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pro vyhledávání: '"Low, A H"'
Autor:
Low, Steven H.
We consider the problem of identifying the admittance matrix of a three-phase radial network from voltage and current measurements at a subset of nodes. These measurements are used to estimate a virtual network represented by the Kron reduction (Schu
Externí odkaz:
http://arxiv.org/abs/2403.17391
Deep reinforcement learning has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power systems has been hindered by a lack of explicit stability and safety guar
Externí odkaz:
http://arxiv.org/abs/2209.07669
Autor:
Low, Steven H.
First we present an approach to formulate unbalanced three-phase power flow problems for general networks that explicitly separates device models and network models. A device model consists of (i) an internal model and (ii) a conversion rule. The con
Externí odkaz:
http://arxiv.org/abs/2207.12519
The existence of multiple load-solution mappings of non-convex AC-OPF problems poses a fundamental challenge to deep neural network (DNN) schemes. As the training dataset may contain a mixture of data points corresponding to different load-solution m
Externí odkaz:
http://arxiv.org/abs/2206.03365
We study the problem of phase optimization for electric-vehicle (EV) charging. We formulate our problem as a non-convex mixed-integer programming problem whose objective is to minimize the charging loss. Despite the hardness of directly solving this
Externí odkaz:
http://arxiv.org/abs/2205.10499
Transmission power systems usually consist of interconnected sub-grids that are operated relatively independently. When a failure happens, it is desirable to localize its impact within the sub-grid where the failure occurs. This paper introduces thre
Externí odkaz:
http://arxiv.org/abs/2205.06315
Autor:
Flannigan, S., Pearson, N., Low, G. H., Buyskikh, A., Bloch, I., Zoller, P., Troyer, M., Daley, A. J.
The rapid development in hardware for quantum computing and simulation has led to much interest in problems where these devices can exceed the capabilities of existing classical computers and known methods. Approaching this for problems that go beyon
Externí odkaz:
http://arxiv.org/abs/2204.13644
As the number of prosumers with distributed energy resources (DERs) grows, the conventional centralized operation scheme may suffer from conflicting interests, privacy concerns, and incentive inadequacy. In this paper, we propose an energy sharing me
Externí odkaz:
http://arxiv.org/abs/2203.04503
Ensuring solution feasibility is a key challenge in developing Deep Neural Network (DNN) schemes for solving constrained optimization problems, due to inherent DNN prediction errors. In this paper, we propose a ``preventive learning'' framework to gu
Externí odkaz:
http://arxiv.org/abs/2112.08091
Autor:
Li, Tongxin, Yang, Ruixiao, Qu, Guannan, Shi, Guanya, Yu, Chenkai, Wierman, Adam, Low, Steven H.
We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances \textit{"consistency"}, which measures the competitive ratio when predictions are accurate, and \textit{"robustness"}, wh
Externí odkaz:
http://arxiv.org/abs/2106.09659