Scenario Reduction Network Based on Wasserstein Distance with Regularization

10.36227/techrxiv.17007967 ◽

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

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Dissipativity-based design of local and wide-area DER controls for large-scale power systems with high penetration of renewables

2017 IEEE Conference on Control Technology and Applications (CCTA) ◽

10.1109/ccta.2017.8062775 ◽

2017 ◽

Cited By ~ 7

Author(s):

Roland Harvey ◽

Ying Xu ◽

Zhihua Qu ◽

Toru Namerikawa

Keyword(s):

Power Systems ◽

Large Scale ◽

Wide Area ◽

High Penetration

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

Sustainability ◽

10.3390/su13148113 ◽

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.

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The energy distance for ensemble and scenario reduction

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2019.0431 ◽

2021 ◽

Vol 379 (2202) ◽

pp. 20190431 ◽

Cited By ~ 1

Author(s):

Florian Ziel

Keyword(s):

Power Systems ◽

Real Data ◽

Wasserstein Distance ◽

Generative Adversarial Networks ◽

Probabilistic Forecasting ◽

Multivariate Distributions ◽

Scenario Reduction ◽

Stochastic Programs ◽

Reduction Techniques ◽

Energy Distance

Scenario reduction techniques are widely applied for solving sophisticated dynamic and stochastic programs, especially in energy and power systems, but are also used in probabilistic forecasting, clustering and estimating generative adversarial networks. We propose a new method for ensemble and scenario reduction based on the energy distance which is a special case of the maximum mean discrepancy. We discuss the choice of energy distance in detail, especially in comparison to the popular Wasserstein distance which is dominating the scenario reduction literature. The energy distance is a metric between probability measures that allows for powerful tests for equality of arbitrary multivariate distributions or independence. Thanks to the latter, it is a suitable candidate for ensemble and scenario reduction problems. The theoretical properties and considered examples indicate clearly that the reduced scenario sets tend to exhibit better statistical properties for the energy distance than a corresponding reduction with respect to the Wasserstein distance. We show applications to a Bernoulli random walk and two real data-based examples for electricity demand profiles and day-ahead electricity prices. This article is part of the theme issue ‘The mathematics of energy systems’.

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An Oscillatory Stability Criterion Based on the Unified $dq$ -Frame Impedance Network Model for Power Systems With High-Penetration Renewables

IEEE Transactions on Power Systems ◽

10.1109/tpwrs.2018.2794067 ◽

2018 ◽

Vol 33 (3) ◽

pp. 3472-3485 ◽

Cited By ~ 40

Author(s):

Huakun Liu ◽

Xiaorong Xie ◽

Wei Liu

Keyword(s):

Power Systems ◽

Network Model ◽

Stability Criterion ◽

High Penetration

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Economic load dispatch of renewable energy-based power systems with high penetration of large-scale hydropower station based on multi-agent glowworm swarm optimization

Energy Strategy Reviews ◽

10.1016/j.esr.2019.100425 ◽

2019 ◽

Vol 26 ◽

pp. 100425 ◽

Cited By ~ 4

Author(s):

Xiaoyu Wang ◽

Kan Yang

Keyword(s):

Renewable Energy ◽

Power Systems ◽

Large Scale ◽

Hydropower Station ◽

Economic Load Dispatch ◽

Swarm Optimization ◽

High Penetration ◽

Glowworm Swarm Optimization ◽

Multi Agent

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Distributed Optimization for Control

Annual Review of Control Robotics and Autonomous Systems ◽

10.1146/annurev-control-060117-105131 ◽

2018 ◽

Vol 1 (1) ◽

pp. 77-103 ◽

Cited By ~ 20

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.

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