towardsan ai assistant - epcc workshop 2019 · •wehave a large domainof plausible futures to...
TRANSCRIPT
EPCC 2019 - Reykjavik
Towards an AI assistant
15/05/2019Rémy CLEMENT, RTE R&D, [email protected] GAMBIER-MOREL, RTE R&D, [email protected]
1. OUR TARGET: AN ASSISTANT FOR SHORT-TERM OPERATION
2. ANTICIPATING THE UPCOMING NETWORK STATE
3. OVERCOMING THE TRACTABILITY BARRIER
4. TOWARDS AN AUTOPILOT FOR THE TRANSMISSION SYSTEM
5. PRACTICAL CONCERNS WITH AI APPLIED TO SYSTEM OPERATION
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Overview
An assistant for short-term operation
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Being an operator today
• Many screens, dispatchedinformation
• Manage activity peaks
• Anticipate as much as possible and be ready for the unexpected
• Think of everything4
Help the operators determine the « best » decisions
Apogée projectGoal
In line with principles developed in the iTesla and Garpur projects
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• Explore a set of possible futures
• Risk assessment
• Recommandations on what to do
Behind the stage
Review of strategies
Real time
Choice Implementation
Real time – x hour(s)
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Anticipating the upcoming network state
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Current process
Classical process today to prepare day-ahead forecast files:• Pick a day of reference that should ressemble the next day
• For each hour, update the planned outages, the expected load, the expected generation
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Problem 1: the process produces a single forecast file per-hour. Assessing a forecast domain would be more relevant.
Problem 2: the resulting forecast files may not respect the reliability criterion. We miss an automatic process that emulates what the dispatcher wouldmanage to do so that we can check whether we should be safe or not.
Forecasting process
Exogenousvariable forecast
ModuleTSO operator
emulator
Probabilistic RiskAssessment(including remedialactions)
Target forecasting process
Forecasting a range of possible futures
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Restitution to the operator
Forecasting a single state è Forecasting a range of possible states
• Analysis of the historical records in order to understand the errordistribution between the forecasted states and the actual snapshots
• Dimensionality reduction technique (PCA,…) to grasp the correlations whilereducing the dimension
SnapshotForecast Delta
Forecast error analysis on wind data11
Forecasting processEmulating a TSO operator
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Exogenous variable forecastModule
TSO operatoremulator
Probabilistic RiskAssessment(including remedialactions)
Target forecasting process
Restitution to the operator
The example of voltage management
Input X• Injections• Monday/…/Sunday• Hour of Day• Reactive margin per generation unit
Output Y• Voltage set points of generation units• Voltage set points of tap-changers• Connection status 0/1 of shunts
• Tentative approach: supervized learning followed by postprocessing
• Train on snapshots• Apply on day-ahead forecast files
Imitation neural network applied to voltage management
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Risk assessment: Overcoming the tractability barrier
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Risk assessment: tractability barrier
• We have a large domain of plausible futures to assess
• We have a significant number of contingencies to consider
• Despite the dimensionality reduction tactic, a direct Monte-Carlo approachis not tractable
• Especially if there are many automatic devices in the network!
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Overcoming the tractability barrier
• Run offline many load-flow computations/dynamic simulations and exploit a machine-learning model to learnthe mapping Inputs->Outputs
Approach 1: replacing traditional solvers with fastapproximators
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Model input
Model output
• Then, either in real-time or in anticipation, use the fast machine-learningmodel on many cases instead of the accurate one on very few cases
Fast machine-learning approximator
Overcoming the tractability barrier
• Given some plausible domain for the network infeeds, an optimizationmodule will seek the injection pattern(s) that lead(s) to the most severeoverload
Approach 2: robust approach filtering
• Then thorough investigation of these few « worst-case » scenarios• Ongoing experimentation on the France-Spain border test case
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Towards an autopilot for the transmission system
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Towards an autopilot for the transmission system
• The recent successes of reinforcement learning applied to the game of Go or Starcraft have raised hope that it might be also applied to real-time TSO operation
PyPowNet: an environment to test agents that findtopological actions to alleviate overloads
• https://l2rpn.chalearn.org/
• Principle of reinforcement-learning:
While not good enough:improve a policy by trial and error with a simulator
• For us, the simulator can be based on a load-flow model
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Practical concerns with AIapplied to TSO operation
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Practical concerns with AI applied to TSO operation
What happens if the current system differs from what was seen in the training set?• New assets in the system, evolution of the behaviour of the (reactive) load,…
=> Growing interest around transfer learning
• Need to be able to identify atypical patterns and answer "I don’t know”
Maintenance of the model and data management
Human-machine interaction• The assistant should not bother too much the human
• Maintenance of competency of the operator: don’t be lazy and solely rely on the AI
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Last words6
In a nutshell
We believe there is a need for a new generation of tools for the exploitation of electricity transmission systems given the energy transition
• Probabilistic assessment of the reliability
• Taking into account the behaviour of the automatic curative devices
• The tool should act as a smart assistant that anticipates the upcoming network states, raisesflags, and suggests actions to do
Ongoing R&D work towards this goal, involving Machine-learning techniques• Approximate models will be mandatory in order to achieve something within a reasonable
duration
• Supervised learning, unsupervised learning, reinforcement learning are being explored
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Some references
Machine-learning to approximate power system solvers• Anticipating contingencies in power grids using fast neural net screening, B. Donnot, I. Guyon, M.
Schoenaueur, A. Marot, P. Panciatici• Graph Neural Solver for Power Systems, B. Donon, B. Donnot, A. Marot & I. Guyon, IJCNN 2019
Worst-case Approach• Hierarchical programming for Worst-case analysis of power grids, H. Djelassi, S. Fliscounakis, A.
Mitsos, P. Panciatici
GARPUR: FP7 project on a new reliability criterion for TSOs• https://www.sintef.no/projectweb/garpur/
iTesla: FP7 project• http://www.itesla-project.eu/
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Open source code
Powsybl project• https://github.com/powsybl
OpFab project• https://github.com/opfab/
Challenge on reinforcement learning• https://pypownet.readthedocs.io/en/latest/• https://github.com/MarvinLer/pypownet
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Thank you!