Distributed Optimization for Control

2018 ◽  
Vol 1 (1) ◽  
pp. 77-103 ◽  
Author(s):  
Angelia Nedić ◽  
Ji Liu
Keyword(s):  
Power Systems ◽  
Network Structure ◽  
Large Scale ◽  
Computational Models ◽  
Optimization Problems ◽  
Distributed Optimization ◽  
Linear Equations ◽  
Time Varying Networks ◽  
Optimization Methodology ◽  
Scale Control

Advances in wired and wireless technology have necessitated the development of theory, models, and tools to cope with the new challenges posed by large-scale control and optimization problems over networks. The classical optimization methodology works under the premise that all problem data are available to a central entity (a computing agent or node). However, this premise does not apply to large networked systems, where each agent (node) in the network typically has access only to its private local information and has only a local view of the network structure. This review surveys the development of such distributed computational models for time-varying networks. To emphasize the role of the network structure in these approaches, we focus on a simple direct primal (sub)gradient method, but we also provide an overview of other distributed methods for optimization in networks. Applications of the distributed optimization framework to the control of power systems, least squares solutions to linear equations, and model predictive control are also presented.

scholarly journals Cost Minimizations and Performance Enhancements of Power Systems Using Spherical Prune Differential Evolution Algorithm Including Modal Analysis

2021 ◽  
Vol 13 (14) ◽  
pp. 8113
Author(s):  
Sherif S.M. Ghoneim ◽  
Mohamed F. Kotb ◽  
Hany M. Hasanien ◽  
Mosleh M. Alharthi ◽  
Attia A. El-Fergany
Keyword(s):  
Power Systems ◽  
Differential Evolution ◽  
Modal Analysis ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Stability Index ◽  
Pareto Solution ◽  
Evolution Algorithm

A novel application of the spherical prune differential evolution algorithm (SpDEA) to solve optimal power flow (OPF) problems in electric power systems is presented. The SpDEA has several merits, such as its high convergence speed, low number of parameters to be designed, and low computational procedures. Four objectives, complete with their relevant operating constraints, are adopted to be optimized simultaneously. Various case studies of multiple objective scenarios are demonstrated under MATLAB environment. Static voltage stability index of lowest/weak bus using modal analysis is incorporated. The results generated by the SpDEA are investigated and compared to standard multi-objective differential evolution (MODE) to prove their viability. The best answer is chosen carefully among trade-off Pareto points by using the technique of fuzzy Pareto solution. Two power system networks such as IEEE 30-bus and 118-bus systems as large-scale optimization problems with 129 design control variables are utilized to point out the effectiveness of the SpDEA. The realized results among many independent runs indicate the robustness of the SpDEA-based approach on OPF methodology in optimizing the defined objectives simultaneously.

scholarly journals New Directions in Streaming Algorithms

2020 ◽  
Author(s):  
Ali Vakilian
Keyword(s):  
Machine Learning ◽  
Combinatorial Optimization ◽  
Design Theory ◽  
Large Scale ◽  
Computational Models ◽  
Optimization Problems ◽  
Low Rank ◽  
Set Cover ◽  
Streaming Algorithms ◽  
Combinatorial Optimization Problems

Large volumes of available data have led to the emergence of new computational models for data analysis. One such model is captured by the notion of streaming algorithms: given a sequence of N items, the goal is to compute the value of a given function of the input items by a small number of passes and using a sublinear amount of space in N. Streaming algorithms have applications in many areas such as networking and large scale machine learning. Despite a huge amount of work on this area over the last two decades, there are multiple aspects of streaming algorithms that remained poorly understood, such as (a) streaming algorithms for combinatorial optimization problems and (b) incorporating modern machine learningtechniques in the design of streaming algorithms. In the first part of this thesis, we will describe (essentially) optimal streaming algorithms for set cover and maximum coverage, two classic problems in combinatorial optimization. Next, in the second part, we will show how to augment classic streaming algorithms of the frequency estimation and low-rank approximation problems with machine learning oracles in order to improve their space-accuracy tradeoffs. The new algorithms combine the benefits of machine learning with the formal guarantees available through algorithm design theory.

scholarly journals Scenario Reduction Network Based on Wasserstein Distance with Regularization

2021 ◽  
Author(s):  
Yingyun Sun ◽  
Xiaochong Dong ◽  
Sarmad Majeed Malik
Keyword(s):  
Power Systems ◽  
Network Model ◽  
Large Scale ◽  
Optimization Problems ◽  
Wasserstein Distance ◽  
Structure Design ◽  
Scenario Reduction ◽  
High Penetration ◽  
Neural Network Structure ◽  
Unconstrained Problem

