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Found 14 papers, 3 papers with code
Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design
Current rapid changes in climate increase the urgency to change energy production and consumption management, to reduce carbon and other green-house gas production.
Learning to run a power network with trust
We first advance an agent with the ability to send to the operator alarms ahead of time when the proposed actions are of low confidence.
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.
Adversarial Training for a Continuous Robustness Control Problem in Power Systems
We propose a new adversarial training approach for injecting robustness when designing controllers for upcoming cyber-physical power systems.
Towards an AI assistant for power grid operators
There is a great need for rethinking the human-machine interface under more unified and interactive frameworks.
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach
The behaviour of this agent is tested on different time-series of generation and demand, demonstrating its ability to operate the grid successfully in 965 out of 1000 scenarios.
Learning to run a power network challenge for training topology controllers
For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities.
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.
Graph Neural Solver for Power Systems
Load flow computation is a well studied and understood problem, but current methods (based on Newton-Raphson) are slow.
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.
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).
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.
