scholarly journals Optimal power flow: an introduction to predictive, distributed and stochastic control challenges

2018 ◽  
Vol 66 (7) ◽  
pp. 573-589 ◽  
Author(s):  
Timm Faulwasser ◽  
Alexander Engelmann ◽  
Tillmann Mühlpfordt ◽  
Veit Hagenmeyer
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problems ◽  
Optimal Power ◽  
Open Questions ◽  
Multi Stage ◽  
Systems And Control ◽  
Control Community ◽  
And Control

Abstract The Energiewende is a paradigm change that can be witnessed at latest since the political decision to step out of nuclear energy. Moreover, despite common roots in Electrical Engineering, the control community and the power systems community face a lack of common vocabulary. In this context, this paper aims at providing a systems-and-control specific introduction to optimal power flow problems which are pivotal in the operation of energy systems. Based on a concise problem statement, we introduce a common description of optimal power flow variants including multi-stage problems and predictive control, stochastic uncertainties, and issues of distributed optimization. Moreover, we sketch open questions that might be of interest for the systems and control community.

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.

scholarly journals SOCP Relaxations of Optimal Power Flow Problem Considering Current Margins in Radial Networks

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3164 ◽  
Author(s):  
Yuwei Chen ◽  
Ji Xiang ◽  
Yanjun Li
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Additional Term ◽  
Transmission Losses ◽  
Stability Margins ◽  
Optimal Power ◽  
Second Order Cone ◽  
Convex Problem ◽  
Generation Costs

Optimal power flow (OPF) is a non-linear and non-convex problem that seeks the optimization of a power system operation point to minimize the total generation costs or transmission losses. This study proposes an OPF model considering current margins in radial networks. The objective function of this OPF model has an additional term of current margins of the line besides the traditional transmission losses and generations costs, which contributes to thermal stability margins of power systems. The model is a reformulated bus injection model with clear physical meanings. Second order cone program (SOCP) relaxations for the proposed OPF are made, followed by the over-satisfaction condition guaranteeing the exactness of the SOCP relaxations. A simple 6-node case and several IEEE benchmark systems are studied to illustrate the efficiency of the developed results.

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