Power systems with high penetration of renewable energy contain various <a></a><a>uncertainties</a>. Scenario-based optimization problems need a large number of discrete scenarios to obtain a reliable approximation for the probabilistic model. It is important to choose typical scenarios and ease the computational burden. This paper presents a scenario reduction network model based on Wasserstein distance. Entropy regularization is used to transform the scenario reduction problem into an unconstrained problem. Through an explicit neural network structure design, the output of the scenario reduction network corresponds to Sinkhorn distance function. The scenario reduction network can generate the typical scenario set through unsupervised learning training. An efficient algorithm is proposed for continuous/discrete scenario reduction. The superiority of the scenario reduction network model is verified through case studies. The numerical results highlight high accuracy and computational efficiency of the proposed model over state-of-the-art model making it an ideal candidate for large-scale scenario reduction problems

scholarly journals Scenario Reduction Network Based on Wasserstein Distance with Regularization

2021 ◽  
Author(s):  
Yingyun Sun ◽  
Xiaochong Dong ◽  
Sarmad Majeed Malik
Keyword(s):  
Power Systems ◽  
Network Model ◽  
Large Scale ◽  
Optimization Problems ◽  
Wasserstein Distance ◽  
Structure Design ◽  
Scenario Reduction ◽  
High Penetration ◽  
Neural Network Structure ◽  
Unconstrained Problem

Power systems with high penetration of renewable energy contain various <a></a><a>uncertainties</a>. Scenario-based optimization problems need a large number of discrete scenarios to obtain a reliable approximation for the probabilistic model. It is important to choose typical scenarios and ease the computational burden. This paper presents a scenario reduction network model based on Wasserstein distance. Entropy regularization is used to transform the scenario reduction problem into an unconstrained problem. Through an explicit neural network structure design, the output of the scenario reduction network corresponds to Sinkhorn distance function. The scenario reduction network can generate the typical scenario set through unsupervised learning training. An efficient algorithm is proposed for continuous/discrete scenario reduction. The superiority of the scenario reduction network model is verified through case studies. The numerical results highlight high accuracy and computational efficiency of the proposed model over state-of-the-art model making it an ideal candidate for large-scale scenario reduction problems

Integrated Power Flow Analysis with Large-scale Solar Photovoltaic Power Systems Employing N-R Method

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Girisha H Navada ◽  
K. N. Shubhanga
Keyword(s):  
Power Systems ◽  
Power System ◽  
Power Flow ◽  
Large Scale ◽  
Control Strategies ◽  
Linear Equations ◽  
Flow Analysis ◽  
Load Flow ◽  
Flow Equations ◽  
Single Machine Infinite Bus

Abstract A method is proposed to modify the conventional load flow programme to accommodate large-scale Solar PhotoVoltaics (SPV) power plant with series power specifications. The programme facilitates easy handling of any number of SPV systems with standard control strategies such as pf-control and voltage-control, considering solar inverter’s power constraints. In this method, the non-linear equations related to SPV systems, located at multiple locations, are solved with the main load flow equations in an integrated fashion, considerably reducing the implementation task. This task is achieved by augmenting the inverter buses to the existing power system network in such a way that the changes required in the conventional programme are minimal. To show the effectiveness of the proposed method, it is compared with the alternate-iteration method popularly followed in the literature. The workability of the proposed method has been demonstrated by using a Single Machine Infinite Bus (SMIB) system and the IEEE14-bus power system with SPV systems. Various test cases pertaining to meteorological variables and control strategies are also presented.

A Case for Nonconvex Distributed Optimization in Large-Scale Power Systems

2017 ◽  
Vol 32 (5) ◽  
pp. 3842-3851 ◽  
Author(s):  
Junyao Guo ◽  
Gabriela Hug ◽  
Ozan K. Tonguz
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Distributed Optimization

scholarly journals Interval Number-Based Safety Reasoning Method for Verification of Decentralized Power Systems in High-Speed Trains

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Peng Wu ◽  
Ning Xiong ◽  
Jiqiang Liu ◽  
Liujia Huang ◽  
Zhuoya Ju ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Power Systems ◽  
High Speed ◽  
Large Scale ◽  
Current System ◽  
Linear Equations ◽  
Interval Number ◽  
Test Cases ◽  
Interval Numbers ◽  
High Speed Trains

Decentralized power systems are commonly used in high-speed trains. However, many parameters in decentralized power systems are uncertain and inevitably have errors. We present a reasoning method based on the interval numbers for decentralized power systems in high-speed trains. Uncertain parameters and their unavoidable errors are quantitatively described by interval numbers. We also define generalized linear equations with interval numbers (LAIs), which can be used to describe the movement of the train. Furthermore, it is proven that the zero sets of LAIs are convex. Therefore, the inside of the fault-tolerance area can be formed by their vertexes and edges and represented by linear inequalities. Consequently, we can judge whether the system is working properly by verifying that the current system state is in the fault-tolerance area. Finally, a fault-tolerance area is obtained, which can be determined by linear equations with an interval number, and we test the correctness of the fault-tolerance area through large-scale random test cases.

Sign in / Sign up

Export Citation Format

Share Document