternative current optimal power flow (AC-OPF) problems more …
Learning to solve the AC-OPF using sensitivity-informed deep neural networks
To shift the computational burden from real-time to offline in delay-critical power systems
applications, recent works entertain the idea of using a deep neural network (DNN) to …
applications, recent works entertain the idea of using a deep neural network (DNN) to …
Deepopf: deep neural networks for optimal power flow
X Pan - Proceedings of the 8th ACM International Conference …, 2021 - dl.acm.org
We develop a Deep Neural Network (DNN) approach, namely DeepOPF, for solving optimal
power flow (OPF) problems that are critical for daily power system operation. DeepOPF …
power flow (OPF) problems that are critical for daily power system operation. DeepOPF …