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

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

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

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

Latent Surgical Interventions in Residual Neural Networks

We propose and study a novel artificial neural network framework, which allows us to model surgical interventions on a physical system. Our approach was developed to predict power flows in power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. However, we anticipate a broader applicability. For several exemplary cases, we illustrate by simulation that our methodology permits learning from empirical data to predict the effect of a subset of interventions (ele-mentary interventions) and then generalize to combinations of interventions never seen during training. We verify this property mathematically in the additive perturbation case. In terms of transfer learning, this is equivalent to training on data from a few source domains then, with a zero-shot learning, generalizing to new target domains (super-generalization). Our architecture bears resemblance with the successful ResNets, with the simple modification that interventions are encoded as an addition of units in the neural network. For applications to real historical data, from the French high voltage power transmission company RTE, we evaluate the viability of this technique to rapidly assess curative actions that human operators take in emergency situations. Integrated in an overall planning and control system, methods deriving from our approach could allow Transmission System Operators (TSO) to assess in real time many more alternative actions, reaching a better exploration-exploitation tradeoff, compared to presently deployed physical system simulator. 1 Background and motivations In this paper, we are interested in speeding up the computation of power flows in power transmission grids using artificial neural networks, to emulate slower physical simulators. Key to our approach is the possibility of simulating the effect of actions on the grid topology. Such neural networks may then be used as part of an overall computer-assisted decision process in which human operators (dispatchers) ensure that the power grid is operated in security at all times, namely that the currents flowing in all lines are below certain thresholds (line thermal limits). We describe our application setting for concreteness, but anticipate a broader applicability of the techniques developed in this paper in various domains of physics, chemistry, manufacturing, biomedicine and others, in which some actions can be combined with each other, but running extensive simulations for each possible combination of such actions is computationally untractable. Electric power generated in production nodes (such as power plants) is transmitted towards consumption nodes in a power grid. The power lines enable this transmission through substations interconnecting them. Each pattern of connections is referred to as a grid topology. This topology is * Benjamin Donnot corresponding authors: [email protected] 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada

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 Grid2Op framework, which is open-source and easily re-usable, allows users to define new environments with its companion GridAlive ecosystem. Grid2Op relies on existing non-linear physical power network simulators and let users 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 give the highlights of the NeurIPS 2020 competition. We present the benchmark suite and analyse the winning solutions, including one super-human performance demonstration. We propose our organizational insights for a successful competition and conclude on open research avenues. Given the challenge success, we expect our work will foster research to create more sustainable solutions for power network operations