Zobrazeno 1 - 10
of 409
pro vyhledávání: '"Zamzam Ahmed"'
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
The increasing number of wildfires in recent years consistently challenges the safe and reliable operations of power systems. To prevent power lines and other electrical components from causing wildfires under extreme conditions, electric utilities o
Externí odkaz:
http://arxiv.org/abs/2402.04444
This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the distributional dependence is unknown. A two-stage approach is proposed
Externí odkaz:
http://arxiv.org/abs/2312.17471
This research explores the joint expansion planning of power and water distribution networks, which exhibit interdependence at various levels. We specifically focus on the dependency arising from the power consumption of pumps and develop models to s
Externí odkaz:
http://arxiv.org/abs/2312.10224
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
As climate change increases the risk of large-scale wildfires, wildfire ignitions from electric power lines are a growing concern. To mitigate the wildfire ignition risk, many electric utilities de-energize power lines to prevent electric faults and
Externí odkaz:
http://arxiv.org/abs/2306.03325
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
With the growing size and complexity of turbulent flow models, data compression approaches are of the utmost importance to analyze, visualize, or restart the simulations. Recently, in-situ autoencoder-based compression approaches have been proposed a
Externí odkaz:
http://arxiv.org/abs/2210.09262
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