Utilizing synchronization to partition power networks into microgrids
- URL: http://arxiv.org/abs/2107.12165v1
- Date: Mon, 26 Jul 2021 12:32:11 GMT
- Title: Utilizing synchronization to partition power networks into microgrids
- Authors: Ricardo Cardona-Rivera, Francesco Lo Iudice, Antonio Grotta, Marco
Coraggio, Mario di Bernardo
- Abstract summary: partitioning a power grid into a set of microgrids, or islands, is of interest for both the design of future smart grids and as a last resort to restore power dispatchment in sections of a grid affected by an extreme failure.
In this paper, we take a different route and obtain the grid partition by exploiting the synchronization dynamics of a cyberlayer of Kuramoto oscillators.
- Score: 1.224954637705144
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The problem of partitioning a power grid into a set of microgrids, or
islands, is of interest for both the design of future smart grids, and as a
last resort to restore power dispatchment in sections of a grid affected by an
extreme failure. In the literature this problem is usually solved by turning it
into a combinatorial optimization problem, often solved through generic
heruristic methods such as Genetic Algorithms or Tabu Search. In this paper, we
take a different route and obtain the grid partition by exploiting the
synchronization dynamics of a cyberlayer of Kuramoto oscillators, each
parameterized as a rough approximation of the dynamics of the grid's node it
corresponds to. We present first a centralised algorithm and then a
decentralised strategy. In the former, nodes are aggregated based on their
internode synchronization times while in the latter they exploit
synchronization of the oscillators in the cyber layer to selforganise into
islands. Our preliminary results show that the heuristic synchronization based
algorithms do converge towards partitions that are comparable to those obtained
via other more cumbersome and computationally expensive optimization-based
methods.
Related papers
- Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks [57.24087627267086]
We consider the task of minimizing the sum of convex functions stored in a decentralized manner across the nodes of a communication network.
Lower bounds on the number of decentralized communications and (sub)gradient computations required to solve the problem have been established.
We develop the first optimal algorithm that matches these lower bounds and offers substantially improved theoretical performance compared to the existing state of the art.
arXiv Detail & Related papers (2024-05-28T10:28:45Z) - Queuing dynamics of asynchronous Federated Learning [15.26212962081762]
We study asynchronous federated learning mechanisms with nodes having potentially different computational speeds.
We propose a non-uniform sampling scheme for the central server that allows for lower delays with better complexity.
Our experiments clearly show a significant improvement of our method over current state-of-the-art asynchronous algorithms on an image classification problem.
arXiv Detail & Related papers (2024-02-12T18:32:35Z) - Asynchronous SGD on Graphs: a Unified Framework for Asynchronous
Decentralized and Federated Optimization [13.119144971868632]
We introduce Asynchronous SGD on Graphs (AGRAF SGD) -- a general algorithmic framework that covers asynchronous versions of many popular algorithms.
We provide rates of convergence under much milder assumptions than previous decentralized asynchronous computation works.
arXiv Detail & Related papers (2023-11-01T11:58:16Z) - Iterative Sketching for Secure Coded Regression [66.53950020718021]
We propose methods for speeding up distributed linear regression.
Specifically, we randomly rotate the basis of the system of equations and then subsample blocks, to simultaneously secure the information and reduce the dimension of the regression problem.
arXiv Detail & Related papers (2023-08-08T11:10:42Z) - Rotation Synchronization via Deep Matrix Factorization [24.153207403324917]
We focus on the formulation of rotation synchronization via neural networks.
Inspired by deep matrix completion, we express rotation synchronization in terms of matrix factorization with a deep neural network.
Our formulation exhibits implicit regularization properties and, more importantly, is unsupervised.
arXiv Detail & Related papers (2023-05-09T08:46:05Z) - On the Convergence of Distributed Stochastic Bilevel Optimization
Algorithms over a Network [55.56019538079826]
Bilevel optimization has been applied to a wide variety of machine learning models.
Most existing algorithms restrict their single-machine setting so that they are incapable of handling distributed data.
We develop novel decentralized bilevel optimization algorithms based on a gradient tracking communication mechanism and two different gradients.
arXiv Detail & Related papers (2022-06-30T05:29:52Z) - Large-Scale Sequential Learning for Recommender and Engineering Systems [91.3755431537592]
In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions.
For the former, we propose novel algorithm called SAROS that take into account both kinds of feedback for learning over the sequence of interactions.
The proposed idea of taking into account the neighbour lines shows statistically significant results in comparison with the initial approach for faults detection in power grid.
arXiv Detail & Related papers (2022-05-13T21:09:41Z) - Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes.
We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
arXiv Detail & Related papers (2021-06-15T09:03:28Z) - Decentralized Optimization with Heterogeneous Delays: a Continuous-Time
Approach [6.187780920448871]
We propose a novel continuous-time framework to analyze asynchronous algorithms.
We describe a fully asynchronous decentralized algorithm to minimize the sum of smooth and strongly convex functions.
arXiv Detail & Related papers (2021-06-07T13:09:25Z) - A Linearly Convergent Algorithm for Decentralized Optimization: Sending
Less Bits for Free! [72.31332210635524]
Decentralized optimization methods enable on-device training of machine learning models without a central coordinator.
We propose a new randomized first-order method which tackles the communication bottleneck by applying randomized compression operators.
We prove that our method can solve the problems without any increase in the number of communications compared to the baseline.
arXiv Detail & Related papers (2020-11-03T13:35:53Z) - Synchronization in 5G: a Bayesian Approach [0.0]
We propose a hybrid approach to synchronize large scale networks.
In particular, we draw on Kalman Filtering (KF) along with time-stamps generated by the Precision Time Protocol (PTP) for pairwise node synchronization.
arXiv Detail & Related papers (2020-02-28T11:27:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.