Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Mak, Terrence W. K."'
This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability issues due to the high dimensionality of the output space. This paper fi
Externí odkaz:
http://arxiv.org/abs/2301.08840
This paper considers optimization proxies for Optimal Power Flow (OPF), i.e., machine-learning models that approximate the input/output relationship of OPF. Recent work has focused on showing that such proxies can be of high fidelity. However, their
Externí odkaz:
http://arxiv.org/abs/2208.07497
One potential future for the next generation of smart grids is the use of decentralized optimization algorithms and secured communications for coordinating renewable generation (e.g., wind/solar), dispatchable devices (e.g., coal/gas/nuclear generati
Externí odkaz:
http://arxiv.org/abs/2205.03787
This paper proposes a novel machine-learning approach for predicting AC-OPF solutions that features a fast and scalable training. It is motivated by the two critical considerations: (1) the fact that topology optimization and the stochasticity induce
Externí odkaz:
http://arxiv.org/abs/2101.06768
The AC Optimal Power Flow (AC-OPF) problem is a core building block in electrical transmission system. It seeks the most economical active and reactive generation dispatch to meet demands while satisfying transmission operational limits. It is often
Externí odkaz:
http://arxiv.org/abs/2101.03973
The AC Optimal Power Flow (AC-OPF) is a key building block in many power system applications. It determines generator setpoints at minimal cost that meet the power demands while satisfying the underlying physical and operational constraints. It is no
Externí odkaz:
http://arxiv.org/abs/2006.16356
This paper studies how to apply differential privacy to constrained optimization problems whose inputs are sensitive. This task raises significant challenges since random perturbations of the input data often render the constrained optimization probl
Externí odkaz:
http://arxiv.org/abs/2001.09508
Publikováno v:
IEEE Transactions on Power Systems ( Volume: 35 , Issue: 2 , March 2020 )
This paper considers the problem of releasing privacy-preserving load data of a decentralized operated power system. The paper focuses on data used to solve Optimal Power Flow (OPF) problems and proposes a distributed algorithm that complies with the
Externí odkaz:
http://arxiv.org/abs/1910.04250
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 generator setpoints for power and voltage, given a set of load demands. It is often
Externí odkaz:
http://arxiv.org/abs/1909.10461
Autor:
Babaeinejadsarookolaee, Sogol, Birchfield, Adam, Christie, Richard D., Coffrin, Carleton, DeMarco, Christopher, Diao, Ruisheng, Ferris, Michael, Fliscounakis, Stephane, Greene, Scott, Huang, Renke, Josz, Cedric, Korab, Roman, Lesieutre, Bernard, Maeght, Jean, Mak, Terrence W. K., Molzahn, Daniel K., Overbye, Thomas J., Panciatici, Patrick, Park, Byungkwon, Snodgrass, Jonathan, Tbaileh, Ahmad, Van Hentenryck, Pascal, Zimmerman, Ray
In recent years, the power systems research community has seen an explosion of novel methods for formulating the AC power flow equations. Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the
Externí odkaz:
http://arxiv.org/abs/1908.02788