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The switching of transmission lines can significantly improve the economic and operational efficiency of power systems. The Direct-Current Optimal Transmission Switching (DC-OTS) problem provides a formal framework for minimizing power…

Discrete Mathematics · Computer Science 2026-03-04 Behnam Jabbari-Marand , Adolfo R. Escobedo

AC optimal power flow (AC OPF) is a fundamental problem in power system operation and control. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem that results in significant…

Optimization and Control · Mathematics 2023-07-06 Nafis Sadik , Mohammad Rasoul Narimani

The optimization of porous infill structures via local volume constraints has become a popular approach in topology optimization. In some design settings, however, the iterative optimization process converges only slowly, or not at all even…

Computational Engineering, Finance, and Science · Computer Science 2022-03-22 Junpeng Wang , Jun Wu , Rüdiger Westermann

Determining contingency aware dispatch decisions by solving a security-constrained optimal power flow (SCOPF) is challenging for real-world power systems, as the high problem dimensionality often leads to impractical computational…

Systems and Control · Electrical Eng. & Systems 2020-05-19 Richard Weinhold , Robert Mieth

The vast spatial dimension of modern interconnected electricity grids challenges the tractability of the DC optimal power flow problem. Grid aggregation methods try to overcome this challenge by reducing the number of network elements. Many…

Optimization and Control · Mathematics 2025-11-12 Benjamin Stöckl , Yannick Werner , Sonja Wogrin

Though the convex optimization has been widely used in power systems, it still cannot guarantee to yield a tight (accurate) solution to some problems. To mitigate this issue, this paper proposes an ensemble learning based convex…

Systems and Control · Electrical Eng. & Systems 2020-05-18 Ren Hu , Qifeng Li , Feng Qiu

This paper presents the input convex neural network architecture. These are scalar-valued (potentially deep) neural networks with constraints on the network parameters such that the output of the network is a convex function of (some of)…

Machine Learning · Computer Science 2017-06-15 Brandon Amos , Lei Xu , J. Zico Kolter

Spatial networks have recently attracted great interest in various fields of research. While the traditional network-theoretic viewpoint is commonly restricted to their topological characteristics (often disregarding existing spatial…

We derive the branch ampacity constraint associated to power losses for the convex optimal power flow (OPF) model based on the branch flow formulation. The branch ampacity constraint derivation is motivated by the physical interpretation of…

Optimization and Control · Mathematics 2020-05-14 Zhao Yuan , Mario Paolone

Chance-constrained optimization has emerged as a promising framework for managing uncertainties in power systems. This work advances its application to the DC Optimal Power Flow (DC-OPF) model, developing a novel approach to uncertainty…

Systems and Control · Electrical Eng. & Systems 2026-03-18 Tianyang Yi , D. Adrian Maldonado , Anirudh Subramanyam

Part I of this paper embeds the AC power flow problem with voltage control and exponential load model in the complex plane. Modeling the action of network controllers that regulate the magnitude of voltage phasors is a challenging task in…

Systems and Control · Computer Science 2016-09-07 Sina Sadeghi Baghsorkhi , Sergey Pavlovich Suetin

We consider the problem of designing an overlay network and routing mechanism that permits finding resources efficiently in a peer-to-peer system. We argue that many existing approaches to this problem can be modeled as the construction of…

Data Structures and Algorithms · Computer Science 2007-05-23 James Aspnes , Zoe Diamadi , Gauri Shah

This paper investigates a distributed optimization problem under edge agreements, where each agent in the network is also subject to local convex constraints. Generalized from the concept of consensus, a group of edge agreements represents…

Optimization and Control · Mathematics 2024-09-04 Zehui Lu , Shaoshuai Mou

We consider the problem of deriving an explicit approximate solution of the nonlinear power equations that describe a balanced power distribution network. We give sufficient conditions for the existence of a practical solution to the power…

Optimization and Control · Mathematics 2019-07-09 Saverio Bolognani , Sandro Zampieri

In this paper, we propose a framework for coordinating distributed energy resources (DERs) connected to a power distribution system, the model of which is not completely known, so that they collectively provide a specified amount of active…

Optimization and Control · Mathematics 2020-06-09 Hanchen Xu , Alejandro D. Domínguez-García , Peter W. Sauer

In this paper, we focus on solving a distributed convex optimization problem in a network, where each agent has its own convex cost function and the goal is to minimize the sum of the agents' cost functions while obeying the network…

Optimization and Control · Mathematics 2020-02-11 Shi Pu , Wei Shi , Jinming Xu , Angelia Nedić

The load-flow equations are the main tool to operate and plan electrical networks. For transmission or distribution networks these equations can be simplified into a linear system involving the graph Laplacian and the power input vector. We…

Classical Physics · Physics 2019-05-15 J. G. Caputo , A. Knippel , N. Retiere

Corrective Transmission Switching can be used by the grid operator to relieve line overloading and voltage violations, improve system reliability, and reduce system losses. Power grid optimization by means of line switching is typically…

Systems and Control · Computer Science 2018-03-19 Shuai Wang , John Baillieul

This article demonstrates how an understanding of the geometry of a family of cost functions can be used to develop efficient numerical algorithms for real-time optimisation. Crucially, it is not the geometry of the individual functions…

Optimization and Control · Mathematics 2012-12-11 Jonathan H. Manton

The ever-increasing parameter counts of deep learning models necessitate effective compression techniques for deployment on resource-constrained devices. This paper explores the application of information geometry, the study of…

Machine Learning · Computer Science 2025-07-15 Zakhar Shumaylov , Vasileios Tsiaras , Yannis Stylianou
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