Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Zamzam, Ahmed S."'
High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load
Externí odkaz:
http://arxiv.org/abs/2411.11791
In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsupervised learning framework to train functions that can closely approx
Externí odkaz:
http://arxiv.org/abs/2403.11068
In multi-timescale multi-agent reinforcement learning (MARL), agents interact across different timescales. In general, policies for time-dependent behaviors, such as those induced by multiple timescales, are non-stationary. Learning non-stationary po
Externí odkaz:
http://arxiv.org/abs/2307.08794
Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms. Most C-MARL algorithms use a primal-dual approach to enforce cons
Externí odkaz:
http://arxiv.org/abs/2211.16069
Autor:
Tasseff, Byron, Bent, Russell, Coffrin, Carleton, Barrows, Clayton, Sigler, Devon, Stickel, Jonathan, Zamzam, Ahmed S., Liu, Yang, Van Hentenryck, Pascal
The classic pump scheduling or Optimal Water Flow (OWF) problem for water distribution networks (WDNs) minimizes the cost of power consumption for a given WDN over a fixed time horizon. In its exact form, the OWF is a computationally challenging mixe
Externí odkaz:
http://arxiv.org/abs/2208.03551
This paper proposes a model-free distribution system state estimation method based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting where the network is divided into multiple areas. The measured physic
Externí odkaz:
http://arxiv.org/abs/2203.00260
Autor:
Biagioni, David, Zhang, Xiangyu, Wald, Dylan, Vaidhynathan, Deepthi, Chintala, Rohit, King, Jennifer, Zamzam, Ahmed S.
We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for rein
Externí odkaz:
http://arxiv.org/abs/2111.05969
Increasing levels of renewable generation motivate a growing interest in data-driven approaches for AC optimal power flow (AC OPF) to manage uncertainty; however, a lack of disciplined dataset creation and benchmarking prohibits useful comparison amo
Externí odkaz:
http://arxiv.org/abs/2111.01228
Many stochastic optimization problems include chance constraints that enforce constraint satisfaction with a specific probability; however, solving an optimization problem with chance constraints assumes that the solver has access to the exact underl
Externí odkaz:
http://arxiv.org/abs/2109.08742
Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a new method, called the pruned physics-aware neural network (P2N2), is
Externí odkaz:
http://arxiv.org/abs/2102.03893