Results 11 to 20 of about 14,952,911 (382)
IEEE Transactions on Power Systems, 2021 This letter is a proof of concept for quantum power flow (QPF) algorithms which underpin various unprecedentedly efficient power system analytics exploiting quantum computing. Our contributions are three-fold: 1) Establish a quantum-state-based fast decoupled model empowered by Hermitian and constant Jacobian matrices; 2) Devise an enhanced Harrow ...
Fei Feng, Yifan Zhou, Peng Zhang
openaire +3 more sources
Distributed optimal power flow. [PDF]
PLoS One, 2021 Objective The objectives of this paper are to 1) construct a new network model compatible with distributed computation, 2) construct the full optimal power flow (OPF) in a distributed fashion so that an effective, non-inferior solution can be found, and 3) develop a scalable algorithm that guarantees the convergence to a local minimum.
Oh H, Oh H.
europepmc +6 more sources
Linear Optimal Power Flow Using Cycle Flows [PDF]
Electric Power Systems Research, 2018 Linear optimal power flow (LOPF) algorithms use a linearization of the alternating current (AC) load flow equations to optimize generator dispatch in a network subject to the loading constraints of the network branches.
Brown, Tom +3 more
core +3 more sources
Physics-Informed Neural Networks for AC Optimal Power Flow [PDF]
Electric power systems research, 2021 This paper introduces, for the first time to our knowledge, physics-informed neural networks to accurately estimate the AC-OPF result and delivers rigorous guarantees about their performance.
Rahul Nellikkath +1 more
semanticscholar +1 more source
Leveraging Power Grid Topology in Machine Learning Assisted Optimal Power Flow [PDF]
IEEE Transactions on Power Systems, 2021 Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in
Thomas Falconer, Letif Mones
semanticscholar +1 more source
Power Flow Balancing With Decentralized Graph Neural Networks [PDF]
IEEE Transactions on Power Systems, 2021 We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at ...
Jonas Berg Hansen +2 more
semanticscholar +1 more source
Physics-Guided Deep Neural Networks for Power Flow Analysis [PDF]
IEEE Transactions on Power Systems, 2020 Solving power flow (PF) equations is the basis of power flow analysis, which is important in determining the best operation of existing systems, performing security analysis, etc. However, PF equations can be out-of-date or even unavailable due to system
Xinyue Hu +3 more
semanticscholar +1 more source
Integration and optimization of electrified LNG steam reforming hydrogen production system
You-qi chuyun, 2023 The global demand for clean hydrogen energy is constantly increasing. However, the methane steam reforming process cannot satisfy the future social demand for clean hydrogen energy due to its high emission, while the development of water electrolysis ...
Huchao SONG +5 more
doaj +1 more source
Data-Driven Optimal Power Flow: A Physics-Informed Machine Learning Approach [PDF]
IEEE Transactions on Power Systems, 2020 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 does not require the time-consuming parameter tuning process compared with the deep
Xingyu Lei +5 more
semanticscholar +1 more source