92 research outputs found
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. In this paper
we propose a new method in two steps which is able to automatically do this
segmentation, while taking into account the real time context, in order to help
them handle shifting dynamics. Our method relies on a "guided" machine learning
approach. As a first step, we define and compute a task specific "Influence
Graph" in a guided manner. We indeed simulate on a grid state chosen
interventions, representative of our task of interest (managing active power
flows in our case). For visualization and interpretation, we then build a
higher representation of the grid relevant to this task by applying the graph
community detection algorithm \textit{Infomap} on this Influence Graph. To
illustrate our method and demonstrate its practical interest, we apply it on
commonly used systems, the IEEE-14 and IEEE-118. We show promising and original
interpretable results, especially on the previously well studied RTS-96 system
for grid segmentation. We eventually share initial investigation and results on
a large-scale system, the French power grid, whose segmentation had a
surprising resemblance with RTE's historical partitioning
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach
System operators are faced with increasingly volatile operating conditions.
In order to manage system reliability in a cost-effective manner, control room
operators are turning to computerised decision support tools based on AI and
machine learning. Specifically, Reinforcement Learning (RL) is a promising
technique to train agents that suggest grid control actions to operators. In
this paper, a simple baseline approach is presented using RL to represent an
artificial control room operator that can operate a IEEE 14-bus test case for a
duration of 1 week. This agent takes topological switching actions to control
power flows on the grid, and is trained on only a single well-chosen scenario.
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. The type and variability of topologies suggested by the
agent are analysed across the test scenarios, demonstrating efficient and
diverse agent behaviour
Towards an AI assistant for human grid operators
Power systems are becoming more complex to operate in the digital age. As a
result, real-time decision-making is getting more challenging as the human
operator has to deal with more information, more uncertainty, more applications
and more coordination. While supervision has been primarily used to help them
make decisions over the last decades, it cannot reasonably scale up anymore.
There is a great need for rethinking the human-machine interface under more
unified and interactive frameworks. Taking advantage of the latest developments
in Human-machine Interactions and Artificial intelligence, we share the vision
of a new assistant framework relying on an hypervision interface and greater
bidirectional interactions. We review the known principles of decision-making
that drives the assistant design and supporting assistance functions we
present. We finally share some guidelines to make progress towards the
development of such an assistant
Graph Neural Solver for Power Systems
International audienceWe propose a neural network architecture that emulates the behavior of a physics solver that solves electricity differential equations to compute electricity flow in power grids (so-called "load flow"). Load flow computation is a well studied and understood problem, but current methods (based on Newton-Raphson) are slow. With increasing usage expectations of the current infrastructure, it is important to find methods to accelerate computations. One avenue we are pursuing in this paper is to use proxies based on "graph neural networks". In contrast with previous neural network approaches, which could only handle fixed grid topologies, our novel graph-based method, trained on data from power grids of a given size, generalizes to larger or smaller ones. We experimentally demonstrate viability of the method on randomly connected artificial grids of size 30 nodes. We achieve better accuracy than the DC-approximation (a standard benchmark linearizing physical equations) on random power grids whose size range from 10 nodes to 110 nodes, the scale of real-world power grids. Our neural network learns to solve the load flow problem without overfitting to a specific instance of the problem
Learning to run a power network with trust
Artificial agents are promising for realtime power system operations,
particularly, to compute remedial actions for congestion management. Currently,
these agents are limited to only autonomously run by themselves. However,
autonomous agents will not be deployed any time soon. Operators will still be
in charge of taking action in the future. Aiming at designing an assistant for
operators, we here consider humans in the loop and propose an original
formulation for this problem. 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. We further model the operator's available attention as a budget
that decreases when alarms are sent. We present the design and results of our
competition "Learning to run a power network with trust" in which we benchmark
the ability of submitted agents to send relevant alarms while operating the
network to their best
Towards an AI assistant for human grid operators
Power systems are becoming more complex to operate in the digital age. As a result, real-time decision-making is getting more challenging as the human operator has to deal with more information, more uncertainty, more applications and more coordination. While supervision has been primarily used to help them make decisions over the last decades, it cannot reasonably scale up anymore. There is a great need for rethinking the human-machine interface under more unified and interactive frameworks. Taking advantage of the latest developments in Human-machine Interactions and Artificial intelligence, we share the vision of a new assistant framework relying on an hypervision interface and greater bidirectional interactions. We review the known principles of decision-making that drives the assistant design and supporting assistance functions we present. We finally share some guidelines to make progress towards the development of such an assistant
Labellisation semi-supervisée de données : Vers une approche experte étendue
National audienceDans cet article, nous proposons une nouvelle approche semi supervisée de labellisation des événements du réseau électrique français. Après une première labellisation partielle par un système expert, nous utilisons un réseau de neurones siamois pour explorer et étendre les labels sur des données nonlabellisés. En appliquant notre approche aux données du système électrique de la région de Lyon sur l'année 2017, les résultats de la métrique créée par le réseau approchent ceux obtenus sur la DTW et nous ouvrent la possibilité d'extension à de plus gros volumes de données à labelliser, tout en intégrant une expertise opérationnelle
Learning to run a Power Network Challenge: a Retrospective Analysis
Power networks, responsible for transporting electricity across large
geographical regions, are complex infrastructures on which modern life
critically depend. Variations in demand and production profiles, with
increasing renewable energy integration, as well as the high voltage network
technology, constitute a real challenge for human operators when optimizing
electricity transportation while avoiding blackouts. Motivated to investigate
the potential of Artificial Intelligence 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. The NeurIPS 2020 competition was well received
by the international community attracting over 300 participants worldwide. The
main contribution of this challenge is our proposed comprehensive Grid2Op
framework, and associated benchmark, which plays realistic sequential network
operations scenarios. The framework is open-sourced and easily re-usable to
define new environments with its companion GridAlive ecosystem. It relies on
existing non-linear physical simulators and let us create a series of
perturbations and challenges that are representative of two important problems:
a) the uncertainty resulting from the increased use of unpredictable renewable
energy sources, and b) the robustness required with contingent line
disconnections. In this paper, we provide details about the competition
highlights. We present the benchmark suite and analyse the winning solutions of
the challenge, observing one super-human performance demonstration by the best
agent. We propose our organizational insights for a successful competition and
conclude on open research avenues. We expect our work will foster research to
create more sustainable solutions for power network operations
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