A complete decomposition and coordination algorithm for large-scale hydrothermal optimal power flow problems

2017 ◽  
Vol 12 (4) ◽  
pp. 491-500
Author(s):  
Chaoqun Wang ◽  
Hua Wei ◽  
Jiancheng Tan
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Complete Decomposition ◽  
Flow Problems ◽  
Optimal Power ◽  
Decomposition And Coordination

Solving large-scale reactive optimal power flow problems by a primal–dual $$\hbox {M}^{2}\hbox {BF}$$ approach

2019 ◽  
Vol 21 (2) ◽  
pp. 485-515 ◽  
Author(s):  
Ricardo B. N. M. Pinheiro ◽  
Leonardo Nepomuceno ◽  
Antonio R. Balbo
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Flow Problems ◽  
Optimal Power ◽  
Primal Dual

Large-scale dynamic optimal power flow problems with energy storage systems

2018 ◽  
Author(s):  
Nico Meyer-Huebner ◽  
Abolfazl Mosaddegh ◽  
Michael Suriyah ◽  
Thomas Leibfried ◽  
Claudio A. Canizares ◽  
...  
Keyword(s):  
Energy Storage ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Storage Systems ◽  
Energy Storage Systems ◽  
Flow Problems ◽  
Optimal Power

scholarly journals A Generic Approach for Solving Nonlinear-Discrete Security-Constrained Optimal Power Flow Problems in Large-Scale Systems

2014 ◽  
Vol 29 (3) ◽  
pp. 1194-1203 ◽  
Author(s):  
Ludovic Platbrood ◽  
Florin Capitanescu ◽  
Christian Merckx ◽  
Horia Crisciu ◽  
Louis Wehenkel
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Large Scale Systems ◽  
Flow Problems ◽  
Optimal Power

Discussion of "Large scale hydrothermal optimal power flow problems based on interior point nonlinear programming" [and reply]

2000 ◽  
Vol 15 (4) ◽  
pp. 1457-1458
Author(s):  
M. Madrigal ◽  
V.H. Quintana ◽  
H. Wei ◽  
H. Sasaki ◽  
J. Kubokawa ◽  
...  
Keyword(s):  
Nonlinear Programming ◽  
Interior Point ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Flow Problems ◽  
Optimal Power

scholarly journals A Clique Merging Algorithm to Solve Semidenite Relaxations of Optimal Power Flow Problems

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Julie Sliwak ◽  
Erling Andersen ◽  
Miguel F Anjos ◽  
Lucas Letocart ◽  
Emiliano Traversi
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problems ◽  
Merging Algorithm ◽  
Optimal Power

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

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 solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

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