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Found 13 papers, 4 papers with code
Online Feedback Optimization for Subtransmission Grid Control
The increasing electric power consumption and the shift towards renewable energy resources demand for new ways to operate transmission and subtransmission grids.
Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling
For this purpose, we formulate the framework of decision-making oriented clustering and propose an algorithm providing a decision-based partition of the data space and good representative decisions.
Learning to run a Power Network Challenge: a Retrospective Analysis
Motivated to investigate the potential of AI methods in enabling adaptability in power network operation, we have designed a L2RPN challenge to encourage the development of reinforcement learning solutions to key problems present in the next-generation power networks.
Decision Set Optimization and Energy-Efficient MIMO Communications
We propose a framework to find a good (finite) decision set which induces a minimal performance loss w. r. t.
LEAP nets for power grid perturbations
We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully.
The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms
Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the primary method for evaluating these emerging methods.
Optimization and Control
Decision-Oriented Communications: Application to Energy-Efficient Resource Allocation
In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task.
Anticipating contingengies in power grids using fast neural net screening
We evaluate that our method scales up to power grids of the size of the French high voltage power grid (over 1000 power lines).
Optimization of computational budget for power system risk assessment
We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators.
Fast Power system security analysis with Guided Dropout
We propose a new method to efficiently compute load-flows (the steady-state of the power-grid for given productions, consumptions and grid topology), substituting conventional simulators based on differential equation solvers.
Guided Machine Learning for power grid segmentation
The segmentation of large scale power grids into zones is crucial for control room operators when managing the grid complexity near real time.
Introducing machine learning for power system operation support
One of the primary goals of dispatchers is to protect equipment (e. g. avoid that transmission lines overheat) with few degrees of freedom: we are considering in this paper solely modifications in network topology, i. e. re-configuring the way in which lines, transformers, productions and loads are connected in sub-stations.